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Adapt_Functions_Meta.R
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##############
# Adapt_Functions_BigRCTs.R
# R code to implement adaptive prespecification
# Modified from Adapt_Functions.R in https://github.com/LauraBalzer/TwoStageTMLE
#-----------------------------------------------------#-----------------------------------------------------
# do.adaptive.prespec: function to implement adaptive prespecification as described in
# Balzer et al. "Adaptive pre-specification in randomized trials with and without pair-matching"
#
# input: goal of analysis ('aRR' or 'RD'),
# target of inference: cluster-level ("clust") or pooled-indv effect ("indv") (target)
# indicator to break the match (break.match)
# dataset (Ldata)
# adjustment variables (cand.QAdj) - CHANGE these do not have to be at the cluster-level,
# selected adjustment variable for the outcome regression (QAdj)
# selected adjustment variable for the pscore regression (gAdj)
# maximum value for outcome scaling (scale_value),
# minimum value for outcome scaling (scale_value_min)
# output: selection for candidate TMLE
#-----------------------------------------------------#-----------------------------------------------------
# UPDATE - expanded candidates for both the outcome regression & pscore estimation
# cand.QAdj = candidate adjustment variable sets for the outcome regression (list)
# cand.Qform = candidate adjustment algorithms for the outcome regression (vector)
# cand.gAdj = candidate adjustment variable sets for the pscore (list)
# cand.gform = candidate adjustment algorithms for the pscore (vector)
#
# remove.pscore: if T, remove the variable(s) selected for adjustment in the outcome regression from
# candidates for the pscore... should only be used if doing adaptive prespec in RCT with few indpt units
do.adaptive.prespec<- function(goal, target='indv', break.match=T, Ldata, V=5,
cand.QAdj, cand.Qform, cand.gAdj, cand.gform, remove.pscore=F,
QAdj=NULL, gAdj=NULL, scale_value, scale_value_min, verbose=F){
# UPDATE: GET THE FOLDS HERE!
# get the indpt units (will be observation in indv RCT)
if( !break.match ){
Ldata$indpt.unit <- Ldata$pair
}else{
Ldata$indpt.unit <- Ldata$id
}
unique.ids <- unique(Ldata$indpt.unit)
# get folds
if(length(unique.ids)>40){
# V-fold CV
folds <- get.folds(V=V, Y=Ldata$Y, ids=unique.ids)
} else{
# leave-one-out CV
folds <- vector("list", length(unique.ids))
for (v in seq(length(unique.ids))){
folds[[v]] <- unique.ids[v]
}
}
if( is.null(QAdj) ){
# if(verbose) print(cand.Qform)
# do adaptive pre-specification to select from candidate approaches for Qbar
select.Q <- suppressWarnings( CV.selector(goal=goal, target=target, break.match=break.match, Ldata=Ldata,
CAND.ADJ=cand.QAdj, CAND.FORM=cand.Qform, forQ=T,
scale_value=scale_value, scale_value_min=scale_value_min,
folds=folds
) )
if(verbose) print(select.Q)
Q.index <- select.Q$adj.index
QAdj <- select.Q$Adj
Qform <- select.Q$form
# if select unadjusted estimator for QbarAW=E(Y|A,W), then stop
if( sum(QAdj == 'U') ){
g.index<- -99; gAdj <- 'U'; gform <- 'glm'
var.CV <- select.Q$var.CV
}
# if did not select the unadjusted for initial estimation of Qbar(A,W),
if( sum(QAdj == 'U')==0 & remove.pscore){
# then need to remove this variable from the candidate for pscore estimation
# useful for very small trials with simple adaptive prespec
# THIS ONLY WORKS IF cand.QAdj === cand.gAdj AND cand.gform='glm
if(sum(unlist(cand.gAdj) != unlist(cand.QAdj))==0 &
sum(cand.gform !='glm')==0){
print('removing selected QAdj from candidates for pscore')
cand.gAdj[[Q.index]] <- NULL
cand.gform <- cand.gform[-Q.index]
}
}
}
if( is.null(gAdj) ){
# if(verbose) print(cand.gform)
select.G <- suppressWarnings( CV.selector(goal=goal, target=target, break.match=break.match, Ldata=Ldata,
CAND.ADJ=cand.gAdj, CAND.FORM=cand.gform, forQ=F,
# input selected variables/form of the outcome regression
QAdj= QAdj, Qform=Qform,
scale_value=scale_value, scale_value_min=scale_value_min,
folds=folds) )
if(verbose) print(select.G)
g.index <- select.G$adj.index
gAdj <- select.G$Adj
gform <- select.G$form
var.CV <- select.G$var.CV
}
list(Q.index=Q.index, QAdj=QAdj, Qform=Qform,
g.index=g.index, gAdj=gAdj, gform=gform, var.CV=var.CV )
}
#-----------------------------------------------------#-----------------------------------------------------
# CV.selector: function to estimate the cross-validated risk
# Loss function is the squared-IC; Risk is then the variance of the TMLE
# See Balzer et al. "Adaptive pre-specification in randomized trials with and without pair-matching"
#
# input: goal of analysis ('aRR' or 'RD),
# target of inference
# indicator to break the match (break.match)
# dataset (Ldata)
# candidate adjustment variables; they do not have to be at the cluster-level
# indicator if for the conditional mean outcome (forQ)
# selected adjustment variable for the outcome regression (QAdj)
# output: selection for adjustment variable (corresponding to a TMLE)
#-----------------------------------------------------#-----------------------------------------------------
CV.selector <- function(goal, target, break.match, Ldata, CAND.ADJ, CAND.FORM,
forQ, QAdj=NULL, Qform=NULL,
scale_value, scale_value_min, folds){
if( length(CAND.FORM)==1 ){
# if exploring only one estimation algorithm (usually GLM)
# then need to replicate the number forms
CAND.FORM <- rep(CAND.FORM, length(CAND.ADJ))
}
if( length(CAND.FORM) != length(CAND.ADJ)){
print('******* PROBLEM- MISMATCH SIZE OF ADJ VAR AND QFORM')
}
# Number of candidate estimators is given by length Qform//gform
num.tmles <- length(CAND.FORM)
CV.risk <- var.CV <- rep(NA, num.tmles)
for(k in 1: num.tmles){
if(forQ){
# if selecting the adjustment approach for the outcome regression
IC.temp<- get.IC.CV(goal=goal, target=target, break.match=break.match, Ldata=Ldata,
QAdj=CAND.ADJ[[k]], Qform=CAND.FORM[k], gAdj=NULL, gform='glm',
scale_value=scale_value, scale_value_min=scale_value_min,
folds=folds)
} else{
# if collaboratively selecting the adjustment approach for the pscore
IC.temp<- get.IC.CV(goal=goal, target=target, break.match=break.match, Ldata=Ldata,
QAdj=QAdj, Qform=Qform,
gAdj= CAND.ADJ[[k]], gform=CAND.FORM[k],
scale_value=scale_value, scale_value_min=scale_value_min,
folds=folds)
}
# estimating the CV risk for each candidate
CV.risk[k]<- IC.temp$CV.risk
# estimating the CV variance for that TMLE
var.CV[k] <- IC.temp$var.CV
}
# select the candidate estimator resulting in the smallest CV-risk
adj.index<- which.min(CV.risk)
list(CV.risk=CV.risk, adj.index=adj.index,
Adj=CAND.ADJ[[adj.index]], form=CAND.FORM[adj.index], var.CV=var.CV[adj.index])
}
#-----------------------------------------------------#-----------------------------------------------------
# getIC.CV: function to obtain a cross-validated estimate of the influence curve
# See Balzer et al. "Adaptive pre-specification in randomized trials with and without pair-matching"
#
# input: goal of analysis ('aRR' or 'RD),
# target (clust/inv)
# indicator to break the match (break.match)
# dataset (Ldata)
# adjustment variable for the outcome regression (QAdj),
# adjustment approach for outcome regression (Qform)
# adjustment variable for the pscore (gAdj),
# adjustment approach for pscore regression (gform)
# output: cross-validated estimate of the IC for pair
#-----------------------------------------------------#-----------------------------------------------------
# UPDATES
# previous version only did leave-one-out (unit or pair)
# this version generalizes to V-fold CV if V>=40
# - can input the number of folds V (default=10)
# - folds created stratified on binary outcomes (by default)
# - if stratify=T and # observations in a given class is <V,
# then sets V=min observations in that fold
get.IC.CV<- function(goal, target, break.match, Ldata, QAdj, Qform, gAdj=NULL, gform='glm',
scale_value, scale_value_min, folds){
nFolds <- length(folds)
DY.CV <- CV.risk <- NULL
# doing a cross-validated estimate
for(i in 1:nFolds) {
these <- Ldata$indpt.unit %in% folds[[i]] #******* IMPT
valid <- Ldata[these, ]
train <- Ldata[!these,]
# run full TMLE algorithm on the training set
train.out <- do.TMLE(goal=goal, target=target, train=train, QAdj=QAdj, Qform=Qform,
gAdj=gAdj, gform=gform,
scale_value=scale_value, scale_value_min=scale_value_min,
doing.CV=T, verbose=F)
# get the relevant components of the IC for the validation set,
# using fits based on the training set
valid.out <- do.TMLE.validset(goal=goal, target=target, valid=valid, train.out=train.out,
scale_value=scale_value, scale_value_min=scale_value_min)
# estimating the CV risk for each candidate
# risk = Expectation of loss with loss as IC-sq
# risk = variance of TMLE
if(break.match){
DY.CV <- c(DY.CV, valid.out$DY)
CV.risk <- c(CV.risk, mean(valid.out$DY^2))
}else{
DY.CV <- c(DY.CV, valid.out$DY.paired)
CV.risk <- c(CV.risk, mean(valid.out$DY.paired^2))
}
}
# ave across folds
CV.risk <- mean(CV.risk)
# estimating the CV variance for that TMLE
var.CV <- var(DY.CV)/length(DY.CV)
list(CV.risk=CV.risk, var.CV=var.CV)
}
#-----------------------------------------------------#-----------------------------------------------------
# do.TMLE.for.valid: function to obtain a cross-validated estimate of the influence curve
# for observations in the validation set
# See Balzer et al. "Adaptive pre-specification in randomized trials with and without pair-matching"
#
# input: goal of analysis ('aRR' or 'RD'),
# validation dataset ('valid')
# TMLE-fits from training set (train.out)
# output: cross-validated estimate of the IC,
# cross-validated risk estimate (loss=IC^2)
#-----------------------------------------------------#-----------------------------------------------------
do.TMLE.validset <- function(goal, target, valid, train.out, scale_value, scale_value_min){
# J <- length(unique(valid$id) )
#=============================================
# Step1 - initial estimation of E(Y|A,W)= Qbar(A,W)
#=============================================
valid<- do.Init.Qbar(train=valid, QAdj=train.out$QAdj, Qform=train.out$Qform,
glm.out=train.out$Q.out)$train
#=============================================
# Step2: Calculate the clever covariate
#=============================================
valid <- get.clever.cov(train=valid, gAdj=train.out$gAdj, gform=train.out$gform,
p.out=train.out$p.out)$train
#=============================================
# Step3: Targeting -
#=============================================
valid <- do.targeting(train=valid, eps=train.out$eps, goal=goal)
#=============================================
# Step5: Variance estimation using treatment-specific means from training set
#=============================================
get.IC.variance(goal=goal, target=target, Vdata=valid, R1=train.out$R1, R0=train.out$R0,
scale_value=scale_value, scale_value_min=scale_value_min, doing.CV=T)
}
#-----------------------------------------------------#-----------------------------------------------------
# get.cand.adj = function to get candidate adjustment strategies (variables + algorithms)
# for estimating the outcome regression and the propensity score
get.cand.adj <- function(all.cand, cand.Qform.fancy=NULL, cand.gform.fancy=NULL){
all.cand.Ulist <- as.list(c('U', all.cand))
# always consider main terms with each candidate adjustment variable
cand.Qform <- cand.gform <- rep('glm', length(all.cand.Ulist))
if(is.null(cand.Qform.fancy)) {
# simple Adaptive Prespec
cand.QAdj <- all.cand.Ulist
}else{
# fancy adaptive prespec with expanded algorithms
cand.QAdj <- c( all.cand.Ulist, rep(list(all.cand), length(cand.Qform.fancy)) )
cand.Qform <- c(cand.Qform, cand.Qform.fancy)
}
if(is.null(cand.gform.fancy)){
# simple adaptive prespec
cand.gAdj <- all.cand.Ulist
}else{
# fancy adaptive prespec with expanded algorithms
cand.gAdj <- c( all.cand.Ulist, rep(list(all.cand), length(cand.gform.fancy)) )
cand.gform <- c(cand.gform, cand.gform.fancy)
}
list(cand.QAdj=cand.QAdj, cand.Qform=cand.Qform,
cand.gAdj=cand.gAdj, cand.gform=cand.gform)
}
#-----------------------------------------------------#-----------------------------------------------------
# adapted from .cvFolds from cvAUC package: https://CRAN.R-project.org/package=cvAUC
# by Erin LeDell
get.folds <- function(V, Y, ids, stratify=T){
if(stratify & length(unique(Y))==2){
# stratify on the outcome
classes <- tapply(1:length(Y), INDEX=Y, FUN=split, 1)
ids.Y1 <- ids[classes$`1`]
ids.noY1 <- ids[classes$`0`]
if(length(ids.Y1) < V | length(ids.noY1) < V) {
V <- min( length(ids.Y1), length(ids.noY1))
}
ids.Y1.split <- split(sample(length(ids.Y1)), rep(1:V, length=length(ids.Y1)))
ids.noY1.split <- split(sample(length(ids.noY1)), rep(1:V, length=length(ids.noY1)))
folds <- vector("list", V)
for (v in seq(V)){
folds[[v]] <- c(ids.Y1[ids.Y1.split[[v]]], ids.noY1[ids.noY1.split[[v]]])
}
}else{
# dont stratify on the outcome
ids.split <- split(sample(length(ids)), rep(1:V, length=length(ids)))
folds <- vector("list", V)
for (v in seq(V)){
folds[[v]] <- ids[ids.split[[v]]]
}
}
folds
}