Jackknife empirical likelihood confidence intervals for assessing heterogeneity in meta-analysis of rare binary event data

Wang, Guanshen; Cheng, Yu‐Shan; Chen, Min; Wang, Xinlei

doi:10.1016/j.cct.2021.106440

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…We would like to point out that this summary may not be representative for the rare binary setting (the focus of our article). This is mainly because for rare binary data, if the number of studies

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is small, meta‐analysis would not help much unless researchers have useful prior information and employ a Bayesian approach to leverage such information 53 . Although it can confirm that the event probabilities are very small, meta‐analysis cannot tell how close to zero they are without prior information, especially in the presence of zero or double zero tables.…”

Section: Discussionmentioning

confidence: 99%

“…This is mainly because for rare binary data, if the number of studies I is small, meta-analysis would not help much unless researchers have useful prior information and employ a Bayesian approach to leverage such information. 53 Although it can confirm that the event probabilities are very small, meta-analysis cannot tell how close to zero they are without prior information, especially in the presence of zero or double zero tables. Thus, people usually apply meta-analysis to rare binary events when I is not small.…”

Section: Discussionmentioning

confidence: 99%

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Bayesian estimation and testing in random‐effects meta‐analysis of rare binary events allowing for flexible group variability

Zhang

Barth

Lim

et al. 2023

Statistics in Medicine

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Rare binary events data arise frequently in medical research. Due to lack of statistical power in individual studies involving such data, meta-analysis has become an increasingly important tool for combining results from multiple independent studies. However, traditional meta-analysis methods often report severely biased estimates in such rare-event settings. Moreover, many rely on models assuming a pre-specified direction for variability between control and treatment groups for mathematical convenience, which may be violated in practice. Based on a flexible random-effects model that removes the assumption about the direction, we propose new Bayesian procedures for estimating and testing the overall treatment effect and inter-study heterogeneity. Our Markov chain Monte Carlo algorithm employs Pólya-Gamma augmentation so that all conditionals are known distributions, greatly facilitating computational efficiency. Our simulation shows that the proposed approach generally reports less biased and more stable estimates compared to existing methods. We further illustrate our approach using two real examples, one using rosiglitazone data from 56 studies and the other using stomach ulcers data from 41 studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Bayesian estimation and testing in random‐effects meta‐analysis of rare binary events allowing for flexible group variability

Zhang

Barth

Lim

et al. 2023

Statistics in Medicine

Self Cite

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“…The goodness-of-fit plots included the following scatter plots: observation (OBS) vs. individual prediction (IPRED), observation (OBS) vs. population prediction (PRED), conditional weighted residual errors (CWRES) vs. population prediction (PRED), and conditional weighted residual errors (CWRES) vs. time. Sensitivity analysis was performed using leave-one-out cross-validation, that is, data from one trial were removed from the original dataset each time, and the model parameters of the remaining data were estimated to investigate the effect of each trial on the model parameters ( 23 ). A visual predictive check (VPC) was used to further validate the established model, and was simulated 1000 times using the Monte Carlo method to obtain the 95% confidence interval (CI) of the model parameters; these parameters were compared with the actual observed values to assess the predictive performance of the model ( 24 ).…”

Section: Methodsmentioning

confidence: 99%

Development and Application of the Placebo Response Model in Clinical Trials for Primary Sjögren’s Syndrome

et al. 2021

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This study aimed to develop a placebo response model for pharmaceutical clinical trials of primary Sjogren’s syndrome,and to quantitatively analyze the distribution and related factors influencing the placebo response to further optimize the design of clinical trials and evaluate the results of single-arm clinical trials. Public databases, including PubMed, Embase, and Cochrane Library were searched for reports on randomized placebo-controlled trials for Sjögren’s syndrome which used the change from baseline in ESSDAI score as the primary outcome. The model-based meta-analysis method was used to evaluate the time course and the related influencing factors of the placebo response for ESSDAI in such clinical trials. A virtual placebo control group was constructed based on the final placebo response model to determine the treatment efficacy of belimumab and cyclosporine A for primary Sjögren’s syndrome in a single-arm study. A total of 12 studies involving 450 subjects were included in the analysis. The established model described the time-course characteristics of the changes in ESSDAI score from the baseline in the 48 weeks placebo group. We found that the onset time of placebo response was approximately 12 weeks, and its efficacy plateaued at 48 weeks. The baseline ESSDAI score had a significant effect on the maximum value of the placebo response; the maximum value of the placebo response decreased by 0.552 for every 1 score rise in the baseline ESSDAI score. The efficacy of belimumab and cyclosporine A in the single-arm trial was comparable to that of the placebo response at the same baseline; no significant therapeutic advantage was observed. The placebo response model established in this study could provide a basis for designing clinical trials for primary Sjogren’s syndrome in the future. It may also provide a reliable external efficacy control standard for single-arm clinical trials.

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Goodness-of-fit testing for meta-analysis of rare binary events

Zhang,

Xiao,

Lim

et al. 2023

Sci Rep

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Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications.

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Jackknife empirical likelihood confidence intervals for assessing heterogeneity in meta-analysis of rare binary event data

Cited by 4 publications

References 40 publications

Bayesian estimation and testing in random‐effects meta‐analysis of rare binary events allowing for flexible group variability

Bayesian estimation and testing in random‐effects meta‐analysis of rare binary events allowing for flexible group variability

Development and Application of the Placebo Response Model in Clinical Trials for Primary Sjögren’s Syndrome

Goodness-of-fit testing for meta-analysis of rare binary events

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