mets (version 1.3.6)

phreg_IPTW: IPTW Cox, Inverse Probaibilty of Treatment Weighted Cox regression

Description

Fits Cox model with treatment weights $$ w(A)= \sum_a I(A=a)/\pi(a|X)$$, where $$\pi(a|X)=P(A=a|X)$$. Computes standard errors via influence functions that are returned as the IID argument. Propensity scores are fitted using either logistic regression (glm) or the multinomial model (mlogit) when there are than treatment categories. The treatment needs to be a factor and is identified on the rhs of the "treat.model". Recurrent events can be considered with start,stop structure and then cluster(id) must be specified. Robust standard errors are computed in all cases.

Usage

phreg_IPTW(
  formula,
  data,
  treat.model = NULL,
  treat.var = NULL,
  weights = NULL,
  estpr = 1,
  pi0 = 0.5,
  se.cluster = NULL,
  ...
)

Arguments

formula

for phreg

data

data frame for risk averaging

treat.model

propensity score model (binary or multinomial)

treat.var

a 1/0 variable that indicates when treatment is given and the propensity score is computed

weights

may be given, and then uses weights*w(A) as the weights

estpr

(=1, default) to estimate propensity scores and get infuence function contribution to uncertainty

pi0

fixed simple weights

se.cluster

to compute GEE type standard errors when additional cluster structure is present

...

arguments for phreg call

Author

Thomas Scheike

Details

Time-dependent propensity score weights can also be computed when treat.var is used, it must be 1 at the time of first (A_0) and 2nd treatment (A_1), then uses weights $$w_0(A_0) * w_1(A_1)^{t>T_r}$$ where $$T_r$$ is time of 2nd randomization.

Examples

library(mets)
data <- mets:::simLT(0.7,100,beta=0.3,betac=0,ce=1,betao=0.3)
dfactor(data) <- Z.f~Z
out <- phreg_IPTW(Surv(time,status)~Z.f,data=data,treat.model=Z.f~X)
summary(out)