Extended Mantel-Haenszel estimating procedure for multivariate logistic regression models

A class of estimating functions is proposed for the estimation of multivariate relative risk in stratified case-control studies. It reduces to the well-known Mantel-Haenszel estimator when there is a single binary risk factor. Large-sample properties of the solutions to the proposed estimating equations are established for two distinct situations. Efficiency calculations suggest that the proposed estimators are nearly fully efficient relative to the conditional maximum likelihood estimator for the parameters considered. Application of the proposed method to family data and longitudinal data, where the conditional likelihood approach fails, is discussed. Two examples from case-control studies and one example from a study on familial aggregation are presented.     

Regressive logistic models for familial disease and other binary traits

The simple Markovian structures of dependence, defined previously for continuous traits, are extended here to familial disease and other binary traits through the use of the logistic function. The regressive models so formulated can incorporate explanatory variables and major gene effects for segregation and linkage analyses. Thus, the goals of epidemiology and genetics in the analysis of familial disease can be accomplished in the same computational scheme.     

Incubation period of coronary heart disease.

Serum cholesterol concentrations and blood pressure were measured during 1958-64 among men aged 40-59 who took part in the Seven Countries Study. In the present study these measurements were related to the national mortality from coronary heart disease in the periods 1959-61, 1964-6, 1969-71, and 1974-6. The correlations increased with time (r = +0.86, 0.90, 0.93, and 0.96 respectively for serum cholesterol concentration and r = +0.48, 0.56, 0.57, and 0.64 for systolic blood pressure), suggesting that the "incubation period" between exposure to major coronary risk factors and the maximum effects on mortality may be 10 years or more.     

Regressive logistic models for familial diseases: a formulation assuming an underlying liability model

Statistical models have been developed to delineate the major-gene and non-major-gene factors accounting for the familial aggregation of complex diseases. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affection is defined by a threshold on the liability scale. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype, phenotypes of antecedents and other covariates. An equivalence between these two approaches cannot be derived analytically. I propose a formulation of the regressive logistic models on the supposition of an underlying liability model of disease. Relatives are assumed to have correlated liabilities to the disease; affected persons have liabilities exceeding an estimable threshold. Under the assumption that the correlation structure of the relatives' liabilities follows a regressive model, the regression coefficients on antecedents are expressed in terms of the relevant familial correlations. A parsimonious parameterization is a consequence of the assumed liability model, and a one-to-one correspondence with the parameters of the mixed model can be established. The logits, derived under the class A regressive model and under the class D regressive model, can be extended to include a large variety of patterns of family dependence, as well as gene-environment interactions.     

阿托伐他汀治疗冠状动脉粥样硬化性心脏病的疗效

目的:研究不同剂量的阿托伐他汀治疗冠状动脉粥样硬化性心脏病的疗效,并探讨性别与年龄对疗效的影响.方法:将320例冠状动脉粥样硬化性心脏病患者随机分为4组(每组80例):10mg/d阿托伐他汀治疗A组、40mg/d阿托伐他汀治疗B组、80mg/d阿托伐他汀治疗C组与常规治疗D组,其中常规治疗组采用硝酸甘油、血管紧张与素转换酶抑制剂阿司匹林等;疗程12周.分析比较各组患者治疗前后:TG、TC、HDL-C、LDL-C和hs-CRP的水平变化.结果:治疗12周后,与常规治疗组相比:①三个不同剂量的阿托伐他汀治疗组中患者的TG、TC、HDL-C、LDL-C和hs-CRP分子的浓度变化差异均有统计学意义,P＜0.05;其中以剂量为40 mg/d的阿托伐他汀治疗组的整体疗效相对最好,P＜0.05;②治疗效果与患者年龄呈负相关趋势;③在45-55岁这个年龄阶段,女性患者的治疗效果要好于同年龄阶段的男性患者,P＜0.05;④在老年患者中,大剂量的阿伐他汀的安全性明显低于中小剂量组.结论:中等剂量的阿托伐他汀对冠状动脉粥样硬化性心脏病的疗效显著,安全性良好.     

Adaptive mining prediction model for content recommendation to coronary heart disease patients

This paper proposes the Fuzzy Rule-based Adaptive Coronary Heart Disease Prediction Support Model (FbACHD_PSM), which gives content recommendation to coronary heart disease patients. The proposed model uses a mining technique validated by medical experts to provide recommendations. FbACHD_PSM consists of three parts for heart disease risk prediction. First, a fuzzy membership function is constructed using medical guidelines and statistical methods. Then, a decision-tree rule induction technique creates mining-based rules that are subjected to validation by medical experts. As the rules may not be medically suitable, the experts add rules that have been verified and delete inappropriate rules. Thirdly, using fuzzy inference based on Mamdani’s method, the model predicts the risk of heart disease. Based on this, final recommendations are provided to patients regarding normal living, nutrition control, exercise, and drugs. To implement our proposed model and evaluate its performance, we use a dataset from a single tertiary hospital.     

Association models for a multivariate binary response

Models for a multivariate binary response are parameterized by univariate marginal probabilities and dependence ratios of all orders. The w-order dependence ratio is the joint success probability of w binary responses divided by the joint success probability assuming independence. This parameterization supports likelihood-based inference for both regression parameters, relating marginal probabilities to explanatory variables, and association model parameters, relating dependence ratios to simple and meaningful mechanisms. Five types of association models are proposed, where responses are (1) independent given a necessary factor for the possibility of a success, (2) independent given a latent binary factor, (3) independent given a latent beta distributed variable, (4) follow a Markov chain, and (5) follow one of two first-order Markov chains depending on the realization of a binary latent factor. These models are illustrated by reanalyzing three data sets, foremost a set of binary time series on auranofin therapy against arthritis. Likelihood-based approaches are contrasted with approaches based on generalized estimating equations. Association models specified by dependence ratios are contrasted with other models for a multivariate binary response that are specified by odds ratios or correlation coefficients.