Bayesian inference and its application to meta-analysis of epidemiologic data

In Part One, the foundations of Bayesian inference are reviewed, and the technicalities of the Bayesian method are illustrated. Part Two applies the Bayesian meta-analysis program, the Confidence Profile Method (CPM), to clinical trial data and evaluates the merits of using Bayesian meta-analysis for overviews of clinical trials.^ The Bayesian method of meta-analysis produced similar results to the classical results because of the large sample size, along with the input of a non-preferential prior probability distribution. These results were anticipated through explanations in Part One of the mechanics of the Bayesian approach. ^

A simulation study using the hybrid statistic and the meta-analysis t-test for data with matched and unmatched subjects in the interim analysis of clinical trials

An interim analysis is usually applied in later phase II or phase III trials to find convincing evidence of a significant treatment difference that may lead to trial termination at an earlier point than planned at the beginning. This can result in the saving of patient resources and shortening of drug development and approval time. In addition, ethics and economics are also the reasons to stop a trial earlier. In clinical trials of eyes, ears, knees, arms, kidneys, lungs, and other clustered treatments, data may include distribution-free random variables with matched and unmatched subjects in one study. It is important to properly include both subjects in the interim and the final analyses so that the maximum efficiency of statistical and clinical inferences can be obtained at different stages of the trials. So far, no publication has applied a statistical method for distribution-free data with matched and unmatched subjects in the interim analysis of clinical trials. In this simulation study, the hybrid statistic was used to estimate the empirical powers and the empirical type I errors among the simulated datasets with different sample sizes, different effect sizes, different correlation coefficients for matched pairs, and different data distributions, respectively, in the interim and final analysis with 4 different group sequential methods. Empirical powers and empirical type I errors were also compared to those estimated by using the meta-analysis t-test among the same simulated datasets. Results from this simulation study show that, compared to the meta-analysis t-test commonly used for data with normally distributed observations, the hybrid statistic has a greater power for data observed from normally, log-normally, and multinomially distributed random variables with matched and unmatched subjects and with outliers. Powers rose with the increase in sample size, effect size, and correlation coefficient for the matched pairs. In addition, lower type I errors were observed estimated by using the hybrid statistic, which indicates that this test is also conservative for data with outliers in the interim analysis of clinical trials.^

Is paromomycin an effective and safe treatment against cutaneous leishmaniasis? A meta-analysis of 14 randomized controlled trials.

BACKGROUND: High cost, poor compliance, and systemic toxicity have limited the use of pentavalent antimony compounds (SbV), the treatment of choice for cutaneous leishmaniasis (CL). Paromomycin (PR) has been developed as an alternative to SbV, but existing data are conflicting. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed, Scopus, and Cochrane Central Register of Controlled Trials, without language restriction, through August 2007, to identify randomized controlled trials that compared the efficacy or safety between PR and placebo or SbV. Primary outcome was clinical cure, defined as complete healing, disappearance, or reepithelialization of all lesions. Data were extracted independently by two investigators, and pooled using a random-effects model. Fourteen trials including 1,221 patients were included. In placebo-controlled trials, topical PR appeared to have therapeutic activity against the old world and new world CL, with increased local reactions, when used with methylbenzethonium chloride (MBCL) compared to when used alone (risk ratio [RR] for clinical cure, 2.58 versus 1.01: RR for local reactions, 1.60 versus 1.07). In SbV-controlled trials, the efficacy of topical PR was not significantly different from that of intralesional SbV in the old world CL (RR, 0.70; 95% confidence interval, 0.26-1.89), whereas topical PR was inferior to parenteral SbV in treating the new world CL (0.67; 0.54-0.82). No significant difference in efficacy was found between parenteral PR and parenteral SbV in the new world CL (0.88; 0.56-1.38). Systemic side effects were fewer with topical or parenteral PR than parenteral SbV. CONCLUSIONS/SIGNIFICANCE: Topical PR with MBCL could be a therapeutic alternative to SbV in selected cases of the old world CL. Development of new formulations with better efficacy and tolerability remains to be an area of future research.

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

A systematic review and meta-analysis of diabetes disease management programs

Context. Despite the rapid growth of disease management programs, there are still questions about their efficacy and effectiveness for improving patient outcomes and their ability to reduce costs associated with chronic disease. ^ Objective. To determine the effectiveness of disease management programs on improving the results of HbA1c tests, lipid profiles and systolic blood pressure (SBP) readings among diabetics. These three quantitative measures are widely accepted methods of determining the quality of a patient's diabetes management and the potential for future complications. ^ Data Sources. MEDLINE and CINAHL were searched from 1950 to June 2008 using MeSH terms designed to capture all relevant studies. Scopus pearling and hand searching were also done. Only English language articles were selected. ^ Study Selection. Titles and abstracts for the 2347 articles were screened against predetermined inclusion and exclusion criteria, yielding 217 articles for full screening. After full article screening, 29 studies were selected for inclusion in the review. ^ Data Extraction. From the selected studies, data extraction included sample size, mean change over baseline, and standard deviation for each control and experimental arm. ^ Results. The pooled results show a mean HbA1c reduction of 0.64%, 95% CI (-0.83 to -0.44), mean SBP reduction of 7.39 mmHg (95% CI to -11.58 to -3.2), mean total cholesterol reduction of 5.74 mg/dL (95% CI, -10.01 to -1.43), and mean LDL cholesterol reduction of 3.74 mg/dL (95% CI, -8.34 to 0.87). Results for HbA1c, SBP and total cholesterol were statistically significant, while the results for LDL cholesterol were not. ^ Conclusions. The findings suggest that disease management programs utilizing five hallmarks of care can be effective at improving intermediate outcomes among diabetics. However, given the significant heterogeneity present, there may be fundamental differences with respect to study-specific interventions and populations that render them inappropriate for meta-analysis. ^

Ascertainment, meta-analysis and reporting of adverse reactions in clinical trials of antibiotics

The ascertainment and analysis of adverse reactions to investigational agents presents a significant challenge because of the infrequency of these events, their subjective nature and the low priority of safety evaluations in many clinical trials. A one year review of antibiotic trials published in medical journals demonstrates the lack of standards in identifying and reporting these potentially fatal conditions. This review also illustrates the low probability of observing and detecting rare events in typical clinical trials which include fewer than 300 subjects. Uniform standards for ascertainment and reporting are suggested which include operational definitions of study subjects. Meta-analysis of selected antibiotic trials using multivariate regression analysis indicates that meaningful conclusions may be drawn from data from multiple studies which are pooled in a scientifically rigorous manner. ^