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At least 613 records · Page 34Linked to original sources
PubMed2000

Patient-controlled analgesia and urinary retention following lower limb joint replacement: prospective audit and logistic regression analysis.

We studied a number of factors that may be associated with urinary retention, in particular the method of postoperative analgesia delivery, in 47 men and 69 women undergoing lower limb joint replacements. The following factors were studied: age, gender, height, weight, previous history of urinary retention, presence of symptoms suggestive of urinary tract obstruction, type of anaesthetic (general anaesthetic or spinal anaesthetic), type of postoperative analgesia (intramuscular or patient-controlled analgesia with morphine) and the total dose of morphine given. Urinary retention developed in 18.1% of patients. Stepwise logistic regression analysis was used to identify independent explanators of an increased probability of developing urinary retention. Three factors emerged - male gender, increasing age and the use of patient-controlled analgesia.

Adult↗
PubMed2002

Color Doppler energy prediction of malignancy in adnexal masses using logistic regression models.

OBJECTIVE: The aim of this study was to assess the usefulness of color Doppler energy in the preoperative diagnosis of ovarian malignancy using multivariate logistic regression analysis. METHODS: One hundred and thirty adnexal masses were studied with transvaginal B-mode, color energy, and pulsed Doppler ultrasonography before surgery in order to develop a model that could be used to determine malignancy. Each ultrasonographic variable (tumor size, wall thickness, septal structure, echogenicity, papillary projection, density (solid or not)) was included individually or combined together as part of the Sassone ultrasound score. Intratumoral blood flow velocity waveforms were obtained to determine pulsatility index and resistance index and a more subjective parameter, location of tumor vascularity, was also assessed. Menopausal status and serum CA 125 levels were also entered as categorical variables. Sonographic parameters were entered alone, then associated with menopausal status and CA 125 serum levels, and finally with Doppler energy measurements. Our model was then validated in a group of 68 adnexal masses and compared to the model of Alcazar. RESULTS: Eighteen adnexal masses (13.8%) were malignant or of low malignant potential. Multivariate analysis showed that papillary projection of the tumor wall, cyst with solid parts, resistance index with a cut-off value of 0.53, CA 125, and central blood flow location, were the only factors to be independent predictors of malignancy. Menopausal status was not an independent factor. For the final model including the Doppler energy parameter the best sensitivity and specificity were 83% and 93%, respectively, at a cut-off value of 10% probability of malignancy compared to 83% and 87% for the morphological variables alone. Validation of the model showed its diagnostic performance to be as good as that reported in the original population and better than the model of Alcazar. CONCLUSION: Sonographic analysis of adnexal masses including color Doppler energy shows the best predictive properties according to histological diagnosis, and improves preoperative diagnosis of malignancy.

Adolescent↗
PubMed2005

The phase-modulated logistic map.

We study the logistic mapping with the nonlinearity parameter varied through a delayed feedback mechanism. This history dependent modulation through a phaselike variable offers an enhanced possibility for stabilization of periodic dynamics. Study of the system as a function of nonlinearity and modulation parameters reveals new phenomena: In addition to period-doubling and tangent bifurcations, there can be bifurcations where the period increases by unity. These are extensions of crises that arise in nonlinear dynamical systems. Periodic orbits in this system can be systematized via the kneading theory, which in the present case extends the analysis of Metropolis, Stein, and Stein for unimodal maps.

Logistic Models↗
PubMed2003

Lower-extremity muscle forces measured by a hand-held dynamometer and the risk of falls among day-care users in Japan: using multinomial logistic regression analysis.

PURPOSE: To assess the relationship between the lower extremity muscle forces and risk of falls among the elderly who were using day-care services in the long-term care insurance system. METHOD: The study population comprised 96 women aged 69 - 91 years. At baseline, we assessed ADL, functional capacity, isometric muscle forces of knee extension and ankle dorsiflexion obtained with a hand-held dynamometer. After 1 year, we asked subjects about falls during the follow up period. RESULTS: In univariate analysis, only the knee extensor strength (p = 0.003) and ankle dorsiflexor strength (p = 0.02) differed significantly according to the fall status. After adjustment for age using multinomial logistic regression analysis, the weak quadriceps group was 7.50 times more likely than the strong group to fall once (p = 0.02), and 5.00 times more likely to fall twice or more (p = 0.02). The weakest ankle dorsiflexor group was 5.09 times more likely than the strongest group to fall twice or more (p = 0.05). CONCLUSIONS: Our results indicated that the forces of knee extensor and ankle dorsiflexor were strongly associated with falls among day-care users. Physiotherapy should focus on the lower-extremity muscle strength to prevent falls and deterioration of physical ability among elderly persons who are partially dependent and need support.

Accidental Falls↗
PubMed2006

Optimal crossover designs for logistic regression models in pharmacodynamics.

Pharmacodynamics (PD) is the study of the biochemical and physiological effects of drugs. The construction of optimal designs for dose-ranging trials with multiple periods is considered in this paper, where the outcome of the trial (the effect of the drug) is considered to be a binary response: the success or failure of a drug to bring about a particular change in the subject after a given amount of time. The carryover effect of each dose from one period to the next is assumed to be proportional to the direct effect. It is shown for a logistic regression model that the efficiency of optimal parallel (single-period) or crossover (two-period) design is substantially greater than a balanced design. The optimal designs are also shown to be robust to misspecification of the value of the parameters. Finally, the parallel and crossover designs are combined to provide the experimenter with greater flexibility.

Algorithms↗
PubMed1998

Accommodating negative intracluster correlation with a mixed effects logistic model for bivariate binary data.

We extend the random intercept logistic model to accommodate negative intracluster correlations for bivariate binary response data. This approach assumes a single random effect per cluster, but entails separate affine transformations of this random effect for the two responses of the pair. We show this approach works for two data sets and a simulation, whereas other mixed effects approaches fail. The two data sets are from a crossover trial and a developmental toxicity study of the effects of chemical exposure on malformation risk among rat pups. Comparisons are made with the conditional likelihood approach and with generalized estimating equations estimation of the population-averaged logit model. Simulations show the conditional likelihood approach does not perform well for moderate to strong negative correlations, as a positive intracluster correlation is assumed. The proposed mixed effects approach appears to be slightly more conservative than the population-averaged approach with respect to coverage of confidence intervals. Nonetheless, the statistical literature suggests that mixed effects models provide information in addition to that provided by population-averaged models under scientific contexts such as crossover trials. Extensions to trivariate and higher-dimensional responses also are addressed. However, such extensions require certain constraints on the correlation structure.

Abnormalities, Drug-Induced↗
PubMed1999

Pelvic relaxation and associated risk factors: the results of logistic regression analysis.

BACKGROUND: To determine the possible risk factors in our population. METHODS: From 250 patients attending our clinic 28 patients with pelvic relaxation were the study group and the rest, 222 in all, were the control group. RESULTS: Age, marriage period, parity and number of abortus in pelvic relaxation group are found to be significantly higher with univariate analysis. With logistic regression analysis menopause, grandmultiparity, abortus (two or more), home deliveries, history of macrosomic infant (4500 gr. or more), deliveries without episiotomy and laceration of uterine cervix are found to be risk factors for pelvic relaxation. CONCLUSIONS: Good family planning programs to reduce grandmultiparity and unnecessary curettage for undesired pregnancies, preventing harmful home deliveries by inexperienced people, and fearless usage of episiotomy in difficult deliveries are necessary to prevent pelvic relaxation.

Abortion, Induced↗
PubMed1999

Pharmacodynamic analysis of analgesic clinical trials: nonlinear mixed-effects logistic models.

In the development of an analgesic product, placebo-controlled clinical trials in patients with defined pain are used to study the dose-time-response relationship of the drug. In such trials, the response is usually an ordered categorical variable with longitudinal and subject-specific repeated measurements. The primary causal variables are time and analgesic concentration. The response may be informative right-hand censored because remedication with a known analgesic may be given if a patient has inadequate pain relief. Mixed-effects logistic models are used to estimate the probabilities of having certain pain relief or pain severity scores. A jackknife method is proposed to estimate the standard errors of parameter estimates. Posterior estimates of these probabilities, or of the scores themselves, allow the evaluation of efficacy for an analgesic. In this evaluation, therapeutic as well as statistical significance is assessed. Two case studies, one focusing on pain relief and the other on pain severity, are used to demonstrate the approach. The level of baseline pain appears to be a determinant of the pattern of response.

Analgesics↗
PubMed2000

A remark on small-sample properties of logistic regression in three-point designs.

Nottingham and Birch (1) recently alleged that the maximum likelihood (ML) estimator beta of the steepness parameter in a logistic regression model could be seriously underestimated. They based their conclusion on a simulation study, investigating in particular a small-sample three-point design with a relatively large spacing between the doses. In the present work we study such situations in more detail and use complete enumeration to find the exact properties of the ML estimators. The result presented here show that the allegation by Nottingham and Birch was misleading. There is a substantial probability for an infinite outcome of beta, which appears to have been neglected by Nottingham and Birch. In fact, it will be demonstrated that the asymptotic normal approximation for beta fits quite well even with small samples, except in the upper tail where outcomes are infinite instead of large finite. The consequences for coverage probabilities of confidence intervals for both of the regression parameters are elucidated.

Algorithms↗
PubMed2000

Optimal designs for the individual and joint exposure general logistic regression models.

Interest in administering compounds in combination lies both in enhancing efficacious effects and in limiting adverse effects. Although much statistical work has focused on developing mathematical functions to model the joint dose-response curves, relatively little work exists in regard to designing experiments for assessing joint action. A variety of parametric dose-response models based on either the normal or logistic probability distribution have been proposed in the literature. These models are typically nonlinear in the parameters, and as such, a nonlinear weighted least squares approach can be employed for the purpose of designing experiments. The approach is applicable across a wide variety of settings commonly associated with joint action data, including continuous and discrete responses, alternative error structures, and nonzero background response. Further, designs can be expressed in terms of proportionate responses associated with the individual compounds rather than dose levels, thereby providing for results that are applicable across compounds. As a precursor to this effort, optimal and minimal experimental designs for the case in which a single compound is administered have also been developed. Although the proposed methodology for deriving experimental designs can be applied to any nonlinear regression model, primary focus is given to the additive and nonadditive independent joint action (IJA) models for individual and combined exposures proposed by Barton, Braunberg, and Friedman (1).

Dose-Response Relationship, Drug↗
PubMed1999

A general conditional-logistic model for affected-relative-pair linkage studies.

Model-free LOD-score methods are often employed to detect linkage between marker loci and common diseases, with samples of affected sib pairs. Although extensions of the basic one-disease-locus model have been proposed that allow separate inclusion of other types of affected relative pairs, discordant relative pairs, covariates, or additional disease loci, a unified framework that can handle all of these features has been lacking. In this report, I propose a conditional-logistic parameterization that generalizes easily to include all of these features. Two data examples, one using simulated data and one using type 1 diabetes, illustrate applications of the models.

Alleles↗
PubMed2006

EEG-like signals generated by a simple chaotic model based on the logistic equation.

We introduce a simple model to produce EEG-like signals. The model is based on the assumption that the number of active nerve cells that generate an electric field, at a given time, is essentially chaotic. In accordance, we use the logistic equation together with a spike-like function to simulate the neuronal activity processes. With this model, we are able to generate EEG-like patterns, with quite a short time of calculation. Real pre-recorded neuronal and simulated signals, as well as their power spectra, are compared in terms of the main conventional EEG frequency peaks.

Action Potentials↗
PubMed2004

Extreme value distribution based gene selection criteria for discriminant microarray data analysis using logistic regression.

One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, L(D|M), and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, L(D(0)|M). Typically, the computational burden for obtaining L(D(0)M) is immense, often exceeding the limits of available computing resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.

Chromosome Mapping↗
PubMed1996

Multivariate statistical analysis for pathologist. Part I, The logistic model.

This paper reviews concepts of multivariate statistical modeling via the logistic regression model, which has become very popular for modeling the relationship between a positive clinical outcome and a variety of predictor variables. The process is illustrated using a composite of data from three large prostate specific antigen based screening studies of prostate cancer.

Discriminant Analysis↗
PubMed2001

Multivariate logistic regression for familial aggregation of two disorders. I. Development of models and methods.

The question of whether two disorders cluster together, or coaggregate, within families often arises. This paper considers how to analyze familial aggregation of two disorders and presents two multivariate logistic regression methods that model both disorder outcomes simultaneously. The first, a proband predictive model, predicts a relative's outcomes (the presence or absence of each of the two disorders) by using the proband's disorder status. The second, a family predictive model derived from the quadratic exponential model, predicts a family member's outcomes by using all of the remaining family members' disorder statuses. The models are more realistic, flexible, and powerful than univariate models. Methods for estimation and testing account for the correlation of outcomes among family members and can be implemented by using commercial software.

Family↗
PubMed2001

Multivariate logistic regression for familial aggregation of two disorders. II. Analysis of studies of eating and mood disorders.

Family studies have suggested that eating disorders and mood disorders may coaggregate within families. Previous studies, however, have been limited by use of univariate modeling techniques and failure to account for the correlation of observations within families. To provide a more efficient analysis and to illustrate multivariate logistic regression models for familial aggregation of two disorders, the authors analyzed pooled data from two previously published family studies (conducted in Massachusetts in 1984-1986 and 1986-1987) by using multivariate proband predictive and family predictive models. Both models demonstrated a significant familial aggregation of mood disorders and familial coaggregation of eating and mood disorders. The magnitude of the coaggregation between eating and mood disorders was similar to that of the aggregation of mood disorders. Similar results were obtained with alternative models, including a traditional univariate proband predictive model. In comparison with the univariate model, the multivariate models provided greater flexibility, improved precision, and wider generality for interpreting aggregation effects.

Family↗
PubMed2005

Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect.

Observational studies often provide the only available information about treatment effects. Control of confounding, however, remains challenging. The authors compared five methods for evaluating the effect of tissue plasminogen activator on death among 6,269 ischemic stroke patients registered in a German stroke registry: multivariable logistic regression, propensity score-matched analysis, regression adjustment with the propensity score, and two propensity score-based weighted methods-one estimating the treatment effect in the entire study population (inverse-probability-of-treatment weights), another in the treated population (standardized-mortality-ratio weights). Between 2000 and 2001, 212 patients received tissue plasminogen activator. The crude odds ratio between tissue plasminogen activator and death was 3.35 (95% confidence interval: 2.28, 4.91). The adjusted odds ratio depended strongly on the adjustment method, ranging from 1.11 (95% confidence interval: 0.67, 1.84) for the standardized-mortality-ratio weighted to 10.77 (95% confidence interval: 2.47, 47.04) for the inverse-probability-of-treatment-weighted analysis. For treated patients with a low propensity score, risks of dying were high. Exclusion of patients with a propensity score of <5% yielded comparable odds ratios of approximately 1 for all methods. High levels of nonuniform treatment effect render summary estimates very sensitive to the weighting system explicit or implicit in an adjustment technique. Researchers need to be clear about the population for which an overall treatment estimate is most suitable.

Aged↗
PubMed2006

Relaxing the rule of ten events per variable in logistic and Cox regression.

The rule of thumb that logistic and Cox models should be used with a minimum of 10 outcome events per predictor variable (EPV), based on two simulation studies, may be too conservative. The authors conducted a large simulation study of other influences on confidence interval coverage, type I error, relative bias, and other model performance measures. They found a range of circumstances in which coverage and bias were within acceptable levels despite less than 10 EPV, as well as other factors that were as influential as or more influential than EPV. They conclude that this rule can be relaxed, in particular for sensitivity analyses undertaken to demonstrate adequate control of confounding.

Bias↗