Catherine M Tangen1, Jeannette Schenk2, Cathee Till3, Phyllis J Goodman3, Wendy Barrington4, M Scott Lucia5, Ian M Thompson6. 1. From the SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, United States. Electronic address: ctangen@fredhutch.org. 2. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States. 3. From the SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, United States. 4. Department of Psychosocial and Community Health, The University of Washington, Seattle, WA, United States. 5. Department of Pathology, The University of Colorado Denver School of Medicine, Denver, CO, United States. 6. The Cancer Therapy and Research Center, Christus Santa Rosa Medical Center, San Antonio, TX, United States.
Abstract
BACKGROUND: Prostate cancer is ubiquitous in older men; differential screening patterns and variations in biopsy recommendations and acceptance will affect which man is diagnosed and, therefore, evaluation of cancer risk factors. We describe a statistical method to reduce prostate cancer detection bias among African American (n = 3398) and Non-Hispanic White men (n = 22,673) who participated in the Selenium and Vitamin E Cancer Prevention trial (SELECT) and revisit a previously reported association between race, obesity and prostate cancer risk. METHODS: For men with screening values suggesting prostate cancer but in whom biopsy was not performed, the Prostate Cancer Prevention Trial Risk Calculator was used to estimate probability of prostate cancer. Associations of body mass index (BMI) and race with incident prostate cancer were compared for observed versus imputation-enhanced outcomes using incident density ratios. RESULTS: Accounting for differential biopsy assessment, the previously reported positive linear trend between BMI and prostate cancer in African American men was not observed; no BMI association was found among Non-Hispanic White men. CONCLUSIONS: Differential disease classification among men who may be recommended to undergo and then consider whether to accept a prostate biopsy leads to inaccurate identification of prostate cancer risk factors. Imputing a man's prostate cancer status reduces detection bias. Covariate adjustment does not address the problem of outcome misclassification. Cohorts evaluating incident prostate cancer should collect longitudinal screening and biopsy data to adjust for this potential bias.
BACKGROUND:Prostate cancer is ubiquitous in older men; differential screening patterns and variations in biopsy recommendations and acceptance will affect which man is diagnosed and, therefore, evaluation of cancer risk factors. We describe a statistical method to reduce prostate cancer detection bias among African American (n = 3398) and Non-Hispanic White men (n = 22,673) who participated in the Selenium and Vitamin ECancer Prevention trial (SELECT) and revisit a previously reported association between race, obesity and prostate cancer risk. METHODS: For men with screening values suggesting prostate cancer but in whom biopsy was not performed, the Prostate Cancer Prevention Trial Risk Calculator was used to estimate probability of prostate cancer. Associations of body mass index (BMI) and race with incident prostate cancer were compared for observed versus imputation-enhanced outcomes using incident density ratios. RESULTS: Accounting for differential biopsy assessment, the previously reported positive linear trend between BMI and prostate cancer in African American men was not observed; no BMI association was found among Non-Hispanic White men. CONCLUSIONS: Differential disease classification among men who may be recommended to undergo and then consider whether to accept a prostate biopsy leads to inaccurate identification of prostate cancer risk factors. Imputing a man's prostate cancer status reduces detection bias. Covariate adjustment does not address the problem of outcome misclassification. Cohorts evaluating incident prostate cancer should collect longitudinal screening and biopsy data to adjust for this potential bias.
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