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Many randomized trials evaluate an intervention effect on time-to-event outcomes. Individual participant data (IPD) from such trials can be obtained and combined in a so-called IPD meta-analysis (IPD-MA), to summarize the overall intervention effect. We performed a narrative literature review to provide an overview of methods for conducting an IPD-MA of randomized intervention studies with a time-to-event outcome. We focused on identifying good methodological practice for modeling frailty of trial participants across trials, modeling heterogeneity of intervention effects, choosing appropriate association measures, dealing with (trial differences in) censoring and follow-up times, and addressing time-varying intervention effects and effect modification (interactions).We discuss how to achieve this using parametric and semi-parametric methods, and describe how to implement these in a one-stage or two-stage IPD-MA framework. We recommend exploring heterogeneity of the effect(s) through interaction and non-linear effects. Random effects should be applied to account for residual heterogeneity of the intervention effect. We provide further recommendations, many of which specific to IPD-MA of time-to-event data from randomized trials examining an intervention effect.We illustrate several key methods in a real IPD-MA, where IPD of 1225 participants from 5 randomized clinical trials were combined to compare the effects of Carbamazepine and Valproate on the incidence of epileptic seizures.

Background: Meta-regression results must be interpreted taking into account the range of covariate values of the contributing studies. Results based on interpolation or extrapolation may be unreliable. In network meta-regression (NMR) models, which include covariates in network meta-analyses, results are estimated using direct and indirect evidence; therefore, it may be unclear which studies and covariate values contribute to which result. We propose graphs to help understand which trials and covariate values contribute to each NMR result and to highlight extrapolation or interpolation. Methods: We introduce methods to calculate the contribution that each trial and covariate value makes to each result and compare them with existing methods. We show how to construct graphs including a network covariate distribution diagram, covariate-contribution plot, heat plot, contribution-NMR plot, and heat-NMR plot. We demonstrate the methods using a dataset with treatments for malaria using the covariate "average age" and a dataset of topical fluoride interventions for preventing dental caries using the covariate "randomisation year." Results: For the malaria dataset, no contributing trials had an average age between 7-25 years and therefore results were interpolated within this range. For the fluoride dataset, there are no contributing trials randomised between 1954-1959 for most comparisons therefore, within this range, results would be extrapolated. Conclusions: Even in a fully connected network, an NMR result may be estimated from trials with a narrower covariate range than the range of the whole dataset. Calculating contributions and graphically displaying them aids interpretation of NMR result by highlighting extrapolated or interpolated results.

Background: Many reviews aim to compare numerous treatments and report results stratified by subgroups (eg, by disease severity). In such cases, a network meta-analysis model including treatment by covariate interactions can estimate the relative effects of all treatment pairings for each subgroup of patients. Two key assumptions underlie such models: consistency of treatment effects and consistency of the regression coefficients for the interactions. Consistency may differ depending on the covariate value at which consistency is assessed. For valid inference, we need to be confident of consistency for the relevant range of covariate values. In this paper, we demonstrate how to assess consistency of treatment effects from direct and indirect evidence at various covariate values. Methods: Consistency is assessed using visual inspection, inconsistency estimates, and probabilities. The method is applied to an individual patient dataset comparing artemisinin combination therapies for treating uncomplicated malaria in children using the covariate age. Results: The magnitude of the inconsistency appears to be decreasing with increasing age for each comparison. For one comparison, direct and indirect evidence differ for age 1 (P = 0.05), and this brings results for age 1 for all comparisons into question. Conclusion: When fitting models including interactions, the consistency of direct and indirect evidence must be assessed across the range of covariates included in the trials. Clinical inferences are only valid for covariate values for which results are consistent.

Context: The use of scrotal ultrasonography (SUS) has increased the detection rate of indeterminate testicular masses. Defining radiological characteristics that identify malignancy may reduce the number of men undergoing unnecessary radical orchidectomy. Objective: To define which SUS or scrotal magnetic resonance imaging (MRI) characteristics can predict benign or malignant disease in pre‐ or post‐pubertal males with indeterminate testicular masses. Evidence Acquisition This systematic review was conducted in accordance with Cochrane Collaboration guidance. Medline, Embase, Cochrane controlled trials and systematic reviews databases were searched from (1970 to 26 March 2021). Benign and malignant masses were classified using the reported reference test: i.e., histopathology, or 12 months progression‐free radiological surveillance. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies‐2 tool (QUADAS‐2). Evidence Synthesis: A total of 32 studies were identified, including 1692 masses of which 28 studies and 1550 masses reported SUS features, four studies and 142 masses reported MRI features. Meta‐analysis of different SUS (B‐mode) values in post‐pubertal men demonstrated that a size of ≤0.5 cm had a significantly lower odds ratio (OR) of malignancy compared to masses of >0.5 cm (P < 0.001). Comparison of masses of 0.6–1.0 cm and masses of >1.5 cm also demonstrated a significantly lower OR of malignancy (P = 0.04). There was no significant difference between masses of 0.6–1.0 and 1.1–1.5 cm. SUS in post‐pubertal men also had a statistically significantly lower OR of malignancy for heterogenous masses vs homogenous masses (P = 0.04), hyperechogenic vs hypoechogenic masses (P < 0.01), normal vs increased enhancement (P < 0.01), and peripheral vs central vascularity (P < 0.01), respectively. There were limited data on pre‐pubertal SUS, pre‐pubertal MRI and post‐pubertal MRI. Conclusions: This meta‐analysis identifies radiological characteristics that have a lower OR of malignancy and may be of value in the management of the indeterminate testis mass. [ABSTRACT FROM AUTHOR]