Simulated treatment comparison (STC) is an established method for performing population adjustment for the indirect comparison of two treatments, where individual patient data (IPD) are available for one trial but only aggregate level information is available for the other. The most commonly used method is what we call 'standard STC'. Here we fit…

A random-effects model is often applied in meta-analysis when considerable heterogeneity among studies is observed due to the differences in patient characteristics, timeframe, treatment regimens, and other study characteristics. Since 2014, the journals "Research Synthesis Methods" and the "Annals of Internal Medicine" have…

During drug development, evidence can emerge to suggest a treatment is more effective in a specific patient subgroup. Whilst early trials may be conducted in biomarker-mixed populations, later trials are more likely to enroll biomarker-positive patients alone, thus leading to trials of the same treatment investigated in different populations. When…

Matching-adjusted indirect comparison (MAIC) enables indirect comparisons of interventions across separate studies when individual patient-level data (IPD) are available for only one study. Due to its similarity with propensity score weighting, it has been speculated that MAIC can be combined with outcome regression models in the spirit of…

Population-adjusted indirect comparisons, developed in the 2010s, enable comparisons between two treatments in different studies by balancing patient characteristics in the case where individual patient-level data (IPD) are available for only one study. Health technology assessment (HTA) bodies increasingly rely on these methods to inform funding…

Clinical prediction models are widely used in modern clinical practice. Such models are often developed using individual patient data (IPD) from a single study, but often there are IPD available from multiple studies. This allows using meta-analytical methods for developing prediction models, increasing power and precision. Different studies,…

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different…

Using the Hartung-Knapp method and 95% prediction intervals (PIs) in random-effects meta-analyses is recommended by experts but rarely applied. Therefore, we aimed to reevaluate statistically significant meta-analyses using the Hartung-Knapp method and 95% PIs. In this methodological study, three databases were searched from January 2010 to July…

Background: Individual patient data meta-analyses (IPD-MA) are regarded as the gold standard for systematic reviews, which also applies to systematic reviews of diagnostic test accuracy (DTA) studies. An increasing number of DTA systematic reviews with IPD-MA have been published in recent years, but there is much variation in how these IPD-MA were…

Rare events meta-analyses of randomized controlled trials (RCTs) are often underpowered because the outcomes are infrequent. Real-world evidence (RWE) from non-randomized studies may provide valuable complementary evidence about the effects of rare events, and there is growing interest in including such evidence in the decision-making process.…

Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap and cannot extrapolate…

Population-adjusted indirect comparisons estimate treatment effects when access to individual patient data is limited and there are cross-trial differences in effect modifiers. Popular methods include matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). There is limited formal evaluation of these methods and…

This paper considers the problem in aggregate data meta-analysis of studies reporting multiple competing binary outcomes and of studies using different summary formats for those outcomes. For example, some may report numbers of patients with at least one of each outcome while others may report the total number of such outcomes. We develop a shared…

The involvement of non-researcher contributors (eg, stakeholders, patients and the public, decision and policy makers, experts, lay contributors) has taken a variety of forms within evidence syntheses. Realist reviews are a form of evidence synthesis that involves non-researcher contributors yet this practice has received little attention. In…

Selective cutoff reporting in primary diagnostic accuracy studies with continuous or ordinal data may result in biased estimates when meta-analyzing studies. Collecting individual participant data (IPD) and estimating accuracy across all relevant cutoffs for all studies can overcome such bias but is labour intensive. We meta-analyzed the…