title: Time-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of bias.
abstract: OBJECTIVE
  To display and discuss the reasons and consequences of length and time-dependent bias. They might occur in presence of a time-dependent study entry or a time-dependent exposure which might change from unexposed to exposed.
  
  
  METHODS
  Recalling the popular study of Oscar nominees and using a real-data example from hospital epidemiology, we give innovative and easy-to-understand graphical presentations of how these biases corrupt the analyses via distorted time-at-risk. Cumulative hazard plots and Cox proportional hazards models were used. We are building bridges to medical disciplines such as critical care medicine, hepatology, pharmaco-epidemiology, transplantation medicine, neurology, gynecology and cardiology.
  
  
  RESULTS
  In presence of time-dependent bias, the hazard ratio (comparing exposed with unexposed) is artificially underestimated. The length bias leads to an artificial underestimation of the overall hazard. When both biases coexist it can lead to different directions of biased hazard ratios.
  
  
  CONCLUSIONS
  Since length and time-dependent bias might occur in several medical disciplines, we conclude that understanding and awareness are the best prevention of survival bias.
authors.affiliation: Institute of Medical Biometry and Medical Informatics, University of Freiburg, Freiburg, Germany. wolke@imbi.uni-freiburg.de
authors.forename: Martin
authors.initials: M
authors.lastname: Wolkewitz
authors.forename: Arthur
authors.initials: A
authors.lastname: Allignol
authors.forename: Stephan
authors.initials: S
authors.lastname: Harbarth
authors.forename: Giulia
authors.initials: G
authors.lastname: de Angelis
authors.forename: Martin
authors.initials: M
authors.lastname: Schumacher
authors.forename: Jan
authors.initials: J
authors.lastname: Beyersmann
mesh_terms: D000022:Abortion, Spontaneous / Q000453:epidemiology; D000544:Alzheimer Disease / Q000401:mortality; D015331:Cohort Studies; D003327:Coronary Disease / Q000453:epidemiology; D003428:Cross Infection / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D003920:Diabetes Mellitus / Q000453:epidemiology; D005260:Female; D016027:Heart Transplantation / Q000401:mortality; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008103:Liver Cirrhosis / Q000401:mortality; D011247:Pregnancy; D016016:Proportional Hazards Models; D015983:Selection Bias; D016019:Survival Analysis; D013997:Time Factors