Hwanhee Hong

Comparing Bayesian and Frequentist Approaches for Multiple Outcome Mixed Treatment Comparisons

Objectives. Bayesian statistical methods are increasingly popular as a tool for meta-analysis of clinical trial data involving both direct and indirect treatment comparisons. However, appropriate selection of prior distributions for unknown model parameters and checking of consistency assumptions required for modeling remain particularly challenging. We compared Bayesian and traditional frequentist statistical methods for mixed treatment comparisons with multiple binary outcomes. Data. We searched major electronic bibliographic databases, Food and Drug Administration reviews, trial registries, and research grant databases up to December 2011 to find randomized studies published in English that examined drugs for female urgency urinary incontinence (UI) on continence, improvement in UI, and treatment discontinuation due to harm. Methods. We describe and fit fixed and random effects models in both Bayesian and frequentist statistical frameworks. In a hierarchical model of 8 treatments, we separately analyze 1 safety and 2 efficacy outcomes. We produce Bayesian and frequentist treatment ranks and odds ratios across all drug v placebo comparisons, as well as Bayesian probabilities that each drug is best overall through a weighted scoring rule that trades off efficacy and safety. Results. In our study, Bayesian and frequentist random effects models generally suggest the same drugs as most attractive, although neither suggests any significant differences between drugs. However, the Bayesian methods more consistently identify one drug (propiverine) as best overall, produce interval estimates that are generally better at capturing all sources of uncertainty in the data, and also permit attractive “rankograms” that visually capture the probability that each drug assumes each possible rank. Conclusions. Bayesian methods are more flexible and their results more clinically interpretable, but they require more careful development and specialized software.

A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons

Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two treatments), and researchers often choose study arms based upon which treatments emerge as superior in previous trials. In this paper, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome and introduce novel Bayesian approaches for multiple outcomes simultaneously, rather than in separate MTC analyses. We do this by incorporating partially observed data and its correlation structure between outcomes through contrast-based and arm-based parameterizations that consider any unobserved treatment arms as missing data to be imputed. We also extend the model to apply to all types of generalized linear model outcomes, such as count or continuous responses. We offer a simulation study under various missingness mechanisms (e.g., missing completely at random, missing at random, and missing not at random) providing evidence that our models outperform existing models in terms of bias, mean squared error, and coverage probability then illustrate our methods with a real MTC dataset. We close with a discussion of our results, several contentious issues in MTC analysis, and a few avenues for future methodological development.

Guidance on the implementation and reporting of a drug safety Bayesian network meta-analysis

The Drug Information Association Bayesian Scientific Working Group (BSWG) was formed in 2011 with a vision to ensure that Bayesian methods are well understood and broadly utilized for design and analysis and throughout the medical product development process, and to improve industrial, regulatory, and economic decision making. The group, composed of individuals from academia, industry, and regulatory, has as its mission to facilitate the appropriate use and contribute to the progress of Bayesian methodology. In this paper, the safety sub-team of the BSWG explores the use of Bayesian methods when applied to drug safety meta-analysis and network meta-analysis. Guidance is presented on the conduct and reporting of such analyses. We also discuss different structural model assumptions and provide discussion on prior specification. The work is illustrated through a case study involving a network meta-analysis related to the cardiovascular safety of non-steroidal anti-inflammatory drugs.

Rejoinder to the discussion of “a Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons,” by S. Dias and A. E. Ades

First, we wish to thank Drs. Dias and Ades (henceforth DA) for their discussion of our work, as well as their thorough and passionate defense of the traditional contrast-based (CB) framework for meta-analysis. We are also very grateful to the editor, Dr. Christopher Schmid, for agreeing to publish our paper, the discussion by DA, and allowing us to provide this rejoinder. While CB methods have been and will likely remain the dominant school of thought in NMA, thanks to the proliferation of randomized clinical trial and observational datasets, hierarchical Bayesian modeling expertise, and associated computing power, arm-based (AB) methods are certainly in ascendancy (much to the chagrin of DA and others). This paper, its discussion, and this rejoinder have allowed all sides of the issue to be fully discussed, and now offers practicing meta-analysts the chance to decide for themselves which model or models they will consider in their own work.

Longitudinal Changes in Nursing Home Resident-Reported Quality of Life: The Role of Facility Characteristics.

Improving quality of nursing homes (NHs) is a major social priority, yet few studies examine the role of facility characteristics for residents’ quality of life (QOL). This study goes beyond cross-sectional analyses by examining the predictors of NH residents’ QOL on the facility level over time. We used three data sources, namely resident interviews using a multidimensional measure of QOL collected in all Medicaid-certified NHs in Minnesota (N = 369), resident clinical data from the minimum data set, and facility-level characteristics. We examined change in six QOL domains from 2007 to 2010, using random coefficient models. Eighty-one facilities improved across most domains and 85 facilities declined. Size, staffing levels (especially activities staff), and resident case mix are some of the most salient predictors of QOL over time, but predictors differ by facility performance status. Understanding the predictors of facility QOL over time can help identify facility characteristics most appropriate for targeting with policy and programmatic interventions.

Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness.

Network meta-analysis expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, synthesizing both direct and indirect information and thus strengthening inference. Since most of trials only compare two treatments, a typical data set in a network meta-analysis managed as a trial-by-treatment matrix is extremely sparse, like an incomplete block structure with significant missing data. Zhang et al. proposed an arm-based method accounting for correlations among different treatments within the same trial and assuming that absent arms are missing at random. However, in randomized controlled trials, nonignorable missingness or missingness not at random may occur due to deliberate choices of treatments at the design stage. In addition, those undertaking a network meta-analysis may selectively choose treatments to include in the analysis, which may also lead to missingness not at random. In this paper, we extend our previous work to incorporate missingness not at random using selection models. The proposed method is then applied to two network meta-analyses and evaluated through extensive simulation studies. We also provide comprehensive comparisons of a commonly used contrast-based method and the arm-based method via simulations in a technical appendix under missing completely at random and missing at random.

Incorporation of individual-patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment

Availability of individual patient-level data (IPD) broadens the scope of network meta-analysis (NMA) and enables us to incorporate patient-level information. Although IPD is a potential gold mine in biomedical areas, methodological development has been slow owing to limited access to such data. In this paper, we propose a Bayesian IPD NMA modeling framework for multiple continuous outcomes under both contrast-based and arm-based parameterizations. We incorporate individual covariate-by-treatment interactions to facilitate personalized decision making. Furthermore, we can find subpopulations performing well with a certain drug in terms of predictive outcomes. We also impute missing individual covariates via an MCMC algorithm. We illustrate this approach using diabetes data that include continuous bivariate efficacy outcomes and three baseline covariates and show its practical implications. Finally, we close with a discussion of our results, a review of computational challenges, and a brief description of areas for future research.