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Life-course epidemiology seeks to better understand the mechanisms that lead to the development of chronic diseases. An example is the mechanism leading from body size to coronary heart disease (CHD); one way to acquire a better understanding of this mechanism is to investigate to what extent it works through other risk factors. In this paper, the dynamic path analysis model is presented as a tool to analyze these dynamic mechanisms in life-course epidemiology. A key feature of dynamic path analysis is its ability to decompose the total effect of a risk factor into a direct effect (not mediated by other variables) and indirect effects (mediated through other variables). This is illustrated by examining the associations between repeated measurements of body mass index (BMI) and systolic blood pressure (SBP) and the risk of CHD in a sample of Danish men between 1976 and 2006. The effect of baseline BMI on the risk of CHD is decomposed into a direct effect and indirect effects going through later BMI, concurrent SBP, or later SBP. In conclusion, dynamic path analysis is a flexible tool that by the decomposition of effects can be used to increase the understanding of mechanisms that underlie the etiology of chronic disease. [ABSTRACT FROM AUTHOR]

Studying the effect of chronic medication exposure by means of a case-crossover design may result in an upward-biased odds ratio. In this study, our aim was to assess the occurrence of this bias and to evaluate whether it is remedied by including a control group (the case-time-control design). Using Danish data resources from 1995-2012, we conducted case-crossover and case-time-control analyses for 3 medications (statins, insulin, and thyroxine) in relation to 3 outcomes (retinal detachment, wrist fracture, and ischemic stroke), all with assumed null associations. Controls were matched on age, sex, and index date, and exposure over the preceding 12 months was ascertained. For retinal detachment, the case-crossover odds ratio was 1.60 (95% confidence interval (CI): 1.42, 1.80) for statins, 1.40 (95% CI: 1.02, 1.92) for thyroxine, and 1.53 (95% CI: 1.04, 2.24) for insulin. Estimates for the retinal detachment controls were similar, leading to near-null case-time-control estimates for all 3 medication classes. For wrist fracture and stroke, the odds ratios were higher for cases than for controls, and case-time-control odds ratios were consistently above unity, thus implying significant residual bias. In case-crossover studies of medications, contamination by persistent users confers a moderate bias upward, which is partly remedied by using a control group. The optimal strategy for dealing with this problem is currently unknown. [ABSTRACT FROM AUTHOR]