Testing goodness-of-fit in logistic case-control studies

We present a goodness-of-fit test for the logistic regressionmodel under case-control sampling. The test statistic is constructedvia a discrepancy between two competing kernel density estimatorsof the underlying conditional distributions given case-controlstatus. The proposed goodness-of-fit test is shown to comparevery favourably with previously proposed tests for case-controlsampling in terms of power. The test statistic can be easilycomputed as a quadratic form in the residuals from a prospectivelogistic regression maximum likelihood fit. In addition, theproposed test is affine invariant and has an alternative representationin terms of empirical characteristic functions.     

Logistic回归模型及其在昆虫学中的应用

介绍了应用Logistic回归分析对二值反应的试验数据进行分析的方法 ,以及Logistic回归分析模型参数估计及其统计检验的方法 ,并结合 1个实际例子说明了Logistic回归模型的应用。     

Conditional logistic analysis of case-control studies with complex sampling

Methods for the analysis of unmatched case-control data based on a finite population sampling model are developed. Under this model, and the prospective logistic model for disease probabilities, a likelihood for case-control data that accommodates very general sampling of controls is derived. This likelihood has the form of a weighted conditional logistic likelihood. The flexibility of the methods is illustrated by providing a number of control sampling designs and a general scheme for their analyses. These include frequency matching, counter-matching, case-base, randomized recruitment, and quota sampling. A study of risk factors for childhood asthma illustrates an application of the counter-matching design. Some asymptotic efficiency results are presented and computational methods discussed. Further, it is shown that a 'marginal' likelihood provides a link to unconditional logistic methods. The methods are examined in a simulation study that compares frequency and counter-matching using conditional and unconditional logistic analyses and indicate that the conditional logistic likelihood has superior efficiency. Extensions that accommodate sampling of cases and multistage designs are presented. Finally, we compare the analysis methods presented here to other approaches, compare counter-matching and two-stage designs, and suggest areas for further research.To whom correspondence should be addressed.     

Evaluating the use of friend or family controls in epidemiologic case-control studies

BackgroundTraditional methodologies for identifying and recruiting controls in epidemiologic case-control studies, such as random digit dialing or neighborhood walk, suffer from declining response rates. Here, we revisit the feasibility and comparability of using alternative sources of controls, specifically friend and family controls.MethodsWe recruited from a recently completed case-control study of non-Hodgkin lymphoma (NHL) among women in Los Angeles County where controls from the parent study were ascertained by neighborhood walk. We calculated participation rates and compared questionnaire responses between the friend/family controls and the original matched controls from the parent study.ResultsOf the 182 NHL case patients contacted, 111 (61%) agreed to participate in our feasibility study. 70 (63%) provided contact information for potential friend and/or family member controls. We were able to successfully contact and recruit a friend/family member for 92% of the case patients. This represented 46 friend controls and 54 family controls. Family controls significantly differed from original matched controls by sex and household income. Other characteristics were similar between friend controls and the original study’s neighborhood controls.ConclusionThe apparent comparability of neighborhood controls to friend and family controls among respondents in this study suggests that these alternative methods of control identification can serve as a complementary source of eligible controls in epidemiologic case-control studies.     

Use of Polytomous Logistic Model in Matched Case-Control Studies

Binary logistic model has been found useful for estimating odds ratio in case of dichotomous exposure variable under matched case-control retrospective design. We describe the use of polytomous logistic model for estimating odds ratios when the exposure of prime interests, relative to disease incidence, has more than two levels. An illustrative example is presented and discussed.     

Regional distribution of bovine Neospora caninum infection in the German state of Rhineland-Palatinate modelled by Logistic regression

To obtain a rapid overview over the distribution of bovine Neospora caninum-infections in the German state of Rhineland-Palatinate, an ELISA to determine specific bovine antibodies against a p38 surface antigen of N. caninum tachyzoites was modified to examine bulk milk samples from cattle herds. Experimental bulk milk samples were used to demonstrate that the seroprevalence in a group of animals can be estimated with this ELISA. A cut-off was selected for the specific detection of herds having a seroprevalence ≥10%. About 90% of the dairy herds located in Rhineland-Palatinate were examined. An overall prevalence of bulk milk-positive herds of 7.9% (95% confidence interval 7.0–8.9%), respectively, was determined. Major regional differences in the distribution of bulk milk-positive herds were observed. Prevalences were higher in regions with an increased degree of urbanisation. Logistic regression was applied to model the prevalence of bulk milk-positive herds on a district and city level. Variables describing the dog density, mean temperature in July, mean temperature in January and the total yearly precipitation in districts and cities were able to explain most of the observed variability in the regional prevalences. Our results provide evidence that in addition to risk factors related to individual farms also risk factors related to the farm location such as dog density in the surrounding and climate factors are important in the epidemiology of bovine neosporosis.