Terminating observation within matched pairs of subjects in a matched cohort analysis: a Monte Carlo simulation study

Stat Med. 2016 Jan 30;35(2):294-304. doi: 10.1002/sim.6621. Epub 2015 Aug 16.


Matched cohort analyses are becoming increasingly popular for estimating treatment effects in observational studies. However, in the applied biomedical literature, analysts and authors are inconsistent regarding whether to terminate follow-up among members of a matched set once one member is no longer under observation. This paper focused on time-to-event outcomes and used Monte Carlo simulation methods to determine the optimal approach. We found that the bias of the estimated treatment effect estimate was negligible under both approaches and that the percentage of censoring had no discernible effect on the magnitude of bias. The mean model-based standard error of the treatment estimate was consistently higher when we terminated observation within matched pairs. Furthermore, the type 1 error rate was consistently lower when we did not terminate follow-up within matched pairs. In conclusion, when the focus was on time-to-event outcomes, we demonstrated that there was no advantage to terminating follow-up within matched pairs. Continuing follow-up on each subject until their observation was naturally complete was superior compared with terminating a subject's observation time once its matched pair had ceased to be under observation. Given the frequency with which these analyses are conducted in the applied literature, our results provide important guidance to analysts and applied researchers as to the preferred analytic approach.

Keywords: Monte Carlo simulations; bias; censoring; matched cohort study; terminate follow-up; treatment effect.

MeSH terms

  • Bias
  • Biostatistics / methods
  • Cohort Studies*
  • Colorectal Neoplasms / therapy
  • Computer Simulation
  • Databases, Factual
  • Humans
  • Monte Carlo Method*
  • Multivariate Analysis
  • Observational Studies as Topic / statistics & numerical data
  • Proportional Hazards Models
  • Treatment Outcome