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PubMed1995

[Logistic model in diagnosis of lung cancer].

Specificity of the diagnosis of lung cancer (l.c.) based upon clinical and radiological criteria was investigated in patients who died in Hospital of Lung Diseases in Lódź between in 1985 and 1989. Autopsy revealed diagnostic errors in 37% cases. Twenty most common disease features were taken into consideration. Logistic analysis helped to create clinical-mathematical model which allows for interpretation of disease features in two distinct categories: l.c. (non-neoplasmatic lung disease). Eight features proved to be significant (changes in RBC, weight loss, chest pain, increased sputum production, haemoptisis, hilar tumor, pleural effusion, peripheral round shadow on chest X-ray). Diagnostic usefulness of the model was checked in prospective study a group of patients who died from properly diagnosed l.c. (499 cases). Our model allowed for proper diagnosis of neoplasmatic cause of the disease with higher probability than morphological methods (including both intravital and autopsy examination).

Diagnostic Errors↗
PubMed1986

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.

Biometry↗
PubMed1999

A general conditional-logistic model for affected-relative-pair linkage studies.

Model-free LOD-score methods are often employed to detect linkage between marker loci and common diseases, with samples of affected sib pairs. Although extensions of the basic one-disease-locus model have been proposed that allow separate inclusion of other types of affected relative pairs, discordant relative pairs, covariates, or additional disease loci, a unified framework that can handle all of these features has been lacking. In this report, I propose a conditional-logistic parameterization that generalizes easily to include all of these features. Two data examples, one using simulated data and one using type 1 diabetes, illustrate applications of the models.

Alleles↗
PubMed1980

Logistic model estimation of death attributable to risk factors for cardiovascular disease in Evans County, Georgia.

Linear logistic analysis of the relationship of cardiovascular disease risk factors to an overall measure of health effect, ten-year mortality, revealed significant associations of death with systolic blood pressure, age, sex, diabetes mellitus, smoking, cholesterol, obesity (Quetelet index), race and social index. Attributable risk and population attributable risk estimates were derived from the model by changing actual variable values to target values. The results confirmed systolic hypertension and smoking as major public health problems and diabetes mellitus as a powerful risk factor for death. The small detrimental effect of cholesterol on probability of death limits the potential for an overall beneficial effect of preventive intervention. In fact, drug intervention in two reported trials of cholesterol reduction had negative overall effects. Demonstration of the association of a characteristic with a specific disease state does not alone justify attempts to eliminate the high risk state from a population. An overall detrimental health effect must be documented by suitable studies before trials of preventive intervention are undertaken or recommendations made to the public.

Adult↗
PubMed2006

Quality control using a multilevel logistic model for the Danish pig Salmonella surveillance antibody-ELISA programme.

In Denmark, the level of Salmonella infection in pig herds is monitored with a surveillance programme using an indirect antibody ELISA. Our purpose with the present study was to determine whether sample results from the programme were useful in the quality control of this ELISA. Test results from the year 2003, in which the laboratory experienced a technical problem with an automatic microtitre-plate washing machine, were examined statistically. We chose 3 months for the analysis: January, where the problem was moderate, June with the problem more serious, and November, where the problem had been solved. A logistic analysis was carried out with outcome 0 for a negative test result and 1 for a positive test result. Row and column on the microtitre plates, multiprobe robot, and their interactions were included as fixed effects, and date, plate, and slaughterhouse were included as random effects. Backward elimination was carried out using alpha=0.05 to achieve a final model for each month. The row and the column were significant in January and June, and a robot effect was also included in the model for January. In June, an interaction between row and column was identified. In November, none of the fixed effects was significant. Breaking the months January and June into shorter time intervals showed that the row and column effects were significant also when data were from only 1 week, whereas the robot main effect was not significant in most periods and the interaction effects were not significant throughout. Analysis of the test results from the wells with test samples gave good information on systematic errors across the microtitre plates, and severe errors appeared significant even when data from short time periods were used.

Animals↗
PubMed1991

Prediction of survival from resuscitation: a prognostic index derived from multivariate logistic model analysis.

Despite advances in resuscitation, the ability to predict survival at cardiac arrests remains unsophisticated. We identified the factors determining outcome of all cardiopulmonary resuscitations performed at our institution over a 4-year period, and used a Cox multivariate regression model to design prognostic indices to assess the probability of successful resuscitation and hospital discharge. Cardiac arrests (710) were studied, and 193 (28%) were successfully resuscitated. The most influential variables, judged by the size and significance of their logistic regression coefficients, were rhythm, resuscitation delay, and age (for successful resuscitation), and rhythm, performance of intubation and defibrillation, defibrillation delay, and age (for survival until discharge). The combination of these in a prognostic index reliably predicted both outcome (area under the receiver operating curve of 0.78), and survival until discharge (area under the curve of 0.80).

Aged↗
PubMed1992

Multinomial logistic models explaining income changes of migrants to high-amenity counties.

"A survey of residents of and migrants to 15 fast-growing wilderness counties [in the United States] showed that only 25 percent of the migrants increased their income, while almost 50 percent accepted income losses upon their moves to high-amenity counties. Concomitantly, amenities and quality of life were more important factors in the migration decision than was employment, for instance. We focused on migrants in the labor force and employed multinomial logistic regression to identify the impact of migrants' characteristics, their satisfaction/dissatisfaction with previous location (push), and the importance of destination features (pull) on income change."

Americas↗
PubMed1985

Intra- or extrauterine transport? Comparison of neonatal outcomes using a logistic model.

In a retrospective study a comparative analysis of mortality by way of transport (maternal, neonatal, or not referred) was performed on 163 liveborn newborns with a gestational age of less than 32 wk or a birth weight of less than 1500 g. The occurrence of potential mortality risk factors in the subgroups was taken into account. Of these, caesarean section, sex, Apgar score, intubation after delivery, birth weight and gestational age showed a relationship with mortality. Logistic regression analysis regarding the relationship between mortality and way of transport revealed that mortality in the neonatal transport group was significantly higher than in the maternal transport group, taking into account the confounding risk factors sex, caesarean section, fetal growth retardation, birth weight and gestational age.

Apgar Score↗
PubMed1987

Place-to-place migration in Israel: estimates of a logistic model.

"A multinomial logit model focusing on economic and other locational factors is formulated and applied to data on place-to-place migration in Israel. Results indicate the effects of expected industrial wage differentials, in accordance with the hypothesis of Harris and Todaro (1970), and of disparities in the structure of industrial employment, suggesting that perceived risk as well as expected return enter into the decision to migrate, as Stark and Levhari (1982) have argued. Other effects include those associated with regional differentials in amenities and agglomeration associated with urbanization, population mobility by age group, center-periphery migration trends, border security hazards, and the like. Implications of the analysis for the Israeli policy of population dispersion are discussed."

Age Factors↗