Perhaps this package could be considered
https://cran.r-project.org/web/packages/hrIPW/hrIPW.pdf
That packages author also has a 2016 article in Statistics in Medicine on the
properties of estimates from such analyses that might be useful.
—
David Winsemius, MD, MPH
Sent from my iPhone
> On May 19, 2021, at 8:01 PM, Sorkin, John wrote:
>
> When running a propensity score weighted analysis using coxph(), are the
> weights entered as the log of the weights, or as the weights on the original
> scale, i.e. coxph(Surv(time,status)~group,weights=weights ,data=mydata)
> or
> coxph(Surv(time,status)~group,weights=log(weights),data=mydata)
>
> I am creating weights using logistic regression as described below.
>
> # Lalonde data from the MatchIt package is used in the pseudo code below
> install.packages("MatchIt")
> library("MatchIt")
>
> #
> # Calculate propensity scores using logistic regression.#
> #
> ps <- glm(treat ~ age + educ +nodegree +re74+
> re75,data=lalonde,family=binomial())
> summary(ps)
> #PS on the scale of the dependent variable
> # Add the propensity scores to the dataset
> lalonde$psvalue <- predict(ps,type="response")
> #
> # END Calculate propensity scores using logistic regression.#
> #
>
> #
> # Convert propensity scores to weights#
> #
> # Different weights for cases (1) and controls
> lalonde$weight.ATE <- ifelse(lalonde$treat == 1,
> 1/lalonde$psvalue,1/(1-lalonde$psvalue))
> summary(lalonde$weight.ATE)
> #
> # END Convert propensity scores to weights#
> #
>
> ##
> # Examples of two possible way to enter weights in the coxph model. #
> ##
> fit1 <- coxph(Surv(time,status)~group,weights=lalonde$weight,data=lalonde)
> or
> fit2 <-
> coxph(Surv(time,status)~group,weights=log(lalonde$weight),data=lalonde)
> ##
> # Examples of two possible way to enter weights in the coxph model. #
> ##
>
>
>[[alternative HTML version deleted]]
>
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