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PubMed2004

A review of two journals found that articles using multivariable logistic regression frequently did not report commonly recommended assumptions.

BACKGROUND AND OBJECTIVE: To examine if commonly recommended assumptions for multivariable logistic regression are addressed in two major epidemiological journals. METHODS: Ninety-nine articles from the Journal of Clinical Epidemiology and the American Journal of Epidemiology were surveyed for 10 criteria: six dealing with computation and four with reporting multivariable logistic regression results. RESULTS: Three of the 10 criteria were addressed in 50% or more of the articles. Statistical significance testing or confidence intervals were reported in all articles. Methods for selecting independent variables were described in 82%, and specific procedures used to generate the models were discussed in 65%. Fewer than 50% of the articles indicated if interactions were tested or met the recommended events per independent variable ratio of 10:1. Fewer than 20% of the articles described conformity to a linear gradient, examined collinearity, reported information on validation procedures, goodness-of-fit, discrimination statistics, or provided complete information on variable coding. There was no significant difference (P>.05) in the proportion of articles meeting the criteria across the two journals. CONCLUSION: Articles reviewed frequently did not report commonly recommended assumptions for using multivariable logistic regression.

Epidemiologic Methods↗
PubMed2004

Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism.

OBJECTIVE: Genetic programming is a search method that can be used to solve complex associations between large numbers of variables. It has been used, for example, for myoelectrical signal recognition, but its value for medical prediction as in diagnostic and prognostic settings, has not been documented. STUDY DESIGN AND SETTING: We compared genetic programming and the commonly used logistic regression technique in the development of a prediction model using empirical data from a study on diagnosis of pulmonary embolism. Using part (67%) of the data, we developed and internally validated (using bootstrapping techniques) a diagnostic prediction model by genetic programming and by logistic regression, and compared both on their predictive ability in the remaining data (validation set). RESULTS: In the validation set, the area under the ROC curve of the genetic programming model was significantly larger (0.73; 95%CI: 0.64-0.82) than that of the logistic regression model (0.68; 0.59-0.77). The calibration of both models was similar, indicating a similar amount of overoptimism. CONCLUSION: Although the interpretation of a genetic programming model is less intuitive and this is the first empirical study quantifying its value for medical prediction, genetic programming seems a promising technique to develop prediction rules for diagnostic and prognostic purposes.

Adult↗
PubMed2002

Using logistic regression to estimate the influence of accident factors on accident severity.

Logistic regression was applied to accident-related data collected from traffic police records in order to examine the contribution of several variables to accident severity. A total of 560 subjects involved in serious accidents were sampled. Accident severity (the dependent variable) in this study is a dichotomous variable with two categories, fatal and non-fatal. Therefore, each of the subjects sampled was classified as being in either a fatal or non-fatal accident. Because of the binary nature of this dependent variable, a logistic regression approach was found suitable. Of nine independent variables obtained from police accident reports, two were found most significantly associated with accident severity, namely, location and cause of accident. A statistical interpretation is given of the model-developed estimates in terms of the odds ratio concept. The findings show that logistic regression as used in this research is a promising tool in providing meaningful interpretations that can be used for future safety improvements in Riyadh.

Accidents↗
PubMed2004

Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression.

The blood flow hemodynamics of carotid arteries were obtained from carotid arteries of 168 individuals with diabetes using the 7.5 MHz ultrasound Doppler M-unit. Fast Fourier Transform (FFT) methods were used for feature extraction from the Doppler signals on the time-frequency domain. The parameters, obtained from the Doppler sonograms, were applied to the mathematical models that were constituted to analyze the effect of diabetes on internal carotid artery (ICA) stenosis. In this study, two different mathematical models such as the traditional statistical method based on logistic regression and a Multi-Layer Perceptron (MLP) neural network were used to classify the Doppler parameters. The correct classification of these data was performed by an expert radiologist using angiograpy before they were executed by logistic regression and MLP neural networks. We classified the carotid artery stenosis into two categories such as non-stenosis and stenosis and we achieved similar results (correctly classified (CC) = 92.8%) in both mathematical models. But, as the degree of stenosis had been increased to 4 (0-39%, 40-59%, 60-79% and 80-99% diameter stenosis), it was found that the neural network (CC = 73.9%) became more efficient than the logistic regression analysis (CC = 67.7%). These outcomes indicate that the Doppler sonograms taken from the carotid arteries may be classified successfully by neural network.

Carotid Stenosis↗
PubMed2000

Hierarchical logistic regression models for clustered binary outcomes in studies of IVF-ET.

OBJECTIVE: To describe a hierarchical logistic regression model for clustered binary data, apply it to data from a study on the effect of hydrosalpinx on embryo implantation, and compare the results with analyses that do not account for clustering. DESIGN: Observational study. SETTING: Academic research environment. PATIENT(S): Women undergoing IVF-ET for tubal disease. MAIN OUTCOME MEASURE(S): Odds of per embryo implantation. RESULT(S): Although regression estimates are largely similar between the models, the hierarchical model properly reflects the added variation due to clustering. Standard errors are higher, confidence intervals are wider, and P values indicate fewer "statistically significant" effects. CONCLUSION(S): Ignoring important sources of variation in any analysis can lead to incorrect confidence intervals and P values. In studies of IVF-ET, where clustered data are common, unexplained heterogeneity can be substantial. In this setting, hierarchical logistic regression is an appropriate alternative to standard logistic regression.

Adult↗
PubMedDate not supplied

Analysis of logistic growth models.

A variety of growth curves have been developed to model both unpredated, intraspecific population dynamics and more general biological growth. Most predictive models are shown to be based on variations of the classical Verhulst logistic growth equation. We review and compare several such models and analyse properties of interest for these. We also identify and detail several associated limitations and restrictions.A generalized form of the logistic growth curve is introduced which incorporates these models as special cases. Several properties of the generalized growth are also presented. We furthermore prove that the new growth form incorporates additional growth models which are markedly different from the logistic growth and its variants, at least in their mathematical representation. Finally, we give a brief outline of how the new curve could be used for curve-fitting.

Animals↗
PubMed2000

Log-linear and logistic modeling of dependence among diagnostic tests.

We developed log-linear and logistic-modeling approaches to investigate dependence among diagnostic tests. To illustrate the approaches, we used published data for swine toxoplasmosis, bovine paratuberculosis, and swine brucellosis. These diseases were selected because each animal's true disease status was known, at least five tests were used, and the serologic tests had been previously shown to have moderate-to-high pairwise dependence in test sensitivities (and sometimes in test specificities). Log-linear and logistic modeling yielded similar results for swine toxoplasmosis and swine brucellosis. However, logistic modeling could not be used to investigate test dependence for bovine paratuberculosis because of quasi-separation in the data attributable to two fecal-based tests having specificities of 100%. Findings from our modeling indicated that 3 (modified agglutination, enzyme-linked immunosorbent assay (ELISA), latex agglutination) of 5 serologic tests for toxoplasmosis and 2 (rivanol and particle concentration fluorescence immunoassay) of 6 serologic tests for brucellosis were adequate for diagnosis. For bovine paratuberculosis, both fecal-based tests (Herrold's egg-yolk culture and radiometric culture) and 1 (ELISA) of 3 serologic tests were necessary in serial and parallel testing schemes.

Animals↗
PubMed2002

Relationship between cadmium concentration in rice and renal dysfunction in individual subjects of the Jinzu River basin determined using a logistic regression analysis.

We investigated the association between the Cd concentration in rice and renal dysfunction in individuals living in the Cd-polluted Jinzu River basin, using a logistic regression analysis. In the cases of logistic regression analysis for people (1) who had either resided in the present hamlet since birth or who had moved there from a non-polluted area and for those (2) who had resided in the present hamlet since birth, except for glucosuria in males, all partial correlation coefficients between the Cd concentration in rice and occurrence of abnormal urinary findings were statistically significant in both sexes. The allowable level of Cd concentration in rice was calculated by substituting the abnormality rates of urinary findings of the controls in the 40-49, 50-59 and 60-69 year age groups into the logistic regression formula for people (3). The value for subjects aged 50 years was 0.13 and 0.17 ppm for males and females, respectively, with regard to proteinuria and 0.15 and 0.10 ppm for males and females, respectively, with regard to proteinuria+glucosuria.

Adult↗
PubMed1998

Logistic regression model to predict outcome after in-hospital cardiac arrest: validation, accuracy, sensitivity and specificity.

OBJECTIVE: To develop and validate a logistic regression model to identify predictors of death before hospital discharge after in-hospital cardiac arrest. DESIGN: Retrospective derivation and validation cohorts over two 1 year periods. Data from all in-hospital cardiac arrests in 1986-87 were used to derive a logistic regression model in which the estimated probability of death before hospital discharge was a function of patient and arrest descriptors, major underlying diagnosis, initial cardiac rhythm, and time of year. This model was validated in a separate data set from 1989-90 in the same hospital. Calculated for each case was 95% confidence limits (C.L.) about the estimated probability of death. In addition, accuracy, sensitivity, and specificity of estimated probability of death and lower 95% C.L. of the estimated probability of death in the derivation and validation data sets were calculated. SETTING: 560-bed university teaching hospital. PATIENTS: The derivation data set described 270 cardiac arrests in 197 inpatients. The validation data set described 158 cardiac arrests in 120 inpatients. INTERVENTIONS: none. MEASUREMENTS AND RESULTS: Death before hospital discharge was the main outcome measure. Age, female gender, number of previous cardiac arrests, and electrical mechanical dissociation were significant variables associated with a higher probability of death. Underlying coronary artery disease or valvular heart disease, ventricular tachycardia, and cardiac arrest during the period July-September were significant variables associated with a lower probability of death. Optimal sensitivity and specificity in the validation set were achieved at a cut-off probability of 0.85. CONCLUSIONS: Performance of this logistic regression model depends on the cut-off probability chosen to discriminate between predicted survival and predicted death and on whether the estimated probability or the lower 95% C.L. of the estimated probability is used. This model may inform the development of clinical practice guidelines for patients who are at risk of or who experience in-hospital cardiac arrest.

Confidence Intervals↗
PubMed2004

Understanding data in clinical research: a simple graphical display for plotting data (up to four independent variables) after binary logistic regression analysis.

In clinical research, suitable visualization techniques of data after statistical analysis are crucial for the researches' and physicians' understanding. Common statistical techniques to analyze data in clinical research are logistic regression models. Among these, the application of binary logistic regression analysis (LRA) has greatly increased during past years, due to its diagnostic accuracy and because scientists often want to analyze in a dichotomous way whether some event will occur or not. Such an analysis lacks a suitable, understandable, and widely used graphical display, instead providing an understandable logit function based on a linear model for the natural logarithm of the odds in favor of the occurrence of the dependent variable, Y. By simple exponential transformation, such a logit equation can be transformed into a logistic function, resulting in predicted probabilities for the presence of the dependent variable, P(Y-1/X). This model can be used to generate a simple graphical display for binary LRA. For the case of a single predictor or explanatory (independent) variable, X, a plot can be generated with X represented by the abscissa (i.e., horizontal axis) and P(Y-1/X) represented by the ordinate (i.e., vertical axis). For the case of multiple predictor models, I propose here a relief 3D surface graphic in order to plot up to four independent variables (two continuous and two discrete). By using this technique, any researcher or physician would be able to transform a lesser understandable logit function into a figure easier to grasp, thus leading to a better knowledge and interpretation of data in clinical research. For this, a sophisticated statistical package is not necessary, because the graphical display may be generated by using any 2D or 3D surface plotter.

Biomedical Research↗
PubMedDate not supplied

Integrating classification trees with local logistic regression in Intensive Care prognosis.

Health care effectiveness and efficiency are under constant scrutiny especially when treatment is quite costly as in the Intensive Care (IC). Currently there are various international quality of care programs for the evaluation of IC. At the heart of such quality of care programs lie prognostic models whose prediction of patient mortality can be used as a norm to which actual mortality is compared. The current generation of prognostic models in IC are statistical parametric models based on logistic regression. Given a description of a patient at admission, these models predict the probability of his or her survival. Typically, this patient description relies on an aggregate variable, called a score, that quantifies the severity of illness of the patient. The use of a parametric model and an aggregate score form adequate means to develop models when data is relatively scarce but it introduces the risk of bias. This paper motivates and suggests a method for studying and improving the performance behavior of current state-of-the-art IC prognostic models. Our method is based on machine learning and statistical ideas and relies on exploiting information that underlies a score variable. In particular, this underlying information is used to construct a classification tree whose nodes denote patient sub-populations. For these sub-populations, local models, most notably logistic regression ones, are developed using only the total score variable. We compare the performance of this hybrid model to that of a traditional global logistic regression model. We show that the hybrid model not only provides more insight into the data but also has a better performance. We pay special attention to the precision aspect of model performance and argue why precision is more important than discrimination ability.

Artificial Intelligence↗
PubMed2000

Diagnosis of MRSA with neural networks and logistic regression approach.

Antibiotic-resistant pathogens are increasingly prevalent in the hospitals and community. A timely and accurate diagnosis of the infection would greatly help physicians effectively treat patients. In this research we investigate the potential of using neural networks (NN) and logistic regression (LR) approach in diagnosing methicillin-resistant Staphylococcus aureus (MRSA). Receiver-Operating Characteristic (ROC) curve and the cross-validation method are used to compare the performances of both systems. We found that NN is better than the logistic regression approach, in terms of both the discriminatory power and the robustness. With modeling flexibility inherent in its techniques, NN is effective in dealing with MRSA and other classification problems involving large numbers of variables and interaction complexity. On the other hand, logistic regression in our case is slightly inferior, offers more clarity and less perplexity. It could be a method of choice when fewer variables are involved and/or justification of the results is desired.

Decision Support Techniques↗
PubMed2001

Do logistic regression and signal detection identify different subgroups at risk? Implications for the design of tailored interventions.

Identifying subgroups of high-risk individuals can lead to the development of tailored interventions for those subgroups. This study compared two multivariate statistical methods (logistic regression and signal detection) and evaluated their ability to identify subgroups at risk. The methods identified similar risk predictors and had similar predictive accuracy in exploratory and validation samples. However, the 2 methods did not classify individuals into the same subgroups. Within subgroups, logistic regression identified individuals that were homogeneous in outcome but heterogeneous in risk predictors. In contrast, signal detection identified individuals that were homogeneous in both outcome and risk predictors. Because of the ability to identify homogeneous subgroups, signal detection may be more useful than logistic regression for designing distinct tailored interventions for subgroups of high-risk individuals.

Adult↗
PubMed1999

A logistics evaluation of visual acuity as applied to the Bailey-Lovie chart.

AIMS: To discover whether as a result of the increasing use of the Bailey-Lovie chart some classes of patients may not be affected by the crowding of the smaller test characters, whose spacing is proportional to their size; and to determine acuities with a logistic function so that all of a patient's responses may be utilized. METHODS: 112 patients were tested both with the original chart and one in which the horizontal distance is kept constant, i.e., the letters are arranged in vertical columns. All of a patient's responses were recorded so that the constants of the logistic function might be determined. RESULTS: No difference was found for very high and very low acuity scores, but, for intermediate ones, the vertical columns yielded acuity ratings increased by some 13%. CONCLUSION: The use of the logistics function was successful in that the correlation between stimulus and response was between 0.9 and 1 for some 80% of those examined. A constant horizontal spacing may be of advantage to some patients with a conventionally measured visual acuity of approximately 0.9.

Adolescent↗
PubMed2003

Logistic regression models for polymorphic and antagonistic pleiotropic gene action on human aging and longevity.

In this paper, we apply logistic regression models to measure genetic association with human survival for highly polymorphic and pleiotropic genes. By modelling genotype frequency as a function of age, we introduce a logistic regression model with polytomous responses to handle the polymorphic situation. Genotype and allele-based parameterization can be used to investigate the modes of gene action and to reduce the number of parameters, so that the power is increased while the amount of multiple testing minimized. A binomial logistic regression model with fractional polynomials is used to capture the age-dependent or antagonistic pleiotropic effects. The models are applied to HFE genotype data to assess the effects on human longevity by different alleles and to detect if an age-dependent effect exists. Application has shown that these methods can serve as useful tools in searching for important gene variations that contribute to human aging and longevity.

Age Factors↗
PubMed2000

Comparison of artificial neural networks with logistic regression in prediction of in-hospital death after percutaneous transluminal coronary angioplasty.

OBJECTIVES: Our objective was to compare artificial neural networks (ANNs) with logistic regression for prediction of in-hospital death after percutaneous transluminal coronary angioplasty and to assess the impact of guiding initial ANN variable selection with univariate analysis. BACKGROUND: ANNs can detect complex patterns within data. Criticisms include the unpredictability of variable selection. They have not previously been applied to outcomes modeling for percutaneous coronary interventions. METHODS: A database of consecutive (n = 3019) percutaneous transluminal coronary angioplasty procedures from an academic tertiary referral center between July 1994 and July 1997 was used. An ANN was developed for 38 variables (unguided model) (n = 1554). A second model was developed with predictors from an univariate analysis (guided model). Both were compared with a logistic regression model developed from the same database. Model validation was performed on independent data (n = 1465). Model predictive accuracy was assessed by the area under receiver operating characteristic curves. Goodness of fit was assessed with the Hosmer-Lemeshow statistic. RESULTS: Sixty unguided and guided ANNs were developed. Predictive accuracy and model calibration for all models were similar for training data but were significantly better for logistic regression for independent validation data. Overestimation of event rate in higher risk patients accounted for the majority of discrepancy in model calibration for the ANNs. This difference was partially amended by guiding variable selection. CONCLUSION: ANNs were able to model in-hospital death after percutaneous transluminal coronary angioplasty when guiding variable selection. However, performance was not better than traditional modeling techniques. Further investigations are needed to understand the impact of this methodology on outcomes analysis.

Aged↗
PubMed2003

Comparing hierarchical modeling with traditional logistic regression analysis among patients hospitalized with acute myocardial infarction: should we be analyzing cardiovascular outcomes data differently?

BACKGROUND: Data in health research are frequently structured hierarchically. For example, data may consist of patients treated by physicians who in turn practice in hospitals. Traditional statistical techniques ignore the possible correlation of outcomes within a given practice or hospital. Furthermore, imputing characteristics measured at higher levels of the hierarchy to the patient-level artificially inflates the amount of available information on the effect of higher-level characteristics on outcomes. METHODS: Conventional logistic regression models and multilevel logistic regression models were fit to a cross-sectional cohort of patients hospitalized with a diagnosis of acute myocardial infarction. The statistical significance of the effect of patient, physician, and hospital characteristics on patient outcomes was compared between the 2 modeling strategies. RESULTS: The 2 analytic strategies agreed well on the effect of patient characteristics on outcomes. According to the traditional analysis, teaching status was statistically significantly associated with 5 of the 9 outcomes, whereas the multilevel models did not find a statistically significant association between teaching status and any patient outcomes. Similarly, the traditional and multilevel models disagreed on the statistical significance of the effect of being treated at a revascularization hospital and 3 patient outcomes. CONCLUSIONS: In comparing the resultant models, we see that false inferences can be drawn by ignoring the structure of the data. Conventional logistic regression tended to increase the statistical significance for the effects of variables measured at the hospital-level compared to the level of significance indicated by the multilevel model.

Adult↗
PubMed1998

Statistical analysis of correlated binary data in ophthalmology: a weighted logistic regression approach.

Ophthalmological studies often deal with correlated binary outcome variables. We propose a weighted logistic regression method to account for the intraclass correlations between eyes. Using simulation studies, we compared this method with two standard logistic regression approaches: a) based on eyes as the unit of analysis and b) treating individuals classified as cases if at least one eye is affected. The considered approaches were evaluated in terms of type I error, power and estimation properties. The simulation results reveal that the subject-based approach can lead to substantial bias in regression coefficient estimates when the correlation between eyes is heterogeneous across groups or when it is low, and that power is directly affected by this bias. Furthermore, the standard eye-based approach, which ignores intrasubject correlations, leads to inflated type I error rates. The proposed weighted approach performed well in all of the situations considered. This is a simple method which can be implemented using any current statistical or epidemiological package that includes logistic regression analysis.

Aged↗