Phase II Study of Single/Repeated Doses of Acumapimod (BCT197) to Treat Acute Exacerbations of COPD.

Mitogen-activated protein kinase p38 is a key regulator in the inflammation pathway and is activated in the lungs of chronic obstructive pulmonary disease (COPD) patients. Acumapimod is a potent, selective, oral, p38 inhibitor under investigation for treatment of acute exacerbations of COPD (AECOPD). In this Phase II, double-blind, randomized, placebo-controlled dose-exploration study of acumapimod in patients with moderate or severe AECOPD (NCT01332097), patients presenting with AECOPD were randomized to receive single-dose acumapimod (20 mg or 75 mg) on Day 1, repeated single-dose acumapimod (20 mg or 75 mg) on Days 1 and 6, oral prednisone 40 mg (10 days), or placebo. Primary outcome: improvement in forced expiratory volume in 1 s (FEV1) versus placebo at Day 5 (single doses) and Day 10 (repeated doses). N = 183 patients were randomized; 169 (92%) patients completed the study. Although the primary endpoint (FEV1 at Day 10) was not met (p = 0.082), there was a significant improvement in FEV1 with acumapimod repeat-dose 75 mg versus placebo at Day 8 (p = 0.022) which, though not a prespecified endpoint, was part of an overall trend. Differences at lower doses did not achieve significance. Mean change in FEV1 AUC from baseline to Day 14 in the 75 mg repeat-dose group was significantly higher versus placebo (p = 0.02), prednisone (p = 0.01), and 20 mg single-dose groups (p = 0.015) (post-hoc analysis). EXACT-PRO showed numerical differences versus placebo that did not reach significance. Acumapimod was well tolerated. In conclusion, repeated single-dose acumapimod showed a clinically relevant improvement in FEV1 over placebo at Day 8, along with consistent numerical differences in EXACT-PRO. These data can be used to determine dose regimens for a proof-of-clinical-concept trial.

Bayesian inference for a principal stratum estimand to assess the treatment effect in a subgroup characterized by postrandomization event occurrence.

The treatment effect in subgroups of patients is often of interest in randomized controlled clinical trials, as this may provide useful information on how to treat which patients best. When a specific subgroup is characterized by the absence of certain events that happen postrandomization, a naive analysis on the subset of patients without these events may be misleading. The principal stratification framework allows one to define an appropriate causal estimand in such settings. Statistical inference for the principal stratum estimand hinges on scientifically justified assumptions, which can be included with Bayesian methods through prior distributions. Our motivating example is a large randomized placebo-controlled trial of siponimod in patients with secondary progressive multiple sclerosis. The primary objective of this trial was to demonstrate the efficacy of siponimod relative to placebo in delaying disability progression for the whole study population. However, the treatment effect in the subgroup of patients who would not relapse during the trial is relevant from both a scientific and patient perspective. Assessing this subgroup treatment effect is challenging as there is strong evidence that siponimod reduces relapses. We describe in detail the scientific question of interest, the principal stratum estimand, the corresponding analysis method for binary endpoints, and sensitivity analyses. Although our work is motivated by a randomized clinical trial, the approach has broader appeal and could be adapted for observational studies.

Model averaging for treatment effect estimation in subgroups.

In many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect. Estimating a treatment effect for these groups, together with an adequate uncertainty statement is challenging, owing to the resulting "random high" / selection bias. In this paper, we will investigate Bayesian model averaging to address this problem. The general motivation for the use of model averaging is to realize that subgroup selection can be viewed as model selection, so that methods to deal with model selection uncertainty, such as model averaging, can be used also in this setting. Simulations are used to evaluate the performance of the proposed approach. We illustrate it on an example early-phase clinical trial.

Group sequential enrichment design incorporating subgroup selection.

An important component of clinical trials in drug development is the analysis of treatment efficacy in patient subgroups (subpopulations). Because of concerns of multiplicity and of the small sample sizes often involved, such analyses can present substantial statistical challenges and may lead to misleading conclusions. As a confirmatory seamless phase II/III design, we propose an adaptive enrichment group sequential procedure whereby resources can be concentrated on subgroups most likely to respond to treatment. Stopping boundaries are defined through upper and lower spending functions. The procedure is presented in terms of the efficient score, enabling the analysis of normal, binary, or time-to-event data. It addresses the dilution effect by eliminating populations at the first stage that appear likely to derive no therapeutic benefit. It subsequently proceeds with the definitive assessment of treatment efficacy among the remaining pooled populations using a group sequential design. The procedure provides strong protection of familywise type I error rate, and we employ a bootstrap algorithm to obtain point and interval estimates that are adjusted for the selection bias. We give examples to demonstrate how the design is used. We make comparisons with adaptive two-stage combination test procedures and with a group sequential test that does not account for the presence of subgroups. Numerical results show that the procedure has high power to detect subgroup-specific effects and the use of multiple interim analysis points can lead to substantial sample size savings.