title: Challenges of guarantee-time bias.
abstract: The potential for guarantee-time bias (GTB), also known as immortal time bias, exists whenever an analysis that is timed from enrollment or random assignment, such as disease-free or overall survival, is compared across groups defined by a classifying event occurring sometime during follow-up. The types of events associated with GTB are varied and may include the occurrence of objective disease response, onset of toxicity, or seroconversion. However, comparative analyses using these types of events as predictors are different from analyses using baseline characteristics that are specified completely before the occurrence of any outcome event. Recognizing the potential for GTB is not always straightforward, and it can be challenging to know when GTB is influencing the results of an analysis. This article defines GTB, provides examples of GTB from several published articles, and discusses three analytic techniques that can be used to remove the bias: conditional landmark analysis, extended Cox model, and inverse probability weighting. The strengths and limitations of each technique are presented. As an example, we explore the effect of bisphosphonate use on disease-free survival (DFS) using data from the BIG (Breast International Group) 1-98 randomized clinical trial. An analysis using a naive approach showed substantial benefit for patients who received bisphosphonate therapy. In contrast, analyses using the three methods known to remove GTB showed no statistical evidence of a reduction in risk of a DFS event with bisphosphonate therapy.
authors.affiliation: Dana-Farber Cancer Institute, Boston, MA, USA. agiohur@jimmy.harvard.edu
authors.forename: Anita
authors.initials: A
authors.lastname: Giobbie-Hurder
authors.forename: Richard D
authors.initials: RD
authors.lastname: Gelber
authors.forename: Meredith M
authors.initials: MM
authors.lastname: Regan
mesh_terms: D000568:Amenorrhea / Q000503:physiopathology; D015982:Bias; D001943:Breast Neoplasms / Q000503:physiopathology / Q000628:therapy; D002986:Clinical Trials as Topic / Q000379:methods*; D015179:Colorectal Neoplasms / Q000235:genetics; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D008495:Medical Oncology / Q000379:methods*; D009154:Mutation; D016016:Proportional Hazards Models; D011518:Proto-Oncogene Proteins / Q000235:genetics; D016283:Proto-Oncogene Proteins p21(ras); D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012107:Research Design; D016019:Survival Analysis; D018631:ras Proteins / Q000235:genetics
chemical_list: C117307:KRAS protein, human; D011518:Proto-Oncogene Proteins; D016283:Proto-Oncogene Proteins p21(ras); D018631:ras Proteins