R/Balance.R
In OHDSI/CohortMethod: New-User Cohort Method with Large Scale Propensity and Outcome Models
Defines functions
getGeneralizabilityTable
plotCovariatePrevalence
plotCovariateBalanceOfTopVariables
.truncRight
plotCovariateBalanceScatterPlot
sampleCohortsAndromeda
sampleCohorts
sampleSingleCohort
computeCovariateBalance
computeMeansPerGroup
filterCovariates
Documented in
computeCovariateBalance
getGeneralizabilityTable
plotCovariateBalanceOfTopVariables
plotCovariateBalanceScatterPlot
plotCovariatePrevalence
# Copyright 2024 Observational Health Data Sciences and Informatics
#
# This file is part of CohortMethod
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# @author Observational Health Data Sciences and Informatics
# @author Patrick Ryan
# @author Marc Suchard
# @author Martijn Schuemie
filterCovariates <- function(covariates, covariateRef, covariateFilter) {
if (is.null(covariateFilter)) {
return(covariates)
} else if (is.numeric(covariateFilter)) {
covariates %>%
filter(.data$covariateId %in% covariateFilter) %>%
return()
} else if (is.data.frame(covariateFilter) && all(c("analysisId", "covariateIds") %in% colnames(covariateFilter))) {
analysisIds <- covariateFilter %>%
filter(is.na(.data$covariateIds)) %>%
pull(.data$analysisId)
covariateIds1 <- covariateRef %>%
filter(.data$analysisId %in% analysisIds) %>%
pull(.data$covariateId)
covariateIds2 <- covariateFilter %>%
filter(!is.na(.data$covariateIds)) %>%
pull(.data$covariateIds) %>%
strsplit(",|;") %>%
unlist() %>%
as.numeric()
covariates %>%
filter(.data$covariateId %in% c(covariateIds1, covariateIds2)) %>%
return()
} else {
stop("Unknown covariateFilter type")
}
}
computeMeansPerGroup <- function(cohorts, cohortMethodData, covariateFilter) {
hasStrata <- "stratumId" %in% colnames(cohorts)
hasIptw <- "iptw" %in% colnames(cohorts)
if (hasStrata) {
stratumSize <- cohorts %>%
group_by(.data$stratumId, .data$treatment) %>%
count() %>%
ungroup() %>%
collect()
}
useWeighting <- (hasStrata && any(stratumSize %>% pull(.data$n) > 1)) ||
(!hasStrata && hasIptw)
# By definition:
sumW <- 1
covariates <- filterCovariates(cohortMethodData$covariates, cohortMethodData$covariateRef, covariateFilter)
if (useWeighting) {
if (hasStrata) {
# Variable strata sizes detected: weigh by size of strata set
w <- stratumSize %>%
mutate(weight = 1 / .data$n) %>%
inner_join(cohorts, by = c("stratumId", "treatment"), copy = TRUE) %>%
select("rowId", "treatment", "weight")
# Overall weight is for computing mean and SD across T and C
overallW <- stratumSize %>%
group_by(.data$stratumId) %>%
summarise(weight = 1 / sum(.data$n, na.rm = TRUE)) %>%
ungroup() %>%
inner_join(cohorts, by = c("stratumId"), copy = TRUE) %>%
select("rowId", "weight")
} else {
w <- cohorts %>%
mutate(weight = .data$iptw) %>%
select("rowId", "treatment", "weight") %>%
collect()
overallW <- w
}
# Normalize so sum(weight) == 1 per treatment arm:
wSum <- w %>%
group_by(.data$treatment) %>%
summarize(wSum = sum(.data$weight, na.rm = TRUE)) %>%
ungroup()
overallWSum <- overallW %>%
summarize(overallWSum = sum(.data$weight, na.rm = TRUE)) %>%
pull()
cohortMethodData$w <- w %>%
inner_join(wSum, by = "treatment") %>%
mutate(weight = .data$weight / .data$wSum) %>%
select("rowId", "treatment", "weight")
cohortMethodData$overallW <- overallW %>%
mutate(overallWeight = .data$weight / overallWSum) %>%
select("rowId", "overallWeight")
# Note: using abs() because due to rounding to machine precision number can become slightly negative:
result <- covariates %>%
inner_join(cohortMethodData$w, by = c("rowId")) %>%
inner_join(cohortMethodData$overallW, by = c("rowId")) %>%
group_by(.data$covariateId, .data$treatment) %>%
summarise(
sum = sum(as.numeric(.data$covariateValue), na.rm = TRUE),
mean = sum(.data$weight * as.numeric(.data$covariateValue), na.rm = TRUE),
overallMean = sum(.data$overallWeight * as.numeric(.data$covariateValue), na.rm = TRUE),
sumSqr = sum(.data$weight * as.numeric(.data$covariateValue)^2, na.rm = TRUE),
sumWSqr = sum(.data$weight^2, na.rm = TRUE),
overallSumSqr = sum(.data$overallWeight * as.numeric(.data$covariateValue)^2, na.rm = TRUE),
overallSumWSqr = sum(.data$overallWeight^2, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(sd = sqrt(abs(.data$sumSqr - .data$mean^2) * sumW / (sumW^2 - .data$sumWSqr))) %>%
ungroup() %>%
select("covariateId", "treatment", "sum", "mean", "sd", "overallMean", "overallSumSqr", "overallSumWSqr") %>%
collect()
cohortMethodData$w <- NULL
cohortMethodData$overallW <- NULL
} else {
# Don't use weighting
cohortCounts <- cohorts %>%
group_by(.data$treatment) %>%
count()
overallCount <- cohorts %>%
count() %>%
pull()
result <- covariates %>%
inner_join(select(cohorts, "rowId", "treatment"), by = "rowId") %>%
group_by(.data$covariateId, .data$treatment) %>%
summarise(
sum = sum(as.numeric(.data$covariateValue), na.rm = TRUE),
sumSqr = sum(as.numeric(.data$covariateValue)^2, na.rm = TRUE),
overallSumWSqr = sum(1 / overallCount^2, na.rm = TRUE),
.groups = "drop"
) %>%
inner_join(cohortCounts, by = "treatment") %>%
mutate(
sd = sqrt((.data$sumSqr - (.data$sum^2 / .data$n)) / .data$n),
mean = .data$sum / .data$n,
overallMean = .data$sum / overallCount,
overallSumSqr = .data$sumSqr / overallCount
) %>%
ungroup() %>%
select("covariateId", "treatment", "sum", "mean", "sd", "overallMean", "overallSumSqr", "overallSumWSqr") %>%
collect()
}
target <- result %>%
filter(.data$treatment == 1) %>%
select("covariateId",
sumTarget = "sum",
meanTarget = "mean",
sdTarget = "sd",
overallMeanTarget = "overallMean",
overallSumSqrTarget = "overallSumSqr",
overallSumWSqrTarget = "overallSumWSqr")
comparator <- result %>%
filter(.data$treatment == 0) %>%
select("covariateId",
sumComparator = "sum",
meanComparator = "mean",
sdComparator = "sd",
overallMeanComparator = "overallMean",
overallSumSqrComparator = "overallSumSqr",
overallSumWSqrComparator = "overallSumWSqr")
result <- target %>%
full_join(comparator, by = "covariateId") %>%
mutate(mean = .data$overallMeanTarget + .data$overallMeanComparator,
overallSumSqr = .data$overallSumSqrTarget + .data$overallSumSqrComparator,
overallSumWSqr = .data$overallSumWSqrTarget + .data$overallSumWSqrComparator) %>%
mutate(sd = sqrt(abs(.data$overallSumSqr - .data$mean^2) * sumW / (sumW^2 - .data$overallSumWSqr))) %>%
select(-"overallMeanTarget",
-"overallMeanComparator",
-"overallSumSqrTarget",
-"overallSumSqrComparator",
-"overallSumWSqrTarget",
-"overallSumWSqrComparator")
return(result)
}
#' Compute covariate balance before and after PS adjustment
#'
#' @description
#' For every covariate, prevalence in treatment and comparator groups before and after
#' matching/trimming/weighting are computed. When variable ratio matching was used
#' the balance score will be corrected according the method described in Austin et
#' al (2008).
#'
#' @template CohortMethodData
#'
#' @param population A data frame containing the people that are remaining after PS adjustment.
#' @param subgroupCovariateId Optional: a covariate ID of a binary covariate that indicates a subgroup of
#' interest. Both the before and after populations will be restricted to this
#' subgroup before computing covariate balance.
#' @param maxCohortSize If the target or comparator cohort are larger than this number, they
#' will be downsampled before computing covariate balance to save time.
#' Setting this number to 0 means no downsampling will be applied.
#' @param covariateFilter Determines the covariates for which to compute covariate balance. Either a vector
#' of covariate IDs, or a table 1 specifications object as generated for example using
#' [FeatureExtraction::getDefaultTable1Specifications()]. If `covariateFilter = NULL`,
#' balance will be computed for all variables found in the data.
#' @details
#' The population data frame should have the following three columns:
#'
#' - rowId (numeric): A unique identifier for each row (e.g. the person ID).
#' - treatment (integer): Column indicating whether the person is in the target (1) or comparator (0) group.
#' - propensityScore (numeric): Propensity score.
#'
#' @return
#' Returns a tibble describing the covariate balance before and after PS adjustment,
#' with one row per covariate, with the same data as the `covariateRef` table in the `CohortMethodData` object,
#' and the following additional columns:
#'
#' - beforeMatchingMeanTarget: The (weighted) mean value in the target before PS adjustment.
#' - beforeMatchingMeanComparator: The (weighted) mean value in the comparator before PS adjustment.
#' - beforeMatchingSumTarget: The (weighted) sum value in the target before PS adjustment.
#' - beforeMatchingSumComparator: The (weighted) sum value in the comparator before PS adjustment.
#' - beforeMatchingSdTarget: The standard deviation of the value in the target before PS adjustment.
#' - beforeMatchingSdComparator: The standard deviation of the value in the comparator before PS adjustment.
#' - beforeMatchingMean: The mean of the value across target and comparator before PS adjustment.
#' - beforeMatchingSd: The standard deviation of the value across target and comparator before PS adjustment.
#' - afterMatchingMeanTarget: The (weighted) mean value in the target after PS adjustment.
#' - afterMatchingMeanComparator: The (weighted) mean value in the comparator after PS adjustment.
#' - afterMatchingSumTarget: The (weighted) sum value in the target after PS adjustment.
#' - afterMatchingSumComparator: The (weighted) sum value in the comparator after PS adjustment.
#' - afterMatchingSdTarget: The standard deviation of the value in the target after PS adjustment.
#' - afterMatchingSdComparator: The standard deviation of the value in the comparator after PS adjustment.
#' - afterMatchingMean: The mean of the value across target and comparator after PS adjustment.
#' - afterMatchingSd: The standard deviation of the value across target and comparator after PS adjustment.
#' - beforeMatchingStdDiff: The standardized difference of means when comparing the target to
#' the comparator before PS adjustment.
#' - afterMatchingStdDiff: The standardized difference of means when comparing the target to
#' the comparator after PS adjustment.
#' - targetStdDiff: The standardized difference of means when comparing the target
#' before PS adjustment to the target after PS adjustment.
#' - comparatorStdDiff: The standardized difference of means when comparing the comparator
#' before PS adjustment to the comparator after PS adjustment.
#' -targetComparatorStdDiff: The standardized difference of means when comparing the entire
#' population before PS adjustment to the entire population after
#' PS adjustment.
#'
#' The 'beforeMatchingStdDiff' and 'afterMatchingStdDiff' columns inform on the balance:
#' are the target and comparator sufficiently similar in terms of baseline covariates to
#' allow for valid causal estimation?
#'
#' The 'targetStdDiff', 'comparatorStdDiff', and 'targetComparatorStdDiff' columns inform on
#' the generalizability: are the cohorts after PS adjustment sufficiently similar to the cohorts
#' before adjustment to allow generalizing the findings to the original cohorts?
#'
#' @references
#' Austin, P.C. (2008) Assessing balance in measured baseline covariates when using many-to-one
#' matching on the propensity-score. Pharmacoepidemiology and Drug Safety, 17: 1218-1225.
#'
#' @export
computeCovariateBalance <- function(population,
cohortMethodData,
subgroupCovariateId = NULL,
maxCohortSize = 250000,
covariateFilter = NULL) {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(population, add = errorMessages)
checkmate::assertClass(cohortMethodData, "CohortMethodData", add = errorMessages)
.assertCovariateId(subgroupCovariateId, len = 1, null.ok = TRUE, add = errorMessages)
checkmate::assertInt(maxCohortSize, lower = 0, add = errorMessages)
if (is.numeric(covariateFilter)) {
checkmate::assertIntegerish(covariateFilter, add = errorMessages)
} else if (!is.null(covariateFilter)) {
checkmate::assertDataFrame(covariateFilter, add = errorMessages)
checkmate::assertNames(colnames(covariateFilter), must.include = c("analysisId", "covariateIds"), add = errorMessages)
}
checkmate::reportAssertions(collection = errorMessages)
start <- Sys.time()
if (!is.null(subgroupCovariateId)) {
subGroupCovariate <- cohortMethodData$covariates %>%
filter(.data$covariateId == subgroupCovariateId) %>%
collect()
if (nrow(subGroupCovariate) == 0) {
stop("Cannot find covariate with ID ", subgroupCovariateId)
}
tempCohorts <- cohortMethodData$cohorts %>%
collect() %>%
filter(.data$rowId %in% subGroupCovariate$rowId) %>%
sampleCohorts(maxCohortSize = maxCohortSize)
if (nrow(tempCohorts) == 0) {
stop("Cannot find covariate with ID ", subgroupCovariateId, " in population before PS adjustment")
}
sumTreatment <- sum(tempCohorts$treatment)
if (sumTreatment == 0 || sumTreatment == nrow(tempCohorts)) {
stop("Subgroup population before PS adjustment doesn't have both target and comparator")
}
tempCohortsAfterMatching <- population %>%
filter(.data$rowId %in% subGroupCovariate$rowId) %>%
sampleCohorts(maxCohortSize = maxCohortSize)
if (nrow(tempCohortsAfterMatching) == 0) {
stop("Cannot find covariate with ID ", subgroupCovariateId, " in population after PS adjustment")
}
sumTreatment <- sum(tempCohortsAfterMatching$treatment)
if (sumTreatment == 0 || sumTreatment == nrow(tempCohortsAfterMatching)) {
stop("Subgroup population before PS adjustment doesn't have both target and comparator")
}
cohortMethodData$tempCohorts <- tempCohorts %>%
select("rowId", "treatment")
cohortMethodData$tempCohortsAfterMatching <- tempCohortsAfterMatching %>%
select("rowId", "treatment", matches("stratumId"), "iptw")
} else {
cohortMethodData$tempCohorts <- cohortMethodData$cohorts %>%
select("rowId", "treatment") %>%
sampleCohortsAndromeda(maxCohortSize = maxCohortSize, label = "before PS adjustment")
cohortMethodData$tempCohortsAfterMatching <- population %>%
select("rowId", "treatment", matches("stratumId"), matches("iptw")) %>%
sampleCohorts(maxCohortSize = maxCohortSize, label = "after PS adjustment")
}
on.exit(cohortMethodData$tempCohorts <- NULL)
on.exit(cohortMethodData$tempCohortsAfterMatching <- NULL, add = TRUE)
beforeMatching <- computeMeansPerGroup(cohortMethodData$tempCohorts, cohortMethodData, covariateFilter)
afterMatching <- computeMeansPerGroup(cohorts = cohortMethodData$tempCohortsAfterMatching, cohortMethodData, covariateFilter)
beforeMatching <- beforeMatching %>%
select("covariateId",
beforeMatchingMeanTarget = "meanTarget",
beforeMatchingMeanComparator = "meanComparator",
beforeMatchingSumTarget = "sumTarget",
beforeMatchingSumComparator = "sumComparator",
beforeMatchingSdTarget = "sdTarget",
beforeMatchingSdComparator = "sdComparator",
beforeMatchingMean = "mean",
beforeMatchingSd = "sd")
afterMatching <- afterMatching %>%
select("covariateId",
afterMatchingMeanTarget = "meanTarget",
afterMatchingMeanComparator = "meanComparator",
afterMatchingSumTarget = "sumTarget",
afterMatchingSumComparator = "sumComparator",
afterMatchingSdTarget = "sdTarget",
afterMatchingSdComparator = "sdComparator",
afterMatchingMean = "mean",
afterMatchingSd = "sd",
matches("overallMean"))
balance <- beforeMatching %>%
full_join(afterMatching, by = "covariateId") %>%
inner_join(collect(cohortMethodData$covariateRef), by = "covariateId") %>%
inner_join(cohortMethodData$analysisRef %>%
select("analysisId", "domainId", "isBinary") %>%
collect() %>%
mutate(domainId = as.factor(.data$domainId)), by = "analysisId") %>%
mutate(
beforeMatchingStdDiff = if_else(
.data$beforeMatchingSd == 0,
0,
(.data$beforeMatchingMeanTarget - .data$beforeMatchingMeanComparator) / .data$beforeMatchingSd
),
afterMatchingStdDiff = if_else(
.data$afterMatchingSd == 0,
0,
(.data$afterMatchingMeanTarget - .data$afterMatchingMeanComparator) / .data$afterMatchingSd
),
targetStdDiff = if_else(
.data$beforeMatchingSdTarget == 0,
0,
(.data$beforeMatchingMeanTarget - .data$afterMatchingMeanTarget) / .data$beforeMatchingSdTarget
),
comparatorStdDiff = if_else(
.data$beforeMatchingSdComparator == 0,
0,
(.data$beforeMatchingMeanComparator - .data$afterMatchingMeanComparator) / .data$beforeMatchingSdComparator
),
targetComparatorStdDiff = if_else(
.data$beforeMatchingSd == 0,
0,
(.data$beforeMatchingMean - .data$afterMatchingMean) / .data$beforeMatchingSd
)
) %>%
arrange(desc(abs(.data$beforeMatchingStdDiff)))
metaData <- attr(population, "metaData")
if (!is.null(metaData) && !is.null(metaData$targetEstimator)) {
attr(balance, "targetEstimator") <- metaData$targetEstimator
}
delta <- Sys.time() - start
message(paste("Computing covariate balance took", signif(delta, 3), attr(delta, "units")))
return(balance)
}
sampleSingleCohort <- function(cohorts, treatment, maxCohortSize) {
variableStrata <- FALSE
if ("stratumId" %in% colnames(cohorts)) {
strataSizes <- cohorts %>%
filter(.data$treatment == !!treatment) %>%
group_by(.data$stratumId) %>%
summarise(count = n())
variableStrata <- nrow(strataSizes) > 20 && any(strataSizes$count > 1)
}
if (variableStrata) {
# If there are many small and large strata (variable ratio matching), small strata are more likely
# to be completely deleted by row-based sampling than larger strata, causing bias. Hence we're sampling
# entire strata to achieve the desired number of rows:
strataSizes <- strataSizes[sample.int(nrow(strataSizes), replace = F), ]
stratumIdsToKeep <- strataSizes$stratumId[cumsum(strataSizes$count) <= maxCohortSize]
idx <- which(cohorts$treatment == !!treatment & cohorts$stratumId %in% stratumIdsToKeep)
} else {
idx <- which(cohorts$treatment == !!treatment)
idx <- sample(idx, maxCohortSize)
}
return(idx)
}
sampleCohorts <- function(cohorts, maxCohortSize, label) {
if (maxCohortSize <= 0) {
return(cohorts)
}
sampled <- FALSE
targetIdx <- which(cohorts$treatment == 1)
comparatorIdx <- which(cohorts$treatment == 0)
targetCount <- length(targetIdx)
comparatorCount <- length(comparatorIdx)
if (targetCount > maxCohortSize) {
targetIdx <- sampleSingleCohort(cohorts, 1, maxCohortSize)
message(
"Downsampling target cohort ",
label,
" from ",
targetCount,
" to ",
length(targetIdx),
" before computing covariate balance"
)
sampled <- TRUE
}
if (comparatorCount > maxCohortSize) {
comparatorIdx <- sampleSingleCohort(cohorts, 0, maxCohortSize)
message(
"Downsampling comparator cohort ",
label,
" from ",
comparatorCount,
" to ",
length(comparatorIdx),
" before computing covariate balance"
)
sampled <- TRUE
}
if (sampled) {
return(cohorts[c(targetIdx, comparatorIdx), ])
} else {
return(cohorts)
}
}
sampleCohortsAndromeda <- function(cohorts, maxCohortSize, label) {
if (maxCohortSize <= 0) {
return(cohorts)
}
cohortCounts <- cohorts %>%
group_by(.data$treatment) %>%
count() %>%
collect()
if (any(cohortCounts$n > maxCohortSize)) {
return(sampleCohorts(collect(cohorts), maxCohortSize, label))
} else {
return(cohorts)
}
}
#' Create a scatterplot of the covariate balance
#'
#' @description
#' Create a scatterplot of the covariate balance, showing all variables with balance before and after
#' matching on the x and y axis respectively. Requires running `computeCovariateBalance` first.
#'
#' @return
#' A ggplot object. Use the [ggplot2::ggsave] function to save to file in a different
#' format.
#'
#' @param balance A data frame created by the `computeCovariateBalance` function.
#' @param absolute Should the absolute value of the difference be used?
#' @param threshold Show a threshold value for after matching standardized difference.
#' @param title The main title for the plot.
#' @param fileName Name of the file where the plot should be saved, for example 'plot.png'. See the
#' function `ggsave` in the ggplot2 package for supported file formats.
#' @param beforeLabel Label for the x-axis.
#' @param afterLabel Label for the y-axis.
#' @param showCovariateCountLabel Show a label with the number of covariates included in the plot?
#' @param showMaxLabel Show a label with the maximum absolute standardized difference after matching/stratification?
#'
#' @export
plotCovariateBalanceScatterPlot <- function(balance,
absolute = TRUE,
threshold = 0,
title = "Standardized difference of mean",
fileName = NULL,
beforeLabel = "Before matching",
afterLabel = "After matching",
showCovariateCountLabel = FALSE,
showMaxLabel = FALSE) {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(balance, add = errorMessages)
checkmate::assertLogical(absolute, len = 1, add = errorMessages)
checkmate::assertNumber(threshold, lower = 0, add = errorMessages)
checkmate::assertCharacter(title, len = 1, add = errorMessages)
checkmate::assertCharacter(fileName, len = 1, null.ok = TRUE, add = errorMessages)
checkmate::assertCharacter(beforeLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(afterLabel, len = 1, add = errorMessages)
checkmate::assertLogical(showCovariateCountLabel, len = 1, add = errorMessages)
checkmate::assertLogical(showMaxLabel, len = 1, add = errorMessages)
checkmate::reportAssertions(collection = errorMessages)
if (absolute) {
balance$beforeMatchingStdDiff <- abs(balance$beforeMatchingStdDiff)
balance$afterMatchingStdDiff <- abs(balance$afterMatchingStdDiff)
}
limits <- c(
min(c(balance$beforeMatchingStdDiff, balance$afterMatchingStdDiff), na.rm = TRUE),
max(c(balance$beforeMatchingStdDiff, balance$afterMatchingStdDiff), na.rm = TRUE)
)
plot <- ggplot2::ggplot(
balance,
ggplot2::aes(x = .data$beforeMatchingStdDiff, y = .data$afterMatchingStdDiff)
) +
ggplot2::geom_point(color = rgb(0, 0, 0.8, alpha = 0.3), shape = 16) +
ggplot2::geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
ggplot2::geom_hline(yintercept = 0) +
ggplot2::geom_vline(xintercept = 0) +
ggplot2::ggtitle(title) +
ggplot2::scale_x_continuous(beforeLabel, limits = limits) +
ggplot2::scale_y_continuous(afterLabel, limits = limits)
if (threshold != 0) {
plot <- plot + ggplot2::geom_hline(yintercept = c(
threshold,
-threshold
), alpha = 0.5, linetype = "dotted")
}
if (showCovariateCountLabel || showMaxLabel) {
labels <- c()
if (showCovariateCountLabel) {
labels <- c(labels, sprintf("Number of covariates: %s", format(nrow(balance), big.mark = ",", scientific = FALSE)))
}
if (showMaxLabel) {
labels <- c(labels, sprintf("%s max(absolute): %.2f", afterLabel, max(abs(balance$afterMatchingStdDiff), na.rm = TRUE)))
}
dummy <- data.frame(text = paste(labels, collapse = "\n"))
plot <- plot + ggplot2::geom_label(x = limits[1] + 0.01, y = limits[2], hjust = "left", vjust = "top", alpha = 0.8, ggplot2::aes(label = text), data = dummy, size = 3.5)
}
if (!is.null(fileName)) {
ggplot2::ggsave(fileName, plot, width = 4, height = 4, dpi = 400)
}
return(plot)
}
.truncRight <- function(x, n) {
nc <- nchar(x)
x[nc > (n - 3)] <- paste("...",
substr(x[nc > (n - 3)], nc[nc > (n - 3)] - n + 1, nc[nc > (n - 3)]),
sep = ""
)
x
}
#' Plot variables with largest imbalance
#'
#' @description
#' Create a plot showing those variables having the largest imbalance before matching, and those
#' variables having the largest imbalance after matching. Requires running
#' `computeCovariateBalance` first.
#'
#' @return
#' A ggplot object. Use the [ggplot2::ggsave] function to save to file in a different
#' format.
#'
#' @param balance A data frame created by the `computeCovariateBalance` function.
#' @param n (Maximum) count of covariates to plot.
#' @param maxNameWidth Covariate names longer than this number of characters are truncated to create
#' a nicer plot.
#' @param title Optional: the main title for the plot.
#' @param fileName Name of the file where the plot should be saved, for example 'plot.png'. See
#' the function `ggsave` in the ggplot2 package for supported file formats.
#' @param beforeLabel Label for identifying data before matching / stratification / trimming.
#' @param afterLabel Label for identifying data after matching / stratification / trimming.
#'
#' @export
plotCovariateBalanceOfTopVariables <- function(balance,
n = 20,
maxNameWidth = 100,
title = NULL,
fileName = NULL,
beforeLabel = "before matching",
afterLabel = "after matching") {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(balance, add = errorMessages)
checkmate::assertInt(n, lower = 1, add = errorMessages)
checkmate::assertInt(maxNameWidth, lower = 1, add = errorMessages)
checkmate::assertCharacter(title, null.ok = TRUE, len = 1, add = errorMessages)
checkmate::assertCharacter(fileName, len = 1, null.ok = TRUE, add = errorMessages)
checkmate::assertCharacter(beforeLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(afterLabel, len = 1, add = errorMessages)
checkmate::reportAssertions(collection = errorMessages)
n <- min(n, nrow(balance))
topBefore <- balance[order(-abs(balance$beforeMatchingStdDiff)), ]
topBefore <- topBefore[1:n, ]
topBefore$facet <- paste("Top", n, beforeLabel)
topAfter <- balance[order(-abs(balance$afterMatchingStdDiff)), ]
topAfter <- topAfter[1:n, ]
topAfter$facet <- paste("Top", n, afterLabel)
filtered <- rbind(topBefore, topAfter)
data <- tibble(
covariateId = rep(filtered$covariateId, 2),
covariate = rep(filtered$covariateName, 2),
difference = c(filtered$beforeMatchingStdDiff, filtered$afterMatchingStdDiff),
group = rep(c(beforeLabel, afterLabel), each = nrow(filtered)),
facet = rep(filtered$facet, 2),
rowId = rep(nrow(filtered):1, 2)
)
filtered$covariateName <- .truncRight(as.character(filtered$covariateName), maxNameWidth)
data$facet <- factor(data$facet, levels = c(paste("Top", n, beforeLabel), paste("Top", n, afterLabel)))
data$group <- factor(data$group, levels = c(beforeLabel, afterLabel))
plot <- ggplot2::ggplot(data, ggplot2::aes(
x = .data$difference,
y = .data$rowId,
color = .data$group,
group = .data$group,
fill = .data$group,
shape = .data$group
)) +
ggplot2::geom_point() +
ggplot2::geom_vline(xintercept = 0) +
ggplot2::scale_fill_manual(values = c(
rgb(0.8, 0, 0, alpha = 0.5),
rgb(0, 0, 0.8, alpha = 0.5)
)) +
ggplot2::scale_color_manual(values = c(
rgb(0.8, 0, 0, alpha = 0.5),
rgb(0, 0, 0.8, alpha = 0.5)
)) +
ggplot2::scale_x_continuous("Standardized difference of mean") +
ggplot2::scale_y_continuous(breaks = nrow(filtered):1, labels = filtered$covariateName) +
ggplot2::facet_grid(facet ~ ., scales = "free", space = "free") +
ggplot2::theme(
axis.text.y = ggplot2::element_text(size = 7),
axis.title.y = ggplot2::element_blank(),
legend.position = "top",
legend.direction = "vertical",
legend.title = ggplot2::element_blank()
)
if (!is.null(title)) {
plot <- plot + ggplot2::ggtitle(title)
}
if (!is.null(fileName)) {
ggplot2::ggsave(fileName, plot, width = 10, height = max(2 + n * 0.2, 5), dpi = 400)
}
return(plot)
}
#' Plot covariate prevalence
#'
#' @description
#' Plot prevalence of binary covariates in the target and comparator cohorts, before and after matching.
#' Requires running `computeCovariateBalance` first.
#'
#' @return
#' A ggplot object. Use the [ggplot2::ggsave] function to save to file in a different
#' format.
#'
#' @param balance A data frame created by the `computeCovariateBalance` function.
#' @param threshold A threshold value for standardized difference. When exceeding the threshold, covariates will be
#' marked in a different color. If `threshold = 0`, no color coding will be used.
#' @param title The main title for the plot.
#' @param fileName Name of the file where the plot should be saved, for example 'plot.png'. See the
#' function `ggsave` in the ggplot2 package for supported file formats.
#' @param beforeLabel Label for the before matching / stratification panel.
#' @param afterLabel Label for the after matching / stratification panel.
#' @param targetLabel Label for the x-axis.
#' @param comparatorLabel Label for the y-axis.
#'
#' @export
plotCovariatePrevalence <- function(balance,
threshold = 0,
title = "Covariate prevalence",
fileName = NULL,
beforeLabel = "Before matching",
afterLabel = "After matching",
targetLabel = "Target",
comparatorLabel = "Comparator") {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(balance, add = errorMessages)
checkmate::assertNumber(threshold, lower = 0, add = errorMessages)
checkmate::assertCharacter(title, len = 1, add = errorMessages)
checkmate::assertCharacter(fileName, len = 1, null.ok = TRUE, add = errorMessages)
checkmate::assertCharacter(beforeLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(afterLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(targetLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(comparatorLabel, len = 1, add = errorMessages)
checkmate::reportAssertions(collection = errorMessages)
balance <- balance %>%
filter(.data$isBinary == "Y")
prevalence <- bind_rows(
balance %>%
select(
target = "beforeMatchingMeanTarget",
comparator = "beforeMatchingMeanComparator",
stdDiff = "beforeMatchingStdDiff"
) %>%
mutate(panel = beforeLabel),
balance %>%
select(
target = "afterMatchingMeanTarget",
comparator = "afterMatchingMeanComparator",
stdDiff = "afterMatchingStdDiff"
) %>%
mutate(panel = afterLabel)
) %>%
mutate(
target = .data$target * 100,
comparator = .data$comparator * 100,
stdDiff = if_else(!is.na(.data$stdDiff) & abs(.data$stdDiff) > threshold,
sprintf("> %0.2f", threshold),
sprintf("<= %0.2f", threshold)
)
)
prevalence$panel <- factor(prevalence$panel, levels = c(beforeLabel, afterLabel))
if (threshold > 0) {
plot <- ggplot2::ggplot(prevalence, ggplot2::aes(x = .data$comparator, y = .data$target, color = .data$stdDiff)) +
ggplot2::geom_point(alpha = 0.3, shape = 16) +
ggplot2::scale_color_manual("Std. diff.", values = c(rgb(0, 0, 0.8), rgb(0.8, 0, 0)))
} else {
plot <- ggplot2::ggplot(prevalence, ggplot2::aes(x = .data$comparator, y = .data$target)) +
ggplot2::geom_point(color = rgb(0, 0, 0.8, alpha = 0.3), shape = 16)
}
plot <- plot + ggplot2::geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
ggplot2::geom_hline(yintercept = 0) +
ggplot2::geom_vline(xintercept = 0) +
ggplot2::ggtitle(title) +
ggplot2::scale_x_continuous(sprintf("Prevalence in %s (%%)", targetLabel), limits = c(0, 100)) +
ggplot2::scale_y_continuous(sprintf("Prevalence in %s (%%)", comparatorLabel), limits = c(0, 100)) +
ggplot2::facet_grid(~ .data$panel)
if (!is.null(fileName)) {
ggplot2::ggsave(filename = fileName, plot = plot, width = 8, height = 4, dpi = 400)
}
return(plot)
}
#' Get information on generalizability
#'
#' @description
#' to assess generalizability we compare the distribution of covariates before and after
#' any (propensity score) adjustments. We compute the standardized difference of mean as
#' our metric of generalizability. (Lipton et al., 2017)
#'
#' Depending on our target estimand, we need to consider a different base population for
#' generalizability. For example, if we aim to estimate the average treatment effect in
#' thetreated (ATT), our base population should be the target population, meaning we
#' should consider the covariate distribution before and after PS adjustment in the target
#' population only. By default this function will attempt to select the right base
#' population based on what operations have been performed on the population. For example,
#' if PS matching has been performed we assume the target estimand is the ATT, and the
#' target population is selected as base.
#'
#' Requires running [computeCovariateBalance()]` first.
#'
#' @param balance A data frame created by the `computeCovariateBalance` function.
#' @param baseSelection The selection of the population to consider for generalizability.
#' Options are "auto", "target", "comparator", and "both". The "auto"
#' option will attempt to use the balance meta-data to pick the most
#' appropriate population based on the target estimator.
#'
#' @return
#' A tibble with the following columns:
#'
#' - covariateId: The ID of the covariate. Can be linked to the `covariates` and `covariateRef`
#' tables in the `CohortMethodData` object.
#' - covariateName: The name of the covariate.
#' - beforeMatchingMean: The mean covariate value before any (propensity score) adjustment.
#' - afterMatchingMean: The mean covariate value after any (propensity score) adjustment.
#' - stdDiff: The standardized difference of means between before and after adjustment.
#'
#' The tibble also has a 'baseSelection' attribute, documenting the base population used
#' to assess generalizability.
#'
#' @references Tipton E, Hallberg K, Hedges LV, Chan W (2017) Implications of Small Samples
#' for Generalization: Adjustments and Rules of Thumb, Eval Rev. Oct;41(5):472-505.
#'
#' @export
getGeneralizabilityTable <- function(balance, baseSelection = "auto") {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(balance, add = errorMessages)
checkmate::assertCharacter(baseSelection, len = 1, add = errorMessages)
checkmate::assertChoice(baseSelection, c("auto", "target", "comparator", "both"), add = errorMessages)
checkmate::reportAssertions(collection = errorMessages)
if (baseSelection == "auto") {
targetEstimator <- attr(balance, "targetEstimator")
if (is.null(targetEstimator)) {
stop("The baseSelection is set to 'auto' but the balance object does not contain a target estimator attribute. ",
"Please set the baseSelection manually.")
}
if (targetEstimator == "ate" | targetEstimator == "ato") {
baseSelection <- "both"
message("Selecting both target and comparator as base for generalizability")
} else if (targetEstimator == "att") {
baseSelection <- "target"
message("Selecting target as base for generalizability")
} else if (targetEstimator == "atu") {
baseSelection <- "comparator"
message("Selecting comparator as base for generalizability")
} else {
stop("Unkown target estimator: ", targetEstimator)
}
}
if (baseSelection == "target") {
generalizability <- balance %>%
mutate(absGeneralizabilityStdDiff = abs(.data$targetStdDiff)) %>%
arrange(desc(.data$absGeneralizabilityStdDiff)) %>%
select("covariateId",
"covariateName",
beforeMatchingMean = "beforeMatchingMeanTarget",
afterMatchingMean = "afterMatchingMeanTarget",
stdDiff = "targetStdDiff")
} else if (baseSelection == "comparator") {
generalizability <- balance %>%
mutate(absGeneralizabilityStdDiff = abs(.data$comparatorStdDiff)) %>%
arrange(desc(.data$absGeneralizabilityStdDiff)) %>%
select("covariateId",
"covariateName",
beforeMatchingMean = "beforeMatchingMeanComparator",
afterMatchingMean = "afterMatchingMeanComparator",
stdDiff = "comparatorStdDiff")
} else {
generalizability <- balance %>%
mutate(absGeneralizabilityStdDiff = abs(.data$targetComparatorStdDiff)) %>%
arrange(desc(.data$absGeneralizabilityStdDiff)) %>%
select("covariateId",
"covariateName",
"beforeMatchingMean",
"afterMatchingMean",
stdDiff = "targetComparatorStdDiff")
}
attr(generalizability, "baseSelection") <- baseSelection
return(generalizability)
}
OHDSI/CohortMethod documentation built on Oct. 9, 2024, 12:50 p.m.
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Defines functions getGeneralizabilityTable plotCovariatePrevalence plotCovariateBalanceOfTopVariables .truncRight plotCovariateBalanceScatterPlot sampleCohortsAndromeda sampleCohorts sampleSingleCohort computeCovariateBalance computeMeansPerGroup filterCovariates
Documented in computeCovariateBalance getGeneralizabilityTable plotCovariateBalanceOfTopVariables plotCovariateBalanceScatterPlot plotCovariatePrevalence
# Copyright 2024 Observational Health Data Sciences and Informatics
#
# This file is part of CohortMethod
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# @author Observational Health Data Sciences and Informatics
# @author Patrick Ryan
# @author Marc Suchard
# @author Martijn Schuemie
filterCovariates <- function(covariates, covariateRef, covariateFilter) {
if (is.null(covariateFilter)) {
return(covariates)
} else if (is.numeric(covariateFilter)) {
covariates %>%
filter(.data$covariateId %in% covariateFilter) %>%
return()
} else if (is.data.frame(covariateFilter) && all(c("analysisId", "covariateIds") %in% colnames(covariateFilter))) {
analysisIds <- covariateFilter %>%
filter(is.na(.data$covariateIds)) %>%
pull(.data$analysisId)
covariateIds1 <- covariateRef %>%
filter(.data$analysisId %in% analysisIds) %>%
pull(.data$covariateId)
covariateIds2 <- covariateFilter %>%
filter(!is.na(.data$covariateIds)) %>%
pull(.data$covariateIds) %>%
strsplit(",|;") %>%
unlist() %>%
as.numeric()
covariates %>%
filter(.data$covariateId %in% c(covariateIds1, covariateIds2)) %>%
return()
} else {
stop("Unknown covariateFilter type")
}
}
computeMeansPerGroup <- function(cohorts, cohortMethodData, covariateFilter) {
hasStrata <- "stratumId" %in% colnames(cohorts)
hasIptw <- "iptw" %in% colnames(cohorts)
if (hasStrata) {
stratumSize <- cohorts %>%
group_by(.data$stratumId, .data$treatment) %>%
count() %>%
ungroup() %>%
collect()
}
useWeighting <- (hasStrata && any(stratumSize %>% pull(.data$n) > 1)) ||
(!hasStrata && hasIptw)
# By definition:
sumW <- 1
covariates <- filterCovariates(cohortMethodData$covariates, cohortMethodData$covariateRef, covariateFilter)
if (useWeighting) {
if (hasStrata) {
# Variable strata sizes detected: weigh by size of strata set
w <- stratumSize %>%
mutate(weight = 1 / .data$n) %>%
inner_join(cohorts, by = c("stratumId", "treatment"), copy = TRUE) %>%
select("rowId", "treatment", "weight")
# Overall weight is for computing mean and SD across T and C
overallW <- stratumSize %>%
group_by(.data$stratumId) %>%
summarise(weight = 1 / sum(.data$n, na.rm = TRUE)) %>%
ungroup() %>%
inner_join(cohorts, by = c("stratumId"), copy = TRUE) %>%
select("rowId", "weight")
} else {
w <- cohorts %>%
mutate(weight = .data$iptw) %>%
select("rowId", "treatment", "weight") %>%
collect()
overallW <- w
}
# Normalize so sum(weight) == 1 per treatment arm:
wSum <- w %>%
group_by(.data$treatment) %>%
summarize(wSum = sum(.data$weight, na.rm = TRUE)) %>%
ungroup()
overallWSum <- overallW %>%
summarize(overallWSum = sum(.data$weight, na.rm = TRUE)) %>%
pull()
cohortMethodData$w <- w %>%
inner_join(wSum, by = "treatment") %>%
mutate(weight = .data$weight / .data$wSum) %>%
select("rowId", "treatment", "weight")
cohortMethodData$overallW <- overallW %>%
mutate(overallWeight = .data$weight / overallWSum) %>%
select("rowId", "overallWeight")
# Note: using abs() because due to rounding to machine precision number can become slightly negative:
result <- covariates %>%
inner_join(cohortMethodData$w, by = c("rowId")) %>%
inner_join(cohortMethodData$overallW, by = c("rowId")) %>%
group_by(.data$covariateId, .data$treatment) %>%
summarise(
sum = sum(as.numeric(.data$covariateValue), na.rm = TRUE),
mean = sum(.data$weight * as.numeric(.data$covariateValue), na.rm = TRUE),
overallMean = sum(.data$overallWeight * as.numeric(.data$covariateValue), na.rm = TRUE),
sumSqr = sum(.data$weight * as.numeric(.data$covariateValue)^2, na.rm = TRUE),
sumWSqr = sum(.data$weight^2, na.rm = TRUE),
overallSumSqr = sum(.data$overallWeight * as.numeric(.data$covariateValue)^2, na.rm = TRUE),
overallSumWSqr = sum(.data$overallWeight^2, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(sd = sqrt(abs(.data$sumSqr - .data$mean^2) * sumW / (sumW^2 - .data$sumWSqr))) %>%
ungroup() %>%
select("covariateId", "treatment", "sum", "mean", "sd", "overallMean", "overallSumSqr", "overallSumWSqr") %>%
collect()
cohortMethodData$w <- NULL
cohortMethodData$overallW <- NULL
} else {
# Don't use weighting
cohortCounts <- cohorts %>%
group_by(.data$treatment) %>%
count()
overallCount <- cohorts %>%
count() %>%
pull()
result <- covariates %>%
inner_join(select(cohorts, "rowId", "treatment"), by = "rowId") %>%
group_by(.data$covariateId, .data$treatment) %>%
summarise(
sum = sum(as.numeric(.data$covariateValue), na.rm = TRUE),
sumSqr = sum(as.numeric(.data$covariateValue)^2, na.rm = TRUE),
overallSumWSqr = sum(1 / overallCount^2, na.rm = TRUE),
.groups = "drop"
) %>%
inner_join(cohortCounts, by = "treatment") %>%
mutate(
sd = sqrt((.data$sumSqr - (.data$sum^2 / .data$n)) / .data$n),
mean = .data$sum / .data$n,
overallMean = .data$sum / overallCount,
overallSumSqr = .data$sumSqr / overallCount
) %>%
ungroup() %>%
select("covariateId", "treatment", "sum", "mean", "sd", "overallMean", "overallSumSqr", "overallSumWSqr") %>%
collect()
}
target <- result %>%
filter(.data$treatment == 1) %>%
select("covariateId",
sumTarget = "sum",
meanTarget = "mean",
sdTarget = "sd",
overallMeanTarget = "overallMean",
overallSumSqrTarget = "overallSumSqr",
overallSumWSqrTarget = "overallSumWSqr")
comparator <- result %>%
filter(.data$treatment == 0) %>%
select("covariateId",
sumComparator = "sum",
meanComparator = "mean",
sdComparator = "sd",
overallMeanComparator = "overallMean",
overallSumSqrComparator = "overallSumSqr",
overallSumWSqrComparator = "overallSumWSqr")
result <- target %>%
full_join(comparator, by = "covariateId") %>%
mutate(mean = .data$overallMeanTarget + .data$overallMeanComparator,
overallSumSqr = .data$overallSumSqrTarget + .data$overallSumSqrComparator,
overallSumWSqr = .data$overallSumWSqrTarget + .data$overallSumWSqrComparator) %>%
mutate(sd = sqrt(abs(.data$overallSumSqr - .data$mean^2) * sumW / (sumW^2 - .data$overallSumWSqr))) %>%
select(-"overallMeanTarget",
-"overallMeanComparator",
-"overallSumSqrTarget",
-"overallSumSqrComparator",
-"overallSumWSqrTarget",
-"overallSumWSqrComparator")
return(result)
}
#' Compute covariate balance before and after PS adjustment
#'
#' @description
#' For every covariate, prevalence in treatment and comparator groups before and after
#' matching/trimming/weighting are computed. When variable ratio matching was used
#' the balance score will be corrected according the method described in Austin et
#' al (2008).
#'
#' @template CohortMethodData
#'
#' @param population A data frame containing the people that are remaining after PS adjustment.
#' @param subgroupCovariateId Optional: a covariate ID of a binary covariate that indicates a subgroup of
#' interest. Both the before and after populations will be restricted to this
#' subgroup before computing covariate balance.
#' @param maxCohortSize If the target or comparator cohort are larger than this number, they
#' will be downsampled before computing covariate balance to save time.
#' Setting this number to 0 means no downsampling will be applied.
#' @param covariateFilter Determines the covariates for which to compute covariate balance. Either a vector
#' of covariate IDs, or a table 1 specifications object as generated for example using
#' [FeatureExtraction::getDefaultTable1Specifications()]. If `covariateFilter = NULL`,
#' balance will be computed for all variables found in the data.
#' @details
#' The population data frame should have the following three columns:
#'
#' - rowId (numeric): A unique identifier for each row (e.g. the person ID).
#' - treatment (integer): Column indicating whether the person is in the target (1) or comparator (0) group.
#' - propensityScore (numeric): Propensity score.
#'
#' @return
#' Returns a tibble describing the covariate balance before and after PS adjustment,
#' with one row per covariate, with the same data as the `covariateRef` table in the `CohortMethodData` object,
#' and the following additional columns:
#'
#' - beforeMatchingMeanTarget: The (weighted) mean value in the target before PS adjustment.
#' - beforeMatchingMeanComparator: The (weighted) mean value in the comparator before PS adjustment.
#' - beforeMatchingSumTarget: The (weighted) sum value in the target before PS adjustment.
#' - beforeMatchingSumComparator: The (weighted) sum value in the comparator before PS adjustment.
#' - beforeMatchingSdTarget: The standard deviation of the value in the target before PS adjustment.
#' - beforeMatchingSdComparator: The standard deviation of the value in the comparator before PS adjustment.
#' - beforeMatchingMean: The mean of the value across target and comparator before PS adjustment.
#' - beforeMatchingSd: The standard deviation of the value across target and comparator before PS adjustment.
#' - afterMatchingMeanTarget: The (weighted) mean value in the target after PS adjustment.
#' - afterMatchingMeanComparator: The (weighted) mean value in the comparator after PS adjustment.
#' - afterMatchingSumTarget: The (weighted) sum value in the target after PS adjustment.
#' - afterMatchingSumComparator: The (weighted) sum value in the comparator after PS adjustment.
#' - afterMatchingSdTarget: The standard deviation of the value in the target after PS adjustment.
#' - afterMatchingSdComparator: The standard deviation of the value in the comparator after PS adjustment.
#' - afterMatchingMean: The mean of the value across target and comparator after PS adjustment.
#' - afterMatchingSd: The standard deviation of the value across target and comparator after PS adjustment.
#' - beforeMatchingStdDiff: The standardized difference of means when comparing the target to
#' the comparator before PS adjustment.
#' - afterMatchingStdDiff: The standardized difference of means when comparing the target to
#' the comparator after PS adjustment.
#' - targetStdDiff: The standardized difference of means when comparing the target
#' before PS adjustment to the target after PS adjustment.
#' - comparatorStdDiff: The standardized difference of means when comparing the comparator
#' before PS adjustment to the comparator after PS adjustment.
#' -targetComparatorStdDiff: The standardized difference of means when comparing the entire
#' population before PS adjustment to the entire population after
#' PS adjustment.
#'
#' The 'beforeMatchingStdDiff' and 'afterMatchingStdDiff' columns inform on the balance:
#' are the target and comparator sufficiently similar in terms of baseline covariates to
#' allow for valid causal estimation?
#'
#' The 'targetStdDiff', 'comparatorStdDiff', and 'targetComparatorStdDiff' columns inform on
#' the generalizability: are the cohorts after PS adjustment sufficiently similar to the cohorts
#' before adjustment to allow generalizing the findings to the original cohorts?
#'
#' @references
#' Austin, P.C. (2008) Assessing balance in measured baseline covariates when using many-to-one
#' matching on the propensity-score. Pharmacoepidemiology and Drug Safety, 17: 1218-1225.
#'
#' @export
computeCovariateBalance <- function(population,
cohortMethodData,
subgroupCovariateId = NULL,
maxCohortSize = 250000,
covariateFilter = NULL) {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(population, add = errorMessages)
checkmate::assertClass(cohortMethodData, "CohortMethodData", add = errorMessages)
.assertCovariateId(subgroupCovariateId, len = 1, null.ok = TRUE, add = errorMessages)
checkmate::assertInt(maxCohortSize, lower = 0, add = errorMessages)
if (is.numeric(covariateFilter)) {
checkmate::assertIntegerish(covariateFilter, add = errorMessages)
} else if (!is.null(covariateFilter)) {
checkmate::assertDataFrame(covariateFilter, add = errorMessages)
checkmate::assertNames(colnames(covariateFilter), must.include = c("analysisId", "covariateIds"), add = errorMessages)
}
checkmate::reportAssertions(collection = errorMessages)
start <- Sys.time()
if (!is.null(subgroupCovariateId)) {
subGroupCovariate <- cohortMethodData$covariates %>%
filter(.data$covariateId == subgroupCovariateId) %>%
collect()
if (nrow(subGroupCovariate) == 0) {
stop("Cannot find covariate with ID ", subgroupCovariateId)
}
tempCohorts <- cohortMethodData$cohorts %>%
collect() %>%
filter(.data$rowId %in% subGroupCovariate$rowId) %>%
sampleCohorts(maxCohortSize = maxCohortSize)
if (nrow(tempCohorts) == 0) {
stop("Cannot find covariate with ID ", subgroupCovariateId, " in population before PS adjustment")
}
sumTreatment <- sum(tempCohorts$treatment)
if (sumTreatment == 0 || sumTreatment == nrow(tempCohorts)) {
stop("Subgroup population before PS adjustment doesn't have both target and comparator")
}
tempCohortsAfterMatching <- population %>%
filter(.data$rowId %in% subGroupCovariate$rowId) %>%
sampleCohorts(maxCohortSize = maxCohortSize)
if (nrow(tempCohortsAfterMatching) == 0) {
stop("Cannot find covariate with ID ", subgroupCovariateId, " in population after PS adjustment")
}
sumTreatment <- sum(tempCohortsAfterMatching$treatment)
if (sumTreatment == 0 || sumTreatment == nrow(tempCohortsAfterMatching)) {
stop("Subgroup population before PS adjustment doesn't have both target and comparator")
}
cohortMethodData$tempCohorts <- tempCohorts %>%
select("rowId", "treatment")
cohortMethodData$tempCohortsAfterMatching <- tempCohortsAfterMatching %>%
select("rowId", "treatment", matches("stratumId"), "iptw")
} else {
cohortMethodData$tempCohorts <- cohortMethodData$cohorts %>%
select("rowId", "treatment") %>%
sampleCohortsAndromeda(maxCohortSize = maxCohortSize, label = "before PS adjustment")
cohortMethodData$tempCohortsAfterMatching <- population %>%
select("rowId", "treatment", matches("stratumId"), matches("iptw")) %>%
sampleCohorts(maxCohortSize = maxCohortSize, label = "after PS adjustment")
}
on.exit(cohortMethodData$tempCohorts <- NULL)
on.exit(cohortMethodData$tempCohortsAfterMatching <- NULL, add = TRUE)
beforeMatching <- computeMeansPerGroup(cohortMethodData$tempCohorts, cohortMethodData, covariateFilter)
afterMatching <- computeMeansPerGroup(cohorts = cohortMethodData$tempCohortsAfterMatching, cohortMethodData, covariateFilter)
beforeMatching <- beforeMatching %>%
select("covariateId",
beforeMatchingMeanTarget = "meanTarget",
beforeMatchingMeanComparator = "meanComparator",
beforeMatchingSumTarget = "sumTarget",
beforeMatchingSumComparator = "sumComparator",
beforeMatchingSdTarget = "sdTarget",
beforeMatchingSdComparator = "sdComparator",
beforeMatchingMean = "mean",
beforeMatchingSd = "sd")
afterMatching <- afterMatching %>%
select("covariateId",
afterMatchingMeanTarget = "meanTarget",
afterMatchingMeanComparator = "meanComparator",
afterMatchingSumTarget = "sumTarget",
afterMatchingSumComparator = "sumComparator",
afterMatchingSdTarget = "sdTarget",
afterMatchingSdComparator = "sdComparator",
afterMatchingMean = "mean",
afterMatchingSd = "sd",
matches("overallMean"))
balance <- beforeMatching %>%
full_join(afterMatching, by = "covariateId") %>%
inner_join(collect(cohortMethodData$covariateRef), by = "covariateId") %>%
inner_join(cohortMethodData$analysisRef %>%
select("analysisId", "domainId", "isBinary") %>%
collect() %>%
mutate(domainId = as.factor(.data$domainId)), by = "analysisId") %>%
mutate(
beforeMatchingStdDiff = if_else(
.data$beforeMatchingSd == 0,
0,
(.data$beforeMatchingMeanTarget - .data$beforeMatchingMeanComparator) / .data$beforeMatchingSd
),
afterMatchingStdDiff = if_else(
.data$afterMatchingSd == 0,
0,
(.data$afterMatchingMeanTarget - .data$afterMatchingMeanComparator) / .data$afterMatchingSd
),
targetStdDiff = if_else(
.data$beforeMatchingSdTarget == 0,
0,
(.data$beforeMatchingMeanTarget - .data$afterMatchingMeanTarget) / .data$beforeMatchingSdTarget
),
comparatorStdDiff = if_else(
.data$beforeMatchingSdComparator == 0,
0,
(.data$beforeMatchingMeanComparator - .data$afterMatchingMeanComparator) / .data$beforeMatchingSdComparator
),
targetComparatorStdDiff = if_else(
.data$beforeMatchingSd == 0,
0,
(.data$beforeMatchingMean - .data$afterMatchingMean) / .data$beforeMatchingSd
)
) %>%
arrange(desc(abs(.data$beforeMatchingStdDiff)))
metaData <- attr(population, "metaData")
if (!is.null(metaData) && !is.null(metaData$targetEstimator)) {
attr(balance, "targetEstimator") <- metaData$targetEstimator
}
delta <- Sys.time() - start
message(paste("Computing covariate balance took", signif(delta, 3), attr(delta, "units")))
return(balance)
}
sampleSingleCohort <- function(cohorts, treatment, maxCohortSize) {
variableStrata <- FALSE
if ("stratumId" %in% colnames(cohorts)) {
strataSizes <- cohorts %>%
filter(.data$treatment == !!treatment) %>%
group_by(.data$stratumId) %>%
summarise(count = n())
variableStrata <- nrow(strataSizes) > 20 && any(strataSizes$count > 1)
}
if (variableStrata) {
# If there are many small and large strata (variable ratio matching), small strata are more likely
# to be completely deleted by row-based sampling than larger strata, causing bias. Hence we're sampling
# entire strata to achieve the desired number of rows:
strataSizes <- strataSizes[sample.int(nrow(strataSizes), replace = F), ]
stratumIdsToKeep <- strataSizes$stratumId[cumsum(strataSizes$count) <= maxCohortSize]
idx <- which(cohorts$treatment == !!treatment & cohorts$stratumId %in% stratumIdsToKeep)
} else {
idx <- which(cohorts$treatment == !!treatment)
idx <- sample(idx, maxCohortSize)
}
return(idx)
}
sampleCohorts <- function(cohorts, maxCohortSize, label) {
if (maxCohortSize <= 0) {
return(cohorts)
}
sampled <- FALSE
targetIdx <- which(cohorts$treatment == 1)
comparatorIdx <- which(cohorts$treatment == 0)
targetCount <- length(targetIdx)
comparatorCount <- length(comparatorIdx)
if (targetCount > maxCohortSize) {
targetIdx <- sampleSingleCohort(cohorts, 1, maxCohortSize)
message(
"Downsampling target cohort ",
label,
" from ",
targetCount,
" to ",
length(targetIdx),
" before computing covariate balance"
)
sampled <- TRUE
}
if (comparatorCount > maxCohortSize) {
comparatorIdx <- sampleSingleCohort(cohorts, 0, maxCohortSize)
message(
"Downsampling comparator cohort ",
label,
" from ",
comparatorCount,
" to ",
length(comparatorIdx),
" before computing covariate balance"
)
sampled <- TRUE
}
if (sampled) {
return(cohorts[c(targetIdx, comparatorIdx), ])
} else {
return(cohorts)
}
}
sampleCohortsAndromeda <- function(cohorts, maxCohortSize, label) {
if (maxCohortSize <= 0) {
return(cohorts)
}
cohortCounts <- cohorts %>%
group_by(.data$treatment) %>%
count() %>%
collect()
if (any(cohortCounts$n > maxCohortSize)) {
return(sampleCohorts(collect(cohorts), maxCohortSize, label))
} else {
return(cohorts)
}
}
#' Create a scatterplot of the covariate balance
#'
#' @description
#' Create a scatterplot of the covariate balance, showing all variables with balance before and after
#' matching on the x and y axis respectively. Requires running `computeCovariateBalance` first.
#'
#' @return
#' A ggplot object. Use the [ggplot2::ggsave] function to save to file in a different
#' format.
#'
#' @param balance A data frame created by the `computeCovariateBalance` function.
#' @param absolute Should the absolute value of the difference be used?
#' @param threshold Show a threshold value for after matching standardized difference.
#' @param title The main title for the plot.
#' @param fileName Name of the file where the plot should be saved, for example 'plot.png'. See the
#' function `ggsave` in the ggplot2 package for supported file formats.
#' @param beforeLabel Label for the x-axis.
#' @param afterLabel Label for the y-axis.
#' @param showCovariateCountLabel Show a label with the number of covariates included in the plot?
#' @param showMaxLabel Show a label with the maximum absolute standardized difference after matching/stratification?
#'
#' @export
plotCovariateBalanceScatterPlot <- function(balance,
absolute = TRUE,
threshold = 0,
title = "Standardized difference of mean",
fileName = NULL,
beforeLabel = "Before matching",
afterLabel = "After matching",
showCovariateCountLabel = FALSE,
showMaxLabel = FALSE) {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(balance, add = errorMessages)
checkmate::assertLogical(absolute, len = 1, add = errorMessages)
checkmate::assertNumber(threshold, lower = 0, add = errorMessages)
checkmate::assertCharacter(title, len = 1, add = errorMessages)
checkmate::assertCharacter(fileName, len = 1, null.ok = TRUE, add = errorMessages)
checkmate::assertCharacter(beforeLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(afterLabel, len = 1, add = errorMessages)
checkmate::assertLogical(showCovariateCountLabel, len = 1, add = errorMessages)
checkmate::assertLogical(showMaxLabel, len = 1, add = errorMessages)
checkmate::reportAssertions(collection = errorMessages)
if (absolute) {
balance$beforeMatchingStdDiff <- abs(balance$beforeMatchingStdDiff)
balance$afterMatchingStdDiff <- abs(balance$afterMatchingStdDiff)
}
limits <- c(
min(c(balance$beforeMatchingStdDiff, balance$afterMatchingStdDiff), na.rm = TRUE),
max(c(balance$beforeMatchingStdDiff, balance$afterMatchingStdDiff), na.rm = TRUE)
)
plot <- ggplot2::ggplot(
balance,
ggplot2::aes(x = .data$beforeMatchingStdDiff, y = .data$afterMatchingStdDiff)
) +
ggplot2::geom_point(color = rgb(0, 0, 0.8, alpha = 0.3), shape = 16) +
ggplot2::geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
ggplot2::geom_hline(yintercept = 0) +
ggplot2::geom_vline(xintercept = 0) +
ggplot2::ggtitle(title) +
ggplot2::scale_x_continuous(beforeLabel, limits = limits) +
ggplot2::scale_y_continuous(afterLabel, limits = limits)
if (threshold != 0) {
plot <- plot + ggplot2::geom_hline(yintercept = c(
threshold,
-threshold
), alpha = 0.5, linetype = "dotted")
}
if (showCovariateCountLabel || showMaxLabel) {
labels <- c()
if (showCovariateCountLabel) {
labels <- c(labels, sprintf("Number of covariates: %s", format(nrow(balance), big.mark = ",", scientific = FALSE)))
}
if (showMaxLabel) {
labels <- c(labels, sprintf("%s max(absolute): %.2f", afterLabel, max(abs(balance$afterMatchingStdDiff), na.rm = TRUE)))
}
dummy <- data.frame(text = paste(labels, collapse = "\n"))
plot <- plot + ggplot2::geom_label(x = limits[1] + 0.01, y = limits[2], hjust = "left", vjust = "top", alpha = 0.8, ggplot2::aes(label = text), data = dummy, size = 3.5)
}
if (!is.null(fileName)) {
ggplot2::ggsave(fileName, plot, width = 4, height = 4, dpi = 400)
}
return(plot)
}
.truncRight <- function(x, n) {
nc <- nchar(x)
x[nc > (n - 3)] <- paste("...",
substr(x[nc > (n - 3)], nc[nc > (n - 3)] - n + 1, nc[nc > (n - 3)]),
sep = ""
)
x
}
#' Plot variables with largest imbalance
#'
#' @description
#' Create a plot showing those variables having the largest imbalance before matching, and those
#' variables having the largest imbalance after matching. Requires running
#' `computeCovariateBalance` first.
#'
#' @return
#' A ggplot object. Use the [ggplot2::ggsave] function to save to file in a different
#' format.
#'
#' @param balance A data frame created by the `computeCovariateBalance` function.
#' @param n (Maximum) count of covariates to plot.
#' @param maxNameWidth Covariate names longer than this number of characters are truncated to create
#' a nicer plot.
#' @param title Optional: the main title for the plot.
#' @param fileName Name of the file where the plot should be saved, for example 'plot.png'. See
#' the function `ggsave` in the ggplot2 package for supported file formats.
#' @param beforeLabel Label for identifying data before matching / stratification / trimming.
#' @param afterLabel Label for identifying data after matching / stratification / trimming.
#'
#' @export
plotCovariateBalanceOfTopVariables <- function(balance,
n = 20,
maxNameWidth = 100,
title = NULL,
fileName = NULL,
beforeLabel = "before matching",
afterLabel = "after matching") {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(balance, add = errorMessages)
checkmate::assertInt(n, lower = 1, add = errorMessages)
checkmate::assertInt(maxNameWidth, lower = 1, add = errorMessages)
checkmate::assertCharacter(title, null.ok = TRUE, len = 1, add = errorMessages)
checkmate::assertCharacter(fileName, len = 1, null.ok = TRUE, add = errorMessages)
checkmate::assertCharacter(beforeLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(afterLabel, len = 1, add = errorMessages)
checkmate::reportAssertions(collection = errorMessages)
n <- min(n, nrow(balance))
topBefore <- balance[order(-abs(balance$beforeMatchingStdDiff)), ]
topBefore <- topBefore[1:n, ]
topBefore$facet <- paste("Top", n, beforeLabel)
topAfter <- balance[order(-abs(balance$afterMatchingStdDiff)), ]
topAfter <- topAfter[1:n, ]
topAfter$facet <- paste("Top", n, afterLabel)
filtered <- rbind(topBefore, topAfter)
data <- tibble(
covariateId = rep(filtered$covariateId, 2),
covariate = rep(filtered$covariateName, 2),
difference = c(filtered$beforeMatchingStdDiff, filtered$afterMatchingStdDiff),
group = rep(c(beforeLabel, afterLabel), each = nrow(filtered)),
facet = rep(filtered$facet, 2),
rowId = rep(nrow(filtered):1, 2)
)
filtered$covariateName <- .truncRight(as.character(filtered$covariateName), maxNameWidth)
data$facet <- factor(data$facet, levels = c(paste("Top", n, beforeLabel), paste("Top", n, afterLabel)))
data$group <- factor(data$group, levels = c(beforeLabel, afterLabel))
plot <- ggplot2::ggplot(data, ggplot2::aes(
x = .data$difference,
y = .data$rowId,
color = .data$group,
group = .data$group,
fill = .data$group,
shape = .data$group
)) +
ggplot2::geom_point() +
ggplot2::geom_vline(xintercept = 0) +
ggplot2::scale_fill_manual(values = c(
rgb(0.8, 0, 0, alpha = 0.5),
rgb(0, 0, 0.8, alpha = 0.5)
)) +
ggplot2::scale_color_manual(values = c(
rgb(0.8, 0, 0, alpha = 0.5),
rgb(0, 0, 0.8, alpha = 0.5)
)) +
ggplot2::scale_x_continuous("Standardized difference of mean") +
ggplot2::scale_y_continuous(breaks = nrow(filtered):1, labels = filtered$covariateName) +
ggplot2::facet_grid(facet ~ ., scales = "free", space = "free") +
ggplot2::theme(
axis.text.y = ggplot2::element_text(size = 7),
axis.title.y = ggplot2::element_blank(),
legend.position = "top",
legend.direction = "vertical",
legend.title = ggplot2::element_blank()
)
if (!is.null(title)) {
plot <- plot + ggplot2::ggtitle(title)
}
if (!is.null(fileName)) {
ggplot2::ggsave(fileName, plot, width = 10, height = max(2 + n * 0.2, 5), dpi = 400)
}
return(plot)
}
#' Plot covariate prevalence
#'
#' @description
#' Plot prevalence of binary covariates in the target and comparator cohorts, before and after matching.
#' Requires running `computeCovariateBalance` first.
#'
#' @return
#' A ggplot object. Use the [ggplot2::ggsave] function to save to file in a different
#' format.
#'
#' @param balance A data frame created by the `computeCovariateBalance` function.
#' @param threshold A threshold value for standardized difference. When exceeding the threshold, covariates will be
#' marked in a different color. If `threshold = 0`, no color coding will be used.
#' @param title The main title for the plot.
#' @param fileName Name of the file where the plot should be saved, for example 'plot.png'. See the
#' function `ggsave` in the ggplot2 package for supported file formats.
#' @param beforeLabel Label for the before matching / stratification panel.
#' @param afterLabel Label for the after matching / stratification panel.
#' @param targetLabel Label for the x-axis.
#' @param comparatorLabel Label for the y-axis.
#'
#' @export
plotCovariatePrevalence <- function(balance,
threshold = 0,
title = "Covariate prevalence",
fileName = NULL,
beforeLabel = "Before matching",
afterLabel = "After matching",
targetLabel = "Target",
comparatorLabel = "Comparator") {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(balance, add = errorMessages)
checkmate::assertNumber(threshold, lower = 0, add = errorMessages)
checkmate::assertCharacter(title, len = 1, add = errorMessages)
checkmate::assertCharacter(fileName, len = 1, null.ok = TRUE, add = errorMessages)
checkmate::assertCharacter(beforeLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(afterLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(targetLabel, len = 1, add = errorMessages)
checkmate::assertCharacter(comparatorLabel, len = 1, add = errorMessages)
checkmate::reportAssertions(collection = errorMessages)
balance <- balance %>%
filter(.data$isBinary == "Y")
prevalence <- bind_rows(
balance %>%
select(
target = "beforeMatchingMeanTarget",
comparator = "beforeMatchingMeanComparator",
stdDiff = "beforeMatchingStdDiff"
) %>%
mutate(panel = beforeLabel),
balance %>%
select(
target = "afterMatchingMeanTarget",
comparator = "afterMatchingMeanComparator",
stdDiff = "afterMatchingStdDiff"
) %>%
mutate(panel = afterLabel)
) %>%
mutate(
target = .data$target * 100,
comparator = .data$comparator * 100,
stdDiff = if_else(!is.na(.data$stdDiff) & abs(.data$stdDiff) > threshold,
sprintf("> %0.2f", threshold),
sprintf("<= %0.2f", threshold)
)
)
prevalence$panel <- factor(prevalence$panel, levels = c(beforeLabel, afterLabel))
if (threshold > 0) {
plot <- ggplot2::ggplot(prevalence, ggplot2::aes(x = .data$comparator, y = .data$target, color = .data$stdDiff)) +
ggplot2::geom_point(alpha = 0.3, shape = 16) +
ggplot2::scale_color_manual("Std. diff.", values = c(rgb(0, 0, 0.8), rgb(0.8, 0, 0)))
} else {
plot <- ggplot2::ggplot(prevalence, ggplot2::aes(x = .data$comparator, y = .data$target)) +
ggplot2::geom_point(color = rgb(0, 0, 0.8, alpha = 0.3), shape = 16)
}
plot <- plot + ggplot2::geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
ggplot2::geom_hline(yintercept = 0) +
ggplot2::geom_vline(xintercept = 0) +
ggplot2::ggtitle(title) +
ggplot2::scale_x_continuous(sprintf("Prevalence in %s (%%)", targetLabel), limits = c(0, 100)) +
ggplot2::scale_y_continuous(sprintf("Prevalence in %s (%%)", comparatorLabel), limits = c(0, 100)) +
ggplot2::facet_grid(~ .data$panel)
if (!is.null(fileName)) {
ggplot2::ggsave(filename = fileName, plot = plot, width = 8, height = 4, dpi = 400)
}
return(plot)
}
#' Get information on generalizability
#'
#' @description
#' to assess generalizability we compare the distribution of covariates before and after
#' any (propensity score) adjustments. We compute the standardized difference of mean as
#' our metric of generalizability. (Lipton et al., 2017)
#'
#' Depending on our target estimand, we need to consider a different base population for
#' generalizability. For example, if we aim to estimate the average treatment effect in
#' thetreated (ATT), our base population should be the target population, meaning we
#' should consider the covariate distribution before and after PS adjustment in the target
#' population only. By default this function will attempt to select the right base
#' population based on what operations have been performed on the population. For example,
#' if PS matching has been performed we assume the target estimand is the ATT, and the
#' target population is selected as base.
#'
#' Requires running [computeCovariateBalance()]` first.
#'
#' @param balance A data frame created by the `computeCovariateBalance` function.
#' @param baseSelection The selection of the population to consider for generalizability.
#' Options are "auto", "target", "comparator", and "both". The "auto"
#' option will attempt to use the balance meta-data to pick the most
#' appropriate population based on the target estimator.
#'
#' @return
#' A tibble with the following columns:
#'
#' - covariateId: The ID of the covariate. Can be linked to the `covariates` and `covariateRef`
#' tables in the `CohortMethodData` object.
#' - covariateName: The name of the covariate.
#' - beforeMatchingMean: The mean covariate value before any (propensity score) adjustment.
#' - afterMatchingMean: The mean covariate value after any (propensity score) adjustment.
#' - stdDiff: The standardized difference of means between before and after adjustment.
#'
#' The tibble also has a 'baseSelection' attribute, documenting the base population used
#' to assess generalizability.
#'
#' @references Tipton E, Hallberg K, Hedges LV, Chan W (2017) Implications of Small Samples
#' for Generalization: Adjustments and Rules of Thumb, Eval Rev. Oct;41(5):472-505.
#'
#' @export
getGeneralizabilityTable <- function(balance, baseSelection = "auto") {
errorMessages <- checkmate::makeAssertCollection()
checkmate::assertDataFrame(balance, add = errorMessages)
checkmate::assertCharacter(baseSelection, len = 1, add = errorMessages)
checkmate::assertChoice(baseSelection, c("auto", "target", "comparator", "both"), add = errorMessages)
checkmate::reportAssertions(collection = errorMessages)
if (baseSelection == "auto") {
targetEstimator <- attr(balance, "targetEstimator")
if (is.null(targetEstimator)) {
stop("The baseSelection is set to 'auto' but the balance object does not contain a target estimator attribute. ",
"Please set the baseSelection manually.")
}
if (targetEstimator == "ate" | targetEstimator == "ato") {
baseSelection <- "both"
message("Selecting both target and comparator as base for generalizability")
} else if (targetEstimator == "att") {
baseSelection <- "target"
message("Selecting target as base for generalizability")
} else if (targetEstimator == "atu") {
baseSelection <- "comparator"
message("Selecting comparator as base for generalizability")
} else {
stop("Unkown target estimator: ", targetEstimator)
}
}
if (baseSelection == "target") {
generalizability <- balance %>%
mutate(absGeneralizabilityStdDiff = abs(.data$targetStdDiff)) %>%
arrange(desc(.data$absGeneralizabilityStdDiff)) %>%
select("covariateId",
"covariateName",
beforeMatchingMean = "beforeMatchingMeanTarget",
afterMatchingMean = "afterMatchingMeanTarget",
stdDiff = "targetStdDiff")
} else if (baseSelection == "comparator") {
generalizability <- balance %>%
mutate(absGeneralizabilityStdDiff = abs(.data$comparatorStdDiff)) %>%
arrange(desc(.data$absGeneralizabilityStdDiff)) %>%
select("covariateId",
"covariateName",
beforeMatchingMean = "beforeMatchingMeanComparator",
afterMatchingMean = "afterMatchingMeanComparator",
stdDiff = "comparatorStdDiff")
} else {
generalizability <- balance %>%
mutate(absGeneralizabilityStdDiff = abs(.data$targetComparatorStdDiff)) %>%
arrange(desc(.data$absGeneralizabilityStdDiff)) %>%
select("covariateId",
"covariateName",
"beforeMatchingMean",
"afterMatchingMean",
stdDiff = "targetComparatorStdDiff")
}
attr(generalizability, "baseSelection") <- baseSelection
return(generalizability)
}
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