Correspondence RE: Prostate Cancer Mortality in Areas With High and Low Prostate Cancer Incidence
1 of 1 Correspondence | JNCI
50 states and regresses these against the states’ corresponding bladder cancer incidence rates (3). Until the standard analyses appropriate for ecologic studies are performed, and the results shared, readers will not have confidence in the authors’ stated conclusion of a statistically significant relationship between PCa mortality and change in PCa incidence. Paul F. Pinsky
References 1. Stattin P, Carlsson S, Holmstrom B, et al. Prostate cancer mortality in areas with high and low prostate cancer incidence. J Nat Cancer Inst. 2014; 106(3): dju007 doi:10.1093/ jnci/dju007. 2. Morgenstern H. Ecologic studies in epidemiology: concepts, principles, and methods. Annu Rev Public Health. 1995;16:16–81. 3. Colli J, Kolettis PN. Bladder cancer incidence and mortality rates compared to ecologic factors among states. Int J Urol Nephrol. 2010;42(3):659–665. Affiliation of author: Division of Cancer Prevention, National Cancer Institute, Bethesda, MD. Correspondence to: Paul F. Pinsky, PhD, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD 20824 (e-mail: [email protected]). DOI:10.1093/jnci/dju292 First published online September 30, 2014 Published by Oxford University Press 2014.
Vol. 106, Issue 10 | dju292 | October 8, 2014
Downloaded from http://jnci.oxfordjournals.org/ at Indiana Univ Library on March 18, 2015
Stattin et al. present intriguing findings from an ecologic study that attempts to ascertain the relationship between PSA screening intensity and prostate cancer (PCa) mortality in the counties of Sweden (1). As a surrogate for PSA intensity, they utilize the change in PCa incidence in the county over two relevant time periods. They classify the 24 Swedish counties into three groups—low, medium, and high change in prostate cancer incidence—and then compare PCa mortality in the “high” counties (n = 8) vs the “low” (n = 6) counties, finding a statistically significant rate ratio of 0.87 (95% confidence interval = 0.81 to 0.95). However, their analysis, including the stated confidence interval, was based solely on the combined number of PCa deaths (and person years) in the “high” counties and the combined number of PCa deaths (and person years) in the “low” counties, an analysis that would be appropriate if the “exposure” was known at the individual (person) level for all subjects, as in a randomized trial.
For an ecologic study, though, where the exposure can only be classified at the aggregate level, which is the case here where the level is the county and the exposure is change in incidence in the county, the unit of analysis must be at that same level and not the level of the individual (2). The effective sample size of the Stattin et al. study therefore should be 14 (8 + 6), and the analysis should compare the distribution of PCa mortality rates in the eight high counties vs that in the six low counties, statistically testing whether the two distributions are different (note: statistical adjustments can be performed to account for the fact that the underlying PCa mortality rates are not known but only estimated from the observed counts). Such an analysis could make it considerably more difficult to find a statistically significant result. Further, to preclude the necessity of somewhat arbitrarily defining cutoffs to determine high vs low counties, a regression analysis of change in incidence vs PCa mortality over all (24) counties would be a standard statistical method for such an ecologic study. For example, a study of ultraviolet (UV) light (among other factors) and bladder cancer takes the mean UV index value (averaged over time) for each of the
1 of 1 Correspondence | JNCI
50 states and regresses these against the states’ corresponding bladder cancer incidence rates (3). Until the standard analyses appropriate for ecologic studies are performed, and the results shared, readers will not have confidence in the authors’ stated conclusion of a statistically significant relationship between PCa mortality and change in PCa incidence. Paul F. Pinsky
References 1. Stattin P, Carlsson S, Holmstrom B, et al. Prostate cancer mortality in areas with high and low prostate cancer incidence. J Nat Cancer Inst. 2014; 106(3): dju007 doi:10.1093/ jnci/dju007. 2. Morgenstern H. Ecologic studies in epidemiology: concepts, principles, and methods. Annu Rev Public Health. 1995;16:16–81. 3. Colli J, Kolettis PN. Bladder cancer incidence and mortality rates compared to ecologic factors among states. Int J Urol Nephrol. 2010;42(3):659–665. Affiliation of author: Division of Cancer Prevention, National Cancer Institute, Bethesda, MD. Correspondence to: Paul F. Pinsky, PhD, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD 20824 (e-mail: [email protected]). DOI:10.1093/jnci/dju292 First published online September 30, 2014 Published by Oxford University Press 2014.
Vol. 106, Issue 10 | dju292 | October 8, 2014
Downloaded from http://jnci.oxfordjournals.org/ at Indiana Univ Library on March 18, 2015
Stattin et al. present intriguing findings from an ecologic study that attempts to ascertain the relationship between PSA screening intensity and prostate cancer (PCa) mortality in the counties of Sweden (1). As a surrogate for PSA intensity, they utilize the change in PCa incidence in the county over two relevant time periods. They classify the 24 Swedish counties into three groups—low, medium, and high change in prostate cancer incidence—and then compare PCa mortality in the “high” counties (n = 8) vs the “low” (n = 6) counties, finding a statistically significant rate ratio of 0.87 (95% confidence interval = 0.81 to 0.95). However, their analysis, including the stated confidence interval, was based solely on the combined number of PCa deaths (and person years) in the “high” counties and the combined number of PCa deaths (and person years) in the “low” counties, an analysis that would be appropriate if the “exposure” was known at the individual (person) level for all subjects, as in a randomized trial.
For an ecologic study, though, where the exposure can only be classified at the aggregate level, which is the case here where the level is the county and the exposure is change in incidence in the county, the unit of analysis must be at that same level and not the level of the individual (2). The effective sample size of the Stattin et al. study therefore should be 14 (8 + 6), and the analysis should compare the distribution of PCa mortality rates in the eight high counties vs that in the six low counties, statistically testing whether the two distributions are different (note: statistical adjustments can be performed to account for the fact that the underlying PCa mortality rates are not known but only estimated from the observed counts). Such an analysis could make it considerably more difficult to find a statistically significant result. Further, to preclude the necessity of somewhat arbitrarily defining cutoffs to determine high vs low counties, a regression analysis of change in incidence vs PCa mortality over all (24) counties would be a standard statistical method for such an ecologic study. For example, a study of ultraviolet (UV) light (among other factors) and bladder cancer takes the mean UV index value (averaged over time) for each of the
