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The use of data from multiple studies or centers for the validation of a clinical test or a multivariable prediction model allows researchers to investigate the test's/model's performance in multiple settings and populations. Recently, meta-analytic techniques have been proposed to summarize discrimination and calibration across study populations. Here, we rather consider performance in terms of net benefit, which is a measure of clinical utility that weighs the benefits of true positive classifications against the harms of false positives. We posit that it is important to examine clinical utility across multiple settings of interest. This requires a suitable meta-analysis method, and we propose a Bayesian trivariate random-effects meta-analysis of sensitivity, specificity, and prevalence. Across a range of chosen harm-to-benefit ratios, this provides a summary measure of net benefit, a prediction interval, and an estimate of the probability that the test/model is clinically useful in a new setting. In addition, the prediction interval and probability of usefulness can be calculated conditional on the known prevalence in a new setting. The proposed methods are illustrated by 2 case studies: one on the meta-analysis of published studies on ear thermometry to diagnose fever in children and one on the validation of a multivariable clinical risk prediction model for the diagnosis of ovarian cancer in a multicenter dataset. Crucially, in both case studies the clinical utility of the test/model was heterogeneous across settings, limiting its usefulness in practice. This emphasizes that heterogeneity in clinical utility should be assessed before a test/model is routinely implemented. [ABSTRACT FROM AUTHOR]

Introduction: For tests reporting continuous results, primary studies usually provide test performance at multiple but often different thresholds. This creates missing data when performing a meta‐analysis at each threshold. A standard meta‐analysis (no imputation [NI]) ignores such missing data. A single imputation (SI) approach was recently proposed to recover missing threshold results. Here, we propose a new method that performs multiple imputation of the missing threshold results using discrete combinations (MIDC). Methods: The new MIDC method imputes missing threshold results by randomly selecting from the set of all possible discrete combinations which lie between the results for 2 known bounding thresholds. Imputed and observed results are then synthesised at each threshold. This is repeated multiple times, and the multiple pooled results at each threshold are combined using Rubin's rules to give final estimates. We compared the NI, SI, and MIDC approaches via simulation. Results: Both imputation methods outperform the NI method in simulations. There was generally little difference in the SI and MIDC methods, but the latter was noticeably better in terms of estimating the between‐study variances and generally gave better coverage, due to slightly larger standard errors of pooled estimates. Given selective reporting of thresholds, the imputation methods also reduced bias in the summary receiver operating characteristic curve. Simulations demonstrate the imputation methods rely on an equal threshold spacing assumption. A real example is presented. Conclusions: The SI and, in particular, MIDC methods can be used to examine the impact of missing threshold results in meta‐analysis of test accuracy studies. [ABSTRACT FROM AUTHOR]

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Health outcomes throughout the life course have been linked to fetal growth restriction and low birthweight. A variety of measures exist to define low birthweight, with a lack of consensus regarding which predict adverse outcome.Objectives: To evaluate the relationship between birthweight standards and childhood and adult outcomes in term-born infants (≥37 weeks' gestation).Search Strategy: MEDLINE (1966-January 2011), EMBASE (1980-January 2011), and the Cochrane Library (2011:1) and MEDION were included.Selection Criteria: Studies comprising live term-born infants (gestation ≥37 completed weeks), with weight or other anthropometric measurements recorded at birth along with childhood and adult outcomes.Data Collection and Analysis: Data were extracted to populate 2 × 2 tables relating birthweight standard with outcome, and meta-analysis was performed where possible.Main Results: Fifty-nine articles (2 600 383 individuals) were selected. There was no significant relationship between birthweight <2.5 kg (odds ratio [OR] 0.98, 95% confidence intervals [CI] 0.87-1.10) and composite measure of childhood morbidity. Weight <10th centile on the population nomogram showed a small association (OR 1.49, 95% CI 1.02-2.19) for the same outcome. There was no significant association between either of the above measures and adult morbidity. The relationship between other measures and individual outcomes varied.Author's Conclusions: The association between low birthweight, by any definition, and childhood and adult morbidity was inconsistent. None of the current standards of low birthweight was a good predictor of adverse outcome. [ABSTRACT FROM AUTHOR]