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A Bayesian mixed-treatment comparison meta-analysis of treatments for alcohol dependence and implications for planning future trials.

ABSTRACT: Several treatments for alcohol dependence have been tested in randomized controlled trials, giving rise to systematic reviews with a network of evidence structure, or mixed treatment comparisons (MTCs). Within the network, there are few direct comparisons of active treatments. Thus far, this network has not been adequately analyzed. For example, "indirect comparisons" between treatments (e.g., the comparison of treatments B:C obtained via estimates from A:B and A:C trials) have not been incorporated into estimates of treatment effects. This has implications for the planning of future randomized controlled trials.We applied recent developments in Bayesian MTC meta-analysis to analyze the network of evidence. Using these results, we proposed a methodology to inform, design, and power a hypothetical trial in the context of an updated meta-analysis for treatments that have been infrequently compared and therefore whose effect sizes are not well informed by a meta-analysis.An MTC meta-analysis provides more accurate estimates than a pairwise meta-analysis and uncovers decisive differences between active treatments that have been infrequently directly compared. Weighting across all outcomes indicates that a combination (naltrexone + acamprosate) treatment has the highest posterior probability of being the "best" treatment. If a new clinical trial were to be conducted of a combination therapy versus acamprosate alone, there is no feasible sample size that would result in a decisive meta-analysis.An MTC meta-analysis should be used to estimate treatment effects in networks in which direct and indirect evidence are consistent and to inform the design of future studies.

SUBMITTER: DeSantis SM

PROVIDER: S-EPMC4318856 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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A Bayesian mixed-treatment comparison meta-analysis of treatments for alcohol dependence and implications for planning future trials.

DeSantis Stacia M SM Zhu Huirong H

Medical decision making : an international journal of the Society for Medical Decision Making 20140616 7

<h4>Background</h4>Several treatments for alcohol dependence have been tested in randomized controlled trials, giving rise to systematic reviews with a network of evidence structure, or mixed treatment comparisons (MTCs). Within the network, there are few direct comparisons of active treatments. Thus far, this network has not been adequately analyzed. For example, "indirect comparisons" between treatments (e.g., the comparison of treatments B:C obtained via estimates from A:B and A:C trials) hav ...[more]

PMID: 24935915

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Project description:Randomized clinical trials (RCT) increasingly investigate combination therapies. Strong biological rationale or early clinical evidence commonly suggest that the effect of the combination treatment is importantly greater than the maximum effect of any of the individual treatments. While these relationships are commonly well-accepted, RCTs do not incorporate them into the design or analysis plans. We therefore propose a simple Bayesian framework for incorporating the known relationships that the effectiveness of a combination treatment exceeds that of any individual treatment, but does not necessarily exceed the sum of individual effects. We term the collation of these two relationships 'fractional additivity'. We performed a binary outcome simulation study of a response adaptive randomized three-arm clinical trial with treatment arms A, B, and A&B that allowed for dropping an inferior treatment arm and terminating the trial early for superiority during any of 4 interim analyses. We compared the Bayesian fractional additivity model to a conventional analysis by measuring the expected proportion of failures, sample size at trial termination, time to termination, and root mean squared error of final estimates. We also compared the fractional additivity model to a 'full additivity' model where the effect of A&B was assumed to be the sum of the effect of A and B. In simulation scenarios where important fractional additivity or full additivity existed, the Bayesian fractional additivity model yielded a 3-4% relative reduction in expected number of failures, and a 30%-50% relative reduction in sample size at trial termination compared to a conventional analysis. These results held true even when the Bayesian fractional additivity model employed a biased prior. The full additivity model had slightly higher gains, but too frequently terminated the trial at the first interim look. In scenarios where no or weak fractional additivity exists, the expected sample size and time to termination were similar for the Bayesian fractional additivity model with a moderately optimistic bias about fractional additivity and the conventional model. Lastly, the fractional additivity model generally yielded similar or lower root mean squared error compared to the other models. In conclusion, our proposed Bayesian fractional additivity model provides an efficient approach for estimating effects of combination treatments in clinical trials. The approach is not only highly applicable in adaptive clinical trials, but also provides added power in a conventional RCT.

Project description:Background: We provided a comprehensive evaluation of efficacy of available treatments for coronavirus disease 2019 (COVID-19). Methods: We searched for candidate COVID-19 studies in WHO COVID-19 Global Research Database up to August 19, 2021. Randomized controlled trials for suspected or confirmed COVID-19 patients published on peer-reviewed journals were included, regardless of demographic characteristics. Outcome measures included mortality, mechanical ventilation, hospital discharge and viral clearance. Bayesian network meta-analysis with fixed effects was conducted to estimate the effect sizes using posterior means and 95% equal-tailed credible intervals (CrIs). Odds ratio (OR) was used as the summary measure for treatment effect. Bayesian hierarchical models were used to estimate effect sizes of treatments grouped by the treatment classifications. Results: We identified 222 eligible studies with a total of 102,950 patients. Compared with the standard of care, imatinib, intravenous immunoglobulin and tocilizumab led to lower risk of death; baricitinib plus remdesivir, colchicine, dexamethasone, recombinant human granulocyte colony stimulating factor and tocilizumab indicated lower occurrence of mechanical ventilation; tofacitinib, sarilumab, remdesivir, tocilizumab and baricitinib plus remdesivir increased the hospital discharge rate; convalescent plasma, ivermectin, ivermectin plus doxycycline, hydroxychloroquine, nitazoxanide and proxalutamide resulted in better viral clearance. From the treatment class level, we found that the use of antineoplastic agents was associated with fewer mortality cases, immunostimulants could reduce the risk of mechanical ventilation and immunosuppressants led to higher discharge rates. Conclusions: This network meta-analysis identified superiority of several COVID-19 treatments over the standard of care in terms of mortality, mechanical ventilation, hospital discharge and viral clearance. Tocilizumab showed its superiority compared with SOC on preventing severe outcomes such as death and mechanical ventilation as well as increasing the discharge rate, which might be an appropriate treatment for patients with severe or mild/moderate illness. We also found the clinical efficacy of antineoplastic agents, immunostimulants and immunosuppressants with respect to the endpoints of mortality, mechanical ventilation and discharge, which provides valuable information for the discovery of potential COVID-19 treatments.

Dataset Information

A Bayesian mixed-treatment comparison meta-analysis of treatments for alcohol dependence and implications for planning future trials.

Publications

A Bayesian mixed-treatment comparison meta-analysis of treatments for alcohol dependence and implications for planning future trials.

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