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PubMed1994

Thyroid diseases among atomic bomb survivors in Nagasaki.

OBJECTIVE: To elucidate the current thyroid disease status for the Nagasaki Adult Health Study cohort of the Radiation Effects Research Foundation. DESIGN: Survey study. SETTING: Nagasaki, Japan. PARTICIPANTS: Cohort members of the Nagasaki Adult Health Study who received biennial health examinations from October 1984 to April 1987 (n = 2856). A total of 2587 subjects remained after exclusion of persons exposed in Hiroshima or in utero and those who were not in Nagasaki at the time of the bombing. Thyroid radiation dose by the dosimetry system established in 1986 was available for 1978 of the 2587 subjects. MAIN OUTCOME MEASURES: Thyroid diseases were diagnosed using uniform procedures including ultrasonic scanning. The relationship of the prevalence of each thyroid disease with thyroid radiation dose, sex, and age was analyzed using logistic models. RESULTS: A significant dose-response relationship was observed for solid nodules, which include cancer, adenoma, adenomatous goiter, and nodules without histological diagnosis, and for antibody-positive spontaneous hypothyroidism (autoimmune hypothyroidism) but not for other diseases. The prevalence of solid nodules showed a monotonic dose-response relationship, yet that of autoimmune hypothyroidism displayed a concave dose-response relationship reaching a maximum (+/- SE) level of 0.7 +/- 0.2 Sv. CONCLUSIONS: The present study confirmed the results of previous studies by showing a significant increase in solid nodules with dose to the thyroid and demonstrated for the first time a significant increase in autoimmune disease among atomic bomb survivors. A concave dose-response relationship indicates the necessity for further studies on the effects of relatively low doses of radiation on thyroid disease.

Cohort Studies↗
PubMed1991

Age-dependent logistic regression model and its application.

In the present paper we introduce the theory and algorithm of the unconditional and conditional age-dependent logistic regression model, which combines logistic regression analysis of case-control study with survival analysis of cases in the data, thus facilitating simultaneous comparison analysis between cases and controls and among cases with different ages of disease onset under study. In age-dependent logistic regression analysis, estimated compound relative risk (CRR) and compound attributable risk (CAR) comprise the variance contributions of risk factors to disease occurrence and the time of disease onset, thereby the role played by various risk factors in etiology and etiopathology can be objectively evaluated. The current logistic regression model is only a particular case of age-dependent logistic regression theory neglecting the variations in onset age of diseases.

Age Factors↗
PubMed1998

Diagnosing breast cancer from FNAs: variable relevance in neural network and logistic regression models.

We compared the selection of variables for building a classification model for the diagnosis of breast cancer using neural networks and logistic regression. A set of 460 cases was used to build neural network and logistic regression models that classify cell samples obtained by fine-needle aspiration (FNA) as malignant or benign, depending on nine pathology features. Variables selected by a step down logistic regression model were compared to those selected by a measure of relevance derived from neural network weights. Since both types of models resulted in similar predictive accuracy, we expected approximately the same variables to be selected. The variables with the highest relevance values for the neural network models corresponded to those of high significance in univariate logistic regression models, but were not the ones selected in the step down procedure of multivariate models. Variable relevance based on weights for neural network models does not seem to be a consistent index of the importance of that variable for multivariate models such as logistic regression.

Analysis of Variance↗
PubMed2000

Interpreting parameters in the logistic regression model with random effects.

Logistic regression with random effects is used to study the relationship between explanatory variables and a binary outcome in cases with nonindependent outcomes. In this paper, we examine in detail the interpretation of both fixed effects and random effects parameters. As heterogeneity measures, the random effects parameters included in the model are not easily interpreted. We discuss different alternative measures of heterogeneity and suggest using a median odds ratio measure that is a function of the original random effects parameters. The measure allows a simple interpretation, in terms of well-known odds ratios, that greatly facilitates communication between the data analyst and the subject-matter researcher. Three examples from different subject areas, mainly taken from our own experience, serve to motivate and illustrate different aspects of parameter interpretation in these models.

Animals↗
PubMed2000

One model, several results: the paradox of the Hosmer-Lemeshow goodness-of-fit test for the logistic regression model.

BACKGROUND: The Hosmer-Lemeshow test, used extensively to assess the fit of the logistic regression model, is performed by several statistical packages. Recent studies have shown some problems in the use of this test when ties are present. These problems were attributed merely to the test implementation. METHODS: We analysed the order of the observations as an alternative explanation of the problem of ties. Using a data-set of 1393 intensive care unit (ICU) patients we performed the Hosmer-Lemeshow test with all possible subjects dispositions. RESULTS: We obtained about one million different P values, ranging from 0.01 to 0.95. DISCUSSION: It is already known that when the Hosmer-Lemeshow goodness-of-fit test is performed with a number of covariate patterns lower than the number of subjects, its result may be inaccurate. We showed that the extent of this problem could be relevant under particular conditions. We also suggest a strategy for estimating the extent of the problem and subsequent interpretation.

Hospital Mortality↗
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↗
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↗
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↗
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↗
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↗
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↗
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↗
PubMed2000

Prospective evaluation of logistic regression models for the diagnosis of ovarian cancer.

OBJECTIVE: To test the accuracy of three logistic regression models in diagnosing malignancy in women with adnexal masses. METHODS: This was a prospective collaborative study. Women were recruited from three hospitals and all assessments were performed at the Gynaecology Ultrasound Unit, King's College Hospital. One hundred women with known adnexal masses were examined preoperatively. The demographic, biochemical, and sonographic data recorded for each patient included age, menopausal status, CA 125 levels, ultrasound morphology, and Doppler blood flow analysis. The diagnosis of malignancy was made for each woman using three logistic regression models previously described by Alcazar et al, Tailor et al, and Timmerman et al. Variables used in these models were then combined to form a new model. The results were compared with the final histopathologic diagnosis. RESULTS: Sixty-seven women had benign tumors and 33 had ovarian cancer. Women with malignant tumors were older than those with benign masses. There were significant differences in CA 125 levels, presence of papillary proliferations, and ascites between the two groups. The sensitivities and specificities achieved respectively by the models were as follows: 45% and 93% with Tailor et al's model, 9% and 99% with Alcazar et al's model, and 73% and 91% with Timmerman et al's model. There was no significant improvement over the performance of Timmerman et al's model and the new combined model. CONCLUSION: All models performed less well than originally reported. Combining the models did not lead to a significant improvement in performance. Larger sample sizes that incorporate all types of ovarian tumors are necessary to design more accurate diagnostic models.

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↗