############ ## README ## ############ ## This code implements the weighted and centered estimator in Boruvka, Almirall, Witkiewitz and Murphy. ## This code is useful when the data arises from a micro-randomized trial in which the randomization ## probabilities are known. In the paper, the known randomization probabilities are p( A_t | H_t ); they are used in ## the denominator of the weights. In this code, the numerator of the weights, \tilde{p}(A_t | S_t) are estimated ## using logistic regression, and this code adjusts the standard errors of \beta for the fact that \tilde{p} is ## is estimated. Recall that S_t is a subset of H_t. ############ ## The MRT data should be in person-period format (long data format) of size n*tmax. ## There are n subjects, with tmax time points (note: all n subjects have data at 1:tmax time points). ## "a" is treatment; "y" is outcome; "id" is the unique person identifier. ## For the analysis of any new MRT data set, the analyst might change the following lines: ## 24, set your working directory where your data is stored ## 34, load new data set from working directory ## 44-45, the known randomization probability ## 49, design matrix for estimating numerator probability, \tilde p ## 56, to incorporate availability (I_t in paper), multiply the weights by the availability indicator ## 60-62, design matrix for yt+1 (q-size working model + p-size causal model) ## clean all objects in R and set working directory rm(list=ls(all=TRUE)) setwd("/Users/dalmiral/Dropbox") ## need an expit calculator expit <- function(x, eta) { e <- as.vector(exp(x %*% eta)) e / (1 + e) } ## load MRT data and get sample size (n) and # of time points (tmax) d <- read.table(file="fakeMRTdata.txt",sep=",") tmax <- length( unique(d$time) ) n <- length( unique(d$id) ) small <- if (n <= 50) {TRUE} else {FALSE} ## do the Mancl and DeRouen small sample adjustment to SEs? ############################################## ## weighting-and-centering estimator ######### ############################################## ## set the randomization probabilities, the "denominator" probabilities ## here, the randomization probs are known d$denprob <- .5 ## p( 1 | H_t ) -- this is the known randomizaiton probability d$den <- d$a*( d$denprob ) + (1-d$a)*( 1-d$denprob ) ## p( A_t | H_t ) -- this is the known one for At=0 and At=1 people ################################## ## get numerator and center action, the "numerator" probabilities ## these are estimated here with ndesign "design matrix" ndesign <- cbind(Int = rep(1,n*(tmax) ) ) ## in this example, it is a intercept-only model glmn <- glm.fit(x = ndesign, y = d$a, weights = rep(1,n*(tmax) ), family = binomial() ) d$numprob <- expit( x = ndesign, eta = glmn$coefficients ) ## \tilde{p}(1 | S_t) d$num <- d$a*( d$numprob ) + (1-d$a)*( 1-d$numprob ) ## \tilde{p}(A_t | S_t) d$ac <- d$a - d$numprob ## A_t - \tilde{p}(1 | S_t) ################################## ## define the weights d$wt <- d$num / d$den ## multiply this by availability indicator, if appropriate for your application ################################## ## get weighted estimator ## design matrix for y is of size n*tmax x (qq+pp) ydesign <- cbind(Int = 1, St=d$St, Ac=d$ac) qq <- 2 ## this is q (dimention of alpha -- working model) pp <- 1 ## this is p (dimension of beta -- causal model) if( (qq+pp)!=dim(ydesign)[2] ) stop("q+p must match the dimension of ydesign.") lm.wt <- lm.wfit(x = ydesign, y = d$y, w = d$wt) ################################################## ## get standard errors for weighting-and-centering #### bread #### Uw <- mapply( function(DD,ww,rr) t(DD) %*% diag(ww) %*% rr, DD = split.data.frame( ydesign, d$id ), ww = split(as.vector( d$wt ), d$id), rr = split(d$y - lm.wt$fitted.values, d$id), SIMPLIFY = FALSE ) Uw <- do.call("rbind", lapply(Uw, FUN=t) ) dUw <- mapply(function(DD, ww) t(DD) %*% diag(ww) %*% DD, DD = split.data.frame( ydesign, d$id ), ww = split(as.vector( d$wt ), d$id), SIMPLIFY = FALSE ) sumdotUw <- Reduce("+", dUw) ## sum of the derivative of the y estimating function EdUw <- sumdotUw / n ## average of the derivative of the y estimating function b <- solve(EdUw) #### meat #### Un <- mapply( function(DD,rr) t(DD) %*% rr, ## numerator estimating function DD = split.data.frame( ndesign, d$id ), rr = split(d$a - d$numprob, d$id), SIMPLIFY = FALSE ) Un <- do.call("rbind", lapply(Un, FUN=t) ) dUn <- mapply(function(DD, ww, prob) t(DD) %*% diag( prob*(1-prob) ) %*% DD, ## derivative of the numerator estimating function DD = split.data.frame( ndesign, d$id ), prob = split(d$numprob, d$id), SIMPLIFY = FALSE ) sumdotUn <- Reduce("+", dUn) ## sum of the derivative of the numerator estimating function EdUn <- sumdotUn / n ## average of the derivative of the numerator estimating function EdUn.inv <- solve(EdUn) Hii <- mapply( function(DDy, ww) DDy %*% solve(sumdotUw) %*% t(DDy) %*% diag(ww), ## tmax x tmax hat matrix for each person DDy = split.data.frame( ydesign, d$id ), ww = split(as.vector( d$wt ), d$id), SIMPLIFY = FALSE ) if (small) { ## small = TRUE, then replace the residual with an adjustment using the hat matrix tilderesidualy <- mapply( function(hatmat,rry) solve(diag(1,tmax) - hatmat) %*% rry , ## new residual for each person (scaled by the hat matrix) hatmat = Hii, rry = split(d$y - lm.wt$fitted.values, d$id), SIMPLIFY = FALSE ) ## re-define the Uw with the adjustment Uw <- mapply( function(DD,ww,rr) t(DD) %*% diag(ww) %*% rr, DD = split.data.frame( ydesign, d$id ), ww = split(as.vector( d$wt ), d$id), rr = tilderesidualy, SIMPLIFY = FALSE ) Uw <- do.call("rbind", lapply(Uw, FUN=t) ) } else { tilderesidualy <- split(d$y - lm.wt$fitted.values, d$id) ## leave Uw as is. } Sigwn1 <- mapply( function(DDy,DDn,ww,rry,rrn) t(DDy) %*% diag(ww) %*% diag( as.vector(rry) ) %*% ( diag(rrn) %*% DDn ), DDy = split.data.frame( ydesign, d$id ), DDn = split.data.frame( ndesign, d$id ), ww = split(as.vector( d$wt ), d$id), rry = tilderesidualy, rrn = split(d$a - d$numprob, d$id), SIMPLIFY = FALSE ) Sigwn2 <- mapply( function(DDy,DDn,ww,rry,rrn) t(DDy) %*% diag(ww) %*% diag( as.vector(rry) ) %*% diag(rrn) %*% DDn , DDy = split.data.frame( cbind( matrix(0,n*(tmax),qq), -1*d$numprob*ydesign[,(qq+1):(qq+pp)]/(d$a - d$numprob) ), d$id ), ww = split(as.vector( d$wt ), d$id), rry = tilderesidualy, rrn = split(1 - d$numprob, d$id), DDn = split.data.frame( ndesign, d$id ), SIMPLIFY = FALSE ) if(pp==1) { ## hack to get just the causal part without centered action justcausal <- ydesign[,(qq+1):(qq+pp)] * lm.wt$coefficients[(qq+1):(qq+pp)] / (d$a - d$numprob) } else { justcausal <- (ydesign[,(qq+1):(qq+pp)] %*% lm.wt$coefficients[(qq+1):(qq+pp)] ) / (d$a - d$numprob) } Sigwn3 <- mapply( function(DDy,rrn,DDn,ptilde,causal,ww) t(DDy) %*% diag( ptilde ) %*% diag( causal ) %*% diag( ww ) %*% diag(rrn) %*% DDn , DDy = split.data.frame( ydesign, d$id ), rrn = split(1 - d$numprob, d$id), DDn = split.data.frame( ndesign, d$id ), ptilde = split(as.vector(d$numprob), d$id), causal = split( justcausal, d$id), ww = split(as.vector( d$wt ), d$id), SIMPLIFY = FALSE ) Sigwn <- Reduce("+",Sigwn1) / n + Reduce("+",Sigwn2) / n + Reduce("+",Sigwn3) / n psi <- Uw + Un %*% EdUn.inv %*% t(Sigwn) m <- ( t(psi) %*% psi ) / n #### sandwich #### varcov <- ( b %*% m %*% t(b) ) / n ## this one adjusts for numerator m <- ( t(Uw) %*% Uw ) / n varcov.noNDadj <- ( b %*% m %*% t(b) ) / n ## this one does NOT adjust for numerator ############################################################# ## get 95% confidence intervals and p-value ## use the adjusted SE for confidence intervals and p-values se.est <- sqrt( diag(varcov) ) lcl <- lm.wt$coefficients - se.est * qt(.975, df = n - qq - 1) ucl <- lm.wt$coefficients + se.est * qt(.975, df = n - qq - 1) pvalue <- pf( (lm.wt$coefficients/se.est)^2, lower.tail = FALSE, df1 = 1, df2 = n - qq - 1) ############################################################# ## print results res <- cbind( est=lm.wt$coefficients, unadjusted.se=sqrt( diag(varcov.noNDadj) ), ## unadjusted SE reported but not used for confidence intervals or pvalues adjusted.se=se.est, lower95CI=lcl, upper95CI=ucl, pval=pvalue ) print( round( res, 4) ) ## eof