Abstract
PurposeTo control for confounding bias from non-random treatment assignment in observational data, both traditional multivariable models and more recently propensity score approaches have been applied. Our aim was to compare a propensity score-stratified model with a traditional multivariable-adjusted model, specifically in estimating survival of hemodialysis (HD) versus peritoneal dialysis (PD) patients.MethodsUsing the Dutch End-Stage Renal Disease Registry, we constructed a propensity score, predicting PD assignment from age, gender, primary renal disease, center of dialysis, and year of first renal replacement therapy. We developed two Cox proportional hazards regression models to estimate survival on PD relative to HD, a propensity score-stratified model stratifying on the propensity score and a multivariable-adjusted model, and tested several interaction terms in both models.ResultsThe propensity score performed well: it showed a reasonable fit, had a good c-statistic, calibrated well and balanced the covariates. The main-effects multivariable-adjusted model and the propensity score-stratified univariable Cox model resulted in similar relative mortality risk estimates of PD compared with HD (0.99 and 0.97, respectively) with fewer significant covariates in the propensity model. After introducing the missing interaction variables for effect modification in both models, the mortality risk estimates for both main effects and interactions remained comparable, but the propensity score model had nearly as many covariates because of the additional interaction variables.ConclusionAlthough the propensity score performed well, it did not alter the treatment effect in the outcome model and lost its advantage of parsimony in the presence of effect modification.
Highlights
Using observational data to compare outcomes associated with different treatments may result in biased estimates because of non-random treatment assignment
We excluded patients younger than 18 years, patients who underwent RRT for less than 30 days, patients who had more than one episode of recovery of renal function, or who died directly following a period of renal recovery, patients who received a pre-emptive transplantation, patients who died during the first 90 days of renal replacement therapy and patients from centers treating fewer than 20 dialysis patients or fewer than 5 peritoneal dialysis (PD) patients
Primary renal diagnosis was coded according to the classification of the European Renal Association-European Dialysis and Transplantation Association (ERAEDTA)
Summary
Using observational data to compare outcomes associated with different treatments may result in biased estimates because of non-random treatment assignment. To correct for variables that may confound an association, the traditional approach is to apply multivariableadjusted modeling, but in recent years, use of propensity scores has become increasingly popular [1]. The concept of a multivariate confounder score was first introduced. Several advantages of propensity score-stratified versus traditional multivariable-adjusted modeling have been suggested. The propensity score enables a direct estimation of comparability of the treatment groups by assessing the covariate balance between groups. Inability to balance confounders alerts investigators that the treatment groups are not sufficiently overlapping with respect to these confounders and that selection bias may not be resolvable [4]. Traditional multivariable regression modeling will not detect this directly
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