Abstract
BackgroundCompeting risks occur when populations may experience outcomes that either preclude or alter the probability of experiencing the main study outcome(s). Many standard survival analysis methods do not account for competing risks. We used mortality risk in people with diabetes with and without albuminuria as a case study to investigate the impact of competing risks on measures of absolute and relative risk.MethodsA population with type 2 diabetes was identified in Clinical Practice Research Datalink as part of a historical cohort study. Patients were followed for up to 9 years. To quantify differences in absolute risk estimates of cardiovascular and cancer, mortality standard (Kaplan-Meier) estimates were compared to competing-risks-adjusted (cumulative incidence competing risk) estimates. To quantify differences in measures of association, regression coefficients for the effect of albuminuria on the relative hazard of each outcome were compared between standard cause-specific hazard (CSH) models (Cox proportional hazards regression) and two competing risk models: the unstratified Lunn-McNeil model, which estimates CSH, and the Fine-Gray model, which estimates subdistribution hazard (SDH).ResultsIn patients with normoalbuminuria, standard and competing-risks-adjusted estimates for cardiovascular mortality were 11.1% (95% confidence interval (CI) 10.8–11.5%) and 10.2% (95% CI 9.9–10.5%), respectively. For cancer mortality, these figures were 8.0% (95% CI 7.7–8.3%) and 7.2% (95% CI 6.9–7.5%). In patients with albuminuria, standard and competing-risks-adjusted estimates for cardiovascular mortality were 21.8% (95% CI 20.9–22.7%) and 18.5% (95% CI 17.8–19.3%), respectively. For cancer mortality, these figures were 10.7% (95% CI 10.0–11.5%) and 8.6% (8.1–9.2%). For the effect of albuminuria on cardiovascular mortality, regression coefficient values from multivariable standard CSH, competing risks CSH, and competing risks SDH models were 0.557 (95% CI 0.491–0.623), 0.561 (95% CI 0.494–0.628), and 0.456 (95% CI 0.389–0.523), respectively. For the effect of albuminuria on cancer mortality, these values were 0.237 (95% CI 0.148–0.326), 0.244 (95% CI 0.154–0.333), and 0.102 (95% CI 0.012–0.192), respectively.ConclusionsStudies of absolute risk should use methods that adjust for competing risks to avoid over-stating risk, such as the CICR estimator. Studies of relative risk should consider carefully which measure of association is most appropriate for the research question.
Highlights
Competing risks occur when populations may experience outcomes that either preclude or alter the probability of experiencing the main study outcome(s)
Cardiovascular mortality was defined as death with International Classification of Diseases (ICD)-10 codes I10-I79, cancer mortality was defined as death with International Classification of Diseases version 10 (ICD-10) codes C00-C97 and other mortality as death with ICD-10 codes other than those listed for cardiovascular or cancer mortality
No significant associations were found between baseline albuminuria and gender, body mass index or total-to-high-density-lipoprotein cholesterol ratio
Summary
Competing risks occur when populations may experience outcomes that either preclude or alter the probability of experiencing the main study outcome(s). D’Alembert produced an alternative solution, which was more generalizable in its application as it was not restricted to immunising diseases In this approach, d’Alembert described a single state of being ‘alive’ and two separate absorbing states of ‘death’, resulting from either the disease or all other causes. The rates of transition to each absorbing state were assumed by d’Alembert to be independent These early disparities in approach continue to be born out to this day with study authors being able to choose from multiple methods to account for competing risks in studies. We consider two standard survival analysis methods [1, 2] and compare the results to those obtained via three survival analysis methods adjusted for the presence of competing risks [4, 6, 7]
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