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.
Funding source: Patient-Centered Outcomes Research Institute
Award Identifier / Grant number: ME-1503-28261
Acknowledgments
All statements in this paper, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee. The authors are sincerely grateful to our PCORI Patient Engagement and Governance Committee for reading a draft of the manuscript and for their efforts supporting the development of the research proposal and the research itself.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This research was supported by Patient-Centered Outcomes Research Institute (ME-1503-28261).
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Conflict of Interest: The authors have no conflicts of interest to report.
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Supplementary material
As supplementary material, we provide the file supplementary. R which contains the R routines used in the Monte Carlo simulations and in the real data examples. The online version of this article offers supplementary material (https://doi.org/10.1515/ijb-2019-0146).
© 2020 Walter de Gruyter GmbH, Berlin/Boston
