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Epidemiology:
doi: 10.1097/EDE.0b013e3182961708
Letters

Time Scale in Follow-up Studies: Considering Disease Prognosis

Chubak, Jessica; Yu, Onchee; Buist, Diana S. M.; Wirtz, Heidi S.; Boudreau, Denise M.

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Group Health Research InstituteSeattle, WADepartment of EpidemiologyUniversity of WashingtonSeattle, WAChubak.j@ghc.org

Group Health Research InstituteSeattle, WA

Group Health Research InstituteSeattle, WADepartment of EpidemiologyUniversity of WashingtonSeattle, WA

Department of PharmacyUniversity of WashingtonSeattle, WA

Group Health Research InstituteSeattle, WADepartment of PharmacyUniversity of WashingtonSeattle, WA

Supported by National Cancer Institute of the National Institutes of Health under Award Number R01CA120562 to D.M.B.

To the Editor:

Colonge et al’s article1 on the choice of primary time scale for epidemiologic follow-up studies makes an important contribution to methods for longitudinal data analysis. We agree with their assessment that the choice of time scale should be based on “the goals of the study and the need for confounder adjustment.” As the authors say, age is the appropriate time scale in most studies of disease incidence (especially when compared with time on study). But there may be other relevant time scales for different applications.

For example, in studies of disease prognosis, time since onset or diagnosis is commonly used.2,3 In a study of the association between a particular medication and breast cancer recurrence, time since initial breast cancer diagnosis may be an important confounder and one that should be accounted for flexibly. Risk of breast cancer recurrence depends on time since diagnosis in a complex way (Figure, unpublished data). Time since diagnosis might also be associated with the exposure of interest, making it a confounder. If time since diagnosis is chosen as the time scale, age should be adjusted for as a covariate in multivariable regression models as it, too, is related to the recurrence risk. The choice of age as a time scale (with adjustment for time since diagnosis) is not incorrect for this application, as age is also associated with recurrence risk and (in some cases) exposure; however, using time since diagnosis may adjust more completely for confounding in some studies of disease prognosis. If both elements of time are potential confounders, both should be accounted for—one as the time scale and one as a covariate in the model. The interpretation of the hazard ratios is essentially the same regardless of which is selected as the time scale and which is modeled; however, the choice may affect the ability to control for confounding.

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Whether it is better to use age or time since diagnosis as the time scale in studies of prognosis may depend on the specific application; in some cases, it may not affect the results.4 With the growing interest in follow-up studies of disease outcomes, additional research on how to select among multiple biologically relevant time scales is necessary.

Jessica Chubak

Group Health Research Institute

Seattle, WA

Department of Epidemiology

University of Washington

Seattle, WA

Chubak.j@ghc.org

Onchee Yu

Group Health Research Institute

Seattle, WA

Diana S. M. Buist

Group Health Research Institute

Seattle, WA

Department of Epidemiology

University of Washington

Seattle, WA

Heidi S. Wirtz

Department of Pharmacy

University of Washington

Seattle, WA

Denise M. Boudreau

Group Health Research Institute

Seattle, WA

Department of Pharmacy

University of Washington

Seattle, WA

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REFERENCES

1. Cologne J, Hsu WL, Abbott RD, et al. Proportional hazards regression in epidemiologic follow-up studies: an intuitive consideration of primary time scale. Epidemiology. 2012;23:565–573

2. Holmes MD, Chen WY, Li L, Hertzmark E, Spiegelman D, Hankinson SE. Aspirin intake and survival after breast cancer. J Clin Oncol. 2010;28:1467–1472

3. O’Meara ES, Rossing MA, Daling JR, Elmore JG, Barlow WE, Weiss NS. Hormone replacement therapy after a diagnosis of breast cancer in relation to recurrence and mortality. J Natl Cancer Inst. 2001;93:754–762

4. Kamineni A, Anderson ML, White E, et al. Body mass index, tumor characteristics, and prognosis following diagnosis of early-stage breast cancer in a mammographically screened population. Cancer Causes Control. 2013;24:305–312

© 2013 by Lippincott Williams & Wilkins, Inc

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