How to conduct an individual participant data meta-analysis in response to an emerging pathogen: Lessons learned from Zika and COVID-19
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.427 | 0.688 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".