Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care

Increased Risk of Unfavorable Metabolic Outcome during Short-Term Follow-Up in Subjects with Nonfunctioning Adrenal Adenomas

Objective: To demonstrate long-term changes in the prevalence of several types of metabolic derangements in subjects with nonfunctioning adrenal adenomas. Subjects and Methods: 273 subjects with adrenal adenomas, including 231 with nonfunctioning adenoma and 42 with subclinical Cushing's syndrome (sCS), were evaluated with respect to anthropometric and laboratory characteristics and prevalence of type 2 diabetes mellitus (T2DM), hypertension, dyslipidemia, metabolic syndrome (MS), prediabetes and cardiovascular disease (CVD). Median duration was 24 months. Follow-up data of 114 participants with nonfunctioning adrenal adenomas are also presented while those of 117 were missing. Follow-up data regarding changes in anthropometric and laboratory parameters and prevalence rates of metabolic disturbances were obtained from the medical records. Results: The prevalence rates for both patients with nonfunctioning adenoma and sCS were: dyslipidemia: 161 (59%), hypertension: 147 (54%), MS: 128 (47%), prediabetes: 62 (23%), T2DM: 49 (18%), and CVD: 21 (8%). Hypertension and CVD were prevalent in subjects with sCS compared to participants with nonfunctioning adenoma. In follow-up, body mass index (p = 0.005), systolic blood pressure (p < 0.001), waist circumference (p = 0.005), homeostasis model assessment (p = 0.046), high-sensitivity C-reactive protein (p = 0.023), total cholesterol (p < 0.001) and low-density lipoprotein cholesterol (p < 0.001) and prevalence of hypertension (p < 0.001), dyslipidemia (p < 0.001), prediabetes (p < 0.001) and MS (p < 0.01) significantly increased in subjects with nonfunctioning adenoma. Conclusion: The data showed that nonfunctioning adrenal adenomas were associated with the development or deterioration of atherosclerotic risk factors. Therefore, follow-up and management strategies should be developed to decrease atherosclerotic morbidity in those individuals. Copyright (c) 2012 S. Karger AG, Base