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.