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At least 523 records · Page 29Linked to original sources
PubMed1997

Clustered binary logistic regression in teratology data using a finite mixture distribution.

The beta-binomial distribution introduced by Skellam has been applied in many teratology problems for modelling the litter effect. Recently, Morel and Nagaraj proposed a new distribution for modelling cluster multinomial data when the clustering is believed to be caused by clumped sampling. It turns out that the distribution is a mixture of two binomial distributions and accommodates the estimation of an additional parameter to account for intra-litter effect. The new distribution arises from a cluster mechanism in which some individuals within a cluster exhibit the same behaviour while the remaining individuals from the cluster react independently of each other. Such a mechanism is a natural model in teratology problems, where typically a genetic trait is passed with a certain probability to the foetuses of the same litter. In this article, we use the new distribution to model binary responses with logistic regression. We analyse data from a teratology experiment to demonstrate that the new model provides a useful addition to current methodology. The experiment investigates the synergistic effect of the anticonvulsant phenytoin and trichloropopene oxide on the prenatal development of inbred mice. In a simulation study we investigate the type I error rate and the power of the maximum likelihood ratio test when the data follow a finite mixture distribution.

Animals↗
PubMed1998

Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach.

Flexible modelling in survival analysis can be useful both for exploratory and predictive purposes. Feed forward neural networks were recently considered for flexible non-linear modelling of censored survival data through the generalization of both discrete and continuous time models. We show that by treating the time interval as an input variable in a standard feed forward network with logistic activation and entropy error function, it is possible to estimate smoothed discrete hazards as conditional probabilities of failure. We considered an easily implementable approach with a fast selection criteria of the best configurations. Examples on data sets from two clinical trials are provided. The proposed artificial neural network (ANN) approach can be applied for the estimation of the functional relationships between covariates and time in survival data to improve model predictivity in the presence of complex prognostic relationships.

Clinical Trials as Topic↗
PubMed1999

A comparison of mixed effects logistic regression models for binary response data with two nested levels of clustering.

We compare mixed effects logistic regression models for binary response data with two nested levels of clustering. The comparison of these models occurs in the context of developmental toxicity data sets, for which multiple types of outcomes (first level) are measured on each rat pup (second level) nested within a litter (third level). Because the nested nature of such data is occasionally accommodated by ignoring one level of clustering, we consider three models: (i) a three-level model adjusting for clustering due to both pup and litter (M1); (ii) a two-level model adjusting for just pup (M2); and (iii) another two-level model adjusting for just litter (M3). The three types of effects of interest are: (i) differences among malformation types (first-level effects); (ii) differences among groups of pups (for example, sex of pup, second-level effects); and (iii) differences among groups of litters (for example, dose, third-level effects). Simulations and data analyses suggest that the M3 model leads to more bias than the M1 or M2 models for all three types of effects. In terms of coverage of confidence intervals for fixed effects log odds ratio parameters, the M1 model achieves nominal coverage, whereas the M2 model reduces coverage for the third-level effects and the M3 model obtains poor coverage for both first- and second-level effects. These reductions in coverage for certain model-parameter combinations worsen as baseline risk increases. The data analyses support these simulation-based conclusions to some extent.

Animals↗
PubMed2000

Prognostic models based on literature and individual patient data in logistic regression analysis.

Prognostic models can be developed with multiple regression analysis of a data set containing individual patient data. Often this data set is relatively small, while previously published studies present results for larger numbers of patients. We describe a method to combine univariable regression results from the medical literature with univariable and multivariable results from the data set containing individual patient data. This 'adaptation method' exploits the generally strong correlation between univariable and multivariable regression coefficients. The method is illustrated with several logistic regression models to predict 30-day mortality in patients with acute myocardial infarction. The regression coefficients showed considerably less variability when estimated with the adaptation method, compared to standard maximum likelihood estimates. Also, model performance, as distinguished in calibration and discrimination, improved clearly when compared to models including shrunk or penalized estimates. We conclude that prognostic models may benefit substantially from explicit incorporation of literature data.

Age Factors↗
PubMed2000

Saddlepoint approximations for small sample logistic regression problems.

Double saddlepoint approximations provide quick and accurate approximations to exact conditional tail probabilities in a variety of situations. This paper describes the use of these approximations in two logistic regression problems. An investigation of regression analysis of the log-odds ratio in a sequence or set of 2x2 tables via simulation studies shows that in practical settings the saddlepoint methods closely approximate exact conditional inference. The double saddlepoint approximation in the test for trend in a sequence of binomial random variates is also shown, via simulation studies, to be an effective approximation to exact conditional inference.

Logistic Models↗
PubMed2000

Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets.

Logistic regression analysis may well be used to develop a prognostic model for a dichotomous outcome. Especially when limited data are available, it is difficult to determine an appropriate selection of covariables for inclusion in such models. Also, predictions may be improved by applying some sort of shrinkage in the estimation of regression coefficients. In this study we compare the performance of several selection and shrinkage methods in small data sets of patients with acute myocardial infarction, where we aim to predict 30-day mortality. Selection methods included backward stepwise selection with significance levels alpha of 0.01, 0.05, 0. 157 (the AIC criterion) or 0.50, and the use of qualitative external information on the sign of regression coefficients in the model. Estimation methods included standard maximum likelihood, the use of a linear shrinkage factor, penalized maximum likelihood, the Lasso, or quantitative external information on univariable regression coefficients. We found that stepwise selection with a low alpha (for example, 0.05) led to a relatively poor model performance, when evaluated on independent data. Substantially better performance was obtained with full models with a limited number of important predictors, where regression coefficients were reduced with any of the shrinkage methods. Incorporation of external information for selection and estimation improved the stability and quality of the prognostic models. We therefore recommend shrinkage methods in full models including prespecified predictors and incorporation of external information, when prognostic models are constructed in small data sets.

Age Factors↗
PubMed1998

Cytologic features of mycobacterial pleuritis: logistic regression and statistical analysis of a blinded, case-controlled study.

Tuberculous pleural effusions are characterized by lymphocytosis; the significance of mesothelial cells is uncertain, as are the cytologic features in concurrent human immunodeficiency virus (HIV) infection. This blinded study compared 38 culture-positive pleural fluids (6 HIV+) with 38 controls from benign exudative processes. Logistic regression analysis selected mature lymphocytes as most predictive of positive culture, and mesothelial cells and eosinophils as negative predictors. Mesothelial cells were scant (< 10% of nucleated cells) in 36/38 cases with mycobacteria (sensitivity 95%); if these cell were > 10%, tuberculosis was virtually ruled out in HIV- patients. Specificity was maximized (82%) when mesothelial cells < 10% were combined with lymphocytes > 50%; positive predictive value with this combination was 76%, but was raised to 96% if moderate/marked cellularity was also identified. Among tuberculosis cases, reactive mesothelial cells differentiated HIV+ from HIV- patients; there was no other significant difference.

Acquired Immunodeficiency Syndrome↗
PubMed1998

Logistic regression analysis of myxoid sarcomas: a cytologic study.

The objective of this study was to identify key diagnostic cytologic criteria for the most common myxoid sarcomas studied by fine-needle aspiration cytology. We reviewed 27 myxoid malignant fibrous histiocytomas, 8 chordomas, 16 chondrosarcomas, and 12 myxoid liposarcomas in which both cytologic specimens and final histopathologic diagnoses were available. All specimens were coded as to the presence or absence of the following variables: high cellularity, low cellularity, tissue fragments, epithelial fragments, pale/loose ground substance, dense ground substance, chondroid fragments, large amount of myxoid material, small amount of myxoid material, capillary vessel networks, pleomorphism, binucleate cells, multinucleate cells, physaliphorous cells, cells in lacunae, signet ring cells, lipoblasts, fibroblast-like cells, histiocyte-like cells, stellate cells, long filamentous cells, short spindle cells, osteoclastic giant cells, nuclei with pointed ends, nuclei with cigar-shaped ends, fish-hook nuclei, round/ovoid nuclei, naked nuclei, large nucleoli, small nucleoli, mitotic figures, abnormal mitotic figures, intracytoplasmic hemosiderin deposits, background cells, fat, cytoplasmic vacuoles, and pleomorphic giant cells. A logistic regression analysis was performed to identify the variables predictive of myxoid malignant fibrous histiocytoma, chordoma, myxoid chondrosarcoma, and myxoid liposarcoma. The statistical analysis selected pleomorphic giant cells and the presence of fibroblast-like cells as most predictive of malignant fibrous histiocytoma, physaliphorous cells as most closely associated with chordoma, chondroid fragments as most predictive of chondrosarcoma, and lipoblasts as most predictive of liposarcoma. While myxoid lesions have many overlapping cytologic features, key criteria including the presence of lipoblasts, physaliphorous cells, chondroid fragments, and pleomorphic giant cells are useful in subclassifying these neoplasms.

Adipocytes↗
PubMed1997

Regressive logistic modeling of familial aggregation for asthma in 7,394 population-based nuclear families.

The aim of this population-based study was to determine whether asthma aggregates in families, and if so, whether aggregation was consistent with environmental and/or genetic etiologies. Data were from 7,394 nuclear families (41,506 individuals) from the 1968 Tasmanian Asthma Survey, in which all Tasmanian schoolchildren born in 1961 were surveyed by respiratory questionnaire completed by their parents. Similar data were obtained for parents and siblings of probands. For a child, having ever had asthma was predicted by a parent or sibling having ever had asthma; odds ratio (OR) = 3.13 (95% confidence interval [CI] 2.82-3.48) for mother, 2.99 (2.69-3.32) for father, and 3.47 (3.23-3.72) for a sibling. Regressive logistic modeling showed that, in addition to parent-offspring effects, the data were consistent with the existence of an unmeasured factor shared by siblings, evident in 15% (SE 2%) of families and associated with a conditional OR of 9.68 (8.27-11.32). Familial aggregation was best described by a general oligogenic model with non-Mendelian transmission probabilities. Of the Mendelian models, a codominant model with an allele frequency of 16% (SE 0.3%) was preferred. Under a dominant model there was evidence for additional parent-offspring and sibling effects of similar magnitude. It is unlikely that there is one major loci influencing asthma susceptibility; the overall effects of asthma genes in the population are more likely to be inherited codominantly, at least for the majority of loci of major etiological importance. The role of environmental factors in explaining part of familial aggregation for asthma cannot be ruled out, as major triggers of asthma attacks are familial.

Asthma↗
PubMed1998

Logistic regression model for analyzing extended haplotype data.

Recently, there has been increased interest in evaluating extended haplotypes in p53 as risk factors for cancer. An allele-specific polymerase chain reaction (PCR) method, confirmed by restriction analysis, has been used to determine absolute extended haplotypes in diploid genomes. We describe statistical analyses for comparing cases and controls, or comparing different ethnic groups with respect to haplotypes composed of several biallelic loci, especially in the presence of other covariates. Tests based on cross-tabulating all possible genotypes by disease state can have limited power due to the large number of possible genotypes. Tests based simply on cross-tabulating all possible haplotypes by disease state cannot be extended to account for other variables measured on the individual. We propose imposing an assumption of additivity upon the haplotype-based analysis. This yields a logistic regression in which the outcome is case or control, and the predictor variables include the number of copies (0, 1, or 2) of each haplotype, as well as other explanatory variables. In a case-control study, the model can be constructed so that each coefficient gives the log odds ratio for disease for an individual with a single copy of the suspect haplotype and another copy of the most common haplotype, relative to an individual with two copies of the most common haplotype. We illustrate the method with published data on p53 and breast cancer. The method can also be applied to any polymorphic system, whether multiple alleles at a single locus or multiple haplotypes over several loci.

Alleles↗
PubMed1994

Low grade transitional cell carcinoma of the bladder. Cytologic diagnosis by key features as identified by logistic regression analysis.

BACKGROUND: Bladder cytology is being used increasingly to detect and monitor early stage bladder cancer. Because of the lack of significant cytomorphologic atypia in low grade transitional cell carcinomas (TCCs), there often is great difficulty in separating these neoplastic cells from benign urothelial cells. METHODS: In an attempt to increase diagnostic sensitivity, 82 bladder wash specimens, which included 33 low grade TCCs and 49 nonneoplastic lesions, were reviewed retrospectively. With a stepwise logistic regression analysis, three key cytomorphologic criteria useful in the separation of low grade TCC from benign urothelium were identified. RESULTS: The key cytologic criteria were increased nuclear/cytoplasmic ratios, irregular nuclear borders, and cytoplasmic homogeneity. By using contingency tables, these three criteria combined had a sensitivity of 45% and a specificity of 98% in the diagnosis of low grade transitional cell carcinoma. If any two of the criteria were present, there was a sensitivity of 85% and a specificity of 96%. CONCLUSIONS: By using these three criteria, the diagnosis of TCC can be made with a high degree of accuracy in bladder wash specimens.

Adult↗
PubMed2007

A logistic model for the prediction of the influence of water on the solid waste methanization in landfills.

This article deals with the impact of water content of solid waste on biogas production kinetics in landfills. This impact has been proved in the laboratory thanks to anaerobic biodegradation experiments on paper/cardboard waste samples. A strong dependence with the moisture level was observed for both kinetic rates and maximum methane production. In this article, a logistic model is proposed to simulate the biogas production rate. It is chosen as simple as possible in order to allow for a correct identification of the model parameters given the experimental data available. The moisture dependency is introduced through a linear weighing of the biomass specific growth rate and of the amount of accessible organic substrate. It is directly linked to physical properties of the waste: the holding capacity and the minimal moisture level allowing the presence of free water.

Bacteria, Anaerobic↗
PubMed1994

Artificial intelligence versus logistic regression statistical modelling to predict cardiac complications after noncardiac surgery.

The traditional approach to developing models predictive of cardiac events has been to perform logistic regression (LR) analysis on a variety of potential predictors. An alternative to use an artificial intelligence system called a neural network (NN) which simulates biological intelligence. To evaluate the potential applicability of the latter method, we compared the ability of LR and NN techniques to predict cardiac events after noncardiac surgery. A total of 200 patients (training group) underwent cardiac risk assessment before major noncardiac surgery using 17 clinical parameters and 7 quantitative indices based on dipyridamole-thallium imaging. There were 21 post-operative myocardial infarctions and/or cardiac deaths. Data from the training group were used to develop two predictive models: one based on backward stepwise LR multivariate statistical analysis and the other one using a neural network. Both models were then validated on a second group of 160 consecutive patients also referred for preoperative risk stratification (validation group). The NN consisted of 14 input, 29 hidden, and 1 output neurons and used a back-propagation algorithm (learning rate 0.2, training tolerance 0.5, sigmoid transfer function). The sensitivity, specificity, positive and negative predictive accuracies for the prediction of postoperative events in the validation group of 160 patients were, respectively, 67% (6/9), 82% (124/151), 18% (6/33), and 98% (124/127) for LR, and 67% (6/9), 96% (145/151), 50% (6/12), and 98% (145/148) for the NN, with a difference in specificity which attained statistical significance (p < 0.01). Artificial intelligence may provide a useful alternative to conventional LR statistical analysis for the purpose of preoperative cardiac risk assessment.

Artificial Intelligence↗
PubMed1992

A logistic model for thyroid lesions.

A stepwise logistic regression analysis was performed in 27 cases of papillary carcinoma thyroid (PC), 20 follicular neoplasms (FN), 30 cases of Grave's disease (GD), and 40 cases of colloid adenomatous goitre (CAG). The three most important variables in predicting PC were papillary clusters, dense cytoplasm, and intranuclear cytoplasmic inclusions, whereas the predictors of FN were high cellularity combined with a prominent acinar pattern. A few cases of GD and CAG showed a cytologic overlap with PC and FN, respectively. Regression analysis established high cellularity, fire flare appearance, and epitheloid granulomas as reliable predictors of GD, whereas abundant colloid with or without foam cells and follicles associated with colloid (FAC) were important variables in CAG.

Adenocarcinoma↗
PubMed1992

Numerical comparisons of two formulations of the logistic regressive models with the mixed model in segregation analysis of discrete traits.

Segregation analysis of discrete traits can be conducted by the classical mixed model and the recently introduced regressive models. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affected persons have a liability exceeding a threshold. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype effects, the phenotypes of older relatives, and other covariates. A formulation of the regressive models, based on an underlying liability model, has been recently proposed. The regression coefficients on antecedents are expressed in terms of the relevant familial correlations and a one-to-one correspondence with the parameters of the mixed model can thus be established. Computer simulations are conducted to evaluate the fit of the two formulations of the regressive models to the mixed model on nuclear families. The two forms of the class D regressive model provide a good fit to a generated mixed model, in terms of both hypothesis testing and parameter estimation. The simpler class A regressive model, which assumes that the outcomes of children depend solely on the outcomes of parents, is not robust against a sib-sib correlation exceeding that specified by the model, emphasizing testing class A against class D. The studies reported here show that if the true state of nature is that described by the mixed model, then a regressive model will do just as well. Moreover, the regressive models, allowing for more patterns of family dependence, provide a flexible framework to understand gene-environment interactions in complex diseases.

Computer Simulation↗
PubMed1999

A logistic regression extension of the transmission disequilibrium test for continuous traits: application to linkage disequilibrium between alcoholism and the candidate genes DRD2 and ADH3.

The transmission disequilibrium test (TDT) recently has become a popular method of testing for linkage in the presence of association due to its simplicity and advantages over other within-family analytic methods. In this paper, we describe a logistic regression extension to the TDT that can be used to test for differences in linkage disequilibrium as a function of one or more continuous and/or categorical explanatory variables. We highlight important features of this method and demonstrate some of its possible uses. We applied these analyses to test for linkage disequilibrium between the dopamine receptor D2 (DRD2) and alcohol dehydrogenase 3 (ADH3) genes and both diagnostic and quantitative indices of alcoholism. Using data from the Collaborative Study on the Genetics of Alcoholism data set, we found evidence suggesting linkage disequilibrium between DRD2 and ADH3 and quantitative indices of alcoholism and correlated phenotypes corresponding to smoking and personality. None of the evidence for linkage disequilibrium varied by sex or age.

Age Factors↗
PubMed2002

A solution to the problem of separation in logistic regression.

The phenomenon of separation or monotone likelihood is observed in the fitting process of a logistic model if the likelihood converges while at least one parameter estimate diverges to +/- infinity. Separation primarily occurs in small samples with several unbalanced and highly predictive risk factors. A procedure by Firth originally developed to reduce the bias of maximum likelihood estimates is shown to provide an ideal solution to separation. It produces finite parameter estimates by means of penalized maximum likelihood estimation. Corresponding Wald tests and confidence intervals are available but it is shown that penalized likelihood ratio tests and profile penalized likelihood confidence intervals are often preferable. The clear advantage of the procedure over previous options of analysis is impressively demonstrated by the statistical analysis of two cancer studies.

Breast Neoplasms↗
PubMed2002

Global goodness-of-fit tests in logistic regression with sparse data.

The logistic regression model has become the standard analysing tool for binary responses in medical statistics. Methods for assessing goodness-of-fit, however, are less developed where this problem is especially pronounced in performing global goodness-of-fit tests with sparse data, that is, if the data contain only a small numbers of observations for each pattern of covariate values. In this situation it has been known for a long time that the standard goodness-of-fit tests (residual deviance and Pearson chi-square) behave unsatisfactorily if p-values are calculated from the chi(2)-distribution. As a remedy in this situation the Hosmer-Lemeshow test is frequently recommended; it relies on a new grouping of the observations to avoid sparseness, where this grouping depends on the estimated probabilities from the model. It has been shown, however, that the Hosmer-Lemeshow test also has some deficiencies, for example, it depends heavily on the calculating algorithm and thus different implementations might lead to different conclusions regarding the fit of the model. We present some alternative tests from the statistical literature which should also perform well with sparse data. Results from a simulation study are given which show that there exist some goodness-of-fit tests (for example, the Farrington test) that have good properties regarding size and power and that even outperform the Hosmer-Lemeshow test. We illustrate the various tests with an example from dermatology on occupational hand eczema in hairdressers.

Beauty Culture↗