This paper reviews Bayesian strategies for monitoring clinical trial data. It focuses on a Bayesian stochastic curtailment method based on the predictive probability of observing a clinically significant outcome at the scheduled end of the study given the observed data. The proposed method is applied to derive efficacy and futility stopping rules in clinical trials with continuous, normally distributed and binary endpoints. The sensitivity of the resulting stopping rules to the choice of prior distributions is examined and guidelines for choosing a prior distribution of the treatment effect are discussed. The Bayesian predictive approach is compared to the frequentist (conditional power) and mixed Bayesian-frequentist (predictive power) approaches. The interim monitoring strategies discussed in the paper are illustrated using examples from a small proof-of-concept study and a large mortality trial.
AimsThe objective of this study was to evaluate the efficacy, safety, and tolerability of LY3015014 (LY), a neutralizing antibody of proprotein convertase subtilisin/kexin type 9 (PCSK9), administered every 4 or 8 weeks in patients with primary hypercholesterolaemia, when added to a background of standard-of-care lipid-lowering therapy, including statins.Methods and resultsDouble-blind, placebo-controlled trial randomized 527 patients with primary hypercholesterolaemia from June 2013 to January 2014 at 61 community and academic centres in North America, Europe, and Japan. Patients were randomized to subcutaneous injections of LY 20, 120, or 300 mg every 4 weeks (Q4W); 100 or 300 mg every 8 weeks (Q8W) alternating with placebo Q4W; or placebo Q4W. The primary endpoint was percentage change from baseline in low-density lipoprotein cholesterol (LDL-C) by beta quantification at Week 16. The mean baseline LDL-C by beta quantification was 136.3 (SD, 45.0)mg/dL. LY3015014 dose-dependently decreased LDL-C, with a maximal reduction of 50.5% with 300 mg LY Q4W and 37.1% with 300 mg LY Q8W compared with a 7.6% increase with placebo maintained at the end of the dosing interval. There were no treatment-related serious adverse events (AEs). The most common AE terms (>10% of any treatment group) reported more frequently with LY compared with placebo were injection site (IS) pain and IS erythema. No liver or muscle safety issues emerged.ConclusionsLY3015014 dosed every 4 or 8 weeks, resulted in robust and durable reductions in LDL-C. No clinically relevant safety issues emerged with the administration of LY. The long-term effects on cardiovascular outcomes require further investigation.
ObjectivesWe investigated the safety, tolerability, pharmacokinetics and pharmacodynamics of evacetrapib.MethodsHealthy volunteers received multiple daily doses of evacetrapib (10–600 mg) administered for up to 15 days in a placebo-controlled study.Key findingsMean peak plasma concentrations of evacetrapib occurred at 4–6 h and terminal half-life ranged 24–44 h. Steady state was achieved at approximately 10 days; all subjects had undetectable levels of evacetrapib 3 weeks after their last dose. The trough inhibition of cholesteryl ester transfer protein (CETP) activity was 65 and 84% at 100 and 300 mg, respectively. At the highest dose (600 mg), evacetrapib significantly inhibited CETP activity (91%), increased HDL-C (87%) and apo AI (42%), and decreased LDL-C (29%) and apo B (26%) relative to placebo. For the highest dose tested, levels of evacetrapib, CETP activity, CETP mass, HDL-C and LDL-C returned to levels at or near baseline after a 2-week washout period. Evacetrapib at the highest dose tested did not produce any significant effect on 24-h ambulatory systolic or diastolic blood pressure.ConclusionsMultiple doses of evacetrapib potently inhibited CETP activity, leading to substantial elevations in HDL-C and lowering of LDL-C. Evacetrapib was devoid of clinically relevant effects on blood pressure and mineralocorticoid levels.
We review a Bayesian predictive approach for interim data monitoring and propose its application to interim sample size reestimation for clinical trials. Based on interim data, this approach predicts how the sample size of a clinical trial needs to be adjusted so as to claim a success at the conclusion of the trial with an expected probability. The method is compared with predictive power and conditional power approaches using clinical trial data. Advantages of this approach over the others are discussed.
The trimmed mean is a method of dealing with patient dropout in clinical trials that considers early discontinuation of treatment a bad outcome rather than leading to missing data. The present investigation is the first comprehensive assessment of the approach across a broad set of simulated clinical trial scenarios. In the trimmed mean approach, all patients who discontinue treatment prior to the primary endpoint are excluded from analysis by trimming an equal percentage of bad outcomes from each treatment arm. The untrimmed values are used to calculated means or mean changes. An explicit intent of trimming is to favor the group with lower dropout because having more completers is a beneficial effect of the drug, or conversely, higher dropout is a bad effect. In the simulation study, difference between treatments estimated from trimmed means was greater than the corresponding effects estimated from untrimmed means when dropout favored the experimental group, and vice versa. The trimmed mean estimates a unique estimand. Therefore, comparisons with other methods are difficult to interpret and the utility of the trimmed mean hinges on the reasonableness of its assumptions: dropout is an equally bad outcome in all patients, and adherence decisions in the trial are sufficiently similar to clinical practice in order to generalize the results. Trimming might be applicable to other inter-current events such as switching to or adding rescue medicine. Given the well-known biases in some methods that estimate effectiveness, such as baseline observation carried forward and non-responder imputation, the trimmed mean may be a useful alternative when its assumptions are justifiable.
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