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At least 253 records · Page 14Linked to original sources
PubMed1989

An integrated logistic formula for prediction of complications from radiosurgery.

An integrated logistic model for predicting the probability of complications when small volumes of tissue receive an inhomogeneous radiation dose is described. This model can be used with either an exponential or linear quadratic correction for dose per fraction and time. Both the exponential and linear quadratic versions of this integrated logistic formula provide reasonable estimates of the tolerance of brain to radiosurgical dose distributions where there are small volumes of brain receiving high radiation doses and larger volumes receiving lower doses. This makes it possible to predict the probability of complications from stereotactic radiosurgery, as well as combinations of fractionated large volume irradiation with a radiosurgical boost. Complication probabilities predicted for single fraction radiosurgery with the Leksell Gamma Unit using 4, 8, 14, and 18 mm diameter collimators as well as for whole brain irradiation combined with a radiosurgical boost are presented. The exponential and linear quadratic versions of the integrated logistic formula provide useful methods of calculating the probability of complications from radiosurgical treatment.

Adult↗
PubMed1990

Detection of restenosis after successful coronary angioplasty: improved clinical decision making with use of a logistic model combining procedural and follow-up variables.

A prospective study of 111 patients who underwent repeat coronary angiography and exercise thallium-201 scintigraphy 6 +/- 2 months after complete revascularization by percutaneous transluminal coronary angioplasty was performed to assess whether clinical, procedure-related and postangioplasty exercise variables yield independent information for the prediction of angiographic restenosis after angioplasty. Complete revascularization was defined as successful angioplasty of one or more vessels that resulted in no residual coronary lesion with greater than 50% diameter stenosis. Restenosis was defined as a residual stenosis at the time of repeat angiography of greater than 50% of luminal diameter. Restenosis occurred in 40% of the patients. The 111 patients were randomly subdivided into a learning group (n = 84) and a testing group (n = 27). A logistic discriminant analysis was performed in the learning group and the logistic model was used to estimate a logistic probability of restenosis. This probability of restenosis was validated in the testing group. In the learning group of 84 patients univariate analysis of 39 factors revealed 8 factors related to restenosis: recurrence of angina (p less than 0.0001), postangioplasty abnormal finding on exercise thallium-201 scintigram (p less than 0.0001), exercise thallium-201 scintigram score (p less than 0.0001), difference between exercise and rest ST segment depression (p less than 0.001), postangioplasty exercise ST segment depression (p less than 0.001), absolute postangioplasty stenosis diameter (p less than 0.003), postangioplasty exercise work load (p less than 0.03) and postangioplasty exercise heart rate (p less than 0.05).(ABSTRACT TRUNCATED AT 250 WORDS)

Angina Pectoris↗
PubMed1989

Polytomous logistic regression analysis of the General Health Questionnaire and the Present State Examination.

First, two examples of dichotomous logistic regression analysis are presented. The probability of being a psychiatric case according to the Present State Examination is predicted from the total score on the General Health Questionnaire and from the general practitioner's judgement on the presence of a mental health problem. Subjects were 292 primary care attenders. Results are compared with those from prior studies. Next, the extension to the polytomous case is demonstrated. The probability of being at any given level of the Index of Definition (computed from PSE data) is estimated from the General Health Questionnaire total score by an ordered polytomous logistic regression model. Several applications of the polytomous logistic regression model are discussed. These range from estimating the proportion of psychiatric cases among individuals who refuse to be interviewed to the formulation of sampling schemes which can be expected to reduce costs while at the same time yielding optimal information for testing specific hypotheses.

Family Practice↗
PubMed1998

Screening for primary aldosteronism with a logistic multivariate discriminant analysis.

OBJECTIVE: Primary aldosteronism (PA) is the most common endocrine cause of curable hypertension, but no single test unequivocally identifies it. Accordingly, we investigated the usefulness of a logistic multivariate discriminant analysis (MDA) approach for PA screening. DESIGN: Generation of a logistic MDA function based on retrospective analysis of biochemical tests in a large cohort of referred patients with/without confirmed Conn's adenoma (CA), followed by prospective validation of the model. PATIENTS: We investigated 574 selected hypertensives: 206 (32 with and 174 without CA) retrospectively, 48 (with a 13% prevalence of CA) prospectively for the validation of the model, and 320 referred hypertensives (with a 3.4% prevalence of CA) similarly evaluated. Patients were referred to a specialised centre for hypertension (4th Clinica Medica--University of Padua) and to a department of Internal Medicine of a regional hospital (Reggio Emilia). MEASUREMENTS: In all patients we measured several demographic and biochemical variables and performed a captopril test. A stepwise analysis of variance, based on a model fitted with several different variables, identified baseline (sALDO) and captopril-suppressed plasma aldosterone (cALDO), supine plasma renin activity (sPRA) and K+ as the most informative. Therefore, two models of logistic MDA with sPRA, K+, and either sALDO (model A) or cALDO (model B) were developed and used. ROC analysis was also performed to assess the optimal cut-off values. RESULTS: The model B of MDA provided the best performance and identified CA with 100% sensitivity and 81% accuracy. When used prospectively it showed 100% sensitivity, both in the Padua (88% accuracy) and in the Reggio Emilia series (90% accuracy). However, at both institutions most patients with idiopathic hyperaldosteronism (IHA) were also detected. CONCLUSIONS: Thus, although developed from patients with confirmed Conn's adenoma, a strategy based on multivariate discriminant analysis can be used prospectively for accurate screening for primary aldosteronism. Furthermore, it was proven to be accurate and applicable to patients tested with similar modalities at a different institution. Although this approach did not provide a clear-cut discrimination of Conn's adenoma from idiopathic hyperaldosteronism, it may avoid unnecessary and costly further testing in patients with a low probability of primary aldosteronism.

Adult↗
PubMed1975

A double logistic comparison of growth patterns of normal children and children with Down's syndrome.

A double logistic model was used to compare six parameters of growth in standing height of 31 children with Down's syndrome with 136 children from the California Guidance Study. Multivariate analysis of variance of the growth data showed that while there were significant differences in all six parameters favouring the normal over the Down's children, there were no significant differences with respect to error of fit. Multivariate analysis with final height as a covariate revealed that differences between the normal and the Down's children in the prepubertal and adolescent components were explainable by differences in final height. In summary, the double logistic model, when applied to this sample of Down's children, identified those well defined logistic components which are characteristic of the growth of normal children, the differences being those of degree, not of form.

Analysis of Variance↗
PubMed1997

A logistic dose-ranging method for phase I clinical investigations trials.

This paper describes an alternative to the continual reassessment method (CRM) for phase I trials. The logistic dose ranging strategy (LDRS) uses logistic regression and a dose allocation scheme similar to the CRM. It can easily be implemented from any logistic regression program. The LDRS can be a stand alone dose allocation scheme or it can be incorporated into standard three on a dose strategies to indicate when escalation can proceed more rapidly. Finally, the effect of covariates such as age or comorbid conditions on the toxicity expected for the dose selected for a phase II trial can be examined.

Bayes Theorem↗
PubMed1998

Prediction and cross-validation of neural networks versus logistic regression: using hepatic disorders as an example.

The authors developed and cross-validated prediction models for newly diagnosed cases of liver disorders by using logistic regression and neural networks. Computerized files of health care encounters from the Fallon Community Health Plan were used to identify 1,674 subjects who had had liver-related health services between July 1, 1992, and June 30, 1993. A total of 219 subjects were confirmed by review of medical records as incident cases. The 1,674 subjects were randomly and evenly divided into training and test sets. The training set was used to derive prediction algorithms based solely on the automated data; the test set was used for cross-validation. The area under the Receiver Operating Characteristic curve for a neural network model was significantly larger than that for logistic regression in the training set (p = 0.04). However, the performance was statistically equivalent in the test set (p = 0.45). Despite its superior performance in the training set, the generalizability of the neural network model is limited. Logistic regression may therefore be preferred over neural network on the basis of its established advantages. More generalizable modeling techniques for neural networks may be necessary before they are practical for medical research.

Algorithms↗
PubMed1986

Fertility of workers. A comparison of logistic regression and indirect standardization.

Estimates of the effect of occupational exposure on the fertility of men employed at three chemical plants were obtained from data stored at the Chemical Industry Institute of Toxicology using logistic regression and indirect standardization. Logistic regression was explored as a possible alternative to indirect standardization because it 1) permits consideration of potential confounding variables, such as the relative spacing between consecutive births, that are not included in the characterization of the fertility of an external reference population, and 2) may enable the study of occupational cohorts for which fertility data from an appropriate external reference population are not available. In addition to the main effects of age, parity, and birth cohort, main effects of certain lag variables which characterize the timing of birth events during the five-year period preceding each person-year were found to be significant at all three plants. Interactions of some of these lag variables with age also emerged as significant. Despite this, both methods were consistent in accepting or rejecting the null hypothesis regarding the effect of exposure at two of the plants. At the third plant, however, the logistic regression yielded a significant interaction between exposure status and one of the lag variables. The net exposure effect was significantly increased fertility in exposed person-years for which no birth had occurred 2-3 years prior, and nonsignificantly decreased fertility in exposed person-years for which such a prior birth had occurred. While this may be a spurious finding, it suggests that both methods of analysis should continue to be explored until a larger body of similar comparative studies has accumulated.

Adolescent↗
PubMed1989

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Algorithms↗
PubMed1998

The interaction of eltanolone and fentanyl with special reference to logistic regression analysis.

UNLABELLED: We investigated whether fentanyl decreases the serum concentrations of the steroid anesthetic eltanolone effective in producing loss of consciousness in 50% of patients (EC50induction) and in preventing movement at skin incision in 50% of patients (EC50incision). For anesthetic induction, patients received effect-site target concentrations of fentanyl 0.0, 1.5, 3.0, or 4.5 ng/mL and eltanolone 500, 750, 1000, or 1200 ng/mL. Loss of response to verbal command was assessed after 10 min. For incision, patients received effect-site target concentrations of fentanyl 0.5,1.5, 3.0, or 4.5 ng/mL and eltanolone 547-2926 ng/mL. Movement at incision was assessed at least 10 min after new targets were entered. Probability of loss of consciousness and of movement versus arterial serum concentration combinations were analyzed by logistic regression. Dixon up-down analysis was used to estimate ET50incision effective target concentration combinations. In the absence of fentanyl, anesthesia was induced in only 1 of 12 patients, which suggests that the EC50induction is >1500 ng/mL at fentanyl 0.0 ng/mL. With fentanyl (38 patients), eltanolone EC50induction was independent of fentanyl concentration, calculated as 628 ng/mL. For the incision phase (52 patients), logistic regression failed to generate a valid model. Dixon analysis (43 patients) produced an eltanolone ET50incision of 2288 ng/mL at fentanyl targets of 0.5 ng/mL, 754 ng/mL at 1.5 ng/mL, 735 ng/mL at 3.0 ng/mL, and 645 ng/mL at 4.5 ng/mL. Fentanyl reduced the serum concentration of eltanolone required to produce loss of consciousness and the target concentration of eltanolone required to prevent movement to skin incision. IMPLICATIONS: Fentanyl reduced the serum concentration of eltanolone required to produce loss of consciousness and the target concentration of eltanolone required to prevent movement to skin incision. Future interaction studies of this nature using logistic regression should model responses to hypnotic alone separately from responses to hypnotic-analgesic combinations.

Adolescent↗
PubMed2000

Thermodynamic properties of a solid exhibiting the energy spectrum given by the logistic map

We show that the infinite-dimensional representation of the recently introduced logistic algebra can be interpreted as a nontrivial generalization of the Heisenberg or oscillator algebra. This allows us to construct a quantum Hamiltonian having the energy spectrum given by the logistic map. We analyze the Hamiltonian of a solid whose collective modes of vibration are described by this generalized oscillator and compute the thermodynamic properties of the model in the two-cycle and r=3.6785 chaotic region of the logistic map.

Journal Article↗
PubMed2026

Sparse Logistic Regression on Genomic Data for Prediction of Tumour Pathological Subtype.

The correct prediction of tumour subtype is critical for the treatment of cancer patients to maximise the chance of survival. The patients' genomic information, such as copy number alterations (CNA) profile, has increasingly become an important factor in the prediction to supplement the traditional pathological subtyping. The incorporation of the CNA information in a prediction model, such as logistic regression, faces two major statistical challenges: first, how to estimate the model parameters in the thousands and, second, how to deal with the correlation of CNA between genomic regions. To address them, we propose a sparse logistic regression model with random effects where some of its parameters are estimated to zero while the other parameters are non-zero. In effect, a variable selection is embedded in the modelling. To deal with the correlation of CNA across genomic regions, we extend further the model to incorporate an additional penalty in the corresponding likelihood function in the logistic regression. The results show that we can identify selected genomic regions that are informative to distinguish different tumour subtypes, while giving a good prediction ability. We illustrate the methodology using CNA dataset from a lung cancer cohort.

Journal Article↗
PubMed2000

A simulation study of measurement error correction methods in logistic regression.

Measurement error models in logistic regression have received considerable theoretical interest over the past 10-15 years. In this paper, we present the results of a simulation study that compares four estimation methods: the so-called regression calibration method, probit maximum likelihood as an approximation to the logistic maximum likelihood, the exact maximum likelihood method based on a logistic model, and the naive estimator, which is the result of simply ignoring the fact that some of the explanatory variables are measured with error. We have compared the behavior of these methods in a simple, additive measurement error model. We show that, in this situation, the regression calibration method is a very good alternative to more mathematically sophisticated methods.

Calibration↗
PubMed2000

Minimax D-optimal designs for the logistic model.

We propose an algorithm for constructing minimax D-optimal designs for the logistic model when only the ranges of the values for both parameters are assumed known. Properties of these designs are studied and compared with optimal Bayesian designs and Sitter's (1992, Biometrics, 48, 1145-1155) minimax D-optimal kk-designs. Examples of minimax D-optimal designs are presented for the logistic and power logistic models, including a dose-response design for rheumatoid arthritis patients.

Algorithms↗
PubMedDate not supplied

Evaluation of a multivariate prostate-specific antigen and percentage of free prostate-specific antigen logistic regression model in the diagnosis of prostate cancer.

The use of prostate-specific antigen (PSA) in the diagnosis of prostate cancer is controversial due to false-positive results caused by benign prostatic hyperplasia. Several groups have suggested the usefulness of the percentage of free PSA (%fPSA) in patients with PSA levels between 4 and 10 microg/l. Based on previously obtained results, biopsy is carried out in our hospital if the PSA is greater than 10 microg/l or if the %fPSA is lower than 20% and PSA is between 4-10 microg/l. In this study, we have compared these results with those obtained with a logistic regression model based on the determination of PSA and %fPSA. The diagnostic efficacy of the logistic regression model is greater than that of the currently used model. The posterior construction of a nomogram based on the data obtained greatly facilitates the application of the logistic regression model.

Aged↗
PubMed1983

The use of radionuclide angiography in the diagnosis of coronary artery disease--a logistic regression analysis.

We applied logistic regression analysis to a group of 736 patients with chest pain to determine which radionuclide angiographic (RNA) parameters were most useful in the diagnosis of significant coronary artery disease. The most useful parameters were exercise ejection fraction, exercise heart rate, "ischemia score," and the presence of a regional wall motion abnormality at exercise. Ten clinical variables were used in one logistic regression model to estimate each patient's pretest probability of disease. A second logistic regression model considered these clinical variables and the four important RNA parameters to estimate each patient's posttest probability. These models were applied prospectively to a group of 76 patients with chest pain who did not have a high pretest probability of disease. Twenty-four patients (32%) could be diagnosed with 90% probability; 32 patients (42%) could be diagnosed with 85% probability. RNA testing is therefore helpful in the noninvasive diagnosis of coronary artery disease. However, a majority of patients who do have a low or intermediate pretest probability of disease will require additional testing for a definitive diagnosis.

Cardiac Catheterization↗
PubMed1997

Logistic regression.

Logistic regression has probably been underutilized in clinical investigations of personality because of its relatively recent development (dictated by the need for computer programs to obtain maximum likelihood estimates), and the fact that use has been largely confined to the fields of biostatistics, epidemiology, and economics Its use should be given serious consideration when the outcome of interest is dichotomous (or polychotomous) in nature and the predictors of interest may be categorical or continuous. The logit transformation is quite tractable mathematically, and it embodies the notion of threshold, which may have relevance for many of the variables that are of interest to investigators in the field of personality. Furthermore, investigators with experience in multiple linear regression or contingency table analysis should have little trouble in transitioning to logistic regression. Logistic regression programs are readily available in the major statistical packages, all of which provide fairly standard output.

Journal Article↗
PubMed2002

A multivariate logistic regression equation to screen for diabetes: development and validation.

OBJECTIVE: To develop and validate an empirical equation to screen for diabetes. RESEARCH DESIGN AND METHODS: A predictive equation was developed using multiple logistic regression analysis and data collected from 1,032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, BMI, postprandial time (self-reported number of hours since last food or drink other than water), and random capillary plasma glucose as independent covariates for prediction of undiagnosed diabetes. These covariates were based on a fasting plasma glucose level >/=126 mg/dl and/or a plasma glucose level 2 h after a 75-g oral glucose load >/=200 mg/dl. The equation was validated using data collected from an independent sample of 1,065 American subjects. Its performance was also compared with that of recommended and proposed static plasma glucose cut points for diabetes screening. RESULTS: The predictive equation was calculated with the following logistic regression parameters: P = 1/(1 - e(-x)), where x = -10.0382 + [0.0331 (age in years) + 0.0308 (random plasma glucose in mg/dl) + 0.2500 (postprandial time assessed as 0 to >/=8 h) + 0.5620 (if female) + 0.0346 (BMI)]. The cut point for the prediction of previously undiagnosed diabetes was defined as a probability value >/=0.20. The equation's sensitivity was 65%, specificity 96%, and positive predictive value (PPV) 67%. When applied to a new sample, the equation's sensitivity was 62%, specificity 96%, and PPV 63%. CONCLUSIONS: This multivariate logistic equation improves on currently recommended methods of screening for undiagnosed diabetes and can be easily implemented in a inexpensive handheld programmable calculator to predict previously undiagnosed diabetes.

Blood Glucose↗