Analysing recurrent hospitalizations in heart failure: a review of statistical methodology, with application to CHARM-Preserved

Eur J Heart Fail. 2014 Jan;16(1):33-40. doi: 10.1002/ejhf.29. Epub 2013 Dec 18.


Aims: Heart failure is characterized by recurrent hospitalizations, but often only the first event is considered in clinical trial reports. In chronic diseases, such as heart failure, analysing all events gives a more complete picture of treatment benefit. We describe methods of analysing repeat hospitalizations, and illustrate their value in one major trial.

Methods and results: The Candesartan in Heart failure Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved study compared candesartan with placebo in 3023 patients with heart failure and preserved systolic function. The heart failure hospitalization rates were 12.5 and 8.9 per 100 patient-years in the placebo and candesartan groups, respectively. The repeat hospitalizations were analysed using the Andersen-Gill, Poisson, and negative binomial methods. Death was incorporated into analyses by treating it as an additional event. The win ratio method and a method that jointly models hospitalizations and mortality were also considered. Using repeat events gave larger treatment benefits than time to first event analysis. The negative binomial method for the composite of recurrent heart failure hospitalizations and cardiovascular death gave a rate ratio of 0.75 [95% confidence interval (CI) 0.62-0.91, P = 0.003], whereas the hazard ratio for time to first heart failure hospitalization or cardiovascular death was 0.86 (95% CI 0.74-1.00, P = 0.050).

Conclusions: In patients with preserved EF, candesartan reduces the rate of admissions for worsening heart failure, to a greater extent than apparent from analysing only first hospitalizations. Recurrent events should be routinely incorporated into the analysis of future clinical trials in heart failure.

Keywords: Candesartan; Clinical trial; Heart failure; Hospitalization; Recurrent events.

Publication types

  • Review

MeSH terms

  • Data Interpretation, Statistical
  • Global Health
  • Heart Failure / epidemiology*
  • Hospitalization / statistics & numerical data*
  • Humans
  • Morbidity / trends
  • Risk Assessment / methods*
  • Survival Rate / trends