Biometrics · 2005 · 248 citations · 22 references
Public HealthBiostatisticsUrologyCausal InferenceEpidemiologic MethodsSurvival AnalysisMatched-pair AnalysisMatched-pair DesignsMatching MethodsPropensity ModelingPropensity Score MethodsTime-dependent CovariatesCovariate Balancing Propensity ScoreMatchingPropensity Score AdjustmentPropensity Score Model SpecificationRisk Set MatchingSimulation StudiesTime-varying Propensity Scores
In observational studies with time‑dependent treatments and covariates, balancing covariate distributions at each time point is desirable. The study proposes a time‑dependent propensity score derived from a Cox proportional hazards model for use in risk‑set matching. The method employs a Cox‑model‑based time‑dependent propensity score for risk‑set matching, with optimal matching designs evaluated on cystoscopy and hydrodistention treatment for interstitial cystitis. Matching on the proposed score balances covariates and, per simulations, yields more accurate point and interval estimates than unmatched analyses.
Summary In observational studies with a time‐dependent treatment and time‐dependent covariates, it is desirable to balance the distribution of the covariates at every time point. A time‐dependent propensity score based on the Cox proportional hazards model is proposed and used in risk set matching. Matching on this propensity score is shown to achieve a balanced distribution of the covariates in both treated and control groups. Optimal matching with various designs is conducted and compared in a study of a surgical treatment, cystoscopy and hydrodistention, given in response to a chronic bladder disease, interstitial cystitis. Simulation studies also suggest that the statistical analysis after matching outperforms the analysis without matching in terms of both point and interval estimations.
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