Bayesian approach to the assessment of the population-specific risk of inhibitors in hemophilia A patients: a case study

Abstract

BackgroundDeveloping inhibitors is a rare event during the treatment of hemophilia A. The multifacets and uncertainty surrounding the development of inhibitors further complicate the process of estimating inhibitor rate from the limited data. Bayesian statistical modeling provides a useful tool in generating, enhancing, and exploring the evidence through incorporating all the available information.MethodsWe built our Bayesian analysis using three study cases to estimate the inhibitor rates of patients with hemophilia A in three different scenarios: Case 1, a single cohort of previously treated patients (PTPs) or previously untreated patients; Case 2, a meta-analysis of PTP cohorts; and Case 3, a previously unexplored patient population – patients with baseline low-titer inhibitor or history of inhibitor development. The data used in this study were extracted from three published ADVATE (antihemophilic factor [recombinant] is a product of Baxter for treating hemophilia A) post-authorization surveillance studies. Noninformative and informative priors were applied to Bayesian standard (Case 1) or random-effects (Case 2 and Case 3) logistic models. Bayesian probabilities of satisfying three meaningful thresholds of the risk of developing a clinical significant inhibitor (10/100, 5/100 [high rates], and 1/86 [the Food and Drug Administration mandated cutoff rate in PTPs]) were calculated. The effect of discounting prior information or scaling up the study data was evaluated.ResultsResults based on noninformative priors were similar to the classical approach. Using priors from PTPs lowered the point estimate and narrowed the 95% credible intervals (Case 1: from 1.3 [0.5, 2.7] to 0.8 [0.5, 1.1]; Case 2: from 1.9 [0.6, 6.0] to 0.8 [0.5, 1.1]; Case 3: 2.3 [0.5, 6.8] to 0.7 [0.5, 1.1]). All probabilities of satisfying a threshold of 1/86 were above 0.65. Increasing the number of patients by two and ten times substantially narrowed the credible intervals for the single cohort study (1.4 [0.7, 2.3] and 1.4 [1.1, 1.8], respectively). Increasing the number of studies by two and ten times for the multiple study scenarios (Case 2: 1.9 [0.6, 4.0] and 1.9 [1.5, 2.6]; Case 3: 2.4 [0.9, 5.0] and 2.6 [1.9, 3.5], respectively) had a similar effect.ConclusionBayesian approach as a robust, transparent, and reproducible analytic method can be efficiently used to estimate the inhibitor rate of hemophilia A in complex clinical settings.

Minimal dataset for post‐registration surveillance of new drugs in hemophilia: communication from the SSC of the ISTH

“…It allows pooling of datasets from hemophilia treatment centers around the world in order to detect low‐frequency adverse effects (not identified in premarketing clinical trials and not detectable in a single cohort) and eventually to link the information to other international databases. This instrument allows also to detect new unlabeled adverse events and unknown long‐term safety issues of novel products in the entire population or specific subgroups of interest .…”

The design of a Bayesian platform trial to prevent and eradicate inhibitors in patients with hemophilia

“…Using Bayesian statistical methodologies, known prior information regarding treatments will be formally incorporated into the analysis. [30][31][32][33] For the Inhibitor Prevention Trial, prior information for the ELOCTATE group was based on data extracted from the open-label single-armed ELOCTATE PUP study. 18 Because actual patient-level data were not made available, we recreated the data based on publicly available Kaplan-Meier curves.…”

“…Approaches to clinical trial design in rare disease settings have been proposed, including using networks of care, relaxed statistical error rates, historical data, carefully selected outcome measures, clinical trial platforms, and Bayesian designs. [30][31][32][33][34][35] The use of Bayesian platform trial design will provide statistical and administrative efficiency for the conduct of the Inhibitor Prevention Trial and the Inhibitor Eradication Trial. Statistical efficiency will be achieved by the (1) use of Bayesian prior distributions to incorporate historical data to increase power and promote efficient use of rare data, (2) use of piecewise exponential survival models to determine mean and 95% confidence interval for each trial arm, and (3) use of thousands of simulations to determine the best model design to optimize power.…”

Survival Rate and Prognostic Factors Among Patients Undergoing Hematopoietic Stem Cell Transplantation: Using the Joint Model

“…If the prior distribution is weakly defined, the posterior distribution will be heavily weighted in data, and if the prior distribution is strongly defined, then data will have little impact. Furthermore, when the sample size is large, posterior distribution will be more heavily affected by the data (11).…”