Estimating the Effect of Preventable Treatment Discontinuation on Health Outcomes.

BACKGROUND: There is increased interest in studying the effects of medication adherence on health outcomes. However, if patients appropriately stop treatment because of side effects and treatment failure, it is neither possible nor clinically meaningful to estimate the effect of full medication adherence.

METHODS: We present an analysis designed to estimate the effect of nonmedical (preventable) discontinuation of cinacalcet, an oral medication approved to treat secondary hyperparathyroidism in patients with end-stage renal disease on dialysis on mortality and heart failure. The approach involves artificially censoring patients who discontinue treatment for a reason that does not appear to be related to an adverse effect of treatment. We address potential bias from informative censoring through inverse-probability of censoring weighted estimation.

RESULTS: Although the analysis is subject to possible residual confounding by the healthy adherer effect and other limitations, we find that potentially preventable discontinuation associates with 2.9 excess deaths at 1 year per 100 patients treated (95% confidence interval, 2.4, 3.5), and 4.6 excess deaths at 2 years (95% confidence interval, 3.5, 5.5). The association between cinacalcet persistence and heart failure hospitalization risk was sensitive to the outcome definition.

CONCLUSIONS: Inverse-probability of censoring weighted estimation can be used to estimate the effect of potentially preventable treatment discontinuation in populations where treatment can be stopped for both medical and nonmedical reasons. Estimates from such approaches may represent an upper bound of what would be achievable by an adherence improvement intervention.