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PubMed1996

A computer program using BALF-analysis results as a diagnostic tool in interstitial lung diseases.

Recently, we showed that it is possible to distinguish between three common interstitial lung diseases (ILD) with similarities in clinical presentation by using a number of selected variables derived from bronchoalveolar lavage fluid (BALF) analysis. The aim of this study was to develop a more general discriminant model, based on polychotomous logistic regression analysis. The 277 patients involved in the study belonged to diagnostic groups with sarcoidosis (n = 193), extrinsic allergic alveolitis (EAA; n = 39), and idiopathic pulmonary fibrosis (IPF; n = 45). The diagnosis had been established independently of the BALF-analysis results. The variables used to discriminate among these patient groups were the yield of recovered BALF, total cell count, and percentages of alveolar macrophages, lymphocytes, neutrophils, and eosinophils. In order to test the predictive power of the logistic model, we used 128 patients having sarcoidosis (n = 91), EAA (n = 5), or IPF (n = 32) from another hospital. In this test set the agreement of predicted with actual diagnostic-group membership was the same as in the learning set in which the logistic model was fitted: 94.5% of the cases were correctly classified. A validated computer program based on the polychotomous logistic regression model can be used to predict the diagnosis for an arbitrary patient with information provided by BALF analysis, and is thought to be of diagnostic value in patients suspected of having ILD.

Adolescent↗
PubMed1996

Prediction of postoperative nausea and vomiting using a logistic regression model.

In a previous study, logistic regression analysis was used to determine the association of independent fixed patient factors with the incidence of postoperative nausea and vomiting (PONV). Female sex, previous history of PONV, use of postoperative opioids, previous history of motion sickness and an interaction between male sex and previous history of PONV were combined in an equation from which risk of PONV could be estimated. The present study was designed to test this equation in a group of patients with wide selection criteria. Data on 400 patients were collected in relation to pre-, per- and postoperative factors which may influence the incidence of PONV. The equation was used to predict PONV, and actual outcome was compared with that predicted. The overall incidence of PONV was 36%. The equation predicted an overall probability of PONV of 27.4%. If the model was used to define individual patients as predicted to have or not to have PONV, it was correct only 71% of the time. However, there was good agreement between the actual incidences of PONV and those predicted among the 16 risk groups created by the model.

Analgesics, Opioid↗
PubMed2006

Benchmark concentrations for methyl mercury obtained from the 9-year follow-up of the Seychelles Child Development Study.

Methyl mercury (MeHg) is highly toxic to the developing nervous system. Human exposure is mainly from fish consumption since small amounts are present in all fish. Findings of developmental neurotoxicity following high-level prenatal exposure to MeHg raised the question of whether children whose mothers consumed fish contaminated with background levels during pregnancy are at an increased risk of impaired neurological function. Benchmark doses determined from studies in New Zealand, and the Faroese and Seychelles Islands indicate that a level of 4-25 parts per million (ppm) measured in maternal hair may carry a risk to the infant. However, there are numerous sources of uncertainty that could affect the derivation of benchmark doses, and it is crucial to continue to investigate the most appropriate derivation of safe consumption levels. Earlier, we published the findings from benchmark analyses applied to the data collected on the Seychelles main cohort at the 66-month follow-up period. Here, we expand on the main cohort analyses by determining the benchmark doses (BMD) of MeHg level in maternal hair based on 643 Seychellois children for whom 26 different neurobehavioral endpoints were measured at 9 years of age. Dose-response models applied to these continuous endpoints incorporated a variety of covariates and included the k-power model, the Weibull model, and the logistic model. The average 95% lower confidence limit of the BMD (BMDL) across all 26 endpoints varied from 20.1 ppm (range=17.2-22.5) for the logistic model to 20.4 ppm (range=17.9-23.0) for the k-power model. These estimates are somewhat lower than those obtained after 66 months of follow-up. The Seychelles Child Development Study continues to provide a firm scientific basis for the derivation of safe levels of MeHg consumption.

Animals↗
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↗
PubMed2004

Validation and updating of predictive logistic regression models: a study on sample size and shrinkage.

A logistic regression model may be used to provide predictions of outcome for individual patients at another centre than where the model was developed. When empirical data are available from this centre, the validity of predictions can be assessed by comparing observed outcomes and predicted probabilities. Subsequently, the model may be updated to improve predictions for future patients. As an example, we analysed 30-day mortality after acute myocardial infarction in a large data set (GUSTO-I, n = 40 830). We validated and updated a previously published model from another study (TIMI-II, n = 3339) in validation samples ranging from small (200 patients, 14 deaths) to large (10,000 patients, 700 deaths). Updated models were tested on independent patients. Updating methods included re-calibration (re-estimation of the intercept or slope of the linear predictor) and more structural model revisions (re-estimation of some or all regression coefficients, model extension with more predictors). We applied heuristic shrinkage approaches in the model revision methods, such that regression coefficients were shrunken towards their re-calibrated values. Parsimonious updating methods were found preferable to more extensive model revisions, which should only be attempted with relatively large validation samples in combination with shrinkage.

Computer Simulation↗
PubMed1992

Numerical comparisons of two formulations of the logistic regressive models with the mixed model in segregation analysis of discrete traits.

Segregation analysis of discrete traits can be conducted by the classical mixed model and the recently introduced regressive models. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affected persons have a liability exceeding a threshold. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype effects, the phenotypes of older relatives, and other covariates. A formulation of the regressive models, based on an underlying liability model, has been recently proposed. The regression coefficients on antecedents are expressed in terms of the relevant familial correlations and a one-to-one correspondence with the parameters of the mixed model can thus be established. Computer simulations are conducted to evaluate the fit of the two formulations of the regressive models to the mixed model on nuclear families. The two forms of the class D regressive model provide a good fit to a generated mixed model, in terms of both hypothesis testing and parameter estimation. The simpler class A regressive model, which assumes that the outcomes of children depend solely on the outcomes of parents, is not robust against a sib-sib correlation exceeding that specified by the model, emphasizing testing class A against class D. The studies reported here show that if the true state of nature is that described by the mixed model, then a regressive model will do just as well. Moreover, the regressive models, allowing for more patterns of family dependence, provide a flexible framework to understand gene-environment interactions in complex diseases.

Computer Simulation↗
PubMed1991

[A case-control study on HBV infection in families of hepatocellular carcinoma-application of conditional logistic regression model].

To assess the risk of HBV infection in families of hepatocellular carcinoma (HCC), methods of case-control study and conditional logistic regression model were applied. The total infection rate of HBV was 88.89% in cases and 55.56% in controls, showing a statistically significant difference (P less than 0.01). Analysis of logistic model showed that the variable interaction of HBsAg and anti-HBc in pointer group and the variable five-HBV-marker in siblings group were the most important risk factors in these families. However, no significant difference was found in each marker of HBV between two spouse-groups (P greater than 0.05). This study indicates a familial clustering of HBV infection in HCC families.

Adult↗
PubMed1991

Validation techniques for logistic regression models.

This paper presents a comprehensive approach to the validation of logistic prediction models. It reviews measures of overall goodness-of-fit, and indices of calibration and refinement. Using a model-based approach developed by Cox, we adapt logistic regression diagnostic techniques for use in model validation. This allows identification of problematic predictor variables in the prediction model as well as influential observations in the validation data that adversely affect the fit of the model. In appropriate situations, recommendations are made for correction of models that provide poor fit.

Benzothiadiazines↗
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↗
PubMed1998

A Rasch measurement model analysis of the Revised Approaches to Studying Inventory.

BACKGROUND: The Revised Approaches to Studying Inventory (Entwistle & Tait, 1994) comprises 38 self-report items designed to measure student approaches to learning in a higher education context. The items have been conceptualised and designed from five learning orientations, corresponding to five subscales of the Inventory: 'a deep approach', 'a surface approach', 'a strategic approach', 'a lack of direction' and 'academic self-confidence'. AIMS: The study aims to create an interval level scale for the Inventory and analyse its psychometric properties using a modern measurement model, the Extended Logistic Model of Rasch (Andrich, 1988a, 1988b; Rasch, 1980), and investigate the conceptual design of the Inventory. SAMPLE: The sample was 346 students (170 females, 176 males, 212 less than 23 years and 134 older than 23), studying in first year Faculty of Business, at a university in Perth, Western Australia. METHOD: A scale was created for the Inventory and analysed for reliability, fit to the model, meaning and validity. The Inventory was analysed separately for each of four subgroups (females, males, younger and older students) to test the invariance of the scale. The five subscales were analysed separately to test the conceptual design and meaning of the Inventory. RESULTS: The Inventory has satisfactory psychometric properties, when items 20 and 33 are deleted. Easier and harder items need to be added to better target the student measures. Its conceptual design from the five learning orientations is confirmed. Only 15 items have satisfactory invariance across the four subgroups. The psychometric properties of three subscales (deep approach, surface approach and strategic approach) are only moderately satisfactory and the item separabilities of the other two (lack of direction and academic self-confidence) are unsatisfactory. CONCLUSIONS: The Extended Logistic Model of Rasch was found to be useful in creating an interval level scale for the Inventory, and for analysing its psychometric properties and conceptual design. It is suggested that the Inventory can be improved conceptually by adding more items relating to attitudes, intentions and behaviour and by adding harder and easier items to target the student measures better.

Adult↗
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↗
PubMed2002

Markers for depression in Parkinson's disease.

OBJECTIVE: To assess whether general risk factors for depression are also markers of depression in patients with Parkinson's disease (PD) and to identify additional disease-specific markers. METHOD: A two-step logistic regression was performed on data from 161 consecutively referred PD patients, 40 of whom suffered from major depressive disorder. A first logistic model was created with five general risk factors for depression. Next, five potential disease-specific markers were added to see whether this would improve the model. RESULTS: The logistic model of general risk factors for depression also predicted depression in PD patients. A family history of depression was the most important marker. 'Right-sided onset' was the only disease-specific marker that improved the model. CONCLUSION: Established risk factors for depression in the general population are also markers of depression in PD. The importance of correcting for general risk factors for depression in the search for disease-specific risk factors is stressed.

Aged↗
PubMed2002

Two goodness-of-fit tests for logistic regression models with continuous covariates.

Assessing goodness-of-fit in logistic regression models can be problematic, in that commonly used deviance or Pearson chi-square statistics do not have approximate chi-square distributions, under the null hypothesis of no lack of fit, when continuous covariates are modelled. We present two easy to implement test statistics similar to the deviance and Pearson chi-square tests that are appropriate when continuous covariates are present. The methodology uses an approach similar to that incorporated by the Hosmer and Lemeshow goodness-of-fit test in that observations are classified into distinct groups according to fitted probabilities, allowing sufficient cell sizes for chi-square testing. The major difference is that the proposed tests perform this grouping within the cross-classification of all categorical covariates in the model and, in some situations, allow for a more powerful assessment of where model predicted and observed counts may differ. A variety of simulations are performed comparing the proposed tests to the Hosmer-Lemeshow test.

Aortic Aneurysm, Abdominal↗
PubMed2003

Regional distribution of bovine Neospora caninum infection in the German state of Rhineland-Palatinate modelled by Logistic regression.

To obtain a rapid overview over the distribution of bovine Neospora caninum-infections in the German state of Rhineland-Palatinate, an ELISA to determine specific bovine antibodies against a p38 surface antigen of N. caninum tachyzoites was modified to examine bulk milk samples from cattle herds. Experimental bulk milk samples were used to demonstrate that the seroprevalence in a group of animals can be estimated with this ELISA. A cut-off was selected for the specific detection of herds having a seroprevalence > or =10%. About 90% of the dairy herds located in Rhineland-Palatinate were examined. An overall prevalence of bulk milk-positive herds of 7.9% (95% confidence interval 7.0-8.9%), respectively, was determined. Major regional differences in the distribution of bulk milk-positive herds were observed. Prevalences were higher in regions with an increased degree of urbanisation. Logistic regression was applied to model the prevalence of bulk milk-positive herds on a district and city level. Variables describing the dog density, mean temperature in July, mean temperature in January and the total yearly precipitation in districts and cities were able to explain most of the observed variability in the regional prevalences. Our results provide evidence that in addition to risk factors related to individual farms also risk factors related to the farm location such as dog density in the surrounding and climate factors are important in the epidemiology of bovine neosporosis.

Animals↗
PubMed2001

Comparison of 'pattern recognition' and logistic regression models for discrimination between benign and malignant pelvic masses: a prospective cross validation.

OBJECTIVES: To test prospectively the diagnostic performance of two logistic regression models for calculation of individual risk of malignancy in adnexal tumors (the 'Tailor model' and the 'Timmerman model'), and to compare them to that of 'pattern recognition' (subjective evaluation of the gray-scale ultrasound image and color Doppler ultrasound examination). DESIGN: Consecutive women with a pelvic mass judged clinically to be of adnexal origin underwent preoperative ultrasound examination including color and spectral Doppler examination. The same examination techniques and definitions as those used in the studies in which the logistic regression models had been created were used. The Tailor model was tested in 133 women (35 of whom hada malignancy) and the Timmerman model in 82 women (29 of whom had a malignancy). A subset of 79 women (28 of whom had a malignancy) was used to compare the performance of the Tailor model and the Timmerman model by calculating and comparing the areas under the receiver operating characteristics curves of the two models. Sensitivity and specificity with regard to malignancy were calculated for all three methods. RESULTS: Pattern recognition performed better than the two logistic regression models (sensitivity around 85%, specificity around 90%). Using a risk of malignancy of > 50% to indicate malignancy (as suggested in the original publications), the sensitivity of the Tailor model was 69% and the specificity 88% (n = 133). The corresponding values for the Timmerman model were 62% and 79% (n = 82). The receiver operating characteristics curves showed the two logistic regression models to have similar diagnostic properties (area under the curve, 0.87 vs. 0.84; P = 0.25; n = 79). The diagnostic performance of the mathematical models was much poorer in this study than in those in which the models had been created. CONCLUSION: The poor diagnostic performance of the mathematical models can probably be explained by subtle differences in definitions and examination technique and by differences between the original tumor populations and the study population. For mathematical models to be generally useful, they probably need to be created on the basis of a very large number of tumors, and the variables in the model must be unequivocally defined and the examination technique meticulously standardized.

Adenofibroma↗
PubMed2002

Mixed effects logistic regression models for multiple longitudinal binary functional limitation responses with informative drop-out and confounding by baseline outcomes.

In the context of analyzing multiple functional limitation responses collected longitudinally from the Longitudinal Study of Aging (LSOA), we investigate the heterogeneity of these outcomes with respect to their associations with previous functional status and other risk factors in the presence of informative drop-out and confounding by baseline outcomes. We accommodate the longitudinal nature of the multiple outcomes with a unique extension of the nested random effects logistic model with an autoregressive structure to include drop-out and baseline outcome components with shared random effects. Estimation of fixed effects and variance components is by maximum likelihood with numerical integration. This shared parameter selection model assumes that drop-out is conditionally independent of the multiple functional limitation outcomes given the underlying random effect representing an individual's trajectory of functional status across time. Whereas it is not possible to fully assess the adequacy of this assumption, we assess the robustness of this approach by varying the assumptions underlying the proposed model such as the random effects structure, the drop-out component, and omission of baseline functional outcomes as dependent variables in the model. Heterogeneity among the associations between each functional limitation outcome and a set of risk factors for functional limitation, such as previous functional limitation and physical activity, exists for the LSOA data of interest. Less heterogeneity is observed among the estimates of time-level random effects variance components that are allowed to vary across functional outcomes and time. We also note that. under an autoregressive structure, bias results from omitting the baseline outcome component linked to the follow-up outcome component by subject-level random effects.

Aged↗
PubMed2000

Mixed effects logistic regression models for longitudinal ordinal functional response data with multiple-cause drop-out from the longitudinal study of aging.

In the context of analyzing ordinal functional limitation responses from the Longitudinal Study of Aging, we investigate the association between current functional limitation and previous year's limitation and its modification by physical activity and multiple causes of drop-out. We accommodate the longitudinal nature of the multiple causes of informative drop-out (death and unknown loss-to-follow-up) with a mixed effects logistic model. Under the proposed model with a random intercept and slope, the ordinal functional outcome and multiple discrete time survival profiles share a common random effect structure. This shared parameter selection model assumes that the multiple causes of drop-out are conditionally independent of the functional limitation outcome given the underlying random effect representing an individual's trajectory of general health status across time. Although it is not possible to fully assess the adequacy of this assumption, we assess the robustness of the approach by varying the assumptions underlying the proposed model, such as the random effects distribution and the drop-out component. It appears that between-subject differences in initial functional limitation are strongly associated with future functional limitation and that this association is stronger for those who do not have physical activity regardless of the random effects and informative drop-out specifications. In contrast, the association between current functional limitation and previous trajectory of functional status within an individual is weaker and more sensitive to changes in the random effects and drop-out assumptions.

Aged↗