binregTSR: 2 Stage Randomization for Survival Data or competing Risks...

In mets: Analysis of Multivariate Event Times

2 Stage Randomization for Survival Data or competing Risks Data

Description

Under two-stage randomization we can estimate the average treatment effect E(Y(i,j)) of treatment regime (i,j). The estimator can be agumented in different ways: using the two randomizations and the dynamic censoring augmetatation. The treatment's must be given as factors.

Usage

binregTSR(
  formula,
  data,
  cause = 1,
  time = NULL,
  cens.code = 0,
  response.code = NULL,
  augmentR0 = NULL,
  treat.model0 = ~+1,
  augmentR1 = NULL,
  treat.model1 = ~+1,
  augmentC = NULL,
  cens.model = ~+1,
  estpr = c(1, 1),
  response.name = NULL,
  offset = NULL,
  weights = NULL,
  cens.weights = NULL,
  beta = NULL,
  kaplan.meier = TRUE,
  no.opt = FALSE,
  method = "nr",
  augmentation = NULL,
  outcome = c("cif", "rmst", "rmst-cause"),
  model = "exp",
  Ydirect = NULL,
  return.dataw = 0,
  pi0 = 0.5,
  pi1 = 0.5,
  cens.time.fixed = 1,
  outcome.iid = 1,
  ...
)

Arguments

formula	formula with outcome (see coxph)
data	data frame
cause	cause of interest
time	time of interest
cens.code	gives censoring code
response.code	code of status of survival data that indicates a response at which 2nd randomization is performed
augmentR0	augmentation model for 1st randomization
treat.model0	logistic treatment model for 1st randomization
augmentR1	augmentation model for 2nd randomization
treat.model1	logistic treatment model for 2ndrandomization
augmentC	augmentation model for censoring
cens.model	stratification for censoring model based on observed covariates
estpr	estimate randomization probabilities using model
response.name	can give name of response variable, otherwise reads this as first variable of treat.model1
offset	not implemented
weights	not implemented
cens.weights	can be given
beta	starting values
kaplan.meier	for censoring weights, rather than exp cumulative hazard
no.opt	not implemented
method	not implemented
augmentation	not implemented
outcome	can be c("cif","rmst","rmst-cause")
model	not implemented, uses linear regression for augmentation
Ydirect	use this Y instead of outcome constructed inside the program (e.g. I(T< t, epsilon=1)), see binreg for more on this
return.dataw	to return weighted data for all treatment regimes
pi0	set up known randomization probabilities
pi1	set up known randomization probabilities
cens.time.fixed	to use time-dependent weights for censoring estimation using weights
outcome.iid	to get iid contribution from outcome model (here linear regression working models).
...	Additional arguments to lower level funtions

Details

The solved estimating eqution is

( I(min(T_i,t) < G_i)/G_c(min(T_i ,t)) I(T \leq t, \epsilon=1 ) - AUG_0 - AUG_1 + AUG_C - p(i,j)) = 0

where using the covariates from augmentR0

AUG_0 = \frac{A_0(i) - \pi_0(i)}{ \pi_0(i)} X_0 \gamma_0

and using the covariates from augmentR1

AUG_1 = \frac{A_0(i)}{\pi_0(i)} \frac{A_1(j) - \pi_1(j)}{ \pi_1(j)} X_1 \gamma_1

and the censoring augmentation is

AUG_C = \int_0^t \gamma_c(s)^T (e(s) - \bar e(s)) \frac{1}{G_c(s) } dM_c(s)

where

\gamma_c(s)

is chosen to minimize the variance given the dynamic covariates specified by augmentC.

In the observational case, we can use propensity score modelling and outcome modelling (using linear regression).

Standard errors are estimated using the influence function of all estimators and tests of differences can therefore be computed subsequently.

Author(s)

Thomas Scheike

Examples

ddf <- mets:::gsim(200,covs=1,null=0,cens=1,ce=2)

bb <- binregTSR(Event(entry,time,status)~+1+cluster(id),ddf$datat,time=2,cause=c(1),
        cens.code=0,treat.model0=A0.f~+1,treat.model1=A1.f~A0.f,
        augmentR1=~X11+X12+TR,augmentR0=~X01+X02,
        augmentC=~A01+A02+X01+X02+A11t+A12t+X11+X12+TR,
        response.code=2)
summary(bb) 

mets documentation built on May 29, 2024, 3:51 a.m.