Inverse probability of treatment-weighted competing risks analysis: an application on long-term risk of urinary adverse events after prostate cancer treatments

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  • Bolch, Charlotte A
    • Other Affiliation: University of Minnesota, Twin Cities Campus, Minneapolis, USA; Gainesville, USA
  • Chu, Haitao
    • Other Affiliation: University of Minnesota, Twin Cities Campus, Minneapolis, USA
  • Jarosek, Stephanie
    • Other Affiliation: University of Minnesota, Twin Cities Campus, Minneapolis, USA
  • Cole, Stephen
  • Elliott, Sean
    • Other Affiliation: University of Minnesota, Twin Cities Campus, Minneapolis, USA
  • Virnig, Beth
    • Other Affiliation: University of Minnesota, Twin Cities Campus, Minneapolis, USA
Abstract
  • Abstract Background To illustrate the 10-year risks of urinary adverse events (UAEs) among men diagnosed with prostate cancer and treated with different types of therapy, accounting for the competing risk of death. Methods Prostate cancer is the second most common malignancy among adult males in the United States. Few studies have reported the long-term post-treatment risk of UAEs and those that have, have not appropriately accounted for competing deaths. This paper conducts an inverse probability of treatment (IPT) weighted competing risks analysis to estimate the effects of different prostate cancer treatments on the risk of UAE, using a matched-cohort of prostate cancer/non-cancer control patients from the Surveillance, Epidemiology and End Results (SEER) Medicare database. Results Study dataset included men age 66 years or older that are 83% white and had a median follow-up time of 4.14 years. Patients that underwent combination radical prostatectomy and external beam radiotherapy experienced the highest risk of UAE (IPT-weighted competing risks: HR 3.65 with 95% CI (3.28, 4.07); 10-yr. cumulative incidence = 36.5%). Conclusions Findings suggest that IPT-weighted competing risks analysis provides an accurate estimator of the cumulative incidence of UAE taking into account the competing deaths as well as measured confounding bias.
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  • doi:10.1186/s12874-017-0367-8
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  • Article
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  • In Copyright
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  • The Author(s).
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  • English
Bibliographic citation
  • BMC Medical Research Methodology. 2017 Jul 10;17(1):93
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  • BioMed Central

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