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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↗
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↗
PubMed1996

Logistic regression model of fotemustine toxicity combining independent phase II studies.

BACKGROUND: To optimize fotemustine chemotherapy, the authors considered how to combine independent Phase II trials to predict the risk of first occurrence of severe toxicity as a function of initial patient characteristics. METHODS: Clinical data from six Phase II trials were collected. Of the 478 patients enrolled 442 (male/female, 259/183; age range, 15-81 years) were evaluable for toxicity (1384 cycles of chemotherapy), including 221 with malignant malignant melanomas, 138 with primary brain tumors, 29 with lung carcinomas, 8 with head and neck carcinomas, and 46 with miscellaneous cancers. The influence of age, sex, performance status, type of tumor, number and location of metastases, and previous treatment by chemotherapy and/or radiotherapy was studied. The logistic regression method was applied to predict occurrence of leukopenia, thrombocytopenia, anemia, digestive tract, and/or hepatic toxicity. RESULTS: Univariate analysis showed that predictive factors for hematologic toxicity were age (> 50 years), type of tumor (brain < melanoma < others), number of metastatic sites (> 3), location of metastases (nonvisceral), and previous chemotherapy. Performance status and previous radiotherapy did not affect the toxicity of fotemustine Nausea and vomiting were predictable based on the type of tumor (head and neck < lung < brain < melanoma < others), the number of metastatic sites (> three) and visceral metastases. Hepatic disorders occurred preferentially in patients with hepatic metastases and more than three metastatic sites. Individual risk of hematologic and hepatic toxicity for patients with melanoma and primary brain tumors was predicted using logistic regression models. CONCLUSIONS: By combining clinical data from independent Phase II trials, the logistic model developed could predict the probability of fotemustine hematologic and hepatic toxicity.

Adolescent↗
PubMed1993

A Bayesian approach to logistic regression models having measurement error following a mixture distribution.

To estimate the parameters in a logistic regression model when the predictors are subject to random or systematic measurement error, we take a Bayesian approach and average the true logistic probability over the conditional posterior distribution of the true value of the predictor given its observed value. We allow this posterior distribution to consist of a mixture when the measurement error distribution changes form with observed exposure. We apply the method to study the risk of alcohol consumption on breast cancer using the Nurses Health Study data. We estimate measurement error from a small subsample where we compare true with reported consumption. Some of the self-reported non-drinkers truly do not drink. The resulting risk estimates differ sharply from those computed by standard logistic regression that ignores measurement error.

Age Factors↗
PubMed1990

Assessing proportionality in the proportional odds model for ordinal logistic regression.

The proportional odds model for ordinal logistic regression provides a useful extension of the binary logistic model to situations where the response variable takes on values in a set of ordered categories. The model may be represented by a series of logistic regressions for dependent binary variables, with common regression parameters reflecting the proportional odds assumption. Key to the valid application of the model is the assessment of the proportionality assumption. An approach is described arising from comparisons of the separate (correlated) fits to the binary logistic models underlying the overall model. Based on asymptotic distributional results, formal goodness-of-fit measures are constructed to supplement informal comparisons of the different fits. A number of proposals, including application of bootstrap simulation, are discussed and illustrated with a data example.

Biometry↗
PubMed1991

Alternative parameterization of polychotomous models: theory and application to matched case-control studies.

A method is proposed for transforming a class of models having an outcome variable with more than two levels into an equivalent binary model. The polychotomous logistic model is used to demonstrate the method. The equivalency to a simple logistic regression model after some data transformation (augmentation) is shown. The method is applied to the data from two case-control studies each with two control groups, and further applications are indicated.

Breast Neoplasms↗
PubMed1982

Prediction of remission in adult acute leukemia: development and testing of predictive models.

Logistic regression methods were applied to derive a set of models relating achievement of CR to prognostic characteristics in a group of 300 adult acute leukemia patients treated with cytosine arabinoside, vincristine, and prednisone combined with adriamycin (ADOAP) or rubidazone (ROAP). These models were tested prospectively in an independent group of 107 subsequent patients treated with ADOAP or ROAP therapy, by comparing observed outcomes to predictions of response based on the models. Several models were able to identify subgroups of patients with good, intermediate, and poor prognoses. A model regarded as clinically useful and which provided a good fit to both the population from which it was derived and the test population included the pretreatment factors age, history of an antecedent hematologic disorder, temperature, blood urea nitrogen, hemoglobin, and liver size.

Acute Disease↗
PubMed1995

Confidence interval estimates of an index of quality performance based on logistic regression models.

This paper considers an index of hospital quality performance defined as the ratio of the observed number deaths to the number predicted by a fitted logistic regression model. We study tests and confidence intervals under two different scenarios depending on the availability of an estimate of the covariance matrix of the coefficients from the fitted logistic regression model. We propose parametric as well as bootstrap-based confidence intervals. We apply the methods to an analysis of the performance of 27 intensive care units.

Aged↗
PubMed1997

A comparison of goodness-of-fit tests for the logistic regression model.

Recent work has shown that there may be disadvantages in the use of the chi-square-like goodness-of-fit tests for the logistic regression model proposed by Hosmer and Lemeshow that use fixed groups of the estimated probabilities. A particular concern with these grouping strategies based on estimated probabilities, fitted values, is that groups may contain subjects with widely different values of the covariates. It is possible to demonstrate situations where one set of fixed groups shows the model fits while the test rejects fit using a different set of fixed groups. We compare the performance by simulation of these tests to tests based on smoothed residuals proposed by le Cessie and Van Houwelingen and Royston, a score test for an extended logistic regression model proposed by Stukel, the Pearson chi-square and the unweighted residual sum-of-squares. These simulations demonstrate that all but one of Royston's tests have the correct size. An examination of the performance of the tests when the correct model has a quadratic term but a model containing only the linear term has been fit shows that the Pearson chi-square, the unweighted sum-of-squares, the Hosmer-Lemeshow decile of risk, the smoothed residual sum-of-squares and Stukel's score test, have power exceeding 50 per cent to detect moderate departures from linearity when the sample size is 100 and have power over 90 per cent for these same alternatives for samples of size 500. All tests had no power when the correct model had an interaction between a dichotomous and continuous covariate but only the continuous covariate model was fit. Power to detect an incorrectly specified link was poor for samples of size 100. For samples of size 500 Stukel's score test had the best power but it only exceeded 50 per cent to detect an asymmetric link function. The power of the unweighted sum-of-squares test to detect an incorrectly specified link function was slightly less than Stukel's score test. We illustrate the tests within the context of a model for factors associated with low birth weight.

Humans↗
PubMed1993

Determinants of success of coronary angioplasty in patients with a chronic total occlusion: a multiple logistic regression model to improve selection of patients.

OBJECTIVE: To study the determinants of success of coronary angioplasty in patients with chronic total occlusions, and to formulate a multiple logistic regression model to improve selection of patients. DESIGN: A retrospective analysis of clinical and angiographic data on a consecutive series of patients. PATIENTS: 312 patients (mean age 55, range 31 to 79 years, 86% men) who underwent coronary angioplasty procedure for a chronic total occlusion between 1981 and 1992. RESULTS: Procedural success was achieved in 191 lesions (61.2%). A major complication occurred in six patients (1.9%). Multiple stepwise logistic regression analysis identified the presence of bridging collaterals (p < 0.001), the absence of a tapered entry configuration (p < 0.001), estimated duration of occlusion of greater than three months (p = 0.001), and a vessel diameter of less than 3 mm (p = 0.003) as independent predictors of procedural failure. The logistic regression model was used to classify patients into groups of high, intermediate, and low probability of procedural success with cut off points of 70% and 30%. The predictive value for procedural success (probability > or = 70%) was 91% (95% confidence intervals (95% CI) 83% to 96%) and predictive value for procedural failure (probability < 30%) was 81% (95% CI 64% to 92%). CONCLUSIONS: Percutaneous transluminal coronary angioplasty of chronic total occlusions is associated with a low risk of acute complication. Procedural success is influenced by easily identifiable clinical and angiographic features and the multiple regression model described may help to improve selection of patients.

Adult↗
PubMed1993

Logistic regression model to estimate the risk of unbalanced offspring in reciprocal translocations.

The aim of this study was to estimate the risk of viable unbalanced offspring for a parental carrier of reciprocal translocation. On a large computerized database of reciprocal translocations we used logistic regression to model this risk. The status of the progeny is the outcome variable. Explanatory covariates are cytogenetic characteristics of the translocation, age and sex of the parental carrier, and potential viability of the gametes. The results obtained by the logistic model demonstrate the important role of certain variables such as the sex of the parental carrier and the R band length of the translocated segments. Within the group of lower risk (risk of viable unbalanced offspring less than 5%), 97% of the individuals are correctly classified with this model. For this group, the choice prenatal diagnosis can be best discussed by considering both the risk for viable unbalanced offspring and the risk of induced abortion following prenatal diagnosis.

Adolescent↗