title: Analysis of Clinical Cohort Data Using Nested Case-control and Case-cohort Sampling Designs. A Powerful and Economical Tool.
abstract: 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 individuals entering the analysis.
  
  
  OBJECTIVE
  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 coefficients 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 precision of parameter estimates is relatively small compared to the saving in resources.
  
  
  CONCLUSIONS
  Nested case-control and case-cohort 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 num-ber of patients using a sampling design instead of analyzing the full cohort.
authors.affiliation: Kristin Ohneberg, Institute for Medical Biometry and Statistics, Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany, E-mail: ohneberg@imbi.uni-freiburg.de.
authors.forename: K
authors.initials: K
authors.lastname: Ohneberg
authors.forename: M
authors.initials: M
authors.lastname: Wolkewitz
authors.forename: J
authors.initials: J
authors.lastname: Beyersmann
authors.forename: M
authors.initials: M
authors.lastname: Palomar-Martinez
authors.forename: P
authors.initials: P
authors.lastname: Olaechea-Astigarraga
authors.forename: F
authors.initials: F
authors.lastname: Alvarez-Lerma
authors.forename: M
authors.initials: M
authors.lastname: Schumacher
mesh_terms: D016022:Case-Control Studies; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D016016:Proportional Hazards Models; D012107:Research Design; D018401:Sample Size
keywords: Case-cohort design; cohort study; nested case-control design; power; sample size