# How to conduct an individual participant data meta-analysis in response to an emerging pathogen: Lessons learned from Zika and COVID-19

## TL;DR

This paper provides a tutorial on conducting individual participant data meta-analyses for emerging pathogens like Zika and COVID-19.

## Contribution

The paper offers practical guidance and lessons learned from IPD-MAs in response to emerging pathogens.

## Key findings

- IPD-MAs help distinguish spurious from clinically relevant heterogeneity in disease progression.
- Statistical challenges like missingness and measurement error were addressed in IPD-MAs during the COVID-19 response.
- The paper outlines steps for conducting IPD-MAs tailored to emerging pathogens.

## Abstract

Sharing, harmonizing, and analyzing participant-level data is of central importance in the rapid research response to emerging pathogens. Individual participant data meta-analyses (IPD-MAs), which synthesize participant-level data from related primary studies, have several advantages over pooling study-level effect estimates in a traditional meta-analysis. IPD-MAs enable researchers to more effectively separate spurious heterogeneity related to differences in measurement from clinically relevant heterogeneity from differences in underlying risk or distribution of factors that modify disease progression. This tutorial describes the steps needed to conduct an IPD-MA of an emerging pathogen and how IPD-MAs of emerging pathogens differ from those of well-studied exposures and outcomes. We discuss key statistical issues, including participant- and study-level missingness and complex measurement error, and present recommendations. We review how IPD-MAs conducted during the COVID-19 response addressed these statistical challenges when harmonizing and analyzing participant-level data related to an emerging pathogen. The guidance presented here is based on lessons learned in our conduct of IPD-MAs in the research response to emerging pathogens, including Zika virus and COVID-19.