Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Logistics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9Linked to original sources
PubMed2006

Properties of R(2) statistics for logistic regression.

Various R(2) statistics have been proposed for logistic regression to quantify the extent to which the binary response can be predicted by a given logistic regression model and covariates. We study the asymptotic properties of three popular variance-based R(2) statistics. We find that two variance-based R(2) statistics, the sum of squares and the squared Pearson correlation, have identical asymptotic distribution whereas the third one, Gini's concentration measure, has a different asymptotic behaviour and may overstate the predictivity of the model and covariates when the model is mis-specified. Our result not only provides a theoretical basis for the findings in previous empirical and numerical work, but also leads to asymptotic confidence intervals. Statistical variability can then be taken into account when assessing the predictive value of a logistic regression model.

Biometry↗
PubMed2006

Random effects logistic models for analysing efficacy of a longitudinal randomized treatment with non-adherence.

We present a random effects logistic approach for estimating the efficacy of treatment for compliers in a randomized trial with treatment non-adherence and longitudinal binary outcomes. We use our approach to analyse a primary care depression intervention trial. The use of a random effects model to estimate efficacy supplements intent-to-treat longitudinal analyses based on random effects logistic models that are commonly used in primary care depression research. Our estimation approach is an extension of Nagelkerke et al.'s instrumental variables approximation for cross-sectional binary outcomes. Our approach is easily implementable with standard random effects logistic regression software. We show through a simulation study that our approach provides reasonably accurate inferences for the setting of the depression trial under model assumptions. We also evaluate the sensitivity of our approach to model assumptions for the depression trial.

Humans↗
PubMed2007

Tree-based model checking for logistic regression.

A tree procedure is proposed to check the adequacy of a fitted logistic regression model. The proposed method not only makes natural assessment for the logistic model, but also provides clues to amend its lack-of-fit. The resulting tree-augmented logistic model facilitates a refined model with meaningful interpretation. We demonstrate its use via simulation studies and an application to the Pima Indians diabetes data.

Biomedical Research↗
PubMed1990

On the use of a logistic risk score in predicting risk of coronary heart disease.

Many studies over the last 20 years have used logistic regression to model the relationship between the risk of developing coronary heart disease (CHD) and the levels of risk factors such as high blood pressure, high serum cholesterol, and cigarette smoking. Subsequently, several investigators have proposed the use of some of the published estimated logistic risk functions to predict risk in new populations. Because of great variation in definition of event, duration of follow-up, population characteristics, definition of risk variables, and selection of other variables in the logistic functions, direct use of such established functions would generally not have validity for the prediction of absolute risk levels. A review of fifteen of these studies indicates on the one hand generally similar results in direction and order of magnitude of effects of the major risk factors, confirming the importance of these risk factors of CHD. On the other hand the reviews indicate sufficient variation to suggest that extrapolation to new populations even to predict relative risk is not justified.

Age Factors↗
PubMed1992

The use of cusums and other techniques in modelling continuous covariates in logistic regression.

The assessment of continuous covariates singly as possible predictors in a multivariable logistic regression model is an important first step in the analysis. An approach to plotting which uses a cusum (cumulative sum) of the binary response variable is described. Extreme-deviation statistics associated with the cusum may be used to detect monotonic and non-monotonic trends. Probability plots of the covariate in the two groups defined by the response variable may help to determine the appropriate scale (transformation) of the covariate and to anticipate possible problems with the logistic fit. The ratio of the variances in the response/non-response groups is informative about the need for a quadratic term in the logistic model. Smoothed scatterplots of the response are valuable in displaying the observed and fitted values. The techniques are illustrated with two data sets.

Binomial Distribution↗
PubMed1993

Sample size determinations using logistic regression with pilot data.

Suppose the goal of a projected study is to estimate accurately the value of a 'prediction' proportion p that is specific to a given set of covariates. Available pilot data show that (1) the covariates are influential in determining the value of p and (2) their relationship to p can be modelled as a logistic regression. A sample size justification for the projected study can be based on the logistic model; the resulting sample sizes not only are more reasonable than the usual binomial sample size values from a scientific standpoint (since they are based on a model that is more realistic), but also give smaller prediction standard errors than the binomial approach with the same sample size. In appropriate situations, the logistic-based sample sizes could make the difference between a feasible proposal and an unfeasible, binomial-based proposal. An example using pilot study data of dental radiographs demonstrates the methods.

Confidence Intervals↗
PubMed1993

A Bayesian approach to logistic regression models having measurement error following a mixture distribution.

To estimate the parameters in a logistic regression model when the predictors are subject to random or systematic measurement error, we take a Bayesian approach and average the true logistic probability over the conditional posterior distribution of the true value of the predictor given its observed value. We allow this posterior distribution to consist of a mixture when the measurement error distribution changes form with observed exposure. We apply the method to study the risk of alcohol consumption on breast cancer using the Nurses Health Study data. We estimate measurement error from a small subsample where we compare true with reported consumption. Some of the self-reported non-drinkers truly do not drink. The resulting risk estimates differ sharply from those computed by standard logistic regression that ignores measurement error.

Age Factors↗
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↗
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↗
PubMed2001

Comparison of logistic and Bayesian classifiers for evaluating the risk of femoral neck fracture in osteoporotic patients.

Femoral neck fracture prediction is an important social and economic issue. The research compares two statistical methods for the classification of patients at risk for femoral neck fracture: multiple logistic regression and Bayes linear classifier. The two approaches are evaluated for their ability to separate femoral neck fractured patients from osteoporotic controls. In total, 272 Italian women are studied. Densitometric and geometric measurements are obtained from the proximal femur by dual energy X-ray absorptiometry. The performances of the two methods are evaluated by accuracy in the classification and receiver operating characteristic curves. The Bayes classifier achieves an accuracy approximately 1% higher than that of the multiple logistic regression. However, the performances of the two methods, evaluated by the area under the curves, are not statistically different. The study demonstrates that the Bayes linear classifier can be a valid alternative to multiple logistic regression in the classification of osteoporotic patients.

Aged↗
PubMed2004

Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS.

For landslide susceptibility mapping, this study applied and verified a Bayesian probability model, a likelihood ratio and statistical model, and logistic regression to Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite imagery and field surveys; and a spatial database was constructed from topographic maps, soil type, forest cover, geology and land cover. The factors that influence landslide occurrence, such as slope gradient, slope aspect, and curvature of topography, were calculated from the topographic database. Soil texture, material, drainage, and effective depth were extracted from the soil database, while forest type, diameter, and density were extracted from the forest database. Land cover was classified from Landsat TM satellite imagery using unsupervised classification. The likelihood ratio and logistic regression coefficient were overlaid to determine each factor's rating for landslide susceptibility mapping. Then the landslide susceptibility map was verified and compared with known landslide locations. The logistic regression model had higher prediction accuracy than the likelihood ratio model. The method can be used to reduce hazards associated with landslides and to land cover planning.

Databases, Factual↗
PubMed1992

Comparison of logistic regression and Bayesian-based algorithms to estimate posttest probability in patients with suspected coronary artery disease undergoing exercise ECG.

Two multivariate methods, a logistic regression-derived algorithm and a Bayesian independence-assuming method (CADENZA), were compared concerning their abilities to estimate posttest probability of coronary disease in patients with suspected coronary disease. All patients underwent exercise testing within 3 months prior to coronary angiography. Coronary disease was defined as the presence of one or more vessels with greater than or equal to 50% luminal diameter narrowing. A group of 300 patients (disease prevalence = 37%) was used to derive the algorithm. Another group of 950 patients was used to validate the algorithm and compare it to CADENZA. Seven variables (age, sex, symptoms, diabetes, mm ST depression, ST slope, and peak heart rate) were used to generate posttest probabilities for each method. The receiver operating characteristic curve area for the logistic regression method (0.81 +/- 0.01) was significantly higher than CADENZA (0.75 +/- 0.01; p less than 0.05). There was, however, no difference in the calibration of the two methods. When given equivalent variable information, the logistic regression algorithm had better discrimination than CADENZA for estimating the probability of coronary disease following exercise electrocardiography.

Algorithms↗
PubMed1991

Using ordinal logistic regression to estimate the likelihood of colorectal neoplasia.

The utility of ordinal logistic regression in the prediction of colorectal neoplasia was demonstrated in a group of 461 consecutive patients undergoing colonoscopy in a community practice. One hundred twenty-nine patients had adenomatous polyps and 34 had colorectal adenocarcinoma. An ordinal logistic regression model developed in a random subset (292 patients) identified five predictors of colorectal neoplasia. Colorectal neoplasia risk could be predicted using the patient's age, sex, hematocrit, fecal occult blood test result and indication for colonoscopy. The risk of colorectal neoplasia in the remaining subset of patients (169) could be reliably estimated from the model. Ordinal logistic regression analysis in this select group of patients can accurately estimate the likelihood of colorectal neoplasia. Because the generalizability of our findings are unknown, the model should not be applied to other patients. However, application of this technique to an unselected group of patients not already referred for colonoscopy could provide unbiased estimates of colorectal neoplasia risk in individual patients.

Adenocarcinoma↗
PubMed1995

An investigation of the relationship between antioxidant vitamin intake and coronary heart disease in men and women using logistic regression analysis.

Antioxidant vitamin intake (C,E and carotene) is assessed from a food frequency questionnaire applied to 10,359 middle-aged men and women participating in the Scottish Heart Health Study. Logistic regression analysis is then used to quantify the relationship between antioxidant vitamin consumption and prevalent coronary heart disease (CHD), analysing diagnosed and undiagnosed cases separately. For men, there is a protective effect of all three antioxidants, before and after adjustment for a comprehensive set of confounding variables. For women the picture is less clear, only vitamin C is negatively associated with CHD, but the effect is removed by adjustment. The logistic regression model is also used to determine classification rules for deciding whether or not an individual has CHD. The classification error rates using the antioxidants are found to be very similar to those found using smoking, blood pressure and serum total cholesterol as classification variables. Significant interactions are found for the antioxidants with smoking, cholesterol and age. It is concluded that antioxidant vitamin intake protects against CHD for men. Logistic regression analysis is compared with discriminant analysis, and is found to have important advantages as an epidemiological tool.

Adult↗
PubMed2006

Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room.

OBJECTIVE: Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. METHODS AND MATERIALS: Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients presenting an emergency department with chest pain. Only data immediately available at patient presentation were used, including electrocardiogram (ECG) data. The models were analyzed using receiver operating characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared with the odds ratios obtained from the logistic model. RESULTS: The ANN ensemble approach together with ECG data preprocessed using principal component analysis resulted in an area under the ROC curve of 80%. At the sensitivity of 95% the specificity was 41%, corresponding to a negative predictive value of 97%, given the ACS prevalence of 21%. Adding clinical data available at presentation did not improve the ANN ensemble performance. Using the area under the ROC curve and model calibration as measures of performance we found an advantage using the ANN ensemble models compared to the logistic regression models. CONCLUSION: Clinically, a prediction model of the present type, combined with the judgment of trained emergency department personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.

Acute Disease↗
PubMed2005

Classification of EEG signals using neural network and logistic regression.

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.

Adult↗
PubMed2006

Predictors of non-calcified plaques in the coronary arteries of 242 subjects using multislice computed tomography and logistic regression models.

OBJECTIVES: We detected non-calcified plaques (NCPs) in the coronary arteries using multislice computed tomography (MSCT) in order to determine the predictors of NCPs using logistic regression models. METHODS: Two hundred and forty-two consecutive subjects (141 males; overall age range, 17-91 years old) underwent enhanced electrocardiogram-gated MSCT to detect NCPs. Logistic models for predicting NCPs were developed which incorporated age, sex, coronary calcified-plaque (CP), and the following coronary risk factors (RFs): hypertension (HT), diabetes mellitus (DM), hyperlipidemia (HL), a smoking habit, and obesity. RESULTS: NCPs were detected in 76 subjects (59 males, 35-82 years old [median=67]) whose average number of coronary RFs was 2.6, 75% of whom presented with HT, 30% with DM, 51% with HL, 64% were present or past cigarette smokers, and 32% were obese. In the 76 subjects with NCPs, the incidence of male sex, presence of HT, a smoking habit, CP and the number of coronary RFs were significantly higher than in the 166 subjects without NCP. Of the 101 female subjects, 17 showed NCPs and in every case the subject was more than 50 years old. In a logistic regression model, male sex, HT and smoking habit (relative risks 2.7, 2.0, and 2.7 [95% confidence interval=1.3-5.6, 1.0-4.0, and 1.5-4.9, respectively]) were associated with increased incidence of NCPs. CONCLUSIONS: The incidence of NCPs was significantly increased in the presence of HT and a smoking habit, suggesting that lesions may be caused by HT or smoking-induced vessel injury even in the young male.

Abstracting and Indexing↗
PubMed2004

Cancer classification and prediction using logistic regression with Bayesian gene selection.

In microarray-based cancer classification and prediction, gene selection is an important research problem owing to the large number of genes and the small number of experimental conditions. In this paper, we propose a Bayesian approach to gene selection and classification using the logistic regression model. The basic idea of our approach is in conjunction with a logistic regression model to relate the gene expression with the class labels. We use Gibbs sampling and Markov chain Monte Carlo (MCMC) methods to discover important genes. To implement Gibbs Sampler and MCMC search, we derive a posterior distribution of selected genes given the observed data. After the important genes are identified, the same logistic regression model is then used for cancer classification and prediction. Issues for efficient implementation for the proposed method are discussed. The proposed method is evaluated against several large microarray data sets, including hereditary breast cancer, small round blue-cell tumors, and acute leukemia. The results show that the method can effectively identify important genes consistent with the known biological findings while the accuracy of the classification is also high. Finally, the robustness and sensitivity properties of the proposed method are also investigated.

Algorithms↗