Investigating the Heterogeneity of "Study Twins".

Meta-analyses are commonly performed based on random-effects models, while in certain cases one might also argue in favor of a common-effect model. One such case may be given by the example of two "study twins" that are performed according to a common (or at least very similar) protocol. Here we investigate the particular case of meta-analysis of a pair of studies, for example, summarizing the results of two confirmatory clinical trials in phase III of a clinical development program. Thereby, we focus on the question of to what extent homogeneity or heterogeneity may be discernible and include an empirical investigation of published ("twin") pairs of studies. A pair of estimates from two studies only provide very little evidence of homogeneity or heterogeneity of effects, and ad hoc decision criteria may often be misleading.

Bayesian random-effects meta-analysis with empirical heterogeneity priors for application in health technology assessment with very few studies.

In Bayesian random-effects meta-analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are adequate in the context of HTA. Therefore, a database of all relevant meta-analyses conducted by the Institute for Quality and Efficiency in Health Care (IQWiG, Germany) was constructed to derive empirical prior distributions for the heterogeneity parameter suitable for HTA. Previously, an extension to the normal-normal hierarchical model had been suggested for this purpose. For different effect measures, this extended model was applied on the database to conservatively derive a prior distribution for the heterogeneity parameter. Comparison of a Bayesian approach using the derived priors with IQWiG's current standard approach for evidence synthesis shows favorable properties. Therefore, these prior distributions are recommended for future meta-analyses in HTA settings and could be embedded into the IQWiG evidence synthesis approach in the case of very few studies.

How trace plots help interpret meta-analysis results.

The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of τ , the between-study standard deviation, and the shrunken estimates of the study effects as a function of τ . With a small or moderate number of studies, τ is not estimated with much precision, and parameter estimates and shrunken study effect estimates can vary widely depending on the correct value of τ . The trace plot allows visualization of the sensitivity to τ along with a plot that shows which values of τ are plausible and which are implausible. A comparable frequentist or empirical Bayes version provides similar results. The concepts are illustrated using examples in meta-analysis and meta-regression; implementation in R is facilitated in a Bayesian or frequentist framework using the bayesmeta and metafor packages, respectively.

Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis.

In Bayesian meta-analysis, the specification of prior probabilities for the between-study heterogeneity is commonly required, and is of particular benefit in situations where only few studies are included. Among the considerations in the set-up of such prior distributions, the consultation of available empirical data on a set of relevant past analyses sometimes plays a role. How exactly to summarize historical data sensibly is not immediately obvious; in particular, the investigation of an empirical collection of heterogeneity estimates will not target the actual problem and will usually only be of limited use. The commonly used normal-normal hierarchical model for random-effects meta-analysis is extended to infer a heterogeneity prior. Using an example data set, we demonstrate how to fit a distribution to empirically observed heterogeneity data from a set of meta-analyses. Considerations also include the choice of a parametric distribution family. Here, we focus on simple and readily applicable approaches to then translate these into (prior) probability distributions.

Using the bayesmeta R package for Bayesian random-effects meta-regression.

BACKGROUND: Random-effects meta-analysis within a hierarchical normal modeling framework is commonly implemented in a wide range of evidence synthesis applications. More general problems may even be tackled when considering meta-regression approaches that in addition allow for the inclusion of study-level covariables. METHODS: We describe the Bayesian meta-regression implementation provided in the bayesmetaR package including the choice of priors, and we illustrate its practical use. RESULTS: A wide range of example applications are given, such as binary and continuous covariables, subgroup analysis, indirect comparisons, and model selection. Example R code is provided. CONCLUSIONS: The bayesmeta package provides a flexible implementation. Due to the avoidance of MCMC methods, computations are fast and reproducible, facilitating quick sensitivity checks or large-scale simulation studies.

Reducing antibiotic use in uncomplicated urinary tract infections in adult women: a systematic review and individual participant data meta-analysis.

BACKGROUND: Randomised controlled trials (RCTs) investigated analgesics, herbal formulations, delayed prescription of antibiotics, and placebo to prevent overprescription of antibiotics in women with uncomplicated urinary tract infections (uUTI). OBJECTIVES: To estimate the effect of these strategies and to identify symptoms, signs, or other factors that indicate a benefit from these strategies. DATA SOURCES: MEDLINE, EMBASE, Web of Science, LILACS, Cochrane Database of Systematic Reviews and of Controlled Trials, and ClinicalTrials. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS AND INTERVENTIONS: RCTs investigating any strategies to reduce antibiotics vs. immediate antibiotics in adult women with uUTI in primary care. METHODS: We extracted individual participant data (IPD) if available, otherwise aggregate data (AD). Bayesian random-effects meta-analysis of the AD was used for pairwise comparisons. Candidate moderators and prognostic indicators of treatment effects were investigated using generalised linear mixed models based on IPD. RESULTS: We analysed IPD of 3524 patients from eight RCTs and AD of 78 patients. Non-antibiotic strategies increased the rates of incomplete recovery (OR 3.0; 95% credible interval (CrI), 1.7-5.5; Bayesian p-value (pB) = 0.0017; τ = 0.6), subsequent antibiotic treatment (OR 3.5; 95% CrI, 2.1-5.8; pB = 0.0003) and pyelonephritis (OR 5.6; 95% CrI, 2.3-13.9; pB = 0.0003). Conversely, they decreased overall antibiotic use by 63%. Patients positive for urinary erythrocytes and urine culture were at increased risk for incomplete recovery (OR 4.7; 95% CrI, 2.1-10.8; pB = 0.0010), but no difference was apparent where both were negative (OR 0.8; 95% CrI, 0.3-2.0; pB = 0.667). In patients treated using non-antibiotic strategies, urinary erythrocytes and positive urine culture were independent prognostic indicators for subsequent antibiotic treatment and pyelonephritis. CONCLUSIONS: Compared to immediate antibiotics, non-antibiotic strategies reduce overall antibiotic use but result in poorer clinical outcomes. The presence of erythrocytes and tests to confirm bacteria in urine could be used to target antibiotic prescribing.

Double arcsine transform not appropriate for meta-analysis.

The variance-stabilizing Freeman-Tukey double arcsine transform was originally proposed for inference on single proportions. Subsequently, its use has been suggested in the context of meta-analysis of proportions. While some erratic behavior has been observed previously, here we point out and illustrate general issues of monotonicity and invertibility that make this transform unsuitable for meta-analysis purposes.

Immunotherapy for people with clinically isolated syndrome or relapsing-remitting multiple sclerosis: treatment response by demographic, clinical, and biomarker subgroups (PROMISE)-a systematic review protocol.

BACKGROUND: Multiple sclerosis (MS) is an inflammatory and degenerative disease of the central nervous system with an increasing worldwide prevalence. Since 1993, more than 15 disease-modifying immunotherapies (DMTs) have been licenced and have shown moderate efficacy in clinical trials. Based on the heterogeneity of the disease and the partial effectiveness of therapies, a personalised medicine approach would be valuable taking individual prognosis and suitability of a chosen therapy into account to gain the best possible treatment effect. The primary objective of this review is to assess the differential treatment effects of all approved DMTs in subgroups of adults with clinically isolated syndrome or relapsing forms of MS. We will analyse possible treatment effect modifiers (TEM) defined by baseline demographic characteristics (gender, age), and diagnostic (i.e. MRI measures) and clinical (i.e. relapses, disability level) measures of MS disease activity. METHODS: We will include all published and accessible unpublished primary and secondary analyses of randomised controlled trials (RCTs) with a follow-up of at least 12 months investigating the efficacy of at least one approved DMT, with placebo or other approved DMTs as control intervention(s) in subgroups of trial participants. As the primary outcome, we will address disability as defined by the Expanded Disability Status Scale or multiple sclerosis functional composite scores followed by relapse frequency, quality of life measures, and side effects. MRI data will be analysed as secondary outcomes. MEDLINE, EMBASE, CINAHL, LILACS, CENTRAL and major trial registers will be searched for suitable studies. Titles and abstracts and full texts will be screened by two persons independently using Covidence. The risk of bias will be analysed based on the Cochrane "Risk of Bias 2" tool, and the certainty of evidence will be assessed using GRADE. Treatment effects will be reported as rate ratio or odds ratio. Primary analyses will follow the intention-to-treat principle. Meta-analyses will be carried out using random-effects models. DISCUSSION: Given that individual patient data from clinical studies are often not available, the review will allow to analyse the evidence on TEM in MS immunotherapy and thus support clinical decision making in individual cases. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021279665 .

A straightforward meta-analysis approach for oncology phase I dose-finding studies.

Phase I early-phase clinical studies aim at investigating the safety and the underlying dose-toxicity relationship of a drug or combination. While little may still be known about the compound's properties, it is crucial to consider quantitative information available from any studies that may have been conducted previously on the same drug. A meta-analytic approach has the advantages of being able to properly account for between-study heterogeneity, and it may be readily extended to prediction or shrinkage applications. Here we propose a simple and robust two-stage approach for the estimation of maximum tolerated dose(s) utilizing penalized logistic regression and Bayesian random-effects meta-analysis methodology. Implementation is facilitated using standard R packages. The properties of the proposed methods are investigated in Monte Carlo simulations. The investigations are motivated and illustrated by two examples from oncology.

COPD mortality and exacerbations in the placebo group of clinical trials over two decades: a systematic review and meta-regression.

A decreasing trend in exacerbation rates has been observed in COPD. Because mortality is linked to exacerbations, it is of interest to investigate whether a similar time trend is also present in mortality rates. We performed a systematic review of placebo groups in published randomised controlled trials. Mortality rate was modelled based on a Poisson distribution for the event counts. Adding information on mortality as well as on newly published studies on a previous database, we performed a meta-regression. Among the 56 included studies representing 14 166 patients, an annual decrease in mortality rates of 6.1% (-0.6%, 12.6%) (p=0.073) was observed. Consistent results were obtained in subgroups as well as when adjusting for potential confounders. The correlation between exacerbation rate and mortality rate was positive but weak as well as insignificant. In summary, analysis of randomised controlled trials in COPD patients showed a decrease in mortality in the placebo arms over the last two decades. This effect is comparable to the previously observed decrease in annual exacerbation rate. Albeit insignificant, our results suggest that care is needed in the design of new trials or when comparing results from trials published many years apart.