Meta-Analysis of Odds Ratios With Incomplete Extracted Data

TL;DR

This paper introduces a new meta-analytic method that accounts for uncertainty in extracted survival data from publications, improving accuracy over naive approaches that ignore such uncertainty.

Contribution

The paper proposes the Uncertain Reading-Estimated Events model, which incorporates extraction uncertainty into meta-analysis of odds ratios, reducing bias and improving reliability.

Findings

Naive meta-analysis leads to biased estimates.

The proposed model reduces overconfidence by accounting for extraction uncertainty.

Standard deviation of log-odds increases, indicating more realistic uncertainty estimates.

Abstract

A typical random effects meta-analysis of odds-ratios assumes binomially distributed numbers of events in a treatment and control group and requires the proportion of deaths to be extracted from published papers. This data is often not available in the publications due to loss to follow-up. When the Kaplan Meier survival plot is available, it is common practice to manually measure the needed information from the plot and infer the probability of survival and then to infer a best-guess of the number of deaths. Uncertainty introduced from theses guesses is not accounted for in current models. This naive approach leads to over-certain results and potentially inaccurate conclusions. We propose the Uncertain Reading-Estimated Events model to construct each study's contribution to the meta-analysis separately using the data available for extraction in the publications. We use real and simulated data to illustrate our methods. Meta-analysis based on the observed number of deaths lead to biased estimates while our proposed model does not. Our results show increases in the standard deviation of the log-odds as compared to a naive meta-analysis that assumes ideal extracted data, equivalent to a reduction of the overall sample size of 43% in our example.