Propensity score matching and persistence correction to reduce bias in comparative effectiveness: the effect of cinacalcet use on all‐cause mortality

Abstract

PurposeThe generalisability of randomised controlled trials (RCTs) may be limited by restrictive entry criteria or by their experimental nature. Observational research can provide complementary findings but is prone to bias. Employing propensity score matching, to reduce such bias, we compared the real‐life effect of cinacalcet use on all‐cause mortality (ACM) with findings from the Evaluation of Cinacalcet Therapy to Lower Cardiovascular Events (EVOLVE) RCT in chronic haemodialysis patients.MethodsIncident adult haemodialysis patients receiving cinacalcet, recruited in a prospective observational cohort from 2007–2009 (AROii; n = 10,488), were matched to non‐exposed patients regardless of future exposure status. The effect of treatment crossover was investigated with inverse probability of censoring weighted and lag‐censored analyses. EVOLVE ACM data were analysed largely as described for the primary composite endpoint.ResultsAROii patients receiving cinacalcet (n = 532) were matched to 1790 non‐exposed patients. The treatment effect of cinacalcet on ACM in the main AROii analysis (hazard ratio 1.03 [95% confidence interval (CI) 0.78–1.35]) was closer to the null than for the Intention to Treat (ITT) analysis of EVOLVE (0.94 [95%CI 0.85–1.04]). Adjusting for non‐persistence by 0‐ and 6‐month lag‐censoring and by inverse probability of censoring weight, the hazard ratios in AROii (0.76 [95%CI 0.51–1.15], 0.84 [95%CI 0.60–1.18] and 0.79 [95%CI 0.56–1.11], respectively) were comparable with those of EVOLVE (0.82 [95%CI 0.67–1.01], 0.83 [95%CI 0.73–0.96] and 0.87 [95%CI 0.71–1.06], respectively).ConclusionsCorrecting for treatment crossover, we observed results in the ‘real‐life’ setting of the AROii observational cohort that closely mirrored the results of the EVOLVE RCT. Persistence‐corrected analyses revealed a trend towards reduced ACM in haemodialysis patients receiving cinacalcet therapy. © 2015 The Authors. Pharmacoepidemiology and Drug Safety Published by John Wiley & Sons Ltd.

The association between cinacalcet use and missed in‐center hemodialysis treatment rate

“…Recently, Gillespie et al conducted a retrospective analysis of cinacalcet‐treated hemodialysis patients ( N = 532) who were matched with untreated hemodialysis control patients ( c = 1790). When investigators made adjustments for treatment cross over—similar to what was done here—using IP of censoring weighting, cinacalcet treatment significantly reduced the hazard ratios for all‐cause mortality (0.79 [95% CI: 0.56, 1.11]), which was comparable with parallel analyses conducted from EVOLVE patient data (0.87 [95% CI: 0.71, 1.06]) …”

“…Therefore, we cannot exclude the possibility of residual confounding, although the variables included in this analysis are at a minimum comparable with, if not more extensive than previous analyses of cinacalcet use. 13,16 In the current study, treatment with cinacalcet was associated with reduced missed in-center hemodialysis treatment rates. However, the study source data did not enable us to observe the reasons for missed hemodialysis treatments; therefore, direct effects of cinacalcet on the particular circumstances that might lead to a missed hemodialysis treatment could not be estimated directly.…”

“…Despite the robustness of the source data, there are potential confounders that are not measured and others that may not be known. Therefore, we cannot exclude the possibility of residual confounding, although the variables included in this analysis are at a minimum comparable with, if not more extensive than previous analyses of cinacalcet use . In the current study, treatment with cinacalcet was associated with reduced missed in‐center hemodialysis treatment rates.…”

“…8 While the study did not meet its primary outcome, analysis of data after prespecified adjustments for baseline imbalances between patient groups revealed that cinacalcet-treated patients showed nominally significant and clinically meaningful reductions in cardiovascular events and death versus patients treated with placebo. 8 13 Based on the prior evidence derived from the EVOLVE trial and an understanding of the complicated treatment paradigm for management of SHPT in hemodialysis patients, we anticipated high and differential rates of censoring and crossover among patients in this study, which was borne out empirically. We therefore determined a priori to use IP censoring or crossover weighting as a means to minimize selection bias that might otherwise result.…”

“…A large proportion of treatment crossover and censoring was anticipated in this population, thus we stipulated two coprimary analyses a priori: (1) stabilized IP of censoring weighted ITT analysis and (2) stabilized IP of censoring or crossover weighted AT analysis. To reduce selection bias due to differential loss to follow‐up and enhance validity and precision, stabilized IP weights were estimated using the method of Robins, Hernán, and Brumback .…”