list(
# Adjustment name ----
Name = "Reporting Delays with trend",
# Adjustment type ----
Type = "REPORTING_DELAYS",
# Adjustment subtype ----
SubType = "TREND",
# Input parameters to the adjustment function ----
Parameters = list(
startYear = list(
label = "Diagnosis start year",
value = 2000L,
input = "numeric"),
endYear = list(
label = "Notification end year",
value = 2017,
input = "numeric"),
endQrt = list(
label = "Notification end quarter (integer between 1 and 4)",
value = 1,
min = 1,
max = 4,
input = "numeric"),
stratGender = list(
name = "stratGender",
label = "Gender",
value = FALSE,
input = "checkbox"),
stratTrans = list(
name = "stratTrans",
label = "Transmission",
value = FALSE,
input = "checkbox"),
stratMigr = list(
name = "stratMigr",
label = "Migration",
value = FALSE,
input = "checkbox")
),
# Names of packages that must be made available to the adjustment function ----
RequiredPackageNames = c(),
## Adjustment function ----
AdjustmentFunction = function(inputData, parameters) {
require(data.table)
require(survival)
# A) SETUP -------------------------------------------------------------------------------------
# Work on a copy
compData <- copy(inputData)
# Start year
startYear <- parameters$startYear
# End quarter
endQrt <- parameters$endYear + parameters$endQrt / 4
# Stratifiation columns
stratVarNames <- c()
if (parameters$stratGender) {
stratVarNames <- union(stratVarNames, "Gender")
}
if (parameters$stratTrans) {
stratVarNames <- union(stratVarNames, "Transmission")
}
if (parameters$stratMigr) {
stratVarNames <- union(stratVarNames, "GroupedRegionOfOrigin")
}
stratVarNames <- stratVarNames[stratVarNames %in% colnames(compData)]
# B) PROCESS DATA ------------------------------------------------------------------------------
# Add dummy "Imputation" column if not found
isOriginalData <- !("Imputation" %in% colnames(compData))
if (isOriginalData) {
compData[, Imputation := 0L]
}
# Make sure the strata columns exist in the data
stratVarNames <- stratVarNames[stratVarNames %in% colnames(compData)]
stratVarNamesTrend <- union("DateOfDiagnosisYear",
stratVarNames)
# Create dimensions to match the weights later
outputData <- copy(compData)
outputData[, VarT := 4 * (pmin.int(MaxNotificationTime, endQrt) - DiagnosisTime) + 1]
# Filter
compData <- compData[!is.na(VarX)]
compData[is.na(DiagnosisTime), DiagnosisTime := DateOfDiagnosisYear + 0.25]
compData[is.na(NotificationTime), NotificationTime := DiagnosisTime + VarX / 4]
compData <- compData[VarX >= 0 &
DiagnosisTime >= (startYear + 0.25) &
NotificationTime <= endQrt]
compData[, ":="(
VarT = 4 * (pmin.int(MaxNotificationTime, endQrt) - DiagnosisTime) + 1,
Tf = 4 * (pmin.int(MaxNotificationTime, endQrt) - pmax.int(min(DiagnosisTime), startYear + 0.25)) + 1,
ReportingDelay = 1L
)]
compData[, ":="(
VarXs = Tf - VarX,
VarTs = Tf - VarT
)]
# NOTE: Otherwise survival model complains
compData <- droplevels(compData[VarXs > VarTs])
totalPlot <- NULL
totalPlotData <- NULL
stratPlotList <- NULL
stratPlotListData <- NULL
rdDistribution <- NULL
reportTableData <- NULL
univAnalysis <- NULL
if (nrow(compData) > 0) {
# ------------------------------------------------------------------------
# Prepare diagnostic table based on original data
mostPrevGender <- compData[!is.na(Gender), .N, by = .(Gender)][frank(-N, ties.method = "first") == 1, as.character(Gender)]
mostPrevTrans <- compData[!is.na(Transmission), .N, by = .(Transmission)][frank(-N, ties.method = "first") == 1, as.character(Transmission)]
mostPrevRegion <- compData[!is.na(GroupedRegionOfOrigin), .N, by = .(GroupedRegionOfOrigin)][frank(-N, ties.method = "first") == 1, as.character(GroupedRegionOfOrigin)]
if (!IsEmptyString(mostPrevGender)) {
compData[, Gender := relevel(Gender, ref = mostPrevGender)]
}
if (!IsEmptyString(mostPrevTrans)) {
compData[, Transmission := relevel(Transmission, ref = mostPrevTrans)]
}
if (!IsEmptyString(mostPrevRegion)) {
compData[, GroupedRegionOfOrigin := relevel(GroupedRegionOfOrigin, ref = mostPrevRegion)]
}
model <- compData[Imputation == 0L,
Surv(time = VarTs,
time2 = VarXs,
event = ReportingDelay)]
# Defining univariate models
univFormulas <- lapply(
stratVarNamesTrend,
function(x) as.formula(sprintf("model ~ %s", x)))
# Applying univariate models
univModels <- lapply(
univFormulas,
function(x) coxph(x, data = compData[Imputation == 0L]))
# Extract results of univariable analysis (whether particular covariates
# are associated with RD)
univAnalysis <- rbindlist(lapply(
univModels,
function(x) {
y <- summary(x)
z <- cox.zph(x)
res <- merge(as.data.table(y$conf.int),
as.data.table(y$coefficients))
res <- cbind(res,
as.data.table(z$table[rownames(z$table) != "GLOBAL", "p", drop = FALSE]))
res[, lapply(.SD, signif, 2), .SDcols = colnames(res)]
setnames(res, c("HR", "1/HR", "HR.lower.95",
"HR.upper.95", "Beta", "SE.Beta",
"Z", "P.value", "Prop.assumpt.p"))
if (!is.null(x$xlevels)) {
varName <- names(x$xlevels)[1]
refLevel <- x$xlevels[[varName]][1]
compLevels <- x$xlevels[[varName]][-1]
predictor <- sprintf("%s (%s vs %s)", varName, compLevels, refLevel)
} else {
predictor <- rownames(y$conf.int)
}
res <- cbind(Predictor = predictor,
res)
return(res)}))
# ------------------------------------------------------------------------
# RD estimation with time trend
# Define parameters
lastYear <- compData[, max(DateOfDiagnosisYear)]
years <- (lastYear - 4):lastYear
tGroups <- 1:3
imputations <- compData[, sort(unique(Imputation))]
formula <- as.formula(sprintf("Surv(time = VarTs, time2 = VarXs, event = ReportingDelay) ~ (%s):strata(tgroup)",
paste(stratVarNamesTrend, collapse = " + ")))
cuts <- compData[, c(max(VarXs) - 10,
max(VarXs) - 3,
max(VarXs))]
# Run fitting per imputation separately
fitStratum <- list()
for (imputation in imputations) {
compDataSplit <- setDT(survSplit(
formula = Surv(time = VarTs, time2 = VarXs, event = ReportingDelay) ~ .,
data = compData[Imputation == imputation],
cut = cuts,
episode = "tgroup"))
fitCox <- coxph(formula,
data = compDataSplit)
estFrame <- CJ(DateOfDiagnosisYear = years,
tgroup = tGroups)
estCov <- na.omit(unique(compData[, ..stratVarNames]))
if (nrow(estCov) > 0) {
estFrame[, MergeDummy := 1]
estCov[, MergeDummy := 1]
estFrame <- estFrame[estCov,
on = .(MergeDummy),
allow.cartesian = TRUE]
estFrame[, MergeDummy := NULL]
}
estFrame[, ":="(
Id = rep(seq_len(.N/length(tGroups)), each = length(tGroups)),
VarTs = c(0, cuts)[tgroup],
VarXs = cuts[tgroup],
ReportingDelay = 0
)]
fit <- try({
survfit(fitCox,
newdata = estFrame,
id = Id)
}, silent = TRUE)
if (is(fit, "try-error")) {
fitStratumImp <- data.table(
Imputation = imputation,
Delay = 0,
P = 1,
Weight = 1,
Var = 0,
unique(estFrame[, ..stratVarNamesTrend])
)
} else {
fitStratumImp <- data.table(
Imputation = imputation,
Delay = fit$time,
P = fit$surv,
Weight = 1/fit$surv,
Var = fit$std.err^2,
unique(estFrame[, ..stratVarNamesTrend])[rep(seq_len(.N), fit$strata)])
}
fitStratumImp[, VarT := max(Delay) - Delay]
# Store this imputation results
fitStratum[[as.character(imputation)]] <- fitStratumImp[VarT >= 0]
}
fitStratum <- rbindlist(fitStratum)
# Merge P, Weight and Var with outputData object
mergeVars <- union(stratVarNamesTrend,
c("VarT", "Imputation"))
outputData[, ":="(
DateOfDiagnosisYearOrig = DateOfDiagnosisYear,
DateOfDiagnosisYear = pmax.int(lastYear - 4, DateOfDiagnosisYear),
Source = ifelse(Imputation == 0, "Reported", "Imputed")
)]
outputData <- merge(outputData,
fitStratum[, c(..mergeVars, "P", "Weight", "Var")],
by = mergeVars,
all.x = TRUE)
outputData[, MissingData := is.na(Weight) | is.infinite(Weight)]
outputData[MissingData == TRUE, ":="(
Weight = 1,
P = 1
)]
outputData[is.na(Var) | is.infinite(Var), Var := 0]
outputData[, ":="(
DateOfDiagnosisYear = DateOfDiagnosisYearOrig,
DateOfDiagnosisYearOrig = NULL
)]
# ------------------------------------------------------------------------
# Get distribution object as artifact
varNames <- setdiff(colnames(fitStratum),
c("Delay", "P", "Weight", "Var", "VarT"))
rdDistribution <- fitStratum[VarT > 0,
union(varNames, c("VarT", "P", "Weight", "Var")),
with = FALSE]
setnames(rdDistribution,
old = "VarT",
new = "Quarter")
setorderv(rdDistribution, union(varNames, "Quarter"))
# Aggregate and keep only required dimensions
agregat <- outputData[, .(Count = .N,
P = mean(P),
Weight = mean(Weight),
Var = mean(Var)),
by = c(mergeVars, "Source", "MissingData")]
# Compute estimated count and its variance
agregat[, ":="(
EstCount = Count * Weight,
EstCountVar = (Count * (Count + 1) / P^4 * Var) + Count * (1 - P) / P^2
)]
# C) TOTAL PLOT ----------------------------------------------------------
totalPlotData <- GetRDPlotData(data = agregat,
by = c("MissingData", "Source", "Imputation",
"DateOfDiagnosisYear"))
setorderv(totalPlotData, c("MissingData", "DateOfDiagnosisYear"))
totalPlot <- GetRDPlots(plotData = totalPlotData,
isOriginalData = isOriginalData)
reportTableData <- dcast(totalPlotData[Source == ifelse(isOriginalData, "Reported", "Imputed")],
DateOfDiagnosisYear + EstCount +
LowerEstCount + UpperEstCount ~ MissingData,
value.var = "Count",
fun.aggregate = sum)
if ("TRUE" %in% colnames(reportTableData)) {
setnames(reportTableData, old = "TRUE", new = "RDWeightNotEstimated")
} else {
reportTableData[, RDWeightNotEstimated := 0]
}
if ("FALSE" %in% colnames(reportTableData)) {
setnames(reportTableData, old = "FALSE", new = "RDWeightEstimated")
} else {
reportTableData[, RDWeightEstimated := 0]
}
reportTableData <- reportTableData[, lapply(.SD, sum),
by = DateOfDiagnosisYear,
.SDcols = setdiff(colnames(reportTableData),
"DateOfDiagnosisYear")]
reportTableData[, Reported := RDWeightEstimated + RDWeightNotEstimated]
reportTableData[, ":="(
EstUnreported = EstCount - Reported,
LowerEstUnreported = LowerEstCount - Reported,
UpperEstUnreported = UpperEstCount - Reported
)]
setcolorder(reportTableData,
c("DateOfDiagnosisYear",
"Reported", "RDWeightEstimated", "RDWeightNotEstimated",
"EstUnreported", "LowerEstUnreported", "UpperEstUnreported",
"EstCount", "LowerEstCount", "UpperEstCount"))
# D) STRATIFIED PLOT (OPTIONAL) ------------------------------------------
if (length(stratVarNames) > 0) {
# Stratification
colNames <- union(c("MissingData", "Source", "DateOfDiagnosisYear", "Count", "EstCount",
"EstCountVar"),
c(stratVarNamesTrend, "Imputation"))
# Keep only required columns, convert data to "long" format...
agregatLong <- melt(agregat[, ..colNames],
measure.vars = stratVarNames,
variable.name = "Stratum",
value.name = "StratumValue")
stratPlotListData <- GetRDPlotData(data = agregatLong,
by = c("MissingData", "Source", "Imputation",
"DateOfDiagnosisYear", "Stratum", "StratumValue"))
stratPlotList <- lapply(stratVarNames,
GetRDPlots,
plotData = stratPlotListData,
isOriginalData = isOriginalData)
names(stratPlotList) <- stratVarNames
}
} else {
outputData[, Weight := 1]
}
# Keep only columns present in the input object plus the weight
outColNames <- union(colnames(inputData),
c("VarT", "Weight"))
outputData <- outputData[, ..outColNames]
artifacts <- list(OutputPlotTotal = totalPlot,
OutputPlotTotalData = totalPlotData,
OutputPlotStrat = stratPlotList,
OutputPlotStratData = stratPlotListData,
ReportTableData = reportTableData,
RdDistribution = rdDistribution,
UnivAnalysis = univAnalysis)
cat("No adjustment specific text outputs.\n")
return(list(Table = outputData,
Artifacts = artifacts))
}
)
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