Propensity ‐score matching with competing risks in survival analysis

We describe how both relative and absolute measures of treatment effect can be obtained when using propensity‐score matching with competing risks data. Estimates of the relative effect of treatment can be obtained by using cause‐specific hazard models in the matched sample. Estimates of absolute t reatment effects can be obtained by comparing cumulative incidence functions (CIFs) between matched treated and matched control subjects. We conducted a series of Monte Carlo simulations to compare the empirical type I error rate of different statistical methods for testing the equality of CIFs esti mated in the matched sample. We also examined the performance of different methods to estimate the marginal subdistribution hazard ratio. We recommend that a marginal subdistribution hazard model that accounts for the within‐pair clustering of outcomes be used to test the equality of CIFs and to e stimate subdistribution hazard ratios. We illustrate the described methods by using data on patients discharged from hospital with acute myocardial infarction to estimate the effect of discharge prescribing of statins on cardiovascular death.
Source: Statistics in Medicine - Category: Statistics Authors: Tags: RESEARCH ARTICLE Source Type: research