Analysis of clinical cohort data using nested case-control and case-cohort sampling designs: A powerful and economical tool

Kristin Ohneberg, M. Wolkewitz, J. Beyersmann, M. Palomar-Martinez, P. Olaechea-Astigarraga, F. Alvarez-Lerma, M. Schumacher

Research output: Contribution to journalArticleResearchpeer-review

15 Citations (Scopus)


© Schattauer 2015. Background: Sampling from a large cohort in order to derive a subsample that would be sufficient for statistical analysis is a frequently used method for handling large data sets in epidemiological studies with limited resources for exposure measurement. For clinical studies however, when interest is in the influence of a potential risk factor, cohort studies are often the first choice with all in dividuals entering the analysis. Objectives: Our aim is to close the gap between epidemiological and clinical studies with respect to design and power considerations. Schoenfeld’s formula for the number of events required for a Cox’ proportional hazards model is fundamental. Our objective is to compare the power of analyzing the full cohort and the power of a nested case control and a case-cohort design. Methods: We compare formulas for power for sampling designs and cohort studies. In our data example we simultaneously apply a nested case-control design with a varying number of controls matched to each case, a case cohort design with varying subcohort size, a random subsample and a full cohort analysis. For each design we calculate the standard error for estimated regression co efficients and the mean number of distinct persons, for whom covariate information is required. Results: The formula for the power of a nested case-control design and the power of a case-cohort design is directly connected to the power of a cohort study using the well known Schoenfeld formula. The loss in pre cision of parameter estimates is relatively small compared to the saving in resources. Conclusions: Nested case-control and casecohort studies, but not random subsamples yield an attractive alternative for analyzing clinical studies in the situation of a low event rate. Power calculations can be conducted straightforwardly to quantify the loss of power compared to the savings in the number of patients using a sampling design instead of analyzing the full cohort.
Original languageEnglish
Pages (from-to)505-514
JournalMethods of Information in Medicine
Issue number6
Publication statusPublished - 1 Jan 2015


  • Case-cohort design
  • Cohort study
  • Nested case-control design
  • Power
  • Sample size


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