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PubMed1999

[Application of the logistic regression procedure to the classification of species on an example of the genu Diplostomum Nord (Digenea)].

We introduce logistic regression procedure and explain how it can be applied for classification of species. This technique is more useful than traditionally used linear discriminant analysis since it does not require multivariate normality in populations being classified. Finally, the proposed approach is illustrated by an example of classification of four species of metacercariae of Diplosomum.

Animals↗
PubMed2006

Two models for outcome prediction - a comparison of logistic regression and neural networks.

OBJECTIVES: Accurately predicting disease progress from a set of predictive variables is an important aspect of clinical work. For binary outcomes, the classical approach is to develop prognostic logistic regression (LR) models. Alternatively, machine learning algorithms were proposed with artificial neural networks (ANN) having become popular over the last decades. Although some studies have compared predictive accuracies of LR and ANN models, some concerns regarding their methodological quality have been voiced. Our comparison has the advantage of being based on two large independent data sets allowing for elaborate model development and independent validation. METHODS: From the German Stroke Database, a learning data set including 1754 prospectively recruited patients with acute ischemic stroke was used. Utilizing LR and ANN, two prognostic models were developed predicting restitution of functional independence and survival after 100 days. The resulting models were applied to classify 1470 patients with acute ischemic stroke; this test data set was collected independently from the learning data. Error fractions in the test data were determined, and differences in error fractions between the algorithms were calculated with 95% confidence intervals. RESULTS: For most prognostic models, error fractions in the test data were below 40%. There was no difference between the algorithms except for the model predicting completely versus incompletely restituted or deceased patients (difference in error fractions = 4.01% [2.10-5.96%], p = 0.0001). CONCLUSIONS: The conscientiously applied LR remains the gold standard for prognostic modelling; however, ANN can be an alternative automated "quick and easy" multivariate analysis.

Aged↗
PubMedDate not supplied

Logistic regression model of the clinical response to 5-fluorouracil based chemotherapy for metastatic colorectal cancer patients.

Several genes have been involved in drug resistance but none are currently used in the drug decision process. To address this problem, mRNA levels were measured for the 5-fluorouracil metabolism-related genes, thymidylate synthase, thymidine phosphorylase and dihydropyrimidine dehydrogenase in tumor samples of 40 patients with synchronous metastatic colon cancer by quantitative RT-PCR. Drug response and overall survival were also obtained for each patient. A logistic regression model was defined to calculate a response predicting score (RPS) with gene expression levels. This RPS split responders from nonresponders as, at the best statistical threshold (0.35), the area of receiver operating characteristic (ROC) curve established with this method was 0.82 and sensitivity and specificity were respectively 100% and 65.4%. Furthermore patients with scores above 0.35 tended to have better overall survival than those with a score less than 0.35 (p = 0.09).

Aged↗
PubMed2006

Combining logistic regression with classification and regression tree to predict quality of care in a home health nursing data set.

In this article, the authors provide an overview of a research method to predict quality of care in home health nursing data set. The results of this study can be visualized through classification an regression tree (CART) graphs. The analysis was more effective, and the results were more informative since the home health nursing dataset was analyzed with a combination of the logistic regression and CART, these two techniques complete each other. And the results more informative that more patients' characters were related to quality of care in home care. The results contributed to home health nurse predict patient outcome in case management. Improved prediction is needed for interventions to be appropriately targeted for improved patient outcome and quality of care.

Forecasting↗
PubMed1991

[Evaluation of a logistic regression model in predicting the prognosis of Graves' disease treated by antithyroid drugs].

One hundred and twelve new cases of Graves' disease were treated by tapazole for 6 months and followed up for another 12 months. The initial dose was 30 mg/d. Clinical and biochemical euthyroidism was achieved within 1 to 3 months, then a maintenance was given until cessation of drugs at 6 months. One hundred and eleven cases completed the study. Remission and relapse were defined at the end of follow-up for 1 year according to the presence or absence of clinical manifestation of hyperthyroidism and the levels of T3 and T4. The results of the 12-month follow-up showed that 46 of 111 cases were in remission, and the remaining 65 cases suffered relapse. A logistic regression model with 4 variables was established, which included thyroid suppression rate and goitre size by palpation at the end of drug treatment, the level of T3 before therapy, and the patients' age. The model had 81.5% sensitivity, 84.8% specificity and 82.9% (92/111) accuracy in predicting the outcome of Graves' disease after withdrawal of drug for 1 year. The results were much better than any other univariate analysis in this study.

Adolescent↗
PubMed1991

[Application of polychotomous logistic regression in the study of risk factors predisposed to postpartum hemorrhage].

To determine the role of history of induced abortion and pregnancy induced hypertension in postpartum hemorrhage, a polychotomous logistic regression method was introduced to analyze the data gathered in an investigation of blood loss among 933 parturients in some counties of Sichuan and Ningxia provinces. The results show that postpartum hemorrhage caused by placenta factors is related to a history of induced abortion and pregnancy induced hypertension with relative risks 2.55 (95% confidence interval = 1.2-5.3) and 4.94 (95% confidence interval = 1.8-13.2), respectively. An increased risk was noted for postpartum hemorrhage associated with pregnancy induced hypertension (relative risk = 2.58, 95% confidence interval = 1.3-5.2), but not with induced abortion. Suggestions were made for prevention of postpartum hemorrhage.

Abortion, Induced↗
PubMed1991

[Logistic regression analysis of risk factors influencing the occurrence of category III pneumoconiosis in the coal mine].

Using 1:4 matched method, we have selected 40 cases of category III pneumoconiosis and 160 controls in a coal mine, and analyzed the factors which influenced the occurrence of category III pneumoconiosis by Multiple Logistic Regression Model. The results were as follows: Tuberculous infection and the type of radiographic appearance manifested mainly as small round opacities were positively correlated with the attack of category III pneumoconiosis. The time of starting exposure to dust, the years and the kinds of work were not led into regression model as significant factors.

Case-Control Studies↗
PubMed1991

[A logistic regression analysis of relations between juvenile myopia and TV-watching, trace elements, and psychological characteristics].

With a paired study, 23 factors were investigated in 204 school children aged 9-14 in Taiyuan. The results of analysis of logistic regression showed that there was a close relationship between juvenile myopia and TV-watching distance, myopia in parents, Zn: Cu ratio in hair, N score of EPQ, Pscore of EPQ, and verbal IQ. The relations between myopia and some factors were discussed.

Adolescent↗
PubMed1991

Attributable risk estimation from case-control data via logistic regression.

By fitting an unconditional logistic regression model to unmatched case-control data, an estimate of the joint population attributable risk for the factor included is obtained. This estimate and its asymptotic variance can easily be computed from the intercept parameter and its asymptotic variance. A generalization to the analysis of stratified data with large strata enables the calculation of stratum-specific attributable risks and their variances via stratum-specific intercept parameters. If sampling of cases is independent of strata, an estimate of the summary attributable risk and its asymptotic variance may be obtained as a weighted sum of the stratum-specific attributable risks.

Biometry↗
PubMed1991

Logistic regression for clustered binary data in proband studies with application to familial aggregation of sleep disorders.

Estimation is considered for the class of conditional logistic regression models for clustered binary data proposed by Qu et al. (Communications in Statistics, Series A 16, 3447-3476, 1987) when clusters are sampled on the basis of the outcome for one or more cluster members. The problem is suggested by data from a study designed to investigate familial aggregation of sleep disorders. After appropriate consideration of the mode of ascertainment of "cases" and "controls," it is shown that the model is preserved under this form of sampling, and a method of estimation is presented. The inconsistency of two alternative methods is demonstrated, and an example is provided.

Biometry↗
PubMed1991

Evaluation of exact and asymptotic interval estimators in logistic analysis of matched case-control studies.

We compare six methods for constructing confidence intervals for a single parameter in stratified logistic regression. Three of these are based on inversion of standard asymptotic tests--namely, the Wald, the score, and the likelihood ratio tests. The other three are based on the exact distribution of the sufficient statistic for the parameter of interest. These include the traditional exact method of constructing confidence intervals, and two others, the mid-P and mean-P methods, which are modifications of this procedure that aim at reducing the conservative bias of the exact method. Using efficient algorithms, the six methods are compared by determination of their exact coverage levels in a series of conditional sample spaces. An incident case-control study of lung cancer in women is used to further illustrate the differences among the various methods. Computation of coverage functions is seen as a useful graphical diagnostic tool for assessing the appropriateness of different methods. The mid-P and the score methods are seen to have better coverage properties than the other four.

Biometry↗
PubMed1991

Maximum likelihood training of connectionist models: comparison with least squares back-propagation and logistic regression.

This paper presents maximum likelihood back-propagation (ML-BP), an approach to training neural networks. The widely reported original approach uses least squares back-propagation (LS-BP), minimizing the sum of squared errors (SSE). Unfortunately, least squares estimation does not give a maximum likelihood (ML) estimate of the weights in the network. Logistic regression, on the other hand, gives ML estimates for single layer linear models only. This report describes how to obtain ML estimates of the weights in a multi-layer model, and compares LS-BP to ML-BP using several examples. It shows that in many neural networks, least squares estimation gives inferior results and should be abandoned in favor of maximum likelihood estimation. Questions remain about the potential uses of multi-level connectionist models in such areas as diagnostic systems and risk-stratification in outcomes research.

Diagnosis, Computer-Assisted↗
PubMed1991

[Logistic regression analysis of risk factors of glaucomatous visual field defects].

30 patients (42 eyes) of early primary glaucoma with normal visual field were followed up for 2.5 years, during which time visual field defects developed in 24 eyes. Multivariate logistic regression analysis of the initial values of suspected risk factors demonstrated that a positive family history, the vertical C/D ratio, and the diurnal peak IOP were significant in predicting impending visual field defects.

Follow-Up Studies↗
PubMed1991

Interpreting the literature in obstetrics and gynecology: II. Logistic regression and related issues.

The goal of epidemiology is accurate measure of the relationship between an exposure and a disease of interest. The control of covariates of the exposure-disease relationship is required to obtain a valid measure. Two types of covariates, confounders and effect modifiers, must be considered. Investigators can design studies to measure and control for the impact of both types of covariates. Design strategies for dealing with covariates include randomization, restriction, and matching. If the impact of a covariate is not eliminated by study design, it must be controlled for during study analysis by use of either stratification or mathematical modeling. Stratified analysis permits an assessment of the exposure-disease relationship for each category of relevant covariates. Although stratification is the best initial approach for controlling covariates, it is often impractical, particularly if more than one or two covariates must be controlled. Multivariate mathematical models are required if multiple covariates are to be controlled. Logistic regression is the mathematical modeling procedure most often used to analyze studies in obstetrics and gynecology. Although there are no uniform rules for building a proper model for regression analysis, useful general strategies are available. It must be emphasized that, though the use of mathematical modeling can control for multiple covariates and thereby improve the chance of obtaining an accurate measure of the exposure-disease relationship, it cannot "fix" data that result from a poorly designed or improperly conducted study.

Gynecology↗
PubMed1991

[Logistic regression and epidemiology. I].

The logistic regression is used in epidemiology to study the relationships between a disease in two modalities (diseased or disease free) and risk factors Xi which may be qualitative or quantitative variables. According to this model, the probability of disease knowing Xi's values is written: [formula: see text]. The coefficients beta i satisfy to the equation: ORi = exp(beta i) where ORi is the odds-ratio linked to the variable Xi adjusted on the other variables of the model. The first part of this paper indicates the way to include in the model qualitative variables with more than two modalities, quantitative variables and interaction between variables. At least, the maximum likelihood method to estimate the parameters of the model is presented.

Epidemiologic Methods↗
PubMed1990

Statistical analysis of the clinical trial of a therapy for Alzheimer's disease. Univariate tests and logistic regression.

A placebo controlled, randomized, double blind, multicentric clinical trial was carried out in Italy to test the effectiveness of a drug in the therapy of Alzheimer's disease. Informed consensus was gained for the inclusion of 130 patients, whose mental performance was assessed before allocation and after 3, 6, 12 months of continuing assumption of treatment. 14 different outcome variables were used. The main analysis was performed on measures adjusted for baseline, using both univariate and multivariate techniques for each of the three assessing times. The former is mainly descriptive in its scope and allows an analytical view of the results; the latter protects from a too high type I error probability, without implying the loss of power produced by the Bonferroni correction. The techniques are respectively Mann-Whitney test, and analysis of deviance of logistic models, which was suggested by Cupples et al. (1984). In the latter we used the allocation of the patients as response variable, and compared the predictor containing all outcome measures with that having just a constant term. It is shown that a significant difference of fit implies that treatment significantly affects outcome. For the 3 and 6 month follow-up assessment, univariate tests do not show clearcut trends (only 1 outcome measure is significantly better for the drug, none is for the placebo). After 12 months, a trend shows: 13 out of 14 variables are in favour of the drug and 4 of them are significant. Of the three multivariate test carried out, that relative to the 12-month follow-up is significant at the 0.05 level, which is consistent with the univariate results.(ABSTRACT TRUNCATED AT 250 WORDS)

Alzheimer Disease↗
PubMed1990

[Conditional logistic regression analysis and path analysis of risk factors of cervical cancer].

A case-control study of 125 pairs was carried out in Jinan during 1987-1988. 39 variables were analysed with the single factor analysis, 14 factors were confirmed as the suspicious risk factors. Conditional Logistic regression analysis of the above 14 factors was used. Results showed that the following five risk factors were associated with cervical cancer, i.e., cervical erosion, low family income, poor personal hygiene during menstrual period, sexual factors and early age at marriage. The causal model with 9 risk factors of cervical cancer was set up and studied with path analysis. The result indicated that 4 risk factors had direct and indirect influence on the occurrence of cervical cancer respectively.

Carcinoma, Squamous Cell↗
PubMed1990

Logistic regression in capture-recapture models.

The effect of population heterogeneity in capture-recapture, or dual registration, models is discussed. An estimator of the unknown population size based on a logistic regression model is introduced. The model allows different capture probabilities across individuals and across capture times. The probabilities are estimated from the observed data using conditional maximum likelihood. The resulting population estimator is shown to be consistent and asymptotically normal. A variance estimator under population heterogeneity is derived. The finite-sample properties of the estimators are studied via simulation. An application to Finnish occupational disease registration data is presented.

Biometry↗