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BACKGROUND: For case–control studies, pooling biologic specimens (cases with cases and controls with controls) can make it affordable to study a biomarker that is expensive to assay, while conserving quantities of archived specimens. For a fixed number of participants, pooling designs incur little loss of estimation precision, and they can even improve precision by enabling inclusion of more participants with the same number of assays. A limitation that has discouraged the use of these methods in environmental epidemiology, however, is the lack of a valid way to adjust for creatinine (urinary dilution) when assaying a biomarker in urine or to adjust for serum lipids when assaying a lipophilic biomarker in serum. OBJECTIVE: We aimed to develop practical strategies to accomplish those adjustments. METHODS: Our strategies either differentially dilute specimens before pooling equal aliquots or, alternatively, pool deliberately unequal aliquots from each specimen, where prior determinations of the adjustment factor in each individual specimen inform their differential dilution or unequal aliquot volumes. In addition, we show how to modify these strategies if, instead of just adjusting for creatinine per se, one needs to account for factors that influence creatinine. We carry out simulations under several causal scenarios. RESULTS: We demonstrate that the proposed strategies perform well in estimating the same adjusted association parameter as would be estimated by using individually-assayed specimens. CONCLUSION: By implementing the proposed strategies when forming specimen pools, one can greatly improve the efficiency of case–control studies that involve an expensive-to-assay exposure measured in biospecimens.

Phytoestrogen exposure from soy formula feeding during infancy may disrupt reproductive system development, resulting in altered menstrual bleeding in adulthood.We investigated this relationship in a cohort of 1,696 young African American women using enrollment data from the Study of Environment, Lifestyle,Fibroids (2010-2012). Questionnaire data on soy formula feeding were available for 1,553 participants, 89% based on mother's report. Menstrual bleeding outcomes including those indicative of heavy menstrual bleeding were ascertained by interview. We estimated relative risks (RRs) and 95% confidence intervals (CI) for associations between soy formula feeding and menstrual bleeding outcomes using log-binomial regression and log-multinomial regression, comparing participants ever fed and never fed soy formula.We observed associations between soy formula feeding and variables indicating a history of heavy menstrual bleeding, including ever experiencing heavy, gushing-type bleeding (RR: 1.2, 95% CI: 1.0, 1.4), ever use of nonsteroidal anti-inflammatory drugs for heavy bleeding (RR: 1.3, 95% CI: 1.0, 1.6), and ever use of a contraceptive method for heavy bleeding (RR: 1.2, 95% CI, 0.9, 1.6). Among the subset of participants with menses in the past year who did not use medication that may alter menstrual flow (n = 762), our data suggested that soy formula feeding was associated with heavier flow and the adverse impact of menstrual bleeding on quality of life, but CIs were wide.Our data suggested that soy formula feeding is associated with heavy menstrual bleeding. Our results support the idea that infancy is a susceptible developmental window for female reproductive function.

Background Previous interview-based studies have suggested that exposure to neurotoxicants including metals might be related to ALS. Methods We evaluated the relation of lead exposure to ALS, using both biological measures and interviews, in a case-control study conducted in New England from 1993 to 1996. Cases (N = 109) were recruited at two hospitals in Boston, MA. Population controls (N = 256) identified by random-digit dialing were frequency-matched to cases by age, sex, and region of residence within New England. Results Risk of ALS was associated with self-reported occupational exposure to lead (odds ratio [OR] = 1.9; 95% confidence interval [CI] = 1.1-3.3), with a dose response for lifetime days of lead exposure. Blood and bone lead levels were measured in most cases (N = 107) and in a subset of controls (N = 41). Risk of ALS was associated with elevations in both blood and bone lead levels. ORs were 1.9 (95% CI = 1.4-2.6) for each microg/dl increase in blood lead, 3.6 (95% CI = 0.6-20.6) for each unit increase in log-transformed patella lead, and 2.3 (95% CI = 0.4-14.5) for each unit increase in log-transformed tibia lead. Conclusions These results are consistent with previous reports and suggest a potential role for lead exposure in the etiology of ALS.

Miettinen and Cook (Am J Epidemiol 1981; 114:593-603) showed that absence of confounding does not imply collapsibility of the odds ratio; that is, the crude odds ratio need not equal a common stratum-specific odds ratio even if the exposed and unexposed study groups have the same distribution of risk factors. Less well known is that absence of confounding does not correspond to collapsibility of the person-time rate ratio or rate difference. For example, two study groups can have the same distribution of all risk factors and yet the crude rate ratio need not equal a common stratum-specific rate ratio. The present paper provides an example and explanation of this phenomenon. The discrepancy between nonconfounding and collapsibility in rate comparisons arises when person-time is a post-exposure variable whose distribution can be altered by the effects of exposure and other risk factors.

Epidemiologists face issues of sample size every time they design a study. Sample size determi? nation has two distinct aspects. One is the tech? nical aspect of how to calculate the sample size re? quired to meet the desired Type I error rate and the power for any specified state of nature. (By "state of nature," I mean the exact point in the relevant multidimensional parameter space for the statistical test? ing problem, the point that corresponds to the hy? pothesized effect that one seeks to detect. If the specified state of nature is "close" to any state that satisfies the null hypothesis, a relatively large sample size will be required to achieve the desired power, whereas if the specified state of nature is "far" from the null hypothesis, a relatively small sample size will do.) The second and more philosophic aspect of sample size determination is the question of how to specify the state of nature that is most relevant to the study. The paper by Yang et al.1 is noteworthy in that it provides an avenue for thinking about this more philosophic aspect. Their core idea is that, in designing a study to detect a certain phenomenon (in this case, "gene-environment interaction"), the size of the effect under investigation can be usefully examined from multiple points of view. The authors consider two: one based on the relative odds ratio for interaction (Rt), and the other based on a population attributable fraction attributable to interac? tion (PAFj). Both points of view describe the same phenomenon, the same state of nature. Mathematically, to fix one is to determine the other. Even so, for a given state of nature, the relation between the value of Rt and the corresponding value of PAFt is not intuitively obvi? ous. Consequently, it is useful to calculate both. A comparison of their values provides perspective on the size of interaction that one views as reasonable to detect. The authors' core idea can be useful for design issues in