There is increasing interest in estimating and drawing inferences about risk or prevalence ratios and differences instead of odds ratios in the regression setting. Recent publications have shown how the GENMOD procedure in SAS (SAS Institute Inc., Cary, North Carolina) can be used to estimate these parameters in non-population-based studies. In this paper, the authors show how model-adjusted risks, risk differences, and risk ratio estimates can be obtained directly from logistic regression models in the complex sample survey setting to yield population-based inferences. Complex sample survey designs typically involve some combination of weighting, stratification, multistage sampling, clustering, and perhaps finite population adjustments. Point estimates of model-adjusted risks, risk differences, and risk ratios are obtained from average marginal predictions in the fitted logistic regression model. The model can contain both continuous and categorical covariates, as well as interaction terms. The authors use the SUDAAN software package (Research Triangle Institute, Research Triangle Park, North Carolina) to obtain point estimates, standard errors (via linearization or a replication method), confidence intervals, and P values for the parameters and contrasts of interest. Data from the 2006 National Health Interview Survey are used to illustrate these concepts.

This paper demonstrates the use of the delta method for estimating the variance of ratio statistics derived from animal carcinogenicity experiments. The Cochran-Armitage test (Cochran, 1954, Biometrika 10, 417-451; and Armitage, 1955, Biometrics 11, 375-386) is routinely applied to carcinogenicity data as a test for linear trend in lifetime tumor incidence rates. The computing formula for this test derives from the assumption that the denominators of the quantal response rates are fixed. However, when time-at-risk weights are introduced to correct for treatment-related differences in survival, the denominators of the quantal response rates are subject to random variation. The delta method and weighted least squares techniques are applied here to approximate the variance of such ratio statistics and test for a linear dose-response relationship among treatments. This technique is compared to that of Bailer and Portier (1988, Biometrics 44, 417-431), who introduced a survival-adjusted quantal response test for trend in lifetime tumor incidence rates. Their test modifies the usual Cochran-Armitage computing formula by weighting the denominators of the response rates to reflect less-than-whole-animal contributions to risk. Within the framework of a weighted least squares linear regression model that underlies the Cochran-Armitage test, the time-at-risk weights of Bailer and Portier are incorporated using the delta method. Although the delta method approach is slightly more computationally intensive, small-sample simulations indicate that it has superior operating characteristics over the Poly-3 trend test of Bailer and Portier when background tumor incidence rates are low (under 3%) and survival patterns differ markedly across treatments.(ABSTRACT TRUNCATED AT 250 WORDS)

Experimental studies of prevention programs often randomize clusters of individuals rather than individuals to treatment conditions. When the correlation among individuals within clusters is not accounted for in statistical analysis, the standard errors are biased, potentially resulting in misleading conclusions about the significance of treatment effects. This study demonstrates the generalized estimating equations (GEE) method, focusing specifically on the GEE-independent method, to control for within-cluster correlation in regression models with either continuous or binary outcomes. The GEE-independent method yields consistent and robust variance estimates. Data from project DARE, a youth substance abuse prevention program, are used for illustration.

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Military personnel and athletes exposed to traumatic brain injury may develop chronic traumatic encephalopathy (CTE). Brain pathology in CTE includes intracellular accumulation of abnormally phosphorylated tau proteins (p-tau), the main constituent of neurofibrillary tangles (NFTs). Recently, we found that cholinergic basal forebrain (CBF) neurons within the nucleus basalis of Meynert (nbM), which provide the major cholinergic innervation to the cortex, display an increased number of NFTs across the pathological stages of CTE. However, molecular mechanisms underlying nbM neurodegeneration in the context of CTE pathology remain unknown. Here, we assessed the genetic signature of nbM neurons containing the p-tau pretangle maker pS422 from CTE subjects who came to autopsy and received a neuropathological CTE staging assessment (Stages II, III, and IV) using laser capture microdissection and custom-designed microarray analysis. Quantitative analysis revealed dysregulation of key genes in several gene ontology groups between CTE stages. Specifically, downregulation of the nicotinic cholinergic receptor subunit β-2 gene (CHRNB2), monoaminergic enzymes catechol-O-methyltransferase (COMT) and dopa decarboxylase (DDC), chloride channels CLCN4 and CLCN5, scaffolding protein caveolin 1 (CAV1), cortical development/cytoskeleton element lissencephaly 1 (LIS1), and intracellular signaling cascade member adenylate cyclase 3 (ADCY3) was observed in pS422-immunreactive nbM neurons in CTE patients. By contrast, upregulation of calpain 2 (CAPN2) and microtubule-associated protein 2 (MAP2) transcript levels was found in Stage IV CTE patients. These single-population data in vulnerable neurons indicate alterations in gene expression associated with neurotransmission, signal transduction, the cytoskeleton, cell survival/death signaling, and microtubule dynamics, suggesting novel molecular pathways to target for drug discovery in CTE.