Propensity Score Matching Meets Survival Analysis

In one of my earlier posts regarding propensity score applications in higher ed research, a reader asked in the comment section about using propensity score methods in the context of survival analysis. Ironically, just a few days prior, I was having a similar discussion with another higher education researcher. Unfortunately, I have not been able to answer their questions adequately, but I think this is an interesting topic. I've recently located a few articles that deal with this. Unfortunately I have not had a chance to read through them but thought they may be of interest. In the least I now have them bookmarked for future reference. Hopefully they address some of these issues.

Effect of radiation therapy on survival in surgically resected retroperitoneal sarcoma: a propensity score-adjusted SEER analysis

 Ann Oncol (2012)

A. H. Choi1,

J. S. Barnholtz-Sloan2 and

J. A. Kim3*

Propensity score methods were used to perform survival analysis in patients who received radiation matched with patients who underwent surgery alone...Propensity scoring (309 matched pairs) and survival analysis using Kaplan–Meier methods demonstrated no difference between propensity score-matched patients receiving radiation therapy and those who did not (P = 0.35).

 Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study.

Pharm Stat. 2012 Mar 12. doi: 10.1002/pst.537.

Gayat E, Resche-Rigon M, Mary JY, Porcher R.

A Monte Carlo simulation study was used to compare the performance of several survival models to estimate both marginal and conditional treatment effects. The impact of accounting or not for pairing when analysing propensity-score-matched survival data was assessed. In addition, the influence of unmeasured confounders was investigated....Our study showed that propensity scores applied to survival data can lead to unbiased estimation of both marginal and conditional treatment effect, when marginal and adjusted Cox models are used. In all cases, it is necessary to account for pairing when analysing propensity-score-matched data, using a robust estimator of the variance.

The performance of different propensity score methods for estimating marginal hazard ratios. Stat Med. 2013 Jul 20;32(16):2837-49. doi: 10.1002/sim.5705. Epub 2012 Dec 12. Austin PC.

...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....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.