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

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...

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Bibliographic Details
Published inThe international journal of biostatistics Vol. 17; no. 2; pp. 223 - 240
Main Authors Martínez-Camblor, Pablo, MacKenzie, Todd A., Staiger, Douglas O., Goodney, Phillip P., O’Malley, A. James
Format Journal Article
LanguageEnglish
Published Germany De Gruyter 01.11.2021
Walter de Gruyter GmbH
Subjects
2SRI-F
Algorithms
Bias
causal average
Causality
Clinical medicine
Confounding Factors, Epidemiologic
Estimates
Humans
instrumental variables
Medicine
Population
Probability
Proportional Hazards Models
survival curves
unmeasured confounder
causal average
instrumental variables
2SRI-F
unmeasured confounder
survival curves
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ISSN1557-4679
2194-573X
1557-4679
DOI10.1515/ijb-2019-0146

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Summary: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.
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ISSN:1557-4679
2194-573X
1557-4679
DOI:10.1515/ijb-2019-0146