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Results for “Logistic Model”

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PubMed1999

Optimum experimental designs for multinomial logistic models.

Multinomial responses frequently occur in dose level experiments. For example, in a study of the influence of gamma radiation on the emergence of house flies (Musca domestica L., 1758), three disjoint outcomes occurred: death before the pupae opened, death during emergence, and life after emergence. Although the flies are easy to breed, this sort of bioassay is, in general, very expensive since it requires the use of a gamma radiation source. Experiments therefore need to be designed to involve the minimum number of different doses. Here the theory of optimum experimental design is applied to provide efficient experiments to estimate the parameters of those multinomial logistic models that are a special case of the multivariate logistic models of Glonek and McCullagh (1995, Journal of the Royal Statistical Society, Series B 57, 533-546). The purpose is to reduce the overall experimental cost. The general equivalence theorem (Fedorov, 1972, Theory of Optimal Experiments) is adapted to this class of models, providing an effective method of generating and checking the optimality of designs. One example on flies demonstrates the method, which can be easily implemented.

Animals↗

PubMed1997

Statistical evaluation of the role of Helicobacter pylori in stress gastritis: applications of splines and bootstrapping to the logistic model.

Stress gastritis is a serious problem in the intensive care unit population. The recent discovery of the causal nature of Helicobacter pylori (H. pylori) in the development of gastric ulcers led us to examine its relationship with stress gastritis. We investigated this relationship in 874 veterans admitted to intensive care units who were tested for the presence of H. pylori and followed for 6 weeks for the development of stress gastritis. We fit spline models to assess functional relationships and used the logistic model to determine the association between H. pylori and stress gastritis. The predictive ability of the model was assessed with receiver operating characteristic (ROC) curve analysis and validated with the bootstrapping technique. Increased anti-H. pylori immunoglobulin A concentrations were found to be an important predictor of stress gastritis independent of other known risk factors.

Aged↗

PubMed2005

Comparing logistic models based on modified GCS motor component with other prognostic tools in prediction of mortality: results of study in 7226 trauma patients.

A simple reproducible and sensitive prognostic trauma tool is still needed. In this article we have introduced modified GCS motor response (MGMR) and evaluated the performance of logistic models based on this variable. The records of 8452 trauma patients admitted to major hospitals of Tehran from 1999 to 2000 were analysed. 7226 records with known outcome were included in our study. Logistic models based on outcome (death versus survival) as a dependent variable and Injury Severity Score (ISS), Revised Trauma Score (RTS), Glasgow Coma Scale (GCS), GCS motor component (GMR) and MGMR (following command [=2], movement but not following [=1] command and without movement [=0]) were compared based on their accuracy and area under the Receiver Operating Characteristic (ROC) curve. The accuracy of the Trauma and Injury Severity Score (TRISS), RTS, GCS, GMR and MGMR models were almost the same. Considering both the area under the ROC curve and accuracy, the age included MGMR model was also comparable with other age included models (RTS+age, GCS+age, GMR+age). We concluded that although in some situations we need more sophisticated models, should our results be reproducible in other populations, MGMR (with or without age added) model may be of considerable practical value.

Adolescent↗

PubMed1991

Regressive logistic models for familial diseases: a formulation assuming an underlying liability model.

Statistical models have been developed to delineate the major-gene and non-major-gene factors accounting for the familial aggregation of complex diseases. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affection is defined by a threshold on the liability scale. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype, phenotypes of antecedents and other covariates. An equivalence between these two approaches cannot be derived analytically. I propose a formulation of the regressive logistic models on the supposition of an underlying liability model of disease. Relatives are assumed to have correlated liabilities to the disease; affected persons have liabilities exceeding an estimable threshold. Under the assumption that the correlation structure of the relatives' liabilities follows a regressive model, the regression coefficients on antecedents are expressed in terms of the relevant familial correlations. A parsimonious parameterization is a consequence of the assumed liability model, and a one-to-one correspondence with the parameters of the mixed model can be established. The logits, derived under the class A regressive model and under the class D regressive model, can be extended to include a large variety of patterns of family dependence, as well as gene-environment interactions.

Environment↗

PubMed1995

Measuring the typicality of objects included in environmental scenes: a logistic model for atypicality.

Some empirical studies have stated that people usually categorize scenes according to the suitability of their elements. This paper proposes a method of measuring the typicality of naturalistic objects contained in environmental scenes. 517 subjects gave a score of suitability for 110 object-scene pairs. We used a logistic model for the measurements which enabled us to obtain two indexes, atypicality and discrimination. Analysis showed that the objects could be arranged on a numerical scale according to their typicality in a scene, and from this we concluded that logistic models are a useful and powerful method of measuring typicality.

Adolescent↗

PubMed1990

Improving the predictive ability of the signal-averaged electrocardiogram with a linear logistic model incorporating clinical variables.

To improve the predictive accuracy of the signal-averaged electrocardiogram, we created a linear logistic model for predicting ventricular tachycardia during electrophysiologic testing. This signal-averaged electrocardiographic model was created from data obtained from 214 patients undergoing electrophysiologic testing (70 had ventricular tachycardia during electrophysiologic testing) by using stepwise logistic regression to rank eight clinical and nine signal-averaged electrocardiographic variables. The best predictors were ejection fraction, history of infarction, ventricular ectopic pairs or nonsustained ventricular tachycardia on Holter monitoring, QRS duration after 25-Hz filtering, and root mean square voltage of the terminal 40 msec of the QRS complex after 40- and 80-Hz filtering. Cross validation (a statistical technique that can be used to accurately evaluate how a predictive model will perform on a prospective patient population) was used to validate the model. After cross validation, the model's sensitivity was 91% and specificity was 59% for predicting ventricular tachycardia during electrophysiologic testing. This model compared favorably with established 25-Hz late-potential criteria (QRS duration of more than 110 msec and root mean square voltage of less than 25 microV of the terminal 40 msec of the QRS complex; sensitivity, 64%; specificity, 85%) and with established 40-Hz late-potential criteria (QRS duration of more than 114 msec or root mean square voltage of less than 20 microV of the terminal 40 msec of the QRS complex or duration of the low-amplitude signal less than 40 microV at the terminal QRS complex that is greater than 38 msec; sensitivity, 84%; specificity, 54%).(ABSTRACT TRUNCATED AT 250 WORDS)

Aged↗

PubMed1990

A family study of panic disorder: reanalysis using a regressive logistic model that incorporates a sibship environment.

Previous analysis of affection status in parents and siblings of 117 probands with panic disorder by a log-linear model for binary pedigree data found a common concordance across biological first-degree relatives and no spouse association [Hopper JL, Judd FK, Derrick PL, Burrows GD: Genet Epidemiol 4:33-41, 1987]. In this paper the data were reanalyzed using a regressive logistic model that modelled both vertical transmission and a shared sibship environment. Ascertainment correction was made by a) an "ascertainment assumption-free" procedure, following Ewens and Shute [Theor Pop Biol 30:388-412, 1986] and compared with b) complete ascertainment and c) single ascertainment. Under every scheme there was evidence for vertical transmission from parents to offspring. Inclusion of a sibship environment gave an improved fit, suggesting that vertical transmission alone may not be sufficient to explain the familial aggregation observed in these families. The effects of an affected parent, of the postulated environmental factor (present for all siblings if it was present for one sibling), and of the prevalence of the rare environmental factor were estimated and found to be roughly similar under the different schemes. Model predictions of lifetime prevalence were consistent with other population-based studies. Under the same assumption-free method, standard errors approximately doubled and computation time increased compared with the other ascertainment schemes that made specific, although not necessarily correct, assumptions. The regressive logistic model used less computation time and gave greater insight into the pattern of familial aggregation than did the previous modelling.

Adult↗

PubMed1998

Estimation of probability of malignancy using a logistic model combining physical examination, ultrasound, serum CA 125, and serum CA 72-4 in postmenopausal women with a pelvic mass: an international multicenter study.

BACKGROUND: To assess the differential diagnostic potential of physical examination, ultrasound, the serum CA 125 assay, and serum CA 72-4 assay, and the contribution of each parameter to a logistic model predicting the probability of malignancy in postmenopausal patients presenting with a pelvic mass. PATIENTS AND METHODS: In a multicenter, prospective study a total of 155 patients were evaluated preoperatively using a standard protocol for pelvic examination, transvaginal (occasionally additional abdominal) ultrasound, and serum CA 72-4 (cutoff level 3 U/ml) and CA 125 (cutoff level 35 U/ml). RESULTS: Fifty-nine malignant (39%) and 92 benign (61%) pelvic tumors were found in addition to 4 borderline tumors (3%). Forty-three patients appeared to have ovarian carcinoma, FIGO Stage III or IV in 28 cases. Borderline tumors were excluded from the statistical calculations. The diagnostic accuracy of each single parameter, i.e., pelvic examination, ultrasound, and serum CA 125 and CA 72-4 in discriminating between benign and malignant pelvic masses gave highly similar results (81, 76, 78, and 81% respectively). Best sensitivity was found in pelvic examination (92%); best specificity was found in CA 72-4 (93%). Using logistic regression analysis the power of pelvic examination appeared to be the most relevant (adjusted odds ratio 12.1), followed by ultrasound (odds ratio 9.7), serum CA 125 (odds ratio 5.0), and serum CA 72-4 (odds ratio 4.9). Age appeared to be nonpredictive. The logistic model gives a correct prediction in 87% of all cases. CONCLUSIONS: The addition of serum CA 72-4 to the combination of pelvic examination, ultrasound, and serum CA 125 leads to an improved discrimination between malignant and benign pelvic masses.

Aged↗

PubMed1982

Resistant fits for some commonly used logistic models with medical application.

Logistic regression-type models are used in many applications. Some examples include the classical dose-response experiment, prospective and retrospective studies of disease incidence (with and without matching), and the analysis of ordinal data. In most instances, the model is fitted by the method of maximum likelihood, which, like least squares, is sensitive to atypical observations. An alternative to maximum likelihood is proposed and illustrated by examples.

Epidemiologic Methods↗

PubMed2001

Assessment of a new logistic model in the preoperative evaluation of adnexal masses.

OBJECTIVE: To assess a new logistic regression model developed to predict malignancy in adnexal masses. METHODS: In the first part of this study, we developed a logistic model by applying logistic regression analysis in a series of 268 adnexal masses (203 benign and 65 malignant lesions) in 248 patients (mean age, 43.6 years; SD, 14.2 years) evaluated and treated at our institution. Eleven parameters were entered in the logistic regression analysis in a forward stepwise way. In the second part of the study, we evaluated the model's diagnostic performance in a further set of 135 adnexal masses (103 benign and 32 malignant tumors) in 129 patients (mean age, 44.4 years; SD, 14.6 years). This diagnostic performance was compared with that of age, tumor volume, Sassone's and Ferrazzi's B-mode ultrasonographic morphologic scoring systems, serum cancer antigen 125 level, and the tumor's lowest resistive index. Comparison was done by calculating the area under the receiver operating characteristic curve. RESULTS: In logistic analysis, only menopausal status, the presence of papillary projections, the logarithm of the cancer antigen 125 value, tumor blood flow location, and the lowest resistive index were retained in the model. The model had the best area under the curve (0.97), significantly higher than patient age (area under the curve, 0.78; P = .001), tumor volume (area under the curve, 0.68; P < .0001), cancer antigen 125 (area under the curve, 0.88; P = .008), lowest resistive index (area under the curve, 0.85; P = .011), Ferrazzi's scoring system (area under the curve, 0.89; P = .01), and maximal peak systolic velocity (area under the curve, 0.71; P< .0001). Comparison with Sassone's scoring system (area under the curve, 0.91) did not reach statistical significance, but a clear trend was found (P = .116). CONCLUSIONS: The model had the best diagnostic performance for discriminating between benign and malignant adnexal masses. A clinical prospective evaluation is needed to confirm its actual value.

Adnexal Diseases↗

PubMed1976

Use of the logistic model in retrospective studies.

A logistic regression model is used to study the association between a dichotomous exposure variable and a disease. The method takes into account factors that may confound the association and leads to a quantitative study of the influence of factors which are related to the strength of the association. Special results are given for matched pair data. A case-control study relating post-menopausal estrogen use and endometrial cancer provides illustration.

Biometry↗

PubMed1988

A modified logistic model applied to human populations.

"The use of the logistic curve for forecasting human populations [in the United Kingdom] considered by Leach is re-examined. A modification of the use of the logistic curve is suggested, which changes the emphasis from fitting a logistic trend to providing a forecast logistic trend. The assumption, used by Leach, that the variance of the additive disturbance term is constant is replaced by a more realistic supposition that the variance of the proportional disturbance is constant. The forecasting performance of the modified logistic model is shown to be superior to that of the model used by Leach." A reply by Leach (pp. 496-7) and a response by the author (p. 498) are included.

Developed Countries↗

PubMed2005

A two-stage logistic model based on the measurement of pro-inflammatory cytokines in bronchial secretions for assessing bacterial, viral, and non-infectious origin of COPD exacerbations.

UNLABELLED: Exacerbations often complicate the progressive course of chronic obstructive pulmonary disease (COPD), mainly due to infectious agents. The precise role of bacterial infections in the course and the pathogenesis of COPD has been a source of controversy for decades. Also viruses and other non-infectious causes of exacerbation play a relevant role and also contribute to persisting airway inflammation. Usually, the etiologic identification of the infective causes of COPD require considerable time and costs. The development of more rapid, reliable, and widely applicable methods to promptly define the etiology of COPD exacerbations should represent a relevant issue in devising earlier and more specific strategies for their effective therapeutic control. AIM: Of the study was to assess the predictive role of some pro-inflammatory cytokines measured in spontaneous bronchial secretions in discriminating the main infectious causes of COPD exacerbations. METHODS: 124 subjects with moderate COPD (51-79 y; mean basal FEV1 = 49.6% pred. +/- 4.6 sd; FEV1 reversibility +3.9% from baseline +/- 4.8 sd after salbutamol 200 mcg) were studied during acute exacerbation. Respiratory viruses were isolated from bronchial secretions in 21 cases; common bacteria (CFU > or = 10(6)/ml) in 28 cases; Pseudomonas Aeruginosa (Ps.Ae.; CFU > or = 10(6)/ml) in 20 cases. The cytokines IL1beta, IL8, and TNFalpha (pg/ml; Immulite; Diagnostic Product Corp, Los Angeles, CA, USA), and neutrophils (% total count) were measured in bronchial secretions of all patients. STATISTICS: A two-stage logistic model was chosen for discriminating the different causes of COPD exacerbations (such as: non-infectious, or viral, bacterial, or due to Ps.Ae.). RESULTS: At the first decisional step, the two-stage logistic model proved that TNFalpha levels in bronchial secretions recognise clearly patients belonging to the Ps.Ae. group from those of all other groups (Area under ROC curve = 0.96; 95% CI = 0.91-0.99), and that, at the second decisional step, IL8 + IL10 levels discriminate patients with bacterial causes (such as all bacteria) from the non-infected ones and from those with a viral cause of exacerbation (Area under ROC curve = 0.87; 95% CI = 0.77-0.94). Neutrophil percent count did not support any contribution in discriminating the different subgroups of COPD subjects. CONCLUSIONS: When exacerbated, COPD subjects express different patterns of pro-inflammatory mediators in bronchial secretions, which appear modulated according to the etiological cause of the exacerbation. In particular, TNFalpha concentration per se enables recognition of COPD exacerbations due to Ps.Ae., while IL8 + IL1beta levels prove helpful in discriminating those to common bacteria from those to viral agents and to non-infectious causes. When present data are further confirmed, the use of a decisional rule based on cytokine measurements might be regarded as a helpful predictive tool. As measures of pro-inflammatory cytokines are low-cost, simple, and faster to perform, they could support rapid clinical decision making at the bedside regarding therapeutic strategy for COPD exacerbations, in particular when they are needed for severe COPD patients.

Aged↗

PubMed1993

Maximum likelihood estimation of the attributable fraction from logistic models.

Bruzzi et al. (1985, American Journal of Epidemiology 122, 904-914) provided a general logistic-model-based estimator of the attributable fraction for case-control data, and Benichou and Gail (1990, Biometrics 46, 991-1003) gave an implicit-delta-method variance formula for this estimator. The Bruzzi et al. estimator is not, however, the maximum likelihood estimator (MLE) based on the model, as it uses the model only to construct the relative risk estimates, and not the covariate-distribution estimate. We here provide maximum likelihood estimators for the attributable fraction in cohort and case-control studies, and their asymptotic variances. The case-control estimator generalizes the estimator of Drescher and Schill (1991, Biometrics 47, 1247-1256). We also present a limited simulation study which confirms earlier work that better small-sample performance is obtained when the confidence interval is centered on the log-transformed point estimator rather than the original point estimator.

Biometry↗

PubMed1992

Distinction between early normal intrauterine pregnancies and pathological pregnancies by means of a logistic model.

The probability of an unclear very early pregnancy being a normal intrauterine pregnancy was estimated using a logistic model. Five diagnostic measures of prognostic value were identified in the model: (i) daily change in human chorionic gonadotrophin (HCG), (ii) results of transvaginal ultrasound, (iii) vaginal bleeding, (iv) serum progesterone level and (v) risk score for ectopic pregnancy. With the use of this model, the probability of a normal intrauterine pregnancy has been estimated as 96.7%.

Chorionic Gonadotropin↗

PubMed1994

Application of a log-logistic model to describe the survival of Yersinia enterocolitica at sub-optimal pH and temperature.

A log-logistic model previously used to describe the thermal inactivation of microorganisms has been applied to predict accurately the survival of Yersinia enterocolitica under conditions of sub-optimal temperatures (0-23 degrees C) and growth inhibitory pH values for a number of different acidulants. Predictions from the model were also compared to observed survival times of Y. enterocolitica in mayonnaise and were found to be in excellent agreement. However in yoghurt the model overestimated survival of this organism at both 20 and 4 degrees C but would 'fail safe' if used for predictive purposes. The discrepancy in yoghurt is discussed.

Food Microbiology↗

PubMed1996

Predictive diagnostics for logistic models.

Novel methodology is implemented to assess the predictive power of covariate information associated with sequential binary events. Logistic models are first fitted on the basis of a subset of the observations and then evaluated sequentially on the rest. The probabilistic forecasts are compared to the outcomes via a scoring function, but as most validation samples are small, the usual reference distribution for the test statistics is inadequate. However, bootstrap-based distributions can easily be constructed. The first example pertains to the evaluation of screening tests for major depression. It illustrates that goodness-of-fit and predictive assessments lead to the selection of very different models. The second example deals with the prediction of a major event in the natural history of HIV-induced disease. It shows that this type of analysis can reveal features missed by other approaches.

Calibration↗

PubMed2004

Improvement of new logistic model for bacterial growth.

Recently Fujikawa et al. [J. Food Hyg. Soc. Japan, 44, 155-160 (2003)] developed a new logistic model for bacterial growth. In the present study, an adjustment factor in the model was improved. The improved model could successfully describe growth curves of Escherichia coli and Salmonella in liquid media. In particular, the model could describe the linear growth at the early logarithmic phase more accurately than the previous model, being similar in this respect to the Baranyi model. However, the improved model more accurately predicted the rate constant of growth and the duration of the lag time as compared with the Baranyi model. These results showed that the improved model has the potential to successfully predict microbial growth.

Bacteria↗