In epidemiology dose-response meta analysis often refers to fitting a meta regression model that describes a linear trend in the outcome ("response") as a function of the exposure ("dose"), based on aggregated data from a number of studies.
Fixed- and random-effects extensions for handling nonlinear dose-response for odds ratios, relative risks and differences in means through the use of...
Updating a meta-analysis (MA) by including additional studies is usually a straightforward exercise, as the relevant data are commonly reported in detail, i.e., effect estimates with standard errors for all studies. Matters are complicated, however, when only the summary of a previous analysis is available, i.e., the overall estimate with standard error. For instance, this is sometimes the...
Meta-analyses often involve transforming bounded effect size measures, such as correlation coefficients or odds ratios, onto a real-valued scale prior to estimation. The results are then back-transformed to the original scale for interpretation purposes. However, in the standard random effects model for meta-analysis, simply applying the inverse transformation function generally does not yield...
In many biomedical research settings, sufficiently large sample sizes can only be achieved by combining data from multiple collection sites (e.g., hospitals). However, pooling individual participant data in a central server is often restricted due to privacy and regulatory constraints. Federated inference addresses this challenge by distributing the statistical analysis across local sites,...
Title:
Evaluating Nonparametric Combination Methods for Aggregating N-of-1 Trials: A Simulation-Based Comparison with Meta-Analysis
Abstract:
Aggregating results from multiple N-of-1 trials has become increasingly relevant for evaluating personalized and digital health interventions, where inter-individual heterogeneity and complex temporal structures challenge traditional study designs....
