Visualizing stability in studies: the moving average meta-analysis (MA2)

Relative clinical benefits are often visually explored and formally analysed through a (cumulative) meta-analysis. In this manuscript, we introduce and further explore the moving average meta-analysis to aid towards the exploration and visualization of stability in a meta-analysis.Comment: 10 pages, 2 figures, 1 tabl

Multiple testing in orthopedic literature: a common problem?

BACKGROUND: Performing multiple tests in primary research is a frequent subject of discussion. This discussion originates from the fact that when multiple tests are performed, it becomes more likely to reject one of the null hypotheses, conditional on that these hypotheses are true and thus commit a type one error. Several correction methods for multiple testing are available. The primary aim of this study was to assess the quantity of articles published in two highly esteemed orthopedic journals in which multiple testing was performed. The secondary aims were to determine in which percentage of these studies a correction was performed and to assess the risk of committing a type one error if no correction was applied. METHODS: The 2010 annals of two orthopedic journals (A and B) were systematically hand searched by two independent investigators. All articles on original research in which statistics were applied were considered. Eligible publications were reviewed for the use of multiple testing with respect to predetermined criteria. RESULTS: A total of 763 titles were screened and 127 articles were identified and included in the analysis. A median of 15 statistical inference results were reported per publication in both journal A and B. Correction for multiple testing was performed in 15% of the articles published in journal A and in 6% from journal B. The estimated median risk of obtaining at least one significant result for uncorrected studies was calculated to be 54% for both journals. CONCLUSION: This study shows that the risk of false significant findings is considerable and that correcting for multiple testing is only performed in a small percentage of all articles published in the orthopedic literature reviewed

Dataset for: Data generating models of dichotomous outcomes: Heterogeneity in simulation studies for a random-effects meta-analysis

Simulation studies to evaluate performance of statistical methods require a well specified Data Generating Model. Details of these models are essential to interpret the results and arrive at proper conclusions. A case in point is random-effects meta-analysis of dichotomous outcomes. We reviewed a number of simulation studies that evaluated approximate normal models for meta-analysis of dichotomous outcomes and we assessed the data generating models that were used to generate events for a series of (heterogeneous) trials. We demonstrate that the performance of the statistical methods, as assessed by simulation, differs between these three alternative Data Generating Models, with larger differences apparent in the small population setting. Our findings are relevant to multilevel binomial models in general

An Application of Sequential Meta-Analysis to Gene Expression Studies

Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is used to evaluate whether the accumulated samples show enough evidence or more experiments should be initiated. We found 313 differentially expressed genes, based on the cumulative information of the experiments. SMA offers an alternative to existing methods in generating a gene list by evaluating the adequacy of the cumulative information

Recommendations for the design of small population clinical trials

Background: Orphan drug development faces numerous challenges, including low disease prevalence, patient population heterogeneity, and strong presence of paediatric patient populations. Consequently, clinical trials for orphan drugs are often smaller than those of non-orphan drugs, and they require the development of efficient trial designs relevant to small populations to gain the most information from the available data. The International Rare Diseases Research Consortium (IRDiRC) is aimed at promoting international collaboration and advance rare diseases research worldwide, and has as one of its goals to contribute to 1000 new therapies for rare diseases. IRDiRC set up a Small Population Clinical Trials (SPCT) Task Force in order to address the shortcomings of our understanding in carrying out clinical trials in rare diseases. Results: The IRDiRC SPCT Task Force met in March 2016 to discuss challenges faced in the design of small studies for rare diseases and present their recommendations, structured around six topics: different study methods/designs and their relation to different characteristics of medical conditions, adequate safety data, multi-arm trial designs, decision analytic approaches and rational approaches to adjusting levels of evidence, extrapolation, and patients' engagement in study design. Conclusions: Recommendations have been issued based on discussions of the Small Population Clinical Trials Task Force that aim to contribute towards successful therapy development and clinical use. While randomised clinical trials are still considered the gold standard, it is recommended to systematically take into consideration alternative trial design options when studying treatments for a rare disease. Combining different sources of safety data is important to give a fuller picture of a therapy's safety profile. Multi-arm trials should be considered an opportunity for rare diseases therapy development, and funders are encouraged to support such trial design via international networks. Patient engagement is critical in trial design and therapy development, a process which sponsors are encouraged to incorporate when conducting trials and clinical studies. Input from multiple regulatory agencies is recommended early and throughout clinical development. Regulators are often supportive of new clinical trial designs, provided they are well thought through and justified, and they also welcome discussions and questions on this topic. Parallel advice for multiregional development programs should also be considered