title: Efficient risk set sampling when a time-dependent exposure is present: matching for time to exposure versus exposure density sampling.
abstract: OBJECTIVE
  The impact of time-dependent exposures on the time until study endpoint may correctly be analyzed with data of a full cohort. Ignoring the time-dependent nature of these exposures leads to time-dependent bias. Matching for time to exposure is often applied to take the time-dependency into account, but prefixed sets of exposed and unexposed may still create bias. This approach is attractive since a subcohort would also save resources, especially when exposure and outcome data are only available in the full cohort but further covariate information is required. The first objective is to show to which extent matching for time to exposure yields biased results. Secondly, exposure density sampling is introduced and explored.
  
  
  METHODS
  To evaluate how both sampling methods perform, they are compared to the correct method as well as to the approach in which the time-dependent nature of the exposure is ignored. Real data of the SIR-3 study (Germany, 2000-2001) and a simulation study are used.
  
  
  RESULTS
  Simulations show that matching may reduce the time-dependent bias but still there is a bias. The matching bias decreases if fewer patients are exposed. Exposure density sampling yields unbiased results.
  
  
  CONCLUSIONS
  Results from studies in which matching for time to exposure was applied are only tolerable for rare exposures. Whenever subcohorting is the intention in order to save resources, exposure density sampling should be preferred instead.
authors: Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, 79104 Freiburg, Germany. wolke@fdm.uni-freiburg.de
authors: Martin
authors: M
authors: Wolkewitz
authors: J
authors: J
authors: Beyersmann
authors: P
authors: P
authors: Gastmeier
authors: M
authors: M
authors: Schumacher
mesh_terms: D015982:Bias; D016022:Case-Control Studies; D015331:Cohort Studies; D003198:Computer Simulation; D003428:Cross Infection / Q000401:mortality / Q000635:transmission*; D005858:Germany; D006785:Hospitals, University / Q000706:statistics & numerical data; D006801:Humans; D015994:Incidence; D007362:Intensive Care Units / Q000706:statistics & numerical data; D007902:Length of Stay / Q000706:statistics & numerical data*; D018410:Pneumonia, Bacterial / Q000401:mortality / Q000635:transmission*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012494:Sampling Studies; D016019:Survival Analysis; D013997:Time Factors