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At least 685 records · Page 38Linked to original sources
PubMed2004

Crossed cerebellar diaschisis in patients with cortical infarction: logistic regression analysis to control for confounding effects.

BACKGROUND AND PURPOSE: Crossed cerebellar diaschisis (CCD) refers to reduced metabolism and blood flow in the cerebellar hemisphere contralateral to a cerebral lesion. Many cortical areas have been reported to cause CCD without consideration of confounding factors. We performed single-photon emission computed tomography (SPECT) in patients with cortical infarction to identify regions independently related to CCD, controlling for possible confounding effects. METHODS: Patients with unilateral cortical infarction (n=113; 75 male, 38 female; mean+/-SD age, 66+/-13 years) underwent SPECT of the brain with N-isopropyl-p-[(123)I]iodoamphetamine ((123)I-IMP). Regional cerebral blood flow was measured autoradiographically. Asymmetry indices (AIs) were calculated on the basis of ratios representing symmetrical regional cerebral blood flow in the cerebellum and 16 cerebral regions. CCD was defined as AI for cerebellum >0.1. AIs for 16 cortical regions were considered for both dichotomous and continuous variables for analysis of CCD occurrence by means of backward logistic regression. RESULTS: For dichotomized variables, hypoperfusion of postcentral (odds ratio [OR]=7.607; 95% CI, 2.299 to 25.174) and supramarginal (OR=3.916; 95% CI, 1.394 to 11.003) regions independently influenced CCD. For continuous variables, hypoperfusion of postcentral (OR=1.044; 95% CI, 1.019 to 1.068) and supramarginal (OR=1.021; 95% CI, 1.001 to 1.041) regions (and, as a negative factor, medial occipital regions; OR=0.942; 95% CI, 0.895 to 0.991) independently influenced CCD. CONCLUSIONS: Many cortical areas apparently do not contribute to CCD. Correspondence of CCD between dichotomized and continuous analyses suggests that location of a lesion, not severity, is the main determinant of CCD.

Adult↗
PubMed1993

A multiple logistic regression analysis of the risk and relative odds of temporomandibular disorders as a function of common occlusal features.

A multiple logistic regression analysis was used to compute the odds ratios for 11 common occlusal features for asymptomatic controls (n = 147) vs. five temporomandibular disorder groups: Disc Displacement with Reduction (n = 81), Disc Displacement without Reduction (n = 48), Osteoarthrosis with Disc Displacement History (n = 75), Primary Osteoarthrosis (n = 85), and Myalgia Only (n = 124). Features that did not contribute included: retruded contact position (RCP) to intercuspal position (ICP) occlusal slides < or = 2 mm, slide asymmetry, unilateral RCP contacts, deep overbite, minimal overjet, dental midline discrepancies, < or = 4 missing teeth, and maxillo-mandibular first molar relationship or cross-arch asymmetry. Groupings of a minimum of two to at most five occlusal variables contributed to the TMD patient groups. Significant increases in risk occurred selectively with anterior open bite (p < 0.01), unilateral maxillary lingual crossbite (p < 0.05 to p < 0.01), overjets > 6-7 mm (p < 0.05 to p < 0.01), > or > 5-6 missing posterior teeth (p < 0.05 to p < 0.01), and RCP-ICP slides > 2 mm (p < 0.05 to p < 0.01). While the contribution of occlusion to the disease groups was not zero, most of the variation in each disease population was not explained by occlusal parameters. Thus, occlusion cannot be considered the unique or dominant factor in defining TMD populations. Certain features such as anterior open bite in osteoarthrosis patients were considered to be a consequence of rather than etiological factors for the disorder.

Age Factors↗
PubMed1996

Predictors of coroner's verdict: a logistic regression model.

This study is an attempt to understand the effect of various factors that are likely to have influenced North Cheshire's Coroners during a five-year period in their decision to return a 'suicide' or an 'open' verdict. Based on the logistic model, the main factors that appeared to have influenced a Coroners decision are: intimation of intent, method of death and alcohol problem.

Adult↗
PubMed1996

Gauss or Bernoulli? A Monte Carlo comparison of the performance of the linear mixed-model and the logistic mixed-model analyses in simulated community trials with a dichotomous outcome variable at the individual level.

This Monte Carlo study compares performance of the linear and the logistic mixed-model analyses of simulated community trials having event rates of 37%, 13%, or 5%, intraclass correlations between 0.01 and 0.05, and 17 or 5 denominator degrees of freedom. Type I or Type II error rates showed no essential difference between the two analysis methods. They showed depressed error rates when the event rate or the denominator degrees of freedom were small. The authors conclude that in studies with adequate denominator degrees of freedom, the researcher may use either method of analysis but should accept negative estimates of components of variance to avoid depression of error rates.

Analysis of Variance↗
PubMedDate not supplied

Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets.

Clinical decision making often requires estimates of the likelihood of a dichotomous outcome in individual patients. When empirical data are available, these estimates may well be obtained from a logistic regression model. Several strategies may be followed in the development of such a model. In this study, the authors compare alternative strategies in 23 small subsamples from a large data set of patients with an acute myocardial infarction, where they developed predictive models for 30-day mortality. Evaluations were performed in an independent part of the data set. Specifically, the authors studied the effect of coding of covariables and stepwise selection on discriminative ability of the resulting model, and the effect of statistical "shrinkage" techniques on calibration. As expected, dichotomization of continuous covariables implied a loss of information. Remarkably, stepwise selection resulted in less discriminating models compared to full models including all available covariables, even when more than half of these were randomly associated with the outcome. Using qualitative information on the sign of the effect of predictors slightly improved the predictive ability. Calibration improved when shrinkage was applied on the standard maximum likelihood estimates of the regression coefficients. In conclusion, a sensible strategy in small data sets is to apply shrinkage methods in full models that include well-coded predictors that are selected based on external information.

Aged↗
PubMedDate not supplied

Application of logistic regression to the analysis of diagnostic data: exact modeling of a probability tree of multiple binary variables.

In the analysis and presentation of diagnostic relationships by means of conventional multiple logistic regression, the following limitations occur: 1) the model starts not from the prior disease odds but from the posterior disease odds for all test variables having a zero value; 2) apart from the odds ratio, other test characteristics cannot be read from the model; 3) the sequence of entering of terms is guided by pure statistical criteria and not primarily by the criterion gain in certainty; 4) interactions are not very comprehensibly represented and are difficult to interpret. A method dealing with these limitations with respect to the analysis of data on the relationships between binary tests and disease outcome is described. Essential is the transformation of any test variable x to (x - x0), where x0 is that specific (virtual) value of x so that: posterior disease odds = prior disease odds, and consequently LRx0 = 1. Moreover, a simple branching structure is introduced while the terms are entered in order of decreasing gain in certainty. Examples are given for one-, two-, and three-test situations with and without dependency and interaction of tests, and general formulas are presented. For situations with the same prior probability, and the same overall discrimination of the separate test variables, all equations clearly have a common basis. Inclusion of new variables does not affect coefficients previously included in the model, and terms without a significant contribution can be skipped without affecting other coefficients. Standard errors and confidence intervals of test characteristics can be computed using the BMDP LR program. Further study needs to be done on the inclusion of continuous variables and cost-benefit aspects, and comparison with the performance of the CART program.

Bayes Theorem↗
PubMedDate not supplied

Measuring decision sensitivity: a combined Monte Carlo-logistic regression approach.

Modeling of the uncertainty of multiple input variables for a complex decision problem complicates sensitivity analysis. A method of analysis comprising stochastic simulation of the model and logistic regression of the simulated dichotomous decision variable against all of the input variables yields a direct measure of the importance of input variables to the decision. This method is demonstrated on a previously analyzed clinical decision either to continue observation or to immediately treat with anticoagulants a woman presenting with deep vein thrombosis in the first trimester of pregnancy. A relative measure of the importance of each input variable in causing a change of decision is estimated by calculating the change in the log odds attributable to variation of each input variable over its range of uncertain values compared with the total change of log odds from variation of all input variables. This method is compared with alternative measures of input variable importance, and is found to be a simple yet powerful tool for gaining quantitative insight into the nuances of a decision model.

Analysis of Variance↗
PubMed2004

Modeling multivariate binary responses with multiple levels of nesting based on alternating logistic regressions: an application to caries aggregation.

Clustered binary responses are commonly encountered in dental research. Data analysis may include modeling both the marginal response probabilities (i.e., risk) and the dependence structure between pairs of responses (i.e., aggregation). While second-order generalized estimating equations (GEE2) is a well-known approach for such data, alternating logistic regressions (ALR) is a computationally efficient alternative method, especially for large clusters. We illustrate ALR with an application to caries aggregation using a dataset with 3 levels of nesting: tooth surfaces within an interproximal (IP) region, IP regions within a jaw, and jaws within a subject. Caries lesions appear to aggregate strongly within subjects with a spatially distributed risk. The minimum within-IP-region odds ratio (OR) was 2.25 (95% confidence interval 1.15, 4.41), and the within-IP-region ORs were always greater than the between-IP-region ORs. ALR is a convenient and useful regression technique for explicit modeling of the dependence structure, and may be applicable to other dental research problems involving clustered or nested responses.

Cluster Analysis↗
PubMed1993

Test of repeated operations and logistic regression as to the efficacy of brain mapping.

The object of this paper is to apply two new methods, those of repeated observations and logistic regression in order to verify the validity of qEEG. These methods are applied using children with hyperactivity and attentional disorders, according to DSM III-R. The results of sensitivity and specificity were similar.

Attention Deficit Disorder with Hyperactivity↗
PubMed2004

An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: the importance of model validation.

BACKGROUND: To examine interactions among the angiotensin converting enzyme (ACE) insertion/deletion, plasminogen activator inhibitor-1 (PAI-1) 4G/5G, and tissue plasminogen activator (t-PA) insertion/deletion gene polymorphisms on risk of myocardial infarction using data from 343 matched case-control pairs from the Physicians Health Study. We examined the data using both conditional logistic regression and the multifactor dimensionality reduction (MDR) method. One advantage of the MDR method is that it provides an internal prediction error for validation. We summarize our use of this internal prediction error for model validation. RESULTS: The overall results for the two methods were consistent, with both suggesting an interaction between the ACE I/D and PAI-1 4G/5G polymorphisms. However, using ten-fold cross validation, the 46% prediction error for the final MDR model was not significantly lower than that expected by chance. CONCLUSIONS: The significant interaction initially observed does not validate and may represent a type I error. As data-driven analytic methods continue to be developed and used to examine complex genetic interactions, it will become increasingly important to stress model validation in order to ensure that significant effects represent true relationships rather than chance findings.

Gene Expression Regulation↗
PubMed2005

Statistics review 14: Logistic regression.

This review introduces logistic regression, which is a method for modelling the dependence of a binary response variable on one or more explanatory variables. Continuous and categorical explanatory variables are considered.

Humans↗
PubMed2004

Comparison and cross-validation of simple and multiple logistic regression models to predict USMLE step 1 performance.

BACKGROUND: Early identification of students who may have difficulty with the United States Medical Licensing Examination-Step 1 examination is important for medical schools and students. Numerous models that predict Step 1 performance have been identified, but few of these models have been cross-validated. PURPOSE: To cross-validate different prediction models of Step 1 performance. METHODS: The development sample was 686 students from a Midwestern medical school. The cross-validation sample was 147 different students. Logistic regression was used to develop the multiple model and Year 1 grade point average (GPA) and Year 2 Fall GPA were used as the simple models. Receiver Operating Characteristic graphs were used to select optimal cutoffs for each model. Kappa coefficients were used to determine level of agreement, and sensitivity and specificity were used to assess classification accuracy. RESULTS: The Year 1 GPA model had relatively poor agreement with actual Step 1 performance, but the other models evidenced fair agreement. The multiple and Year 1 GPA models demonstrated statistically significant loss of classification accuracy on cross-validation, whereas the Year 2 Fall GPA model did not. CONCLUSIONS: Cross-validation is necessary to determine the generalizability and overall utility of prediction models.

Education, Medical↗
PubMed2005

Logistic analysis of veterans' eligibility-status changes.

Currently, the US Department of Veterans Affairs provides medical care to more than four million veterans across the nation. Given the limited resources and increasing demand, the US Department of Veterans Affairs Health Administration (VA) is required by law to ensure that veterans with similar economic status and eligibility priority have similar access to VA health care, regardless of where they reside. This study, using descriptive statistics and logistic regression techniques, examines the factors that affect veterans' eligibility-status changes. This study found that veterans' demographics are correlated with the likelihood of eligibility-status conversion. More importantly, this study concludes that eligibility-status changes have a geographic pattern. These findings are important and useful in planning workload, as well as improving equal access of health care.

Adult↗
PubMed1991

[A study on health examination services under the Health Service Law for the Aged--multiple logistic regression analysis of service methods and the service-acceptance rate].

A survey on health services provided in accordance with the Health Service Law for the Aged was conducted in 3,278 cities, wards, towns, and villages throughout Japan in May 1985. Responses were returned from 2,645 districts and data on beneficiaries of these health services in 2,625 districts became available. Because the exact number of people constituting the subjects of health services could not be obtained, the service-acceptance rate was calculated by the number of people utilizing the services divided by the those in the population who were at least 40 years old. The service-acceptance rate was affected by both the size of the population of the local community and the methods of implementation of health services. Therefore, the mutual effects of population size and service methods were corrected by logistic regression analysis to find the relationship between methods of implementing health services and the response rate of the public in accepting the services. The service-acceptance rate was divided into two categories: one less than 30% and the other 30% and over. Odds ratios were calculated for an acceptance ratio of 30% and over. The results were summarized as follows: 1. In the jurisdictions where the local organizations cooperated in notifying citizens about the results of their general health examination, in comparison with the areas where there was no cooperation, the odds ratio was found to be 2.40. A similar comparison was made between the communities with local cooperation for notification of a general health examination and those communities without it. The odds ratio for the former was found to be 1.66.(ABSTRACT TRUNCATED AT 250 WORDS)

Aged↗
PubMed1994

Prediction of occult neck disease in laryngeal cancer by means of a logistic regression statistical model.

The ability to accurately predict the presence of subclinical metastatic neck disease in clinically N0 patients with primary epidermoid cancer of the larynx would be of great value in determining whether to perform an elective neck dissection. We describe a statistical approach to estimating the probability of occult neck disease given pretreatment clinical parameters. A retrospective study was performed involving 736 clinically N0 patients with primary laryngeal cancer who were treated surgically with primary resection and ipsilateral neck dissection. Nodal involvement was determined histologically after surgical lymphadenectomy. A logistic regression model was used to derive an equation that calculated the probability of occult neck metastasis based on pretreatment T stage, tumor location, and histologic grade. The model has a sensitivity of 74%, a specificity of 87%, and can be entered into a programmable calculator.

Adult↗
PubMed1990

On the consequences of model misspecification in logistic regression.

Logistic regression models are commonly used to study the association between a binary response variable and an exposure variable. Besides the exposure of interest, other covariates are frequently included in the fitted model in order to control for their effects on outcome. Unfortunately, misspecification of the main exposure variable and the other covariates is not uncommon, and this can adversely affect tests of the association between the exposure and response. We allow the term "misspecification" to cover a broad range of modeling errors including measurement errors, discretizing continuous explanatory variables, and completely excluding covariates from the model. This paper reviews some recent results on the consequences of model misspecification on the large sample properties of likelihood score tests of association between exposure and response.

Animals↗
PubMed2002

Risk assessment in localized primary cutaneous melanoma: a Southwest Oncology Group study evaluating nine factors and a test of the Clark logistic regression prediction model.

We studied 9 clinical and pathologic factors in 259 patients using Cox model regression analysis to determine which factors have independent predictive value. Median follow-up time in all patients still alive was 12.3 years (range, 1.7 to 16.7 years). Tumor-infiltrating lymphocytes (P = .005), primary site (P = .006), and thickness (P = .02) had independent predictive value. Ulceration (P = .06) and age (P = .07) had marginal value. We used 6 of those factors to test the Clark logistic regression prediction model, which accurately predicted 8-year survival in 121 (72.9%) of 166 patients and accurately predicted melanoma-specific mortality in 32 (43%) of 74 patients. The combined or overall accuracy of the Clark model was only 64%.

Female↗