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PubMed2006

[Logistic regression of the environmental risk factors in areas with Kashin-Beck disease].

OBJECTIVE: In order to find the factors influencing the prevalence of KBD, the possible nosogenetic factors of the family in Kashin-Beck disease (KBD) areas were analyzed. METHODS: The possible nosogenetic factors in mild, middle, high prevalence KBD areas were analyzed by logistic regression. The differences of the factors between three kinds of KBD areas were compared. RESULTS: Univariate analysis found sanitary conditions and meat-egg-mild were associated with KBD prevalence in all kinds KBD areas. Binary logistic analysis of multivariate suggested the possible nosogenetic factors were different in the different kind of KBD areas. It was wheat in mild prevalence area, sanitary conditions and meat-egg-mild in middle prevalence area, and sanitary conditions, rice and meat-egg-mild in high prevalence area. CONCLUSION: The risk factors are associated with the kind of KBD areas. The different preventive methods should be taken according to the kind of KBD areas, which will improve the effect of prevention.

China↗
PubMed2005

Predicting outcomes of hospitalization for heart failure using logistic regression and knowledge discovery methods.

The purpose of this study is to determine the best prediction of heart failure outcomes, resulting from two methods -- standard epidemiologic analysis with logistic regression and knowledge discovery with supervised learning/data mining. Heart failure was chosen for this study as it exhibits higher prevalence and cost of treatment than most other hospitalized diseases. The prevalence of heart failure has exceeded 4 million cases in the U.S.. Findings of this study should be useful for the design of quality improvement initiatives, as particular aspects of patient comorbidity and treatment are found to be associated with mortality. This is also a proof of concept study, considering the feasibility of emerging health informatics methods of data mining in conjunction with or in lieu of traditional logistic regression methods of prediction. Findings may also support the design of decision support systems and quality improvement programming for other diseases.

Databases as Topic↗
PubMed2006

[Risk factors for postoperative gastroparesis syndrome after pancreaticoduodenectomy with binary logistic regression].

OBJECTIVE: To study the risk factors of postoperative gastroparesis syndrome after pancreaticoduodenectomy. METHOD: Clinical data of 166 patients who underwent pancreaticoduodenectomy from January 2002 to April 2006 were analyzed with binary logistic regression in order to explore the risk factors of postoperative gastroparesis syndrome after pancreaticoduodenectomy. RESULTS: There were 60 patients occurred postoperative gastroparesis syndrome after pancreaticoduodenectomy. Among 15 factors, 6 factors were proved to be the risk factors of postoperative gastroparesis syndrome after pancreaticoduodenectomy by unitary anylasis; 7 factors were proved related factors of postoperative gastroparesis syndrome after pancreaticoduodenectomy by analyzed with binary logistic regression. CONCLUSIONS: The risk factors are preoperative gastro-intestinal obstruction syndrome, extensive lymphadenectomy, postoperative abdominal cavity infection, starting time of postoperative enteral nutrition, postoperative blood glucose level; The protective factors are preoperative percutaneous transhepatic cholangial drainage (PTCD) and postoperative blood terminal protein (TP) level.

Adolescent↗
PubMed1991

Regressive logistic models for familial diseases: a formulation assuming an underlying liability model.

Statistical models have been developed to delineate the major-gene and non-major-gene factors accounting for the familial aggregation of complex diseases. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affection is defined by a threshold on the liability scale. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype, phenotypes of antecedents and other covariates. An equivalence between these two approaches cannot be derived analytically. I propose a formulation of the regressive logistic models on the supposition of an underlying liability model of disease. Relatives are assumed to have correlated liabilities to the disease; affected persons have liabilities exceeding an estimable threshold. Under the assumption that the correlation structure of the relatives' liabilities follows a regressive model, the regression coefficients on antecedents are expressed in terms of the relevant familial correlations. A parsimonious parameterization is a consequence of the assumed liability model, and a one-to-one correspondence with the parameters of the mixed model can be established. The logits, derived under the class A regressive model and under the class D regressive model, can be extended to include a large variety of patterns of family dependence, as well as gene-environment interactions.

Environment↗
PubMed1990

[Epidemiological analysis of rank category exposure effects using cumulative logistic models].

Logistic regression is the fundamental procedure to analyze the dependence of the frequency of a binary disease indicator from exposure and other factors. Disease indicators with ranked levels contain a larger amount of information and permit a more precise prediction. For that, the cumulative logistic model was developed. Its application, demonstrated with an epidemiologic study for the dependence of arthritic changes in the knee-joint from intensity of kneeling stress and age, is very recommendable for the evaluation of well-designed epidemiologic studies concerning the prevalence of chronic diseases.

Environmental Exposure↗
PubMed1993

[Factors influencing survival in patients with coronary heart disease. II. Multivariate logistic function analysis].

A group of 683 patients with the significant narrowing (> 70%) of at least one coronary vessel diagnosed by coronarography performed between 1976-1988 in the Institute of Cardiology in Warsaw was followed during one to seven years. Two hundred ninety of them were treated surgically, 393 non surgically. A multivariate logistic function (MLF) analysis of 10 variables is presented obtained from anamnesis and hemodynamic data and their significance upon survival after 2, 4 and 6 years. In the group treated non surgically the number of narrowed vessels was a factor independently significant after 2, 4 and 6 years. After 4 and 6 years the ejection fraction and the left ventricle end diastolic volume index were also significant. In the group treated surgically none of those were significant after 2 years. After 4 and 6 years anamnesis of arterial hypertonia was significant after 6 years also left ventricle end diastolic volume index. Coefficients calculated from multivariate logistic function analysis allow the calculation of probability of survival for an individual patient.

Adult↗
PubMed1997

The effect of incomplete knowledge of parameter values on single- and multiple-stage designs for logistic regression.

We consider the design of single- and multiple-stage dose-response trials in which the probability of response is a logistic function of the dose. Knowledge of the parameters of the logistic at the time of planning the trial is represented by a Gaussian prior distribution. Methods are presented for determining a design that approximately optimizes a measure of the accuracy of estimation averaged over the prior distribution. Changes in design due to uncertainty of parameter values are described as well as the changes in sample size required to produce a specified precision. In multiple-stage trials, the initial stage is planned as if it were the only stage. For succeeding stages, the initial Gaussian prior distribution is updated using outcomes at the previous stages. At each such stage, the design optimizes a chosen criterion averaged over the updated prior distribution. The effectiveness of this methodology is evaluated by comparing the operating characteristics of two-stage designs with those of single-stage designs.

Algorithms↗
PubMed1996

[Non-conditional logistic regression analysis of affecting factors in patients with NIDDM].

According to the data of investigation, country from the southern part of Jiangsu Province, we analyzed 88 patients with non-insulindependent diabetes mellitus (NIDDM) through a case-control study. Simple and multiple logistic regression analysis demonstrated that the rates of history of hypertension, history of cardiovascular diseases, over nutrition or obesity in patients with NIDDM were significantly higher than those in controls (P < 0.01):OR of hypertension was 4.50, and it was still 2.94 when excluding other factors. These results indicated that hypertension might serve as one of the independent predicting factors. The model of multiple factors non-conditional logistic regression sholwed that NIDDM directly effected on the development of cardiovascular disease.

Adult↗
PubMed1997

Biochemical model of logistic regression for early prediction of the etiology of acute pancreatitis.

OBJECTIVE: To define a simple model for the early prediction of the biliary or alcoholic etiology in acute pancreatitis, according to the results of several biochemical variables. PATIENTS AND METHODS: Forty-five patients with acute pancreatitis were included in the study (33 of biliary and 12 of alcoholic etiology). Plasma levels of standard biochemical parameters (glucose, urea, albumin, calcium and C-reactive protein), liver function tests (glutamic oxalacetic transaminase, glutamic pyruvic transaminase, alkaline phosphatase, (gamma-glutamyl-transpeptidase, lactic dehydrogenase, bilirubin and bile acids) and pancreatic enzymes (lipase, amylase and p-amylase) were measured daily throughout the first three days of hospitalization. The lipase/amylase ratio was also calculated. Univariate and logistic regression analyses were performed. RESULTS: Age, sex and plasma levels of C-reactive protein, glutamic oxalacetic transaminase, glutamic pyruvic transaminase, alkaline phosphatase, (gamma-glutamyl-transpeptidase, bilirubin and amylase were significantly different in the two groups. The lipase/amylase ratio was not useful. Logistic regression analysis based on four variables: sex, age, C-reactive protein(day) 1 and glutamic pyruvic transaminase(day) 2 allowed the correct classification in 44 of 45 cases (97.7%). CONCLUSIONS: Biliary and alcoholic acute pancreatitis present differing biochemical profiles-Glutamic pyruvic transaminase(day)2 and C-reactive protein(day) 1 were the variables with the highest predictive value. Taking into account these two biochemical parameters plus age and sex, an accurate and early etiologic classification was possible in the vast majority of cases in the present study.

Acute Disease↗
PubMed1998

Conditional logistic regression with sandwich estimators: application to a meta-analysis.

Motivated by a meta-analysis of animal experiments on the effect of dietary fat and total caloric intake on mammary tumorigenesis, we explore the use of sandwich estimators of variance with conditional logistic regression. Classical conditional logistic regression assumes that the parameters are fixed effects across all clusters, while the sandwich estimator gives appropriate inferences for either fixed effects or random effects. However, inference using the standard Wald test with the sandwich estimator requires that each parameter is estimated using information from a large number of clusters. Since our example violates this condition, we introduce two modifications to the standard Wald test. First, we reduce the bias of the empirical variance estimator (the middle of the sandwich) by using standardized residuals. Second, we approximately account for the variance of these estimators by using the t-distribution instead of the normal distribution, where the degrees of freedom are estimated using Satterthwaite's approximation. Through simulations, we show that these sandwich estimators perform almost as well as classical estimators when the true effects are fixed and much better than the classical estimators when the true effects are random. We achieve simulated nominal coverage for these sandwich estimators even when some parameters are estimated from a small number of clusters.

Animals↗
PubMed1998

Differentiation of benign and malignant breast tumors by logistic regression and a classification tree using Doppler flow signals.

In breast examinations with Doppler, an increased flow is found in malignant tumors. With the relatively new color Doppler, we measured different flow values in 133 cancer patients and in 325 women with benign disease. These measurements were used to develop diagnostic rules. For the highly correlated flow values, we used a stepwise procedure to select a final logistic regression model and a tree-based approach, which is a different way of modeling. With both approaches we developed simple diagnostic rules of which the sensitivity and the specificity exceeded 90%. There are no differences between the two approaches concerning discriminative ability. As complex statistical modeling leads to an overoptimism in the assessment of the error rates, we applied sensitivity analysis, investigated the stability of the selected logistic regression model, and estimated the magnitude of the overoptimism of the diagnostic rules with resampling methods. The results indicate that the estimates of sensitivity and specificity are probably close to realistic values for a clinical setting.

Blood Flow Velocity↗
PubMedDate not supplied

Logistic regression generated probability estimates for trisomy 21 outcomes from serum alpha fetoprotein and beta human chorionic gonadotrophin: simplification with increased specificity.

Traditional Gaussian approaches to biochemical screening make calculations very cumbersome. We have sought to develop a logistic regression mathematical model for the calculation of risk for trisomy 21 in multiple marker screening that would be more "user friendly." Biochemical screening data from 15,444 patients with confirmed normal outcomes and 28 with trisomy 21 were analyzed by logistic regression analysis (LRA) to produce a direct mathematical approach to risk calculation that combines the improvements in sensitivity and specificity already demonstrated by us with extreme value cumulative function surface fittings, but which can be extended for other exploratory variables. LRA yields a 71.4% sensitivity with a 95% specificity which is comparable to our previously published discriminant aneuploidy detection (DADs) approach; both are an improvement over the commonly used Gaussian approach of risk assessment. In our study population, LRA performed as well as DADs; both performed better than the Gaussian method, and LRA is a simpler approach.

Aneuploidy↗
PubMed1982

Duchenne muscular dystrophy carrier detection using logistic discrimination: serum creatine kinase, hemopexin, pyruvate kinase, and lactate dehydrogenase in combination.

In the absence of an unambiguous test for identifying Duchenne muscular dystrophy (DMD) heterozygotes, methods are needed for combination of the results of individually equivocal tests as effectively and rationally as possible. Tw used logistic discrimination to assess the effectiveness of measurements of serum creatine kinase, hemopexin, pyruvate kinase, and lactate dehydrogenase alone and in various combinations in identifying DMD carriers. We analyzed 127 serum samples from 63 normal female controls (20-40 years old) and 67 from 38 obligate DMD carriers. The best two tests to use in combination were creatine kinase and hemopexin, and these two, with lactate dehydrogenase, were the best three. t the 95% level (with 5% of controls misclassified), 54% of the carriers were identified by CK alone, whereas 88% were identified by means of the four tests. Although a small proportion of known carriers still cannot be identified, application of the four tests to a group of 45 possible carrier mothers of isolated cases of DMD resolves the population into fairly discrete "normal" and "abnormal" subgroups. Thus, if bias of selection can be eliminated, application of logistic discrimination may permit a direct estimate of the proportion of mothers of affected boys who are homozygous normal.

Adult↗
PubMed1984

Serum myoglobin in Duchenne muscular dystrophy carrier detection: a comparison with creatine kinase and hemopexin using logistic discrimination.

Logistic discrimination was used to assess the effectiveness of serum myoglobin (Mb), creatine kinase (CK), and hemopexin (H) measurements in identifying Duchenne muscular dystrophy (DMD) carriers. Subjects included 36 obligate carriers, 46 age-matched control women, 30 mothers of isolated cases, and 14 DMD patients. The percentages of obligate carriers with logistic carrier probabilities exceeding the upper normal 95th centile (or 97.5th centile) were: CK alone, 63% (50%); Mb alone, 65% (62%); CK and Mb, 66% (62%); CK and H, 78% (65%); CK H and Mb, 72% (65%). In this study, Mb identified more carriers than CK at the 97.5% level, but there was no advantage in using Mb measurements with CK. CK provided slightly better overall separation of the control and carrier groups than Mb. CK, Mb, and H in combination provided significantly better separation than CK and H, or CK alone.

Adult↗
PubMed1992

Cytopathology of malignant mesothelioma: a stepwise logistic regression analysis.

Twenty-four cytologic features, previously reported to be useful in the distinction of malignant mesothelioma, adenocarcinoma, and benign mesothelial proliferation in serous effusions were assessed. Forty-four cases of malignant mesotheliomas, 46 cases of metastatic adenocarcinomas, and 30 cases of benign mesothelial proliferations were examined for these parameters. When these cytologic features were subjected to a stepwise logistic regression analysis, five features were selected to distinguish malignant mesothelioma from adenocarcinoma. These were true papillary aggregates, multinucleation with atypia, cell-to-cell apposition, acinus-like structures, and balloon-like vacuolation, the latter two features being characteristic of adenocarcinoma. The four variables selected to distinguish malignant mesothelioma from benign mesothelial proliferations were nuclear pleomorphism, macronucleoli, cell-in-cell engulfment, and monolayer cell groups, the latter being a feature of benign proliferations. Using these selected variables, the logistic model correctly predicted 95.4% of cases of malignant mesothelioma versus 100% of adenocarcinoma and 100% of malignant mesotheliomas versus 90% of benign mesothelial proliferations. The results of regression analysis suggest that many of the previously described cytologic features are not important diagnostic discriminators.

Adenocarcinoma↗
PubMed1989

Familial analysis of bipolar affective disorder using logistic models.

Data on bipolar affective disorder in 187 pedigrees from the Collaborative Depression Study were analyzed using logistic models that have been extended to incorporate age of onset information. Logistic regression analysis and segregation analysis revealed evidence for complex familial effects on this disorder.

Age Factors↗
PubMedDate not supplied

Hypothesis testing in the polychotomous logistic model with an application to detecting gastrointestinal cancer.

We discuss the use of the trichotomous logistic model to discriminate between patients with gastrointestinal (GI) cancer, patients with benign GI disease and 'normal' subjects, using symptoms and the concentrations of some serum proteins that are potentially indicative of malignancy as covariates. A parsimonious model can be obtained by invoking an indistinguishability hypothesis which is appropriate when a covariate is considered to have no predictive value between categories. It is shown that the polychotomous model can be re-parameterised under the null hypothesis to give a 'reduced form', which can be fitted by maximum likelihood. The validity of the use of the same methods for retrospective sampling is discussed. The approach is illustrated by the development of a logistic model to identify symptomatic and asymptomatic subjects with a high risk of GI cancer.

C-Reactive Protein↗
PubMed1989

Efficiency of the logistic regression and Cox proportional hazards models in longitudinal studies.

Both logistic regression and Cox proportional hazards models are used widely in longitudinal epidemiologic studies for analysing the relationship between several risk factors and a time-related dichotomous event. The two models yield similar estimates of regression coefficients in studies with short follow-up and low incidence of event occurrence. Further, with just one dichotomous covariate and identical censoring times for all subjects, the asymptotic relative efficiency of the two models is very close to 1 unless the duration of follow-up is extended. We generalize this result to several qualitative or quantitative covariates. This was motivated by the analysis of mortality data from a study where all subjects are followed up during the same fixed period without loss except by death. Logistic and Cox models were applied to these data. Similar results were obtained for the two models in shorter periods of follow-up of five years or less, but not in longer periods of ten years or more, where the survival rate was lower.

France↗