Summarizing causal differences in survival curves in the presence of unmeasured confounding

Martínez-Camblor Pablo (), MacKenzie Todd A., Staiger Douglas O., Goodney Phillip P. and O’Malley A. James
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Martínez-Camblor Pablo: Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA
MacKenzie Todd A.: Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA
Staiger Douglas O.: The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Lebanon, New Hampshire, USA
Goodney Phillip P.: The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Lebanon, New Hampshire, USA
O’Malley A. James: Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA

The International Journal of Biostatistics, 2021, vol. 17, issue 2, 223-240

Abstract: Proportional hazard Cox regression models are frequently used to analyze the impact of different factors on time-to-event outcomes. Most practitioners are familiar with and interpret research results in terms of hazard ratios. Direct differences in survival curves are, however, easier to understand for the general population of users and to visualize graphically. Analyzing the difference among the survival curves for the population at risk allows easy interpretation of the impact of a therapy over the follow-up. When the available information is obtained from observational studies, the observed results are potentially subject to a plethora of measured and unmeasured confounders. Although there are procedures to adjust survival curves for measured covariates, the case of unmeasured confounders has not yet been considered in the literature. In this article we provide a semi-parametric procedure for adjusting survival curves for measured and unmeasured confounders. The method augments our novel instrumental variable estimation method for survival time data in the presence of unmeasured confounding with a procedure for mapping estimates onto the survival probability and the expected survival time scales.

Keywords: 2SRI-F; causal average; instrumental variables; survival curves; unmeasured confounder (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/ijb-2019-0146

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