Estimation of exponential rates in non-homogeneous populations using the EAIR estimator

The exposure-adjusted incidence rate (EAIR) is an estimator proposed for assessing safety events in clinical trials and is sometimes used on efficacy endpoints when the follow-up time varies over the subjects, regardless of whether the follow-up time refers to duration of exposure to treatment or time in the study. The estimator relies on a strong assumption that the time to event is exponentially distributed with the same rate parameter for all subjects within a treatment group. Despite its apparent simplicity, the interpretation of EAIR might not be straightforward, especially in the presence of heterogeneity in the individual rates of event occurrence in the population under study. This article discusses potential issues with the EAIR estimator, as well as the interpretation of the resulting estimate, both within a given study and when considering a combination of multiple studies (aggregation, integration, pooling, meta-analysis). Considerations for confidence interval estimation are also discussed.

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Publication Details

Journal
Journal of Biopharmaceutical Statistics
Published
2026-05-16
DOI
https://doi.org/10.1080/10543406.2026.2664116
Primary Topic
Census and Population Estimation
Type
article
Field-Weighted Citation Impact
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article

Estimation of exponential rates in non-homogeneous populations using the EAIR estimator

Katarina Hedman, Gregory Levin, George Kordzakhia, Li Hongjian et al.
Journal of Biopharmaceutical Statistics
Census and Population Estimation
article

Estimation of exponential rates in non-homogeneous populations using the EAIR estimator

Katarina Hedman, Gregory Levin, George Kordzakhia, Li Hongjian, C. Torres, Per Nyström
article en

Abstract

The exposure-adjusted incidence rate (EAIR) is an estimator proposed for assessing safety events in clinical trials and is sometimes used on efficacy endpoints when the follow-up time varies over the subjects, regardless of whether the follow-up time refers to duration of exposure to treatment or time in the study. The estimator relies on a strong assumption that the time to event is exponentially distributed with the same rate parameter for all subjects within a treatment group. Despite its apparent simplicity, the interpretation of EAIR might not be straightforward, especially in the presence of heterogeneity in the individual rates of event occurrence in the population under study. This article discusses potential issues with the EAIR estimator, as well as the interpretation of the resulting estimate, both within a given study and when considering a combination of multiple studies (aggregation, integration, pooling, meta-analysis). Considerations for confidence interval estimation are also discussed.

Journal of Biopharmaceutical Statistics
Center for Drug Evaluation and Research (US), AstraZeneca (Brazil) (BR)
Openalex Percentile: Top 3%
Census and Population Estimation
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