ROTECTION OF HUMAN SUBjects in research is an evolving process. The current system of institutional review board (IRB) assessment of human subjects protection was established in 1974 in response to highly publicized human research scandals in the 1960s and early 1970s. 1,2 Federal regulation of research conduct and IRB function was implemented in 1979. When IRBs were created, the common paradigm for human subjects research consisted of a single investigator at one institution enrolling local participants, with the major emphasis of regulation on the review of clinical trials. Over the past 25 years, research strategies and technologies have changed, often bringing together investigators from multiple institutions to enroll geographically diverse pools of participants into epidemiological studies. However, IRB procedures and their federal underpinnings have not correspondingly kept pace. 2,3 Because of the focus of IRBs on clinical trials, others have asserted that IRBs "often have little insight into the needs of epidemiology." 4 Indeed, it is worth noting that one infamous human subjects re

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Detection of familial aggregation of a disease is important for studying possible genetic and environmental factors contributing to disease etiology. Accurate quantification of familial aggregation can provide guidance for subsequent, more sophisticated genetic studies. This article presents a statistical model and method for detecting both inter- and intra-class aggregation of a binary trait with family data. The method used here is based on the logistic regression model which incorporates effects of individual covariates while measuring familial aggregation of risk as the odds ratios among classes of relatives. An estimation equation approach is presented where the joint distribution of binary traits among family members need not be fully specified. Data from a genetic epidemiologic study on liver cancer in Shanghai are analyzed for illustration, and reveal strong aggregation of risk even after adjusting for covariates. Effects of non-random sampling and ascertainment bias are also discussed.

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Antihypertensive medications complicate studies of blood pressure (BP) natural history; BP if untreated (“underlying BP”) needs to be estimated. Our objectives were to compare validity of five missing data imputation methods to estimate underlying BP and longitudinal associations of underlying BP and age. We simulated BP treatment in untreated hypertensive participants from Atherosclerosis Risk in Communities (ARIC) in visits 1–5 (1987–2013) using matched treated hypertensive participants. The underlying BP was imputed: #1, set as missing; #2, add 10 mmHg for systolic, 5 mmHg for diastolic; #3, add medication class-specific constant; #4, truncated normal regression; and #5, truncated normal regression including prior visit data. Longitudinal associations were estimated using linear mixed models of imputed underlying BP for simulated treated and measured BP for untreated participants. Method 3 was the best-performing for systolic BP; lowest relative bias (5.3% for intercept at age 50, 0% for age coefficient) and average deviation from expected (0.04 to -1.79). Method 2 performed best for diastolic BP; lowest relative bias (0.6% intercept at age 50, 33.3% age <60, 9.1% age 60+) and average deviation (-1.25 to -1.68). Methods 4 and 5 were comparable or slightly inferior. In conclusion, constant addition methods yielded valid and precise underlying BP and longitudinal associations.