Richard D Riley

Interpretation of random effects meta-analyses

Summary estimates of treatment effect from random effects meta-analysis give only the average effect across all studies. Inclusion of prediction intervals, which estimate the likely effect in an individual setting, could make it easier to apply the results to clinical practice

Meta-analysis of individual participant data: rationale, conduct, and reporting.

The use of individual participant data instead of aggregate data in meta-analyses has many potential advantages, both statistically and clinically. Richard D Riley and colleagues describe the rationale for an individual participant data meta-analysis and outline how to conduct this type of study

Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research

In this article, the third in the PROGRESS series on prognostic factor research, Sara Schroter and colleagues review how prognostic models are developed and validated, and then address how prognostic models are assessed for their impact on practice and patient outcomes, illustrating these ideas with examples.

Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data The PRISMA-IPD Statement

IMPORTANCE Systematic reviews and meta-analyses of individual participant data (IPD) aim to collect, check, and reanalyze individual-level data from all studies addressing a particular research question and are therefore considered a gold standard approach to evidence synthesis. They are likely to be used with increasing frequency as current initiatives to share clinical trial data gain momentum and may be particularly important in reviewing controversial therapeutic areas. OBJECTIVE To develop PRISMA-IPD as a stand-alone extension to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement, tailored to the specific requirements of reporting systematic reviews and meta-analyses of IPD. Although developed primarily for reviews of randomized trials, many items will apply in other contexts, including reviews of diagnosis and prognosis. DESIGN Development of PRISMA-IPD followed the EQUATOR Network framework guidance and used the existing standard PRISMA Statement as a starting point to draft additional relevant material. A web-based survey informed discussion at an international workshop that included researchers, clinicians, methodologists experienced in conducting systematic reviews and meta-analyses of IPD, and journal editors. The statement was drafted and iterative refinements were made by the project, advisory, and development groups. The PRISMA-IPD Development Group reached agreement on the PRISMA-IPD checklist and flow diagram by consensus. FINDINGS Compared with standard PRISMA, the PRISMA-IPD checklist includes 3 new items that address (1) methods of checking the integrity of the IPD (such as pattern of randomization, data consistency, baseline imbalance, and missing data), (2) reporting any important issues that emerge, and (3) exploring variation (such as whether certain types of individual benefit more from the intervention than others). A further additional item was created by reorganization of standard PRISMA items relating to interpreting results. Wording was modified in 23 items to reflect the IPD approach. CONCLUSIONS AND RELEVANCE PRISMA-IPD provides guidelines for reporting systematic reviews and meta-analyses of IPD.

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochrans heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright

Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research

In the second article in the PROGRESS series on prognostic factor research, Sara Schroter and colleagues discuss the role of prognostic factors in current clinical practice, randomised trials, and developing new interventions, and explain why and how prognostic factor research should be improved.

Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes.

Understanding and improving the prognosis of a disease or health condition is a priority in clinical research and practice. In this article, the authors introduce a framework of four interrelated themes in prognosis research, describe the importance of the first of these themes (understanding future outcomes in relation to current diagnostic and treatment practices), and introduce recommendations for the field of prognosis research

Multivariate meta‐analysis: Potential and promise

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the days discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright

Prognosis research strategy (PROGRESS) 4: Stratified medicine research

In patients with a particular disease or health condition, stratified medicine seeks to identify those who will have the most clinical benefit or least harm from a specific treatment. In this article, the fourth in the PROGRESS series, the authors discuss why prognosis research should form a cornerstone of stratified medicine, especially in regard to the identification of factors that predict individual treatment response

A Systematic Review of Molecular and Biological Tumor Markers in Neuroblastoma

Purpose: The aim of this study was to conduct a systematic review, and where possible meta-analyses, of molecular and biological tumor markers described in neuroblastoma, and to establish an evidence-based perspective on their clinical value for the screening, diagnosis, prognosis, and monitoring of patients. Experimental Design: A well-defined, reproducible search strategy was used to identify the relevant literature from 1966 to February 2000. Results: A total of 428 papers studying the use of 195 different tumor markers in neuroblastoma were identified. Small sample sizes, poor statistical reporting, large heterogeneity across studies (e.g., in cutoff levels), and publication bias limited meta-analysis to the area of prognosis only; MYCN, chromosome 1p, DNA index, vanillylmandelic acid:homovanillic acid ratio, CD44, Trk-A, neuron-specific enolase, lactate dehydrogenase, ferritin, and multidrug resistance were all identified as potentially important prognostic tools. Conclusions: This systematic review forms a knowledge base of the tumor markers studied thus far in neuroblastoma, and has identified some of the most important prognostic markers, which should be considered in future research and treatment strategies. Importantly, the review has also highlighted some general problems across primary tumor marker studies, in particular poor and heterogeneous reporting. These need to be addressed to allow better clinical interpretation and enable more appropriate evidence-based reviews in the future. In particular, collaboration of cancer research groups is needed to enable bigger sample sizes, standardize methods of analysis and reporting, and facilitate the pooling of individual patient data.