Purpose: Economic evaluation of interventions for chronic diseases is frequently based on data at the end of clinical trials. This approach is based on the assumptions that relative hazards are constant across time. The aim of this study was to explore whether this assumption is violated in terms of survival curve crossings or convergences.
Method: We identified all time-to-event graphs during 2007 in Annals of Internal Medicine, BMJ, JAMA, New England Journal of Medicine and Lancet. The following data were extracted: type of disease, type of exposure, number of comparator groups, number of paired comparisons, type of primary and secondary end-points, sample size, maximum follow-up time, survival curve convergences, survival curve crossings and type of epidemiologic design.
Result: In total 78% of the 177 publications had survival curve convergences and 42% survival curve crossings. In multivariate logistic regression, survival curve convergence was positively associated with ‘more than one paired comparison’ (OR 3.7, 95% CI 1.3-10.8) and death as a secondary endpoint (OR 8.1, 95% CI 1.1-65.5). No association was found between survival curve crossings and any of the explanatory variables.
Conclusion: Survival curve convergences and crossings are common phenomena in medical research. The results warrant care in making inferences about the effectiveness of interventions for chronic diseases on the basis of measurement at a single point in time.
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