PubMed · 14071467
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L M SOLOMON, H BEERMAN. 1963. COLD PANNICULITIS.. https://doi.org/10.1001/archderm.1963.01590240221036
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The authors evaluated the relation between consumption of alcoholic beverages and incidence of coronary heart disease in White and African-American participants in the Atherosclerosis Risk in Communities Study. The average duration of follow-up was 9.8 years between 1987 and 1998. The association was analyzed by means of Cox proportional hazards regression models. The authors found a positive association between ethanol consumption and incident coronary heart disease for Black men (for a 13-g/day increment in ethanol consumption, adjusted hazard ratio (HR) = 1.13, 95% confidence interval (CI): 1.01, 1.28) and an inverse association for White men (HR = 0.88, 95% CI: 0.79, 0.99). There was an inverse association of coronary heart disease with rare drinking (HR = 0.47, 95% CI: 0.28, 0.80) and with consumption of > or =70 g of ethanol per week (HR = 0.49, 95% CI: 0.24, 0.98) in White women and with consumption of > or =210 g/week (HR = 0.56, 95% CI: 0.33, 0.95) in White men. In Black men, the association was positive for consumption of 140-<210 g/week (HR = 2.61, 95% CI: 1.11, 6.17). The contrasting findings in Whites and Black men in this cohort raise the question of whether the cardioprotective effect of alcohol is real or may be confounded by lifestyle characteristics of drinkers.
We propose a simple method to compute sample size for an arbitrary test hypothesis in population pharmacokinetics (PK) studies analysed with non-linear mixed effects models. Sample size procedures exist for linear mixed effects model, and have been recently extended by Rochon using the generalized estimating equation of Liang and Zeger. Thus, full model based inference in sample size computation has been possible. The method we propose extends the approach using a first-order linearization of the non-linear mixed effects model and use of the Wald chi(2) test statistic. The proposed method is general. It allows an arbitrary non-linear model as well as arbitrary distribution of random effects characterizing both inter- and intra-individual variability of the mixed effects model. To illustrate possible uses of the method we present tables of minimum sample sizes, in particular, with an illustration of the effect of sampling design on sample size. We demonstrate how (D-)optimal or frequent sampling requires fewer subjects in comparison to a sparse sampling design. We also present results from Monte Carlo simulations showing that the computed sample size can produce the desired power. The proposed method greatly reduces computing times compared with simulation-based methods of estimating sample sizes for population PK studies.
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