Abstract
Observational studies are increasingly being used to estimate the effect of treatments, interventions and exposures on outcomes that can occur over time. Historically, the hazard ratio, which is a relative measure of effect, has been reported. However, medical decision making is best informed when both relative and absolute measures of effect are reported. When outcomes are time-to-event in nature, the effect of treatment can also be quantified as the change in mean or median survival time due to treatment and the absolute reduction in the probability of the occurrence of an event within a specified duration of follow-up. We describe how three different propensity score methods, propensity score matching, stratification on the propensity score and inverse probability of treatment weighting using the propensity score, can be used to estimate absolute measures of treatment effect on survival outcomes. These methods are all based on estimating marginal survival functions under treatment and lack of treatment. We then conducted an extensive series of Monte Carlo simulations to compare the relative performance of these methods for estimating the absolute effects of treatment on survival outcomes. We found that stratification on the propensity score resulted in the greatest bias. Caliper matching on the propensity score and a method based on earlier work by Cole and Hernán tended to have the best performance for estimating absolute effects of treatment on survival outcomes. When the prevalence of treatment was less extreme, then inverse probability of treatment weighting-based methods tended to perform better than matching-based methods.
Highlights
Health researchers are increasingly using observational studies to estimate the effects of treatments, interventions and exposures on outcomes
We used an extensive series of Monte Carlo simulations to examine the ability of different propensity score methods to estimate the absolute effects of treatment on survival or time-to-event outcomes
We considered estimating both the absolute effect of treatment on mean and median survival time and the absolute reduction in the probability of the occurrence of the event within a specified duration of follow-up time
Summary
Health researchers are increasingly using observational studies to estimate the effects of treatments, interventions and exposures on outcomes. Due to the lack of random treatment assignment, treated or exposed subjects frequently differ from untreated or unexposed subjects. Propensity score methods are increasingly being used to reduce or minimize the confounding that occurs frequently in observational studies of the effect of treatment on outcomes.[1,2] The propensity score is the probability of treatment assignment conditional on measured baseline covariates.[3] There are four ways of using the propensity score to reduce confounding: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score and covariate adjustment using the propensity score.[3,4,5,6]
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