The performance of different propensity score methods for estimating marginal hazard ratios

Abstract

Propensity score methods are increasingly being used to reduce or minimize the effects of confounding when estimating the effects of treatments, exposures, or interventions when using observational or non-randomized data. Under the assumption of no unmeasured confounders, previous research has shown that propensity score methods allow for unbiased estimation of linear treatment effects (e.g., differences in means or proportions). However, in biomedical research, time-to-event outcomes occur frequently. There is a paucity of research into the performance of different propensity score methods for estimating the effect of treatment on time-to-event outcomes. Furthermore, propensity score methods allow for the estimation of marginal or population-average treatment effects. We conducted an extensive series of Monte Carlo simulations to examine the performance of propensity score matching (1:1 greedy nearest-neighbor matching within propensity score calipers), stratification on the propensity score, inverse probability of treatment weighting (IPTW) using the propensity score, and covariate adjustment using the propensity score to estimate marginal hazard ratios. We found that both propensity score matching and IPTW using the propensity score allow for the estimation of marginal hazard ratios with minimal bias. Of these two approaches, IPTW using the propensity score resulted in estimates with lower mean squared error when estimating the effect of treatment in the treated. Stratification on the propensity score and covariate adjustment using the propensity score result in biased estimation of both marginal and conditional hazard ratios. Applied researchers are encouraged to use propensity score matching and IPTW using the propensity score when estimating the relative effect of treatment on time-to-event outcomes.

Figure 1

Estimated hazard ratio using different propensity score methods. IPTW, inverse probability of treatment weight; ATE, average treatment effect; ATT, average treatment effect in the treated.

Figure 2

Relative bias of different propensity score methods for estimating marginal hazard ratios. IPTW, inverse probability of treatment weight; ATE, average treatment effect; ATT, average treatment effect in the treated.

Figure 3

Relative bias of different propensity score methods for estimating conditional hazard ratios.

Figure 4

Ratio of mean standard error to standard deviation of estimated log-hazard ratios. IPTW, inverse probability of treatment weight; ATE, average treatment effect; ATT, average treatment effect in the treated.

Figure 5

Coverage rates of 95% confidence intervals (CIs) for estimated hazard ratios. IPTW, inverse probability of treatment weight; ATE, average treatment effect; ATT, average treatment effect in the treated.

Figure 6

Mean length of 95% confidence intervals (CIs) for marginal hazard ratios. IPTW, inverse probability of treatment weight; ATE, average treatment effect; ATT, average treatment effect in the treated.

Figure 7

Mean squared error of estimated log-hazard ratio. IPTW, inverse probability of treatment weight; ATE, average treatment effect; ATT, average treatment effect in the treated.