Risk factors for prostate cancer : analysis of primary data, pooling, and related methodological aspects

Prostate cancer is the second most common cancer among men worldwide, yet its etiology remains
poorly understood. Obesity, on the other hand, is a prevalent but preventable medical condition that is
associated with hormonal and metabolic changes. Since prostate cancer is a hormone-related cancer, the
hypothesis of a link between body fatness and prostate cancer risk has been formulated. Furthermore,
the considerable biologic heterogeneity of prostate cancer warrants analyses to be carried out separately
by aggressiveness of the disease, differentiating indolent from potentially lethal tumors. 



This thesis has two aims. First, to elucidate the association between obesity, as measured by body
mass index (BMI), and the risk of localized, advanced, and fatal prostate cancer. This is done using
both primary data (Paper I) and aggregated data extracted from published epidemiological studies
(Paper IV). Second, to deal with some methodological aspects related to the analysis of primary and
aggregated data (Paper II; Paper III; Paper V).



In Paper I, we used primary data from the Cohort of Swedish Men to examine the association of
BMI during early adulthood (30 years of age) and middle-late adulthood (45–79 years of age) with the
incidence of localized and advanced prostate cancer and with prostate cancer mortality. BMI during
middle-late adulthood was observed to be inversely associated with the incidence of localized prostate
cancer, while it was directly associated with the incidence of advanced prostate cancer and with prostate
cancer mortality. At the same time, we observed limited evidence of an inverse association between
BMI during early-adulthood and the risk of advanced and fatal prostate cancer.



In Paper II, we extended the use of quantile regression for censored data to those situations where
the time scale of interest is attained age at the event instead of follow-up time. In particular, we
described how to use Laplace regression to model percentiles of age at the event in the presence of
delayed entries, by conditioning on age at entry.
In Paper III, we identified three major misinterpretations of risk and rate advancement periods
(RAP): first, equating RAP with the difference in mean survival times; second, interpreting RAP as
the time by which the survival curve for the exposed individuals is shifted compared with that for the
unexposed; third, equating the RAP to a simple ratio of two log–relative risks. Furthermore, we showed
how RAP estimation is sensitive to the specification of the age-disease association.



In Paper IV, we carried out a dose–response meta-analysis to summarize the available evidence on
the association between BMI during middle-late adulthood and the incidence of localized and advanced
prostate cancer. Based on aggregated data extracted from 13 prospective studies, we observed that BMI
was inversely associated with the incidence of localized prostate cancer, while it was directly associated
with the incidence of advanced prostate cancer.



In Paper V, we stressed the importance of assessing the goodness of fit of dose–response metaanalysis
models. We presented and discussed three tools (deviance, coefficient of determination, and
decorrelated-residuals–versus–exposure plot) that are useful to test, quantify, and visually display the
fit of dose–response meta-analysis models, while taking into account the correlation structure of the
study-specific log–relative risks. 



In conclusion, Paper I and Paper IV supported the hypothesis of etiological heterogeneity of prostate
cancer in relation to obesity during middle-late adulthood. In particular, BMI was observed to be directly
associated with advanced prostate cancer and with prostate cancer mortality. Paper II extended the
use of quantile regression for censored data to those situations where attained age is the time scale of
interest, Paper III clarified the appropriate use and interpretation of RAP, and Paper V proposed useful
and relevant methods for assessing the goodness of fit of dose–response models in research synthesis