README.md
In idasomm/genRCT: Generalizing the average treatment effect (ATE) from the trial using observational studies (OS)
genRCT
Generalizing the average treatment effect (ATE) from the trial using observational studies (OS)
Installation
devtools::install_github("idasomm/genRCT")
Continous / Binary outcomes
Usage
genRCT(Y.trial, X.trial, A.trial, Y.rwe, X.rwe, A.rwe, family =
“gaussian”, estimators = c(“Naive”, “IPSW”, “AIPSW”, “CW”, “ACW-t”,
“ACW-b”), sieve = TRUE, inference = TRUE, n.boot = 100, conf.level =
0.05, seed = NULL, plot.boot = TRUE, verbose = TRUE)
Help functions
library(genRCT)
?genRCT
Arguments
| Argument | |
| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Y.trial | Observed outcome from a trial; vector of size n (trial sample size). |
| A.trial | Treatment received from a trial; vector of size n. |
| X.trial | Matrix of p baseline covariates from a trial; dimension n by p. |
| Y.rwe | Observed outcome from OS; if obtained, vector of size m (OS sample size); otherwise, set Y.rwe = NULL. |
| A.rwe | Treatment received from OS; if obtained, vector of size m; otherwise, set A.rwe = NULL. |
| X.rwe | Matrix of p baseline covariates from OS; dimension m by p. |
| family | The type of outcome; “gaussian” for gaussian regression or “binomial” for logistic regression Default is “gaussian”. |
| estimators | A vector of one or multiple methods to estimate the ATE. Allowed values are “Naive”, “IPSW”, “AIPSW”, “CW”, “ACW-t”, “ACW-b”. The “ACW-b” is allowed only when both “Y.rwe” and “A.rwe” are obtained. Default specifies all 6 methods. |
| sieve | A logical value indicating whether the method of sieves are used for estimating sampling score and outcome models. Used only if estimators = “AIPSW” or “ACW-t” or “ACW-b”. Default is TRUE. |
| inference | A logical value indicating whether inference for the ATE via bootstrap should be provided. Default it TRUE. |
| n.boot | A numeric value indicating the number of bootstrap samples used. This is only relevant if inference = TRUE. Default is 100. |
| conf.level | The level of bootstrap confidence interval; Default is 0.05. |
| seed | An optional integer specifying an initial randomization seed for reproducibility. Default is NULL, corresponding to no seed. |
| plot.boot | A logical value indicating whether histograms of the bootstrap samples should be produced. Default is TRUE. |
| verbose | A logical value indicating whether intermediate progress messages should be printed. Default is TRUE. |
Value
| Value | |
| ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| fit | A table of estimated ATEs with bootstrap SE and confidence interval. |
| plot | A set of histograms displaying the distribution of the bootstrapped estimates. The red vertical reference lines represent the estimated ATEs from each method. |
Example
library(genRCT)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
#> ✔ ggplot2 3.3.3 ✔ purrr 0.3.4
#> ✔ tibble 3.1.0 ✔ dplyr 1.0.5
#> ✔ tidyr 1.1.3 ✔ stringr 1.4.0
#> ✔ readr 1.4.0 ✔ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
library(data.table)
#>
#> Attaching package: 'data.table'
#> The following objects are masked from 'package:dplyr':
#>
#> between, first, last
#> The following object is masked from 'package:purrr':
#>
#> transpose
data("simulData")
data.trial <- simulData %>% dplyr::filter(delta == 1)
data.rwe <- simulData %>% dplyr::filter(delta == 0)
Y.trial <- data.trial %>% dplyr::select(Y)
A.trial <- data.trial %>% dplyr::select(A)
X.trial <- data.trial %>% dplyr::select(X1:X5)
Y.rwe <- data.rwe %>% dplyr::select(Y)
A.rwe <- data.rwe %>% dplyr::select(A)
X.rwe <- data.rwe %>% dplyr::select(X1:X5)
fit <- genRCT(Y.trial = Y.trial, A.trial = A.trial, X.trial = X.trial, Y.rwe = Y.rwe,
A.rwe = A.rwe, X.rwe = X.rwe, family = "gaussian",
estimators = c("Naive", "IPSW", "AIPSW", "CW", "ACW-t", "ACW-b"),
sieve = TRUE, inference = TRUE, n.boot = 500, conf.level = 0.05,
seed = 12345, plot.boot = TRUE, verbose = FALSE)
#> Fitting estimators..
#> Bootstrapping..
#> Total runtime : 7.565105 mins
fit$fit
#> ATE SE 2.5% 97.5%
#> Naive 50.31553 2.4868271 45.342828 55.36317
#> IPSW 35.69669 8.1734416 19.479857 49.27143
#> AIPSW 26.63354 0.6464247 25.283526 27.78797
#> CW 27.74951 14.7089762 1.937296 52.01549
#> ACW-t 26.88160 0.7517235 25.197760 28.06301
#> ACW-b 27.57737 0.7093065 25.914611 28.65249
truth <- 27.4
fit$plot + geom_vline(xintercept = truth, color = 'blue', linetype = "dashed")
The
red solid lines represent the estimated ATEs and the blue dashed lines
represent the true ATE. The differences are
#> Naive IPSW AIPSW CW ACW-t ACW-b
#> -22.9155252 -8.2966905 0.7664578 -0.3495104 0.5184023 -0.1773662
Survival outcomes
Usage
genRCT.surv(Y.trial, d.trial, A.trial, X.trial, X.rwe, tau, n.boot =
100, conf.level = 0.05, seed = NULL, verbose = TRUE)
Arguments
| Argument | |
| ---------- | ---------------------------------------------------------------------------------------------------------------------------- |
| Y.trial | Observed outcome from a trial; vector of size n (trial sample size). |
| d.trial | The event indicator, normally 1 = event, 0 = censored; vector of size n. |
| A.trial | Treatment received from a trial; vector of size n. |
| X.trial | Matrix of p baseline covariates from a trial; dimension n by p. |
| X.rwe | Matrix of p baseline covariates from OS; dimension m by p. |
| tau | A vector of truncation time for defining restricted mean survival time; e.g., seq(10, 50, by = 10) |
| n.boot | A numeric value indicating the number of bootstrap samples used. This is only relevant if inference = TRUE. Default is 100. |
| conf.level | The level of bootstrap confidence interval; Default is 0.05. |
| seed | An optional integer specifying an initial randomization seed for reproducibility. Default is NULL, corresponding to no seed. |
| verbose | A logical value indicating whether intermediate progress messages should be printed. Default is TRUE. |
Value
| Value | |
| ----- | -------------------------------------------------------------------- |
| rmst | A list of estimated RMSTs with bootstrap SE and confidence interval. |
| surv | A list of estimated treatment-specific survival function. |
Example
data("simulData.surv")
library(tidyverse)
library(ggplot2)
library(survival)
library(data.table)
library(nleqslv)
library(ncvreg)
#>
#> Attaching package: 'ncvreg'
#> The following object is masked from 'package:survival':
#>
#> heart
library(MASS)
#>
#> Attaching package: 'MASS'
#> The following object is masked from 'package:dplyr':
#>
#> select
library(Matrix)
#>
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#>
#> expand, pack, unpack
data.trial <- simulData.surv$trial
Y.trial <- data.trial %>% dplyr::select(Y)
d.trial <- data.trial$d
A.trial <- data.trial %>% dplyr::select(A)
X.trial <- data.trial %>% dplyr::select(X1:X3)
X.rwe <- simulData.surv$rwe
tau <- seq(10, 50, by = 10)
tau <- round(tau)
## Fit genRCT survival
fit <- genRCT.surv(Y.trial = Y.trial, d.trial = d.trial, A.trial = A.trial, X.trial = X.trial, X.rwe = X.rwe,
tau = tau, n.boot = 100, conf.level = 0.05, seed = 123, verbose = FALSE)
#> Fitting estimators..
#> Bootstrapping..
#> Total runtime : 8.039888 mins
lower1 <- fit$surv$lower1
lower0 <- fit$surv$lower0
upper1 <- fit$surv$upper1
upper0 <- fit$surv$upper0
sv1 <- as.data.frame(fit$surv$surv1) %>% mutate(est = 's1', time = fit$surv$time1)
sv0 <- as.data.frame(fit$surv$surv0) %>% mutate(est = 's0', time = fit$surv$time0)
surv.comb <- rbind(sv1, sv0)
datas <- surv.comb %>% pivot_longer(cols = c(surv.nv:denom.dacw.sv), names_to = "method", values_to = "surv") %>%
mutate(estimators = factor(method, levels = c("denom.nv", "surv.nv", "denom.dr", "surv.dr", "denom.or", "surv.or", "denom.ipsw", "surv.ipsw",
"denom.cw", "surv.cw", "denom.dacw", "surv.dacw", "denom.dacw.sv", "surv.dacw.sv"),
labels = c("Naive", "Naive2", "DR", "DR2", "OR", "OR2", "IPSW", "IPSW2", "CW", "CW2", "ACW1", "ACW2", "ACW1(S)", "ACW2(S)")))
lower <- data.frame(rbind(lower1, lower0)) %>% pivot_longer(cols = c(surv.nv:denom.dacw.sv), names_to = "method", values_to = "lower") %>%
mutate(estimators = factor(method, levels = c("denom.nv", "surv.nv", "denom.dr", "surv.dr", "denom.or", "surv.or", "denom.ipsw", "surv.ipsw",
"denom.cw", "surv.cw", "denom.dacw", "surv.dacw", "denom.dacw.sv", "surv.dacw.sv"),
labels = c("Naive", "Naive2", "DR", "DR2", "OR", "OR2", "IPSW", "IPSW2", "CW", "CW2", "ACW1", "ACW2", "ACW1(S)", "ACW2(S)")))
upper <- data.frame(rbind(upper1, upper0)) %>% pivot_longer(cols = c(surv.nv:denom.dacw.sv), names_to = "method", values_to = "upper") %>%
mutate(estimators = factor(method, levels = c("denom.nv", "surv.nv", "denom.dr", "surv.dr", "denom.or", "surv.or", "denom.ipsw", "surv.ipsw",
"denom.cw", "surv.cw", "denom.dacw", "surv.dacw", "denom.dacw.sv", "surv.dacw.sv"),
labels = c("Naive", "Naive2", "DR", "DR2", "OR", "OR2", "IPSW", "IPSW2", "CW", "CW2", "ACW1", "ACW2", "ACW1(S)", "ACW2(S)")))
datas$lower <- lower$lower
datas$upper <- upper$upper
datas %>% filter(estimators %in% c("Naive", "DR", "OR", "IPSW", "CW", "ACW1", "ACW2", "ACW1(S)", "ACW2(S)")) %>%
ggplot(aes(x = time)) + geom_line(aes(y = surv, color = est)) +
geom_ribbon(aes(ymin=lower, ymax=upper, fill=est),
alpha=0.3, colour=NA) +
facet_wrap( ~ estimators, scales = "free", nrow=2) +
coord_cartesian(xlim = c(0, 30)) +
theme_bw()
idasomm/genRCT documentation built on April 15, 2023, 7:16 a.m.
R Package Documentation
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genRCT
Generalizing the average treatment effect (ATE) from the trial using observational studies (OS)
Installation
devtools::install_github("idasomm/genRCT")
Continous / Binary outcomes
Usage
genRCT(Y.trial, X.trial, A.trial, Y.rwe, X.rwe, A.rwe, family = “gaussian”, estimators = c(“Naive”, “IPSW”, “AIPSW”, “CW”, “ACW-t”, “ACW-b”), sieve = TRUE, inference = TRUE, n.boot = 100, conf.level = 0.05, seed = NULL, plot.boot = TRUE, verbose = TRUE)
Help functions
library(genRCT)
?genRCT
Arguments
| Argument | | | ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Y.trial | Observed outcome from a trial; vector of size n (trial sample size). | | A.trial | Treatment received from a trial; vector of size n. | | X.trial | Matrix of p baseline covariates from a trial; dimension n by p. | | Y.rwe | Observed outcome from OS; if obtained, vector of size m (OS sample size); otherwise, set Y.rwe = NULL. | | A.rwe | Treatment received from OS; if obtained, vector of size m; otherwise, set A.rwe = NULL. | | X.rwe | Matrix of p baseline covariates from OS; dimension m by p. | | family | The type of outcome; “gaussian” for gaussian regression or “binomial” for logistic regression Default is “gaussian”. | | estimators | A vector of one or multiple methods to estimate the ATE. Allowed values are “Naive”, “IPSW”, “AIPSW”, “CW”, “ACW-t”, “ACW-b”. The “ACW-b” is allowed only when both “Y.rwe” and “A.rwe” are obtained. Default specifies all 6 methods. | | sieve | A logical value indicating whether the method of sieves are used for estimating sampling score and outcome models. Used only if estimators = “AIPSW” or “ACW-t” or “ACW-b”. Default is TRUE. | | inference | A logical value indicating whether inference for the ATE via bootstrap should be provided. Default it TRUE. | | n.boot | A numeric value indicating the number of bootstrap samples used. This is only relevant if inference = TRUE. Default is 100. | | conf.level | The level of bootstrap confidence interval; Default is 0.05. | | seed | An optional integer specifying an initial randomization seed for reproducibility. Default is NULL, corresponding to no seed. | | plot.boot | A logical value indicating whether histograms of the bootstrap samples should be produced. Default is TRUE. | | verbose | A logical value indicating whether intermediate progress messages should be printed. Default is TRUE. |
Value
| Value | | | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | | fit | A table of estimated ATEs with bootstrap SE and confidence interval. | | plot | A set of histograms displaying the distribution of the bootstrapped estimates. The red vertical reference lines represent the estimated ATEs from each method. |
Example
library(genRCT)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
#> ✔ ggplot2 3.3.3 ✔ purrr 0.3.4
#> ✔ tibble 3.1.0 ✔ dplyr 1.0.5
#> ✔ tidyr 1.1.3 ✔ stringr 1.4.0
#> ✔ readr 1.4.0 ✔ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
library(data.table)
#>
#> Attaching package: 'data.table'
#> The following objects are masked from 'package:dplyr':
#>
#> between, first, last
#> The following object is masked from 'package:purrr':
#>
#> transpose
data("simulData")
data.trial <- simulData %>% dplyr::filter(delta == 1)
data.rwe <- simulData %>% dplyr::filter(delta == 0)
Y.trial <- data.trial %>% dplyr::select(Y)
A.trial <- data.trial %>% dplyr::select(A)
X.trial <- data.trial %>% dplyr::select(X1:X5)
Y.rwe <- data.rwe %>% dplyr::select(Y)
A.rwe <- data.rwe %>% dplyr::select(A)
X.rwe <- data.rwe %>% dplyr::select(X1:X5)
fit <- genRCT(Y.trial = Y.trial, A.trial = A.trial, X.trial = X.trial, Y.rwe = Y.rwe,
A.rwe = A.rwe, X.rwe = X.rwe, family = "gaussian",
estimators = c("Naive", "IPSW", "AIPSW", "CW", "ACW-t", "ACW-b"),
sieve = TRUE, inference = TRUE, n.boot = 500, conf.level = 0.05,
seed = 12345, plot.boot = TRUE, verbose = FALSE)
#> Fitting estimators..
#> Bootstrapping..
#> Total runtime : 7.565105 mins
fit$fit
#> ATE SE 2.5% 97.5%
#> Naive 50.31553 2.4868271 45.342828 55.36317
#> IPSW 35.69669 8.1734416 19.479857 49.27143
#> AIPSW 26.63354 0.6464247 25.283526 27.78797
#> CW 27.74951 14.7089762 1.937296 52.01549
#> ACW-t 26.88160 0.7517235 25.197760 28.06301
#> ACW-b 27.57737 0.7093065 25.914611 28.65249
truth <- 27.4
fit$plot + geom_vline(xintercept = truth, color = 'blue', linetype = "dashed")
The
red solid lines represent the estimated ATEs and the blue dashed lines
represent the true ATE. The differences are
#> Naive IPSW AIPSW CW ACW-t ACW-b
#> -22.9155252 -8.2966905 0.7664578 -0.3495104 0.5184023 -0.1773662
Survival outcomes
Usage
genRCT.surv(Y.trial, d.trial, A.trial, X.trial, X.rwe, tau, n.boot = 100, conf.level = 0.05, seed = NULL, verbose = TRUE)
Arguments
| Argument | | | ---------- | ---------------------------------------------------------------------------------------------------------------------------- | | Y.trial | Observed outcome from a trial; vector of size n (trial sample size). | | d.trial | The event indicator, normally 1 = event, 0 = censored; vector of size n. | | A.trial | Treatment received from a trial; vector of size n. | | X.trial | Matrix of p baseline covariates from a trial; dimension n by p. | | X.rwe | Matrix of p baseline covariates from OS; dimension m by p. | | tau | A vector of truncation time for defining restricted mean survival time; e.g., seq(10, 50, by = 10) | | n.boot | A numeric value indicating the number of bootstrap samples used. This is only relevant if inference = TRUE. Default is 100. | | conf.level | The level of bootstrap confidence interval; Default is 0.05. | | seed | An optional integer specifying an initial randomization seed for reproducibility. Default is NULL, corresponding to no seed. | | verbose | A logical value indicating whether intermediate progress messages should be printed. Default is TRUE. |
Value
| Value | | | ----- | -------------------------------------------------------------------- | | rmst | A list of estimated RMSTs with bootstrap SE and confidence interval. | | surv | A list of estimated treatment-specific survival function. |
Example
data("simulData.surv")
library(tidyverse)
library(ggplot2)
library(survival)
library(data.table)
library(nleqslv)
library(ncvreg)
#>
#> Attaching package: 'ncvreg'
#> The following object is masked from 'package:survival':
#>
#> heart
library(MASS)
#>
#> Attaching package: 'MASS'
#> The following object is masked from 'package:dplyr':
#>
#> select
library(Matrix)
#>
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#>
#> expand, pack, unpack
data.trial <- simulData.surv$trial
Y.trial <- data.trial %>% dplyr::select(Y)
d.trial <- data.trial$d
A.trial <- data.trial %>% dplyr::select(A)
X.trial <- data.trial %>% dplyr::select(X1:X3)
X.rwe <- simulData.surv$rwe
tau <- seq(10, 50, by = 10)
tau <- round(tau)
## Fit genRCT survival
fit <- genRCT.surv(Y.trial = Y.trial, d.trial = d.trial, A.trial = A.trial, X.trial = X.trial, X.rwe = X.rwe,
tau = tau, n.boot = 100, conf.level = 0.05, seed = 123, verbose = FALSE)
#> Fitting estimators..
#> Bootstrapping..
#> Total runtime : 8.039888 mins
lower1 <- fit$surv$lower1
lower0 <- fit$surv$lower0
upper1 <- fit$surv$upper1
upper0 <- fit$surv$upper0
sv1 <- as.data.frame(fit$surv$surv1) %>% mutate(est = 's1', time = fit$surv$time1)
sv0 <- as.data.frame(fit$surv$surv0) %>% mutate(est = 's0', time = fit$surv$time0)
surv.comb <- rbind(sv1, sv0)
datas <- surv.comb %>% pivot_longer(cols = c(surv.nv:denom.dacw.sv), names_to = "method", values_to = "surv") %>%
mutate(estimators = factor(method, levels = c("denom.nv", "surv.nv", "denom.dr", "surv.dr", "denom.or", "surv.or", "denom.ipsw", "surv.ipsw",
"denom.cw", "surv.cw", "denom.dacw", "surv.dacw", "denom.dacw.sv", "surv.dacw.sv"),
labels = c("Naive", "Naive2", "DR", "DR2", "OR", "OR2", "IPSW", "IPSW2", "CW", "CW2", "ACW1", "ACW2", "ACW1(S)", "ACW2(S)")))
lower <- data.frame(rbind(lower1, lower0)) %>% pivot_longer(cols = c(surv.nv:denom.dacw.sv), names_to = "method", values_to = "lower") %>%
mutate(estimators = factor(method, levels = c("denom.nv", "surv.nv", "denom.dr", "surv.dr", "denom.or", "surv.or", "denom.ipsw", "surv.ipsw",
"denom.cw", "surv.cw", "denom.dacw", "surv.dacw", "denom.dacw.sv", "surv.dacw.sv"),
labels = c("Naive", "Naive2", "DR", "DR2", "OR", "OR2", "IPSW", "IPSW2", "CW", "CW2", "ACW1", "ACW2", "ACW1(S)", "ACW2(S)")))
upper <- data.frame(rbind(upper1, upper0)) %>% pivot_longer(cols = c(surv.nv:denom.dacw.sv), names_to = "method", values_to = "upper") %>%
mutate(estimators = factor(method, levels = c("denom.nv", "surv.nv", "denom.dr", "surv.dr", "denom.or", "surv.or", "denom.ipsw", "surv.ipsw",
"denom.cw", "surv.cw", "denom.dacw", "surv.dacw", "denom.dacw.sv", "surv.dacw.sv"),
labels = c("Naive", "Naive2", "DR", "DR2", "OR", "OR2", "IPSW", "IPSW2", "CW", "CW2", "ACW1", "ACW2", "ACW1(S)", "ACW2(S)")))
datas$lower <- lower$lower
datas$upper <- upper$upper
datas %>% filter(estimators %in% c("Naive", "DR", "OR", "IPSW", "CW", "ACW1", "ACW2", "ACW1(S)", "ACW2(S)")) %>%
ggplot(aes(x = time)) + geom_line(aes(y = surv, color = est)) +
geom_ribbon(aes(ymin=lower, ymax=upper, fill=est),
alpha=0.3, colour=NA) +
facet_wrap( ~ estimators, scales = "free", nrow=2) +
coord_cartesian(xlim = c(0, 30)) +
theme_bw()
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