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Sensitivity Analysis for Observational Studies with Recurrent Events

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  • Jeffrey Zhang

    (The Wharton School, University of Pennsylvania, Academic Research Building)

  • Dylan S. Small

    (The Wharton School, University of Pennsylvania, Academic Research Building)

Abstract

We conduct an observational study of the effect of sickle cell trait Haemoglobin AS (HbAS) on the hazard rate of malaria fevers in children. Assuming no unmeasured confounding, there is strong evidence that HbAS reduces the rate of malarial fevers. Since this is an observational study, however, the no unmeasured confounding assumption is strong. A sensitivity analysis considers how robust a conclusion is to a potential unmeasured confounder. We propose a new sensitivity analysis method for recurrent event data and apply it to the malaria study. We find that for the causal conclusion that HbAS is protective against malarial fevers to be overturned, the hypothesized unmeasured confounder must be as influential as all but one of the measured confounders.

Suggested Citation

  • Jeffrey Zhang & Dylan S. Small, 2024. "Sensitivity Analysis for Observational Studies with Recurrent Events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(1), pages 237-261, January.
    • Handle: RePEc:spr:lifeda:v:30:y:2024:i:1:d:10.1007_s10985-023-09607-6
    DOI: 10.1007/s10985-023-09607-6
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    References listed on IDEAS

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    1. Andrea Ichino & Fabrizia Mealli & Tommaso Nannicini, 2008. "From temporary help jobs to permanent employment: what can we learn from matching estimators and their sensitivity?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 305-327.
    2. Guildo W. Imbens, 2003. "Sensitivity to Exogeneity Assumptions in Program Evaluation," American Economic Review, American Economic Association, vol. 93(2), pages 126-132, May.
    3. B Karmakar & B French & D S Small, 2019. "Integrating the evidence from evidence factors in observational studies," Biometrika, Biometrika Trust, vol. 106(2), pages 353-367.
    4. Bo Zhang & Dylan S. Small, 2020. "A calibrated sensitivity analysis for matched observational studies with application to the effect of second‐hand smoke exposure on blood lead levels in children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1285-1305, November.
    5. Carlos Cinelli & Chad Hazlett, 2020. "Making sense of sensitivity: extending omitted variable bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 39-67, February.
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