#' Calculate STIPW based on propensity for being study
#'
#' Names are hard coded, so stick
#' to script in the examples.
#' @param data_in an object returned from apply_censoring()
#' @param na.action "keep" means the dataset returned will be same number of rows as data_in and "omit" discards all data past censoring observation
#' @export
#' @return a dataset similar to data_in with stipw in columns; and potentially fewer rows or NA-filled rows
#' for induced censoring.
#' @examples
#' \dontrun{
#' d<-prep_data()
#' head(d)
#' over_samp_mat<-sample_data(d,1000)
#' with_ses <- calculate_ses(over_samp_mat)
#' long <-make_long(with_ses)
#' head(long)
#' censored <- apply_censoring(long)
#' head(censored,18)
#' observed_with_stipw <- calculate_stipw(censored,"keep")
#' }
calculate_stipw_hh2 <- function(data_in=NULL, na.action="omit"){
######################################################
# BL edits
######################################################
# only do the propensity among observations upto dropout.
# This is because the pooled logistic is a time to event propensity score.
# Therefore keeping in censored observations
# is like the individual is censored over and over again
######################################################
# BJS notes on BL edits
######################################################
# indcen.age is just switching 1's and 0's of instudy.sim
# see this with a
# table(data_in$indcen.age,data_in$instudy.sim,useNA="ifany")
# cumsum.ind, and cumsum.ind2 are shown in the following two code-lines:
# tail(data_in[,c("newid","indcen.age", "cumsum.ind", "cumsum.ind2")])
# (data_in[data_in$newid==1000,c("newid","indcen.age", "cumsum.ind", "cumsum.ind2")])
# cumsum.ind adds up 1's and 0's, which are independent
# cumsum.ind2 adds up cumsum.ind. By taking data_in.sub to be <=1, we
# only keep the data up to the first indcen.age==1. cumsum.ind2 is necessary
# b/c we see multiple rows of cumsum.ind can equal 1.
data_in$indcen.age[data_in$instudy.sim==0] <- c(1)
data_in$indcen.age[is.na(data_in$indcen.age)] <- 0
data_in$cumsum.ind <- ave(data_in$indcen.age,data_in$newid,FUN=cumsum)
data_in$cumsum.ind2 <- ave(data_in$cumsum.ind,data_in$newid,FUN=cumsum)
data_in.sub <- data_in[data_in$cumsum.ind2<=1,]
## we calculate the numerator and denominators for the standardize inverse probability wts
## aka STIPW on data_in.sub. However, if the models only contain baseline info
## and not subject-specific time-varying info, we can actually FIT/PREDICT weights
## for every single observation, regardless of missing outcomes (inches, cd4,etc)
##bl.logit.nocov <- glm(instudy~bs(time,intercept=FALSE,df=6) , family="binomial", data=data_in.sub)
#simplog$terms[[3]]
##bl.logit.ses <- glm(instudy~bs(time,intercept=FALSE,df=6)+age + sex + race + hetero + msm + ivdu,
## family="binomial", data=data_in.sub)
## post 2015-07-08 edit: re-read Cain and Cole, and talk with Dean
## helped me get "time-specific" intercepts...
bl.logit.nocov <- glm(instudy~as.factor(time)-1 , family="binomial", data=data_in.sub)
#simplog$terms[[3]]
bl.logit.ses <- glm(instudy~as.factor(time)-1 +
cd4_baseline + cd4_delta +
age + sex + race + hetero + msm + ivdu,
family="binomial", data=data_in.sub)
## post 2015-07-08 edit: re-read Cain and Cole, and talk with Dean
## can still do a stipw02 thing, but only with baseline covariates...
bl.logit.base <- glm(instudy~as.factor(time)-1 +
cd4_baseline +
age + sex + race + hetero + msm + ivdu,
family="binomial", data=data_in.sub)
#qplot(bl.logit.nocov$fitted);range(bl.logit.nocov$fitted)
#qplot(bl.logit.ses$fitted);range(bl.logit.ses$fitted)
## add the fitted probabilities to the dataframe
#data_in$fitted.nocov <- (logit.nocov$fitted)
#data_in$fitted.ses <- (logit.ses$fitted)
## this is what we were doing; before Dean on 6/19 said
## if only baseline we could fit wts for all:
data_in.sub$bl.fitted.nocov <- (bl.logit.nocov$fitted)
data_in.sub$bl.fitted.ses <- (bl.logit.ses$fitted)
## wts for all, not just .sub
data_in$bl.fitted.nocov2 <- predict(bl.logit.nocov,
newdata = data_in,
type="response")
data_in$bl.fitted.base2 <- predict(bl.logit.base,
newdata = data_in,
type="response")
data_in$cumprod.nocov2 <- ave(data_in$bl.fitted.nocov2,data_in$newid,FUN=cumprod)
data_in$cumprod.base2 <- ave(data_in$bl.fitted.base2,data_in$newid,FUN=cumprod)
data_in$stipw2 <- data_in$cumprod.nocov2/data_in$cumprod.base2
## now sub again so that we only have observed for weight calculation...
data_in.sub2 <- data_in.sub[data_in.sub$cumsum.ind2==0,]
data_in.sub2$cumprod.nocov <- ave(data_in.sub2$bl.fitted.nocov,data_in.sub2$newid,FUN=cumprod)
data_in.sub2$cumprod.ses <- ave(data_in.sub2$bl.fitted.ses,data_in.sub2$newid,FUN=cumprod)
data_in.sub2$stipw <- data_in.sub2$cumprod.nocov/data_in.sub2$cumprod.ses
## double check:
head(data_in.sub2[data_in.sub2$id==500, c("id","time", "prob.cens", "instudy.sim", "instudy", "bl.fitted.nocov", "bl.fitted.ses","cumprod.nocov", "cumprod.ses", "stipw" )],13)
summary(data_in.sub2$stipw)
## instead of setting this to 0, once weights are calculated for everyone else,
## remove the censored line for each individual...
## data_in.sub$stipw[!data_in.sub$instudy] <- 0
#qplot(cumprod.nocov,cumprod.ses,color=age,size=ses,data=data_in.sub)
#qplot(stipw,cumprod.ses,color=age,size=ses,data=data_in.sub)
#qplot(stipw,cumprod.nocov,color=age,size=ses,data=data_in.sub)
##sub.wtd_avg <- ddply(data_in.sub, .(age), function(w) sum(w$stipw*w$inches)/sum(w$stipw))
##names(sub.wtd_avg)[2] <- "wtd_avg"
if(na.action=="omit"){
return_obj=data_in.sub2
}
if(na.action=="keep"){
setDT(data_in)
setDT(data_in.sub2)
setkeyv(data_in , names(data_in.sub2)[1:15])
setkeyv(data_in.sub2, names(data_in.sub2)[1:15])
setDF(data_in)
setDF(data_in.sub2)
back_in <- merge(data_in, data_in.sub2,all.x=TRUE,sort=FALSE)
return_obj=back_in
}
#setDT(return_obj)
#setkey(return_obj, newid, time)
return_obj
}
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