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At least 19 recordsLinked to original sources
PubMed1990

[Epidemiological analysis of rank category exposure effects using cumulative logistic models].

Logistic regression is the fundamental procedure to analyze the dependence of the frequency of a binary disease indicator from exposure and other factors. Disease indicators with ranked levels contain a larger amount of information and permit a more precise prediction. For that, the cumulative logistic model was developed. Its application, demonstrated with an epidemiologic study for the dependence of arthritic changes in the knee-joint from intensity of kneeling stress and age, is very recommendable for the evaluation of well-designed epidemiologic studies concerning the prevalence of chronic diseases.

Environmental Exposure↗
PubMed1994

Advantages and inconveniences of the Cox model compared with the logistic model: application to a study of risk factors of nursing cow infertility.

The survival Cox model and the logistic model were compared on a data set obtained from an ecopathological survey relative to the risk factors of nursing cow infertility. The risk factors resulting from the 2 models were the same. The Cox model has the advantage of preserving the variable in its original quantitative form, and of using a maximum of information. However, very restrictive conditions of application of this model make its use rather limited.

Animals↗
PubMed2000

Carrying capacity and demographic stochasticity: scaling behavior of the stochastic logistic model.

The stochastic logistic model is the simplest model that combines individual-level demography with density dependence. It explicitly or implicitly underlies many models of biodiversity of competing species, as well as non-spatial or metapopulation models of persistence of individual species. The model has also been used to study persistence in simple disease models. The stochastic logistic model has direct relevance for questions of limiting similarity in ecological systems. This paper uses a biased random walk heuristic to derive a scaling relationship for the persistence of a population under this model, and discusses its implications for models of biodiversity and persistence. Time to extinction of a species under the stochastic logistic model is approximated by the exponential of the scaling quantity U=(R-1)(2) N/R(R+1), where N is the habitat size and R is the basic reproductive number.

Animals↗
PubMed2002

Simple polynomial multiplication algorithms for exact conditional tests of linearity in a logistic model.

The linear logistic model is often employed in the analysis of binary response data. The well-known asymptotic chi-square and likelihood ratio tests are usually used to detect the assumption of linearity in such a model. For small, sparse, or skewed data, the asymptotic theory is however dubious and exact conditional chi-square and likelihood ratio tests may provide reliable alternatives. In this article, we propose efficient polynomial multiplication algorithms to compute exact significance levels as well as exact powers of these tests. Two options, namely the cell- and stage-wise approaches, in implementing these algorithms will be discussed. When sample sizes are large, we propose an efficient Monte Carlo method for estimating the exact significance levels and exact powers. Real data are used to demonstrate the performance with an application of the proposed algorithms.

Algorithms↗
PubMed1998

On the cumulants of population size for the stochastic power law logistic model.

The deterministic power law logistic model is used to describe density-dependent population growth in cases where the ordinary logistic model is insufficient. This paper investigates an analogous stochastic power law logistic model. The exact (unconditional) population size distributions and the cumulant functions for this stochastic model are intractable for large population sizes. Approximating cumulant functions are derived for populations of any size, and are illustrated with examples of assumed Africanized honey bee population dynamics. Outstanding among the findings is that the approximations for the cumulant functions are very accurate for these examples. The stochastic power law logistic model is very general and may be applied to describe the growth of many other natural populations.

Animals↗
PubMedDate not supplied

Pneumoconiosis risk assessment in agate workers--multiple logistic model.

A multivariate logistic model for measuring and comparing pneumoconiosis risk is described. In the first stage variables are screened on the basis of contributed variability via Pearson Chi Square statistic. Age, dust years and pack years so chosen as explanatory variables are fitted in the above model. The coefficients are estimated as linear discriminant function co-efficient. The model gives quite a good fit between observed and expected frequencies. Dust years discriminate maximum between the normal and pneumoconiosis group. Nearly sixty per cent of the variation is explained by these variables.

Adult↗
PubMed1984

Treatment allocation for nonlinear models in clinical trials: the logistic model.

Many clinical trials have a binary outcome variable. If covariate adjustment is necessary in the analysis, the logistic-regression model is frequently used. Optimal designs for allocating treatments for this model, or for any nonlinear or heteroscedastic model, are generally unbalanced with regard to overall treatment totals and totals within strata. However, all treatment-allocation methods that have been recommended for clinical trials in the literature are designed to balance treatments within strata, either directly or asymptotically. In this paper, the efficiencies of balanced sequential allocation schemes are measured relative to sequential Ds-optimal designs for the logistic model, using as examples completed trials conducted by the Eastern Cooperative Oncology Group and systematic simulations. The results demonstrate that stratified, balanced designs are quite efficient, in general. However, complete randomization is frequently inefficient, and will occasionally result in a trial that is very inefficient.

Biometry↗
PubMed1989

Modelling sibship environment in the regressive logistic model for familial disease.

Recently analytical models for pedigree disease data have been developed that combine genetic and epidemiological modelling techniques. The regressive logistic model [Bonney, Biometrics 42: 611-625; 1986] relies on decomposing the likelihood of a pedigree into the product of conditional probabilities, one for each individual, by imposing a (natural) order on pedigree members. In addition to modelling measured epidemiological variables, vertical transmission, transmission of unmeasured ousiotypes (a special case being genotypes), and some modelling of sibship dependencies have been proposed. In this paper the model is extended to include an unmeasured sibship environment factor using a log-linear model for binary pedigree traits [Hopper et al., Genet Epidemiol 1: 183-188; 1984], which breaks the pedigree into conditionally independent groups. Statistical issues, such as designs for which these factors will be discernible and tests of fit, are discussed.

Environment↗
PubMed1982

Comparing two head injury treatments by linear logistic model.

A linear logistic model is used to compare the performance of a series of head injured patients treated in Auckland with a series obtained from the International Data Bank (IDB), on head injured patients. The IDB patients were treated along conventional lines of neurosurgical management. The Auckland patients were submitted to a regime of elective artificial ventilation of the lungs and heavy sedation, directed against diffuse brain swelling. Two types of comparison were used. First, a model was constructed from the Auckland data of the relationship of outcome to factors relating to the severity of the head injury. This input-output relationship was used to predict the distributions of outcome in the IDB series. Secondly, a descriptive model on the combined Auckland and IDB data was given the option of selecting a dummy variable to indicate whether the source of the patient, Auckland or IDB, had significantly influenced outcome for a given set of other determinants. Differences between Auckland and IDB were only significant if the severity of the head injury in the IDB cases was represented by the set of scores indicating their best condition over the first 24 hours of coma. The scores indicating the condition of the Auckland patients might be comparable to either the 24 hour best or the 24 hour worst IDB scores. One cannot say whether any differences in input-output relationships between the two series arise from differences in coding the input data or from real differences in outcome for given sets of determinants of outcome.

Adolescent↗
PubMed2003

A new logistic model for bacterial growth.

A new logistic model for bacterial growth was developed in this study. The model is based on a logistic model, which is often applied for biological and ecological population kinetics. The new model is described by a differential equation and contains an additional term for suppression of the growth rate during the lag phase, compared with the original logistic equation. The new model successfully described sigmoidal growth curves of Escherichia coli and Salmonella under various initial conditions. Data for E. coli were obtained from our experiments and data for Salmonella from the literature. When the new model was compared with a modified Gompertz model, which is widely used by many predictive microbiology researchers, it proved to be superior to the Gompertz model. Further, Salmonella growth at varying temperature could be well simulated by the new model. These results indicate that the new model will be a useful tool to predict bacterial growth under various temperature profiles.

Bacteria↗
PubMed1992

The logistic modeling of interobserver agreement.

An approach to the logistic modeling of interobserver agreement is described that allows for the estimation of a commonly employed measure of agreement. The dependent variable is defined to be 1 if the two raters agree, and 0 otherwise. Covariates may be included in the regression equation in order to obtain adjusted or subgroup-specific estimates of percent agreement. As an empirical example, logistic models were fitted to data from a validation study of the agreement between interview information and physician records on the history of post-menopausal estrogen use, from a case-control study of breast cancer conducted on Oahu, Hawaii. Variables found to be related to agreement in previous univariate analyses were examined as covariates in the logistic model. The directly calculated estimates of percent agreement agreed well with the modeled estimates derived from the regression coefficients. Thus, the logistic model may provide a useful alternative to existing methods for the description of interobserver agreement.

Breast Neoplasms↗
PubMed2002

Constrained four parameter logistic model.

The constrained four parameter logistic model has found wide application in describing dose response relationships across many assay systems. This discussion examines the basic model and its practical application to potency testing in the context of the 96 well plate. A two step procedure is recommended for the analysis: (i) the constrained logistic model to generate potency estimates, (ii) a linear mixed-effects model to account for within-plate and between plate variability for producing the final combined estimate of potency. The method is outlined in a case study. Design issues related to possible location effects on the plate may be ameliorated by use of a Latin square design.

Biological Assay↗
PubMed1987

The use of the logistic model in space motion sickness prediction.

The one-equation and the two-equation logistic models were used to predict tested subjects' susceptibility to motion sickness in KC-135 parabolic flights using data from other ground-based motion sickness tests. A data set containing data from 6 provocative tests, 2 vestibular function tests, and 1 motion sickness experience questionnaire from 162 subjects was used in this study. The prediction results from the logistic models were compared with those from the previously-used Bayes linear discriminant analysis procedures. The results based on this data set show that the logistic models correctly predicted substantially more cases (an average of 13%) in the data subset used for model building. In the data subset used for model cross-validation, the logistic models correctly predicted 4% and 5% more cases in the prediction of vomit or nonvomit, and of degree of susceptibility, respectively. Overall, the logistic models ranged from 53 to 65% predictions of the three endpoint parameters, whereas the Bayes linear discriminant procedure ranged from 48 to 65% correct for the cross validation sample.

Adult↗
PubMed1993

[From multiple regression analysis to logistic model, proportional hazard model and log linear model. Its concept and application].

Recently logistic model, proportional hazard model and log linear model have been used frequently in the medical literatures. Here, each model is reviewed briefly from basics to its application, pointing out pitfalls in its application, some of which are common to any regression analysis. The logistic model is especially useful for the analysis of retrospective data where odds ratio is utilized to evaluate the outcome probability. On the other hand, proportional hazard model is useful when we analyze censored data, utilizing hazard function. Log linear model has been used where contingency table has more than three independent variables, the situation where its applicability in clinical medicine is wide. Familiarity with these statistical methods would enable us to evaluate data more effectively and efficiently and ultimately to read literature more easily.

Models, Statistical↗
PubMed2000

Log-linear and logistic modeling of dependence among diagnostic tests.

We developed log-linear and logistic-modeling approaches to investigate dependence among diagnostic tests. To illustrate the approaches, we used published data for swine toxoplasmosis, bovine paratuberculosis, and swine brucellosis. These diseases were selected because each animal's true disease status was known, at least five tests were used, and the serologic tests had been previously shown to have moderate-to-high pairwise dependence in test sensitivities (and sometimes in test specificities). Log-linear and logistic modeling yielded similar results for swine toxoplasmosis and swine brucellosis. However, logistic modeling could not be used to investigate test dependence for bovine paratuberculosis because of quasi-separation in the data attributable to two fecal-based tests having specificities of 100%. Findings from our modeling indicated that 3 (modified agglutination, enzyme-linked immunosorbent assay (ELISA), latex agglutination) of 5 serologic tests for toxoplasmosis and 2 (rivanol and particle concentration fluorescence immunoassay) of 6 serologic tests for brucellosis were adequate for diagnosis. For bovine paratuberculosis, both fecal-based tests (Herrold's egg-yolk culture and radiometric culture) and 1 (ELISA) of 3 serologic tests were necessary in serial and parallel testing schemes.

Animals↗
PubMed1991

Polychotomous multivariate models for coronary heart disease simulation. I. Tests of a logistic model.

Stochastic compartmental modeling techniques have been employed to simulate coronary heart disease morbidity and mortality. In the current paper, polychotomous logistic models are used to describe the relationship between risk of disease and multiple risk factors, effect modification and confounding variables. The process of estimating the parameters for two risk factors and three types of outcomes is described for a population followed for five years. A Statistical Analysis System (SAS) procedure was used to estimate risk factor coefficients based on two partial periods and on the entire five year epoch. Most of the estimated coefficients were found to be statistically significant. The model performance was evaluated by comparing the observational data with simulated outcomes using a micropopulation and Monte Carlo techniques. Two different tests of goodness of fit were used. Satisfactory fits were obtained both for the risk coefficients based on two partial periods and those based on the entire epoch. This indicates that the model is suitable for simulation of the effects of intervention strategies. The use of the entire epoch involved estimates of one half as many parameters as did the use of two partial periods. Accordingly, it is concluded that only the entire epoch need be considered for future studies of this population.

Adult↗
PubMed1999

Prospective evaluation of a logistic model based on sonographic morphologic and color Doppler findings developed to predict adnexal malignancy.

To assess prospectively a logistic model based on sonographic morphologic and color Doppler findings, which had been developed to predict adnexal malignancy, 167 consecutive and unselected patients (mean age, 45.7 yr; range, 17 to 81 yr; 113 [67.7%] premenopausal and 54 [32.3%] postmenopausal) diagnosed as having an adnexal mass and scheduled for surgery were prospectively included in this study. All patients were evaluated by transvaginal color Doppler ultrasonography. The probability of adnexal malignancy was estimated prior to surgery, applying a logistic model developed previously. A probability of malignancy greater than 75% was considered to assess model performance. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for the model. In all cases definitive histopathologic diagnosis was obtained. One hundred and twenty-five (74.9%) benign and 42 (25.1%) malignant tumors were found. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 85.7% (95% confidence intervals, 71.4% to 94.6%), 100% (95% confidence intervals, 97.1% to 100%), 100% (95% confidence intervals, 90.3% to 100%), and 95.4% (95% confidence intervals, 90.3% to 98.3%), respectively. Overall accuracy was 96.4% (95% confidence intervals, 91.3% to 98.7%). Our results confirm the validity of the proposed logistic model in predicting adnexal malignancy.

Adnexal Diseases↗