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Purpose The etiology of young-onset breast cancer (BC) is poorly understood, despite its greater likelihood of being hormone receptor-negative with a worse prognosis and persistent racial and socioeconomic inequities. We conducted a population-based case–control study of BC among young Black and White women and here discuss the theory that informed our study, exposures collected, study methods, and operational results. Methods Cases were non-Hispanic Black (NHB) and White (NHW) women age 20–49 years with invasive BC in metropolitan Detroit and Los Angeles County SEER registries 2010–2015. Controls were identified through area-based sampling from the U.S. census and frequency matched to cases on study site, race, and age. An eco-social theory of health informed life-course exposures collected from in-person interviews, including socioeconomic, reproductive, and energy balance factors. Measured anthropometry, blood (or saliva), and among cases SEER tumor characteristics and tumor tissue (from a subset of cases) were also collected. Results Of 5,309 identified potentially eligible cases, 2,720 sampled participants were screened and 1,812 completed interviews (682 NHB, 1140 NHW; response rate (RR): 60%). Of 24,612 sampled control households 18,612 were rostered, 2,716 participants were sampled and screened, and 1,381 completed interviews (665 NHB, 716 NHW; RR: 53%). Ninety-nine% of participants completed the main interview, 82% provided blood or saliva (75% blood only), and SEER tumor characteristics (including ER, PR and HER2 status) were obtained from 96% of cases. Conclusions Results from the successfully established YWHHS should expand our understanding of young-onset BC etiology overall and by tumor type and identify sources of racial and socioeconomic inequities in BC.

The Population Assessment of Tobacco and Health (PATH) Study is a nationally representative study of the US population on tobacco use and its effects on health, with four waves of data collection between 2013 and 2017. Prior work described the methods of the first three waves. In this paper, we describe the methods of Wave 4, during which a replenishment sample was added to the ongoing cohort. We describe the design and estimation methods of the continuing Wave 1 cohort (with four waves of data) and the Wave 4 cohort (the new cohort created at Wave 4). We provide survey quality metrics, including response rates for both cohorts and a panel conditioning analysis, and guidance on understanding the target populations for both cohorts.

Objectives: The Population Assessment of Tobacco and Health (PATH) Study is a nationally representative study of the US population on tobacco use and its effects on health, with 3 waves of data collection between 2013 and 2016. Prior work described the methods of the first wave. In this paper, we describe the methods of the subsequent 2 waves and provide recommendations for how to conduct longitudinal analyses of PATH Study data. Methods: We use standard survey quality metrics to evaluate the results of the follow-up waves of the PATH Study. The recommendations and examples of longitudinal and cross-sectional analyses of PATH Study data follow a design-based statistical inference framework. Results: The quality metrics indicate that the PATH Study sample of approximately 40,000 continuing respondents remains representative of its target population. Depending on the intended analysis, different survey weights may be appropriate. Conclusion: The PATH Study data are a valuable resource for regulatory scientists interested in longitudinal analysis of tobacco use and its effects on health. The availability of multiple sets of specialized survey weights enables researchers to target a wide range of tobacco-related analytic questions.

Summary Complex sample designs, involving stratified and/or multistage sampling with sample weighting, along with frequency matching, are used to select controls or cases for case–control studies. Examples that motivated this paper are the Kaposi sarcoma case–control study that was conducted in Sicily and the US kidney cancer case–control study. Survey design-based approaches can be inefficient for the analysis of case–control studies with frequency matching. We propose a weighting method that post-stratifies control sample weights to the estimated population distribution of the matching variables among cases. This weighting maintains the efficiency of frequency matching. The method proposed is evaluated by using simulation studies and is applied to the two case–control studies.

A sample frame is the listing of the units from which a sample is to be selected. When deciding upon a file to serve as a source for a sample frame for a survey, perhaps the most important consideration is the extent to which the target population will be covered by the frame. However, other issues also come into play such as the accuracy of contact and other information appearing on the file as well as its cost. The American Medical Association Masterfile has long been considered the preferred choice for surveys of physicians, although it does have drawbacks. Here we consider two alternative files, discussing their relative advantages and disadvantages. For surveys of physician practices (or other organizations that employ physicians), there have been no files that are obvious choices to serve as the basis for a sample frame. Here we discuss data collection on physician practices from an analytic perspective and consider how sampling physicians to obtain practice level data may be a desirable approach.

Renal cell carcinoma and hypertension (a well-established renal cancer risk factor) are both more frequent among blacks than whites in the United States. The association between hypertension and renal cell carcinoma has not been examined in black Americans. We investigated the hypertension-renal cancer association by race, and we assessed the role of hypertension in the racial disparity of renal cancer incidence.Participants were enrolled in a population-based case-control study in Detroit and Chicago during 2002-2007 (number of cases: 843 whites, 358 blacks; number of controls: 707 whites, 519 blacks). Participants reported their history of hypertension and antihypertensive drug use. We used unconditional logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusted for demographic characteristics, smoking, body mass index, and family history of cancer.Hypertension doubled renal cancer risk (OR = 2.0 [CI = 1.7-2.5]) overall. For whites, the OR was 1.9 (CI = 1.5-2.4), whereas for blacks it was 2.8 (2.1-3.8) (P for interaction = 0.11). ORs increased with time after hypertension diagnosis (P for trend0.001), reaching 4.1 (CI = 2.3-7.4) for blacks and 2.6 (CI = 1.7-4.1) for whites after 25 years. ORs for poorly controlled hypertension were 4.5 (CI = 2.3-8.8) for blacks and 2.1 (CI = 1.2-3.8) for whites. If these estimates correctly represent causal effects and if, hypothetically, hypertension could be prevented entirely among persons aged 50-79 years, the black/white disparity in renal cancer could be reversed among women and reduced by two-thirds among men.Hypertension is a risk factor for renal cancer among both blacks and whites, and might explain a substantial portion of the racial disparity in renal cancer incidence. Preventing and controlling hypertension might reduce renal cancer incidence, adding to the known benefits of blood pressure control for heart disease and stroke reduction, particularly among blacks.

The recent article, "Comparison of Telephone Sampling and Area Sampling: Response Rates and Within-Household Coverage" (Am J Epidemiol 2001;153:1119-27), raised a number of issues related to two sampling methodologies that can be used for selecting population-based controls for case-control studies: random digit dialing (RDD) and area probability sampling. Some of these issues are discussed in this commentary in more detail to help in making sample design decisions, including the need to take the analysis plan into account when developing a sample design. Data from the paper are used to illustrate how the choice of sample design can affect analyses. Relative costs associated with the two methodologies as well as variance and bias concerns are also discussed in detail. Sample coverage issues, including those associated with list-assisted RDD, are considered, as are some advantages of the list-assisted approach. A discussion of the use of concurrent screening and sampling with an RDD approach as an alternative to periodically selecting fixed sample sizes is provided.