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PubMed2000

Investigations using logistic regression models on the effect of the LMA on morphine induced vomiting after tonsillectomy.

The effect of intraoperative airway management on postoperative vomiting after tonsillectomy is unknown. Logistic regression was used in a retrospective study to investigate the effect of the laryngeal mask airway (LMA) on a morphine dose-vomiting response curve. Charts were reviewed in 351 children in whom the airway was managed with either a LMA (n=177) or a tracheal tube (n=174). A mean perioperative morphine dose of 0.10 mg.kg(-1) (SD 0.09) was used in 248 children and a further 103 children were given no opioid. One hundred and eighteen of these 248 children vomited (47.6%) compared to 14 of 103 children given no morphine (13.6%). The probability of vomiting was related to morphine dose using logistic regression with both a linear and an E(max) model. Both the calibration (Hosmer-Lemishow goodness of fit chi-squared test lambda(2), P=0.81) and discrimination (area under the receiver operating characteristic plot, AUC ROC=0.67) of the E(max) model were better than the linear model (lambda(2), P=0.49; AUC ROC=0.64). Pharmacodynamic parameter estimates for the Emax model were P(0) (the baseline probability of vomiting) 0.139, P(max) (the maximal probability of vomiting due to morphine) 0.96, ED(50) (morphine dose that induces an effect equivalent to 50% of the logit P(max)) 0.09 mg.kg-1. The probability of vomiting was 50% after morphine 0.125 mg.kg-1. The use of the LMA had no effect on this dose-response curve. A covariate analysis investigating propofol for induction or isoflurane for the intraoperative maintenance of anaesthesia, however, showed that both drugs shifted the curve to the right. The probability of vomiting was 50% after morphine 0.17 mg.kg(-1) and 0.21 mg.kg(-1) for the isoflurane and propofol use curves, respectively. The concomitant use of propofol and isoflurane, but not the use of the LMA, decreases the probability of vomiting due to morphine.

Adolescent↗
PubMed2005

Prediction of small for gestational age by logistic regression in twins.

BACKGROUND: Small for gestational age (SGA) is one of the major determinants of perinatal mortality and morbidity, and may relate in adult diseases. Early prediction of SGA could be helpful for health care providers and public health workers in guiding antenatal management and prevention. The reported methods of SGA prediction are not satisfactory because the diagnostic performance is poor and the interval between prediction and delivery is too short. AIMS: To establish a SGA prediction model for twin pregnancies based on variables obtainable in early gestation. METHODS: We used a large twin registry United States data (1995-1997). The study subjects were randomly divided into two groups: group 1 to establish the prediction model by logistic regression and group 2 to validate the prediction model. SGA was defined as birth weight for gestational age z scores less than 10th percentiles. Pair of twin was the unit of analysis. Two sets of multiple logistic regression analyses with different outcome measures - one or both twins SGAs and both twins SGAs - were used to establish the prediction model. RESULTS: The sensitivity, specificity, and positive predictive value were 52.3, 62.5, and 21.5%, respectively, at the cutoff value 0.16 in a SGA prediction model based on maternal race, education, marital status, parity, prenatal care visit initiation, cigarette smoking, and paternal race. CONCLUSIONS: A prediction model based on determinants that can be obtained at early gestation might be useful in the management of pregnancies with high risk of SGA in twins.

Adult↗
PubMedDate not supplied

Using logistic regression analysis in preliminary differential diagnosis of adnexal masses.

The aim of our study was to generate predictive model, which would allow to estimate the influence of analyzed parameters on predictive accuracy of differential diagnosis of adnexal masses and to evaluate prospectively diagnostic efficacy of the statistic model in the new set of patients. A total of 686 women diagnosed and surgically treated in the Gynecological and Obstetrical Teaching Hospital of University of Medical Sciences in Poznan, Poland, were recruited into the study. Preoperative diagnostics included gynecological examination, ultrasonographic evaluation, tumor Doppler blood flow analysis, and serum levels of CA125 and TPS. In order to find the best combination of features and to calculate the individual probability of the malignancy, stepwise logistic regression analysis with quasi-Newton estimation was applied. The essential part of the best prognostic model, described by foregoing variables, is as follows: [z = -6.005 + 0.058 x age + 1.174 x septa + 1.317 x tumor localization + 1.185 x ascites + 2.28 x solid element + 2.429 x vessels localization -2.386 x PI + 0.084 x MEDV]. The highest sensitivity and specificity for the obtained model were 87.84% and 93.74%, respectively. Prognostic model, constructed with the use of logistic regression analysis, is characterized by higher sensitivity and specificity than individually applied diagnostic tests. Prospective evaluation of this model application in a larger group of patients with adnexal masses will enable precise assessment of its objective clinical usefulness.

Adnexal Diseases↗
PubMed1996

Relative risk for the development of hepatocellular carcinoma in alcoholic patients with cirrhosis: a multiple logistic-regression coefficient analysis.

The hepatitis B virus (HBV) or hepatitis C virus (HVC) markers are frequently positive in alcoholic patients with hepatocellular carcinoma (HCC). However, the role of the relationship between HBV or HCV infection and alcohol drinking in the development of HCC has not been clearly documented. In the present study, the relative risk in 1200 cirrhotic patients with different etiologies who were admitted to five different hospitals in Japan was calculated using the multiple logistic-regression coefficient analysis. In the HCV+ alcohol group, HCC patients tended to be younger, and the odds ratio for the development of HCC was significantly higher compared with the HCV-alone group. Furthermore, the interaction coefficient of alcohol and HCV for the development of HCC was significant statistically. However, the interaction between HBV and alcohol was not significant. Because the proportion of male patients with HCC was significantly higher in the alcohol-alone and HBV-related groups than in the HCV-related group, the multiple logistic-regression analysis was also performed in male patients only. The results were nearly the same as those in male and female patients combined. These results suggest strongly that alcohol and HCV together accelerate the development of HCC. However, a similar relationship was not found between alcohol and HBV.

Adult↗
PubMed2005

Application of logistic regression to case-control association studies involving two causative loci.

Models in which two susceptibility loci jointly influence the risk of developing disease can be explored using logistic regression analysis. Comparison of likelihoods of models incorporating different sets of disease model parameters allows inferences to be drawn regarding the nature of the joint effect of the loci. We have simulated case-control samples generated assuming different two-locus models and then analysed them using logistic regression. We show that this method is practicable and that, for the models we have used, it can be expected to allow useful inferences to be drawn from sample sizes consisting of hundreds of subjects. Interactions between loci can be explored, but interactive effects do not exactly correspond with classical definitions of epistasis. We have particularly examined the issue of the extent to which it is helpful to utilise information from a previously identified locus when investigating a second, unknown locus. We show that for some models conditional analysis can have substantially greater power while for others unconditional analysis can be more powerful. Hence we conclude that in general both conditional and unconditional analyses should be performed when searching for additional loci.

Case-Control Studies↗
PubMed2006

Prediction of empirical p values from asymptotic p values for conditional logistic affected relative pair linkage analysis.

OBJECTIVE: p Values are inaccurate for model-free linkage analysis using the conditional logistic model if we assume that the LOD score is asymptotically distributed as a simple mixture of chi-square distributions. When analyzing affected relative pairs alone, permuting the allele sharing of relative pairs does not lead to a useful permutation distribution. As an alternative, we have developed regression prediction models that provide more accurate p values. METHODS: Let E(alpha) be the empirical p value, which is the proportion of statistical tests whose LOD score under the null hypothesis exceeds a threshold determined by alpha, the nominal single test significance value. We used simulated data to obtain values of E(alpha) and compared them with alpha. We also developed a regression model, based on sample size, number of covariates in the model, alpha and marker density, to derive predicted p values for both single-point and multipoint analyses. To evaluate our predictions we used another set of simulated data, comparing the Ealpha for these data with those obtained by using the prediction model, referred to as predicted p values (P(alpha)). RESULTS: Under almost all circumstances the values of P(alpha) were closer to the E(alpha) than were the values of alpha. CONCLUSION: The regression models suggested by our analysis provide more accurate alternative p values for model-free linkage analysis when using the conditional logistic model.

Animals↗
PubMedDate not supplied

Exercise testing in patients with chest pain and normal coronary arteries: improving test specificity by use of a simple logistic model.

Despite an exercise electrocardiogram (ECG) positive for ischemia by established criteria, many patients referred for coronary angiography to evaluate chest pain are found to have angiographically normal coronary arteries (NCA). Exercise ECG were analyzed from 27 patients with chest pain and angiographically NCA and 28 patients with chest pain and coronary artery disease (CAD) using univariate and multivariate logistic regression analysis. We derived the following logistic model for the logit probability of CAD: 3 + SEX x 4 - METs x 0.7 + STDV5 x 0.8, where SEX = 0 for female and SEX = 1 for male, METs = maximal estimated work load (metabolic equivalents) and STDV5 = horizontal or downsloping ST depression (mm) in V5. A logit probability > or = 0 identified CAD with a sensitivity of 79% and a specificity of 89%. The model correctly identified 28/36 (78%) patients with CAD, and 7/10 (70%) patients with NCA (correct diagnosis 76%; p < 0.02) in a separate random group of 46 unselected patients with positive exercise tests undergoing diagnostic coronary angiography.

Adult↗
PubMed1994

Ovarian tumors: prediction of the probability of malignancy by using patient's age and tumor morphologic features with a logistic model.

An attempt was made to predict the probability of malignancy of a given ovarian tumor in a certain patient by using the age and simple morphologic features of the tumor. A cohort of 959 patients with ovarian tumors was analysed retrospectively according to the patient's age and tumor characteristics such as greatest diameter, consistency, bilaterality and diagnosis as malignant (271 patients) or benign (688 patients). All variables were entered unconditionally in a logistic regression. The presence of solid/multilocular elements has a 9.6-fold increased risk of malignancy, where a bilateral tumor has a 2.8-fold increase. Significant increase in risk of malignancy was observed in ages under 20 and over 40 years, as well as in tumors with a diameter larger than 9 cm. All variables were highly significant associated with the discrimination between benign and malignant. A formula including all variables has been developed so that the probability of malignancy can be estimated by a scientific calculator. In conclusion, simple, easily determined by ultrasound and reproducible criteria such as patient's age, tumor size, consistency and bilaterality were assembled in a logistic model in order to predict the probability of malignancy for a given ovarian tumor, in an individual patient.

Adult↗
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↗
PubMed1991

Logistic discriminant analysis improves diagnostic accuracy of exercise testing for coronary artery disease in women.

BACKGROUND: Diagnostic accuracy of the exercise electrocardiogram in women has been shown to be limited for the detection of coronary artery disease. New diagnostic methods based on computer analysis of the exercise electrocardiogram and multivariate analysis have improved the diagnostic value of exercise testing in male subjects. The aim of the present study was to assess whether the diagnostic value of exercise testing can be enhanced in women by using multivariate analysis of exercise data. METHODS AND RESULTS: Between 1978 and 1984, 135 infarct-free women underwent exercise testing and coronary angiography. Significant coronary artery disease was present in 41% of the patients. In this first group, maximal exercise variables were submitted to a stepwise logistic analysis. Work load, heart rate, and ST60X were selected to build a diagnostic model. The model was tested in a second group of 115 catheterized women (significant coronary artery disease in 47%) and of 76 volunteers. We compared the present model with conventional analysis of the exercise electrocardiogram, with ST changes adjusted for heart rate, and with a previously described analysis. In both groups, sensitivity was better with the present model (66% and 70%) than by conventional (68% and 59%) and by the previously described analysis (57% and 44%) without a loss of specificity (85% and 93%). Receiver-operator characteristic curves showed also a better diagnostic accuracy with the present model. CONCLUSIONS: In women, logistic analysis of exercise variables improves the diagnostic value of exercise testing. It yields a significantly better sensitivity without a loss of specificity.

Coronary Disease↗
PubMedDate not supplied

Fitting multistate transition models with autoregressive logistic regression: supervised exercise in intermittent claudication.

The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a six-month exercise program. The levels of the polytomous outcome variable correspond to states they defined in a Markov decision model comparing treatment strategies for intermittent claudication. Autoregressive logistic regression can be used to fit multistate transition models to observed longitudinal data with standard statistical software. The technique allows exploration of alternative assumptions about the dependence in the outcome series and provides transition probabilities for different covariate patterns. Of the alternatives examined, a Markov model including two preceding responses, time, age, ankle brachial index, and duration of disease best described the data.

Decision Support Techniques↗
PubMed2002

Feasibility of predicting the risk of atrial fibrillation after coronary artery bypass surgery with logistic regression model.

BACKGROUND AND AIMS: The aim of this study was to determine the risk factors of postoperative atrial fibrillation (AF) after coronary artery bypass grafting and to create predictive model and to evaluate the effects of AF on patients outcome. MATERIAL AND METHODS: Data of 3,676 consecutive patients were analysed to identify the predictors of AF. Multivariate logistic regression model was validated prospectively in 1,107 patients. RESULTS: Increasing age (p < 0.001), preoperative use of digoxin (p = 003), need of intra-aortic balloon pump or inotropic medication in the weaning off cardiopulmonary by pass or during the first 24 hours postoperatively (p = 0.013), increasing body surface area (p = 0.006) and lower ejection fraction (p = 0.048) were independent risk factors for postoperative AF. The predictive model gave area under the receiver-operating characteristic (ROC) curve 0.682, 95% confidence interval 0.663-0.701, and p < 0.001. The patients with AF incidence had more postoperative stroke (p = 0.008), confusion (p < 0.001) severe gastrointestinal complications (p = 0.005), readmission to ICU (p < 0.001), longer ICU (p < 0.001) and hospital stay (p < 0.001) when compared with the patients who remained in sinus rhythm. CONCLUSION: Logistic regression model with the parameters used was not accurate enough for clinical purposes. Postoperative AF is associated with postoperative stroke, severe gastrointestinal complications, readmission to ICU, and longer ICU and hospital stay.

Aged↗
PubMed2004

Overdispersed logistic regression for SAGE: modelling multiple groups and covariates.

BACKGROUND: Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample t-test. When the number of groups involved is more than two, however, a more general approach is needed. RESULTS: We describe how logistic regression with overdispersion supplies this generalization, carrying with it the framework for incorporating other covariates into the model as a byproduct. This approach has the advantage that logistic regression routines are available in several common statistical packages. CONCLUSIONS: The described method provides an easily implemented tool for analyzing SAGE data that correctly handles multiple types of variation and allows for more flexible modelling.

Analysis of Variance↗
PubMed2003

Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio.

BACKGROUND: Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. METHODS: We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. RESULTS: Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by chi2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. CONCLUSIONS: Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.

Cross-Sectional Studies↗
PubMed2004

Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Adult↗
PubMed2006

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study.

BACKGROUND: Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. METHODS: The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. RESULTS: It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. CONCLUSION: The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

Adult↗
PubMed2003

Monte carlo conditional inference for log-linear and logistic models: a survey of current methodology.

Nuisance parameters are parameters that are not of immediate interest to the experimenter. For log-linear and logistic models the null distribution of (most) statistics of interest depends on such parameters. Traditionally, nuisance parameters were eliminated by performing inference with respect to the chi-squared limiting distribution of common test statistics. An alternative solution is to eliminate the nuisance parameters by conditioning on their minimal sufficient statistics. The support of the resulting conditional distribution is often intractable, making null probability calculations challenging. An often feasible way to avoid complete enumeration of this support is to approximate conditional probabilities using Monte Carlo methods. In this article we survey recent developments in Monte Carlo conditional analysis for log-linear and logistic models focusing on the algorithms proposed by Booth and Butler, Diaconis and Sturmfels, Smith et al., and Mehta et al. We illustrate these algorithms with simple motivating examples.

Algorithms↗
PubMed2005

Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group.

PURPOSE: To collect data for the development of a more universally useful logistic regression model to distinguish between a malignant and benign adnexal tumor before surgery. PATIENTS AND METHODS: Patients had at least one persistent mass. More than 50 clinical and sonographic end points were defined and recorded for analysis. The outcome measure was the histologic classification of excised tissues as malignant or benign. RESULTS: Data from 1,066 patients recruited from nine European centers were included in the analysis; 800 patients (75%) had benign tumors and 266 (25%) had malignant tumors. The most useful independent prognostic variables for the logistic regression model were as follows: (1) personal history of ovarian cancer, (2) hormonal therapy, (3) age, (4) maximum diameter of lesion, (5) pain, (6) ascites, (7) blood flow within a solid papillary projection, (8) presence of an entirely solid tumor, (9) maximal diameter of solid component, (10) irregular internal cyst walls, (11) acoustic shadows, and (12) a color score of intratumoral blood flow. The model containing all 12 variables (M1) gave an area under the receiver operating characteristic curve of 0.95 for the development data set (n = 754 patients). The corresponding value for the test data set (n = 312 patients) was 0.94; and a probability cutoff value of .10 gave a sensitivity of 93% and a specificity of 76%. CONCLUSION: Because the model was constructed from multicenter data, it is more likely to be generally applicable. The effectiveness of the model will be tested prospectively at different centers.

Adnexal Diseases↗