Incorporating uncertainty in the baseline risk: An R Shiny tool and an empirical study

The common practice in meta-analysis and clinical practice guidelines is to derive the absolute treatment effect (also called risk difference, RD) from a combination of a pooled relative risk (RR) that resulted from a meta-analysis, and a user-provided baseline risk (BR). However, this method does not address the uncertainty in BR. We developed a web-based R Shiny tool to perform simple microsimulation and incorporate uncertainty in BR into the precision of RD. We empirically evaluated this approach by estimating the impact of incorporating this uncertainty when BR is derived from the control group rates in 3,128 meta-analyses curated from the Cochrane Library (26,964 individual studies). When BR was derived from the largest study in each meta-analysis, the median width of the CI of BR was 11.6% (interquartile range (IQR), 6.30%-18.5%). Incorporating this uncertainty in BR led to expansion of the RD CI by a median of 8 per 1,000 persons (IQR 2-24). This expansion increased in a linear fashion with BR imprecision and was more prominent in meta-analyses with low BR. This study provides a web-based tool to perform simple microsimulation and incorporate uncertainty in BR into the CI of RD.

Authors

Institutions

Publication Details

Journal
Cochrane Evidence Synthesis and Methods
Published
2025-01-28
DOI
https://doi.org/10.1002/cesm.70018
Primary Topic
Meta-analysis and systematic reviews
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Incorporating uncertainty in the baseline risk: An R Shiny tool and an empirical study

M. Hassan Murad, Lifeng Lin
Cochrane Evidence Synthesis and Methods
Meta-analysis and systematic reviews
article

Incorporating uncertainty in the baseline risk: An R Shiny tool and an empirical study

M. Hassan Murad, Lifeng Lin
article en

Abstract

The common practice in meta-analysis and clinical practice guidelines is to derive the absolute treatment effect (also called risk difference, RD) from a combination of a pooled relative risk (RR) that resulted from a meta-analysis, and a user-provided baseline risk (BR). However, this method does not address the uncertainty in BR. We developed a web-based R Shiny tool to perform simple microsimulation and incorporate uncertainty in BR into the precision of RD. We empirically evaluated this approach by estimating the impact of incorporating this uncertainty when BR is derived from the control group rates in 3,128 meta-analyses curated from the Cochrane Library (26,964 individual studies). When BR was derived from the largest study in each meta-analysis, the median width of the CI of BR was 11.6% (interquartile range (IQR), 6.30%-18.5%). Incorporating this uncertainty in BR led to expansion of the RD CI by a median of 8 per 1,000 persons (IQR 2-24). This expansion increased in a linear fashion with BR imprecision and was more prominent in meta-analyses with low BR. This study provides a web-based tool to perform simple microsimulation and incorporate uncertainty in BR into the CI of RD.

Cochrane Evidence Synthesis and MethodsVol. 3(2)
University of Arizona (US), Mayo Clinic in Arizona (US)
Openalex Percentile: Top 98%
Meta-analysis and systematic reviews
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Incorporating uncertainty in the baseline risk: An R Shiny tool and an empirical study — M. Hassan Murad, Lifeng Lin · Cochrane Evidence Synthesis and Methods (2025) | TGRS Research Map | TGRS