Carl van Walraven1, Steven Hawken2. 1. Medicine, University of Ottawa; Epidemiology and Community Medicine, University of Ottawa; Ottawa Hospital Research Institute; ICES uOttawa. Electronic address: carlv@ohri.ca. 2. Ottawa Hospital Research Institute.
Abstract
BACKGROUND: Kaplan-Meier (KM) analyses are frequently used to measure outcome risk over time. These analyses overestimate risk whenever competing events are present. Many published KM analyses are susceptible to such competing risk bias. This study derived and validated a model that predicted true outcome risk based on the biased KM risk. METHODS: We simulated survival data sets having a broad range of 1-year true outcome and competing event risk. Unbiased true outcome risk estimates were calculated using the cumulative incidence function (CIF). Multiple linear regression was used to determine the independent association of CIF-based true outcome risk with the biased KM risk and the proportion of all outcomes that were competing events. RESULTS: The final model found that both the biased KM-based risk and the proportion of all outcomes that were competing events were strongly associated with CIF-based risk. In validation populations that used a variety of distinct survival hazard functions, the model accurately predicted the CIF (R(2) = 1). CONCLUSIONS: True outcome risk can be accurately predicted from KM estimates susceptible to competing risk bias.
BACKGROUND: Kaplan-Meier (KM) analyses are frequently used to measure outcome risk over time. These analyses overestimate risk whenever competing events are present. Many published KM analyses are susceptible to such competing risk bias. This study derived and validated a model that predicted true outcome risk based on the biased KM risk. METHODS: We simulated survival data sets having a broad range of 1-year true outcome and competing event risk. Unbiased true outcome risk estimates were calculated using the cumulative incidence function (CIF). Multiple linear regression was used to determine the independent association of CIF-based true outcome risk with the biased KM risk and the proportion of all outcomes that were competing events. RESULTS: The final model found that both the biased KM-based risk and the proportion of all outcomes that were competing events were strongly associated with CIF-based risk. In validation populations that used a variety of distinct survival hazard functions, the model accurately predicted the CIF (R(2) = 1). CONCLUSIONS: True outcome risk can be accurately predicted from KM estimates susceptible to competing risk bias.