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Hierarchical Bayes Approach to Estimate the Treatment Effect for Randomized Controlled Trials.

ABSTRACT: One important issue in Bayesian estimation is the determination of an effective informative prior. In hierarchical Bayes models, the uncertainty of hyperparameters in a prior can be further modeled via their own priors, namely, hyper priors. This study introduces a framework to construct hyper priors for both the mean and the variance hyperparameters for estimating the treatment effect in a two-group randomized controlled trial. Assuming a random sample of treatment effect sizes is obtained from past studies, the hyper priors can be constructed based on the sampling distributions of the effect size mean and precision. The performance of the hierarchical Bayes approach was compared with the empirical Bayes approach (hyperparameters are fixed values or point estimates) and the ordinary least squares (OLS) method via simulation. The design factors for data generation included the sample treatment effect size, treatment/control group size ratio, and sample size. Each generated data set was analyzed using the hierarchical Bayes approach with three hyper priors, the empirical Bayes approach with twelve priors (including correct and inaccurate priors), and the OLS method. Results indicated that the proposed hierarchical Bayes approach generally outperformed the empirical Bayes approach and the OLS method, especially with small samples. When more sample effect sizes were available, the treatment effect was estimated more accurately regardless of the sample sizes. Practical implications and future research directions are discussed.

SUBMITTER: Liang X

PROVIDER: S-EPMC7565114 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Recent methodologic evidence suggests that lack of blinding in randomized trials can result in under- or overestimation of the treatment effect size. The objective of this study is to quantify the extent of bias associated with blinding in randomized controlled trials of oral health interventions. METHODS:We selected all oral health meta-analyses that included a minimum of five randomized controlled trials. We extracted data, in duplicate, related to nine blinding-related criteria, namely: patient blinding, assessor blinding, care-provider blinding, investigator blinding, statistician blinding, blinding of both patients and assessors, study described as "double blind", blinding of patients, assessors, and care providers concurrently, and the appropriateness of blinding. We quantified the impact of bias associated with blinding on the magnitude of effect size using a two-level meta-meta-analytic approach with a random effects model to allow for intra- and inter-meta-analysis heterogeneity. RESULTS:We identified 540 randomized controlled trials, included in 64 meta-analyses, analyzing data from 137,957 patients. We identified significantly larger treatment effect size estimates in trials that had inadequate patient blinding (difference in treatment effect size?=?0.12; 95% CI: 0.00 to 0.23), lack of blinding of both patients and assessors (difference?=?0.19; 95% CI: 0.06 to 0.32), and lack of blinding of patients, assessors, and care-providers concurrently (difference?=?0.14; 95% CI: 0.03 to 0.25). In contrast, assessor blinding (difference?=?0.06; 95% CI: -0.06 to 0.18), caregiver blinding (difference?=?0.02; 95% CI: -0.04 to 0.09), principal-investigator blinding (difference?=?-?0.02; 95% CI: -0.10 to 0.06), describing a trial as "double-blind" (difference?=?0.09; 95% CI: -0.05 to 0.22), and lack of an appropriate method of blinding (difference?=?0.06; 95% CI: -0.06 to 0.18) were not associated with over- or underestimated treatment effect size. CONCLUSIONS:We found significant differences in treatment effect size estimates between oral health trials based on lack of patient and assessor blinding. Treatment effect size estimates were 0.19 and 0.14 larger in trials with lack of blinding of both patients and assessors and blinding of patients, assessors, and care-providers concurrently. No significant differences were identified in other blinding criteria. Investigators of oral health systematic reviews should perform sensitivity analyses based on the adequacy of blinding in included trials.

Project description:BackgroundAntipsychotics improve the positive symptoms of schizophrenia. However, little is known about the extent of antidepressive effects of antipsychotics and their correlation with effects on other symptom domains in schizophrenia. The aim was to investigate whether antidepressive effects of antipsychotics have a significant correlation with the effects on specific symptom domains of schizophrenia.MethodsElectronic databases were searched to identify eligible studies that reported antidepressive effects of antipsychotics for the treatment of adult patients with schizophrenia in double-blind, randomized placebo-controlled trials (RCTs). Mean change from baseline in depressive symptoms was meta-analyzed, and the correlation with the effects on other symptom domains was examined through meta-regression analysis.ResultsThirty-five RCTs (13 890 patients) were included in this meta-analysis. Overall, antipsychotics showed greater efficacy than placebo in reducing depressive symptoms, with small to medium effect sizes (standardized mean difference = -0.27, 95% confidence interval -0.32 to -0.22, P < .001). All the antipsychotics, except for chlorpromazine, haloperidol, and ziprasidone, were associated with significantly greater decreases in depressive symptoms compared with placebo (standardized mean difference = -0.19 to -0.40). A higher antidepressive effect was significantly correlated with a higher improvement in Positive and Negative Syndrome Scale/Brief Psychiatric Rating Scale total, positive, and negative, and Positive and Negative Syndrome Scale-general psychopathology symptoms (β = .618, P < .001; β = .476, P < .001; β = .689, P < .001; β = .603, P < .001, respectively).ConclusionsSecond-generation antipsychotics (except for ziprasidone) were associated with small to medium effects sizes on improvement in depressive symptoms among adult patients with schizophrenia. The antidepressive effect of antipsychotics was significantly correlated with improvement in other symptom domains, with the highest correlation observed for improvement in negative symptoms.Prospero registration numberCRD42019133015.

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Hierarchical Bayes Approach to Estimate the Treatment Effect for Randomized Controlled Trials.

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