On Estimation of Relative Risk in Case Control Studies

An estimator of relative risk in a case control study has been proposed in terms of observed cell frequencies and the probability of disease. The bias of the usual estimator i.e odds ratio as compared to the new estimator has been workedout. The expression of Mean Square Error of proposed estimator has been derived in situations where probability of disease is exactly known and when it is estimated through an independent survey. It has been observed that there is a serious error using odds ratio as an estimate of relative risk when probability of disease is not negligible. In such situations the proposed estimator can be used with advantage.     

Estimation of Partial Regression Coefficient

A sampling scheme providing unbiased partial regression coefficient has been proposed. The proposed sampling scheme is not only unbiased but also superior to simple random sampling and that due to Singh and Bathla (1990) for estimation of partial regression coefficient.     

外科病房医院感染相关危险因素的Logistic 回归分析

目的:探讨外科病房医院感染的相关危险因素,为临床的外科病房进行医院感染的防控提供理论支持。方法:选取本院外科病房住院进行手术治疗的8046例患者作为本研究的研究对象,其中发生医院感染253例,采用目标监测的调查研究方法,对患者进行医院感染相关危险因素的问卷调查,对外科病房医院感染相关危险因素进行单因素和多因素非条件Logistic回归分析。结果:单因素Logistic回归分析发现年龄、治疗专科(心胸外科)、治疗专科(神经外科)、入住ICU病房、急诊手术、住院治疗天数、气管插管全麻醉、切口类型(污染切口)、切口长度、侵袭性操作、侵袭性操作时间、抗菌药物治疗、术前抗菌药物治疗、术前抗菌药物治疗时间、术后抗菌药物治疗、术后抗菌药物治疗时间是医院感染的相关危险因素(P0.05)。多因素Logistic回归分析发现年龄、治疗专科(心胸外科)、治疗专科(神经外科)、入住ICU病房、急诊手术、住院治疗天数、切口类型(污染切口)、切口长度、侵袭性操作、侵袭性操作时间、抗菌药物治疗均是医院感染的相关危险因素(P0.05)。结论:外科病房医院感染是多种因素共同作用的结果,要针对外科病房医院感染相关危险因素制订相应的防治对策,对于降低医院感染率、防控外科病房医院感染的发生具有重要的意义。     

重症医学病房中早产儿死亡危险因素的单变量logistic 回归 分析研究

目的:探讨重症医学病房内早产儿的相关性危险因素对其死亡的影响.方法:回顾性收集我重症医学病房从2008年6月1日至2011年8月31日收治的早产患儿共45例,分组后,对相关死亡危险因素进行logistics回归分析研究.结果:45例早产儿死亡的相关危险因素为出生体重(OR=4.157),体重越低,死亡率越高.结论:加强孕期保健,优化围产期管理,促进胎儿成熟,加强对低出生体重儿的管理,可提高早产儿的存活率和存活质量.     

Expected Error Rates for Logistic Regression Versus Normal Discriminant Analysis

The expected error rates associated with using the allocation rule based on logistic regression are derived in the context of two multivariate normal populations with a common covariance matrix and compared with the corresponding error rates of the classical rule based on this normality assumption. It is shown in terms of the actual sizes of the asymptotic expected error rates that the performance of the logistic procedure does not fall far short of the normality based method, even for widely separated populations. This latter result is not obvious from previously available work on the asymptotic relative efficiency of the logistic procedure.     

青年卒中后抑郁/焦虑危险因素logistic回归分析

目的:探讨青年卒中患者抑郁/焦虑发生的危险因素,为制定科学的管理策略提供依据。方法:选择2012年7月至2013年7月我院收治的69例青年卒中患者,采用《Hamilton抑郁量表(HAMD)》和《Hamilton焦虑量表(HAMA)》筛选出抑郁/焦虑病例作为病例组,其他病例作为对照组,通过多因素非条件Logistic回归模型分析青年卒中后抑郁/焦虑产生的危险因素。结果:69例青年卒中病人中,抑郁、焦虑的发生率分别约26.0%和36.2%。多因素Logistic回归分析显示NIHSS评分(OR值5.002,95%CI为4.015~6.023)、幕下病变(OR值0.466,95%CI为0.145~1.505)、伴随基础疾病(OR值0.093,95%CI为0.008~1.129)是青年卒中后抑郁/焦虑出现的危险因素。结论:对青年卒中患者要积极控制基础疾病,重视幕下病变和严重残疾,有助于预防及早期识别卒中后抑郁/焦虑的产生。     

选择回归方程自变量的条件数法及其在RK手术中的应用

选择合适的自变量是确定线性回归模型的首要问题,本文以消除自变量之间的复共线性为目标,介绍了一种选择回归方程自变量的条件数法,并在ＲＫ手术的结果预测问题中采用了这一方法。     

Misclassification in Logistic Regression with Discrete Covariates

We study the effect of misclassification of a binary covariate on the parameters of a logistic regression model. In particular we consider 2 × 2 × 2 tables. We assume that a binary covariate is subject to misclassification that may depend on the observed outcome. This type of misclassification is known as (outcome dependent) differential misclassification. We examine the resulting asymptotic bias on the parameters of the model and derive formulas for the biases and their approximations as a function of the odds and misclassification probabilities. Conditions for unbiased estimation are also discussed. The implications are illustrated numerically using a case control study. For completeness we briefly examine the effect of covariate dependent misclassification of exposures and of outcomes.     

Estimation of Prevalence and Relative Risk in Repetitive Surveys

The Theory of double sampling as proposed by Neyman (1938) and subsequently used for successive sampling by Jesson (1942), Yates (1960), Patterson (1950), Eckler (1955), Kuldroff (1963) and Tikkiwal (1960, 1967) has been explored to develop a general estimator which can be used for estimation of parameters such as mean, ratio or double ratio. A simple case of sampling on two occasions has only been considered but the logic can easily be extended for more than two occasions. The results show that the generalised estimator will be very useful for the applied statisticians.     

Estimation of a Conditional Mean in a Linear Regression Model

Consider the two linear regression models of Y_ij on X_ij, namely Y_ij = β_io + β_il X_ij + ε_ij,j = 1,2,…,n_i, i = 1,2, where ε_ij are assumed to be normally distributed with zero mean and common unknown variance σ². The estimated value of a mean of Y₁ for a given value of X₁ is made to depend on a preliminary test of significance of the hypothesis β₁₁ = β₂₁. The bias and the mean square error of the estimator for the conditional mean of Y₁ are given. The relative efficiency of the estimator to the usual estimator is computed and is used to determine a proper choice of the significance level of the preliminary test.     

Complementary Log–Log Regression for the Estimation of Covariate‐Adjusted Prevalence Ratios in the Analysis of Data from Cross‐Sectional Studies

We assessed complementary log–log (CLL) regression as an alternative statistical model for estimating multivariable‐adjusted prevalence ratios (PR) and their confidence intervals. Using the delta method, we derived an expression for approximating the variance of the PR estimated using CLL regression. Then, using simulated data, we examined the performance of CLL regression in terms of the accuracy of the PR estimates, the width of the confidence intervals, and the empirical coverage probability, and compared it with results obtained from log–binomial regression and stratified Mantel–Haenszel analysis. Within the range of values of our simulated data, CLL regression performed well, with only slight bias of point estimates of the PR and good confidence interval coverage. In addition, and importantly, the computational algorithm did not have the convergence problems occasionally exhibited by log–binomial regression. The technique is easy to implement in SAS (SAS Institute, Cary, NC), and it does not have the theoretical and practical issues associated with competing approaches. CLL regression is an alternative method of binomial regression that warrants further assessment.     

Binary Regression Analysis with Pooled Exposure Measurements: A Regression Calibration Approach

Summary It has become increasingly common in epidemiological studies to pool specimens across subjects to achieve accurate quantitation of biomarkers and certain environmental chemicals. In this article, we consider the problem of fitting a binary regression model when an important exposure is subject to pooling. We take a regression calibration approach and derive several methods, including plug‐in methods that use a pooled measurement and other covariate information to predict the exposure level of an individual subject, and normality‐based methods that make further adjustments by assuming normality of calibration errors. Within each class we propose two ways to perform the calibration (covariate augmentation and imputation). These methods are shown in simulation experiments to effectively reduce the bias associated with the naive method that simply substitutes a pooled measurement for all individual measurements in the pool. In particular, the normality‐based imputation method performs reasonably well in a variety of settings, even under skewed distributions of calibration errors. The methods are illustrated using data from the Collaborative Perinatal Project.     

高龄患者下肢深静脉血栓超声诊断的Logistic回归分析

目的：应用Logistic回归筛选高龄患者下肢深静脉血栓灰阶及彩色多普勒超声诊断特征。方法：对我院150例主动要求下肢深静脉血栓灰阶及彩色多普勒超声检查的高龄患者,应用超声检查观察血管管径、管腔内回声及血流动力学等特征,进行Logistic回归分析,筛选超声诊断特征,对Logistic回归模型预测诊断绘制受试者工作曲线图（ROC）,评估模型效果。结果：150例超声检查高龄患者,发生下肢深静脉栓塞129例,占86.00%。Logistic回归筛选,血管管径、管腔内回声、管壁内壁、血流信号改变4个变量进入回归模型,Logistic回归模型预测超声检出的ROC曲线下面积为0.903,灵敏度为90.3%,特异度为93.8%。结论：以高龄患者下肢深静脉血栓超声特征建立的Logistic回归模型对该病具有较好的预测诊断价值。     

Improved Estimation of Heritability Through Scaling

Two minimum mean square error estimators of heritability are proposed and compared with the conventional regression estimator with live data.     

Monte Carlo Investigations for Relative Efficiency of Stratified Random Sampling Scheme for Estimation of Relative Risk in Case-Control Studies

Monte Carlo Investigations have been widely used in Sample Surveys in Comparing the efficiency of various methods when exact mathematical comparisons are not possible. In this paper the same has been used for comparing the efficiency of Stratified Random Sampling with respect to Simple Random Sampling for estimation of Relative Risk in Case-Control Studies. The data used relate to a Case Control study on peptic ulcer. On the basis of Monte Carlo Investigations on 50 samples of size 10–20 (Cases and Controls), it has been observed that there is considerable gain in efficiency in using Stratified Random Sampling over Simple Random Sampling. The sensitivity of the results with the change in Sample Size has also been investigated.     

The Treatment of Missing Values in Logistic Regression

The efficiencies of the estimators in the linear logistic regression model are examined using simulations under six missing value treatments. These treatments use either the maximum likelihood or the discriminant function approach in the estimation of the regression coefficients. Missing values are assumed to occur at random. The cases of multivariate normal and dichotomous independent variables are both considered. We found that in general, there is no uniformly best method. However, mean substitution and discriminant function estimation using existing pairs of values for correlations turn out to be favourable for the cases considered.     

生长曲线参数估计的一种新方法-优化回归组合法

在现有文献研究的基础上,对生长曲线参数估计问题又作了进一步研究,给出了生长曲线参数估计的一种新方法优化回归组合法,该方法创造性地将最优化方法与回归方法结合在一起,利用最优化理论中的区间搜索和一维搜索,可以得到一系列c^＊值,利用回归方法可求得与其相对应的一系列a和b的值．当c取最优值c时,a和b便得到最优值a^＊和b^＊经示例计算表明,这种参数估计法具有较高的精度,     

Univariate Dose-Response Models in Case-Control Studies

For modelling dose-response relationships in case-control studies the multiplicative logistic regression model, assuming the relative risk to be an exponential function of the dose, is widely known. If the relative risk is assumed to be a linear function of the dose, several authors (see e.g. BERRY (1980)) have proposed an additive (linear) model. This model has a better fit with the data if such a linear relation holds. Confidence limits for the relative risk derived from the information matrix, however, appear to be rather inaccurate. Therefore, use of the ‘standard’ logistic model in two different ways was studied: extension with a quadratic term or a logarithmic transformation of the dose. By applying the methods both to an empirical data set and in a simulation experiment, it is shown that appropriate transformation (often logarithmic) of the dosage and then applying the ‘standard’ logistic model is an useful approach if a linear dose-response relationship holds.