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PubMed1986

[Differential diagnosis of jaundice by combined noninvasive diagnostic means using a logistic discriminant analysis].

Using a logistic discriminant function we retrospectively evaluated the diagnostic value of laboratory features and abdominal sonography in 70 patients with jaundice. 18 patients had an extrahepatic obstruction of the common bile duct (EHO), 22 patients had metastatic liver disease (MLD) and 30 patients had an infectious or toxic hepatocellular disease (HCD). The sensitivity resp. specificity of the 5 laboratory values AP, GGT, GPT, GOT and bilirubin was 22% resp. 90% for diagnosing EHO, 82% resp. 79% for diagnosing MLD and 67% resp. 68% for diagnosing HCD. The diagnostic value determined by their Chi2-value was AP greater than GPT greater than bilirubin greater than GOT greater than GGT. An undoubtedly dilated common bile duct was seen in 56% of the patients with EHO and in none of the other patients. Metastatic lesions were seen exclusively in 81% of the patients with MLD. No distinct sonographic sign could be found for the patients with HCD. The combination of AP, GPT and bilirubin with the result of abdominal sonography in a logistic discriminant function led to a correct a posteriori classification of all patients. Using a mathematical classification model jaundiced patients can be diagnosed on the basis of noninvasive methods alone and invasive procedures should be restricted to therapeutic interventions.

Cholestasis, Extrahepatic↗
PubMedDate not supplied

Logistic growth curve of chickens: a comparison of techniques to estimate parameters.

Parameters of a mathematical function of growth, fit to the body weight curve of two randombred control populations of each sex of chickens from hatching through 45 weeks of age, were estimated. The logistic function was chosen from among growth formulae that express rate of gain as a function of weight at a given time and gain to be made. Two logistic parameters, growth-rate constant and age at the point of inflection, were estimated by the methods of sample quantiles and nonlinear regression from weekly mean body weights of 225 males and 281 females of the Rhode Island Red (RIR) line, and 164 males and 239 females of the White Leghorn (WL) line. Males had a larger growth-rate constant than females of the same line. The RIR line had a larger rate constant than the WL line, for each sex. Age at the point of inflection was similar for males and females in the RIR line, but smaller for males than females in the WL line. Sample quantiles yielded larger, less precise estimates of the growth-rate constant than nonlinear regression. Estimates of age at the point of inflection were usually smaller using sample quantiles.

Aging↗
PubMed1985

Application of the log-linear and logistic regression models in the prediction of systemic lupus erythematosus in the dog.

This study sought to mathematically define canine systemic lupus erythematosus (SLE) by unifying diagnostic criteria proposed by others. Thirty-one cases of canine SLE were selected for modeling when 4 different published schemes agreed on the diagnosis, and 122 controls were selected when a patient's status met no scheme's criteria. The log-linear method showed an association between SLE and polyarthritis, hematologic abnormalities, renal damage, dermatologic disorders, and antinuclear antibody test response (positive). Logistic regression was then used to derive a predictive algorithm that could identify cases and controls with which all published criteria would be in accordance. The final equation correctly classified 93.5% of the affected dogs and 98.4% of the controls. It was concluded that the log-linear and logistic regression models are useful for the diagnosis of clinically similar, but distinguishable, disease states.

Animals↗
PubMed1984

Treatment allocation for nonlinear models in clinical trials: the logistic model.

Many clinical trials have a binary outcome variable. If covariate adjustment is necessary in the analysis, the logistic-regression model is frequently used. Optimal designs for allocating treatments for this model, or for any nonlinear or heteroscedastic model, are generally unbalanced with regard to overall treatment totals and totals within strata. However, all treatment-allocation methods that have been recommended for clinical trials in the literature are designed to balance treatments within strata, either directly or asymptotically. In this paper, the efficiencies of balanced sequential allocation schemes are measured relative to sequential Ds-optimal designs for the logistic model, using as examples completed trials conducted by the Eastern Cooperative Oncology Group and systematic simulations. The results demonstrate that stratified, balanced designs are quite efficient, in general. However, complete randomization is frequently inefficient, and will occasionally result in a trial that is very inefficient.

Biometry↗
PubMed1981

The logistic analysis of categorical data from dental and oral experiments.

Logistic analysis methodology is being increasingly used in the analysis of categorical data from the biomedical sciences. We describe how the relationships between a dichotomous outcome variable and several qualitative classificatory or experimental variables can be modeled in a format similar to that used for quantitative data. The technique of weighted least squares is used to estimate the underlying parameters in the model and to perform various tests of statistical significance. The methodology is illustrated using data from a randomized dental anaesthetic study comparing three local anaesthetic solutions under two operative procedures. The similarities between the logistic analysis of dichotomous data and the analysis of variance applied to quantitative data are stressed.

Analysis of Variance↗
PubMed1994

The use of logistic models for the analysis of codon frequencies of DNA sequences in terms of explanatory variables.

The development of the regressive logistic model applicable to the analysis of codon frequencies of DNA sequences in terms of explanatory variables is presented. A codon is a triplet of nucleotides that code for an amino acid, and may be considered as a trivariate response (B1, B2, B3), where Bi (i = 1, 2, 3) is a categorical random variable with values A, C, G, T. The linear order of bases in the DNA and possible statistical dependence of the bases in a given codon make the regressive logistic model a suitable tool for the analysis of codon frequencies. A problem of structural zeros arises from the fact that the stopping codons (terminators) do not code for amino acids; this is solved by normalizing the likelihood function. Codon frequencies may also depend on the function of the gene and they are known to differ between genes of the same genome. Differences also occur between synonymous codons for the same amino acid. Thus, the use of covariates that differ between synonymous codons as well as covariates that are constant within codons of the same amino acid may be useful in explaining the frequencies. As an illustration, the method is applied to the human mitochondrial genome using the following as explanatory variables: (1) TSCORE, a measure of the number of single base mutations required for a given codon to become a terminator; (2) AARISK, an indicator of a codon's ability of changing by a single base substitution to triplets coding for amino acids with very different characteristics; (3) AVDIST, a measure of the typicality of the amino acid coded for by the triplets. The results indicate that models that incorporate dependency structure and covariates are to be preferred to either the models comprising covariates alone or dependency structure alone.

Amino Acid Sequence↗
PubMed1995

Estimating the diagnostic yields resulting from renography and deconvolution parameters: a logistic regression analysis.

METHODS: Seventy patients with established diagnoses of normal, parenchymally insufficient or acutely obstructed kidneys were subjected to gamma camera renography. Deconvolution was then performed using three main techniques subdivided into six variants. Parameters from time-activity curves as well as retention curves were calculated. Logistic regression analysis was performed to assess the ability of renography and deconvolution methods to differentiate between kidney groups. RESULTS: Discrimination between the groups was achieved by standard renography using six of 17 tested renogram parameters. Based on a set of six curve parameters, the correct classification rates ranged 86%-100%. Five of the six variants of the deconvolution technique used produced similar results. None, however, produced results which were as robust as those from renography. The sixth deconvolution method was consistently worse than the others. CONCLUSION: Standard renography was consistently better than any of the deconvolution techniques used in the separation of the kidney groups. Conceptually, the results of a logistic regression analysis of renogram parameters may raise possibilities in the field of computer-aided diagnosis.

Adolescent↗
PubMed1994

Prediction of postoperative complications after elective aortic surgery using stepwise logistic regression analysis.

Perioperative risk factors that contribute to postoperative morbidity have been identified for various forms of vascular disease, but it is not clear to what degree each type of disease contributes to morbidity when these diseases are seen in combination. We used stepwise logistic regression analysis to determine the relative importance of 25 risk factors in predicting postoperative complications in 100 consecutive patients undergoing elective intra-abdominal aortic surgery (59% aneurysmal disease, 37% occlusive disease, 26% renal artery lesions). Thirty-one patients developed postoperative complications, including three deaths. The most common complication was deterioration in renal function (17 patients, 24% of those at risk) followed by the need for prolonged endotracheal intubation (> 48 hours, 8%). All other events occurred uncommonly (4% or less). Stepwise logistic regression demonstrated four criteria that were significantly associated with complications. In descending order of importance these were: renal artery occlusive disease, intraoperative blood replacement, the preoperative APACHE II score, and a history of heavy smoking. Since both physiologic status and comorbid conditions contribute to morbidity and costs of elective vascular surgery, outcome studies must be adjusted to account for these preoperative characteristics. Additionally, since two-thirds of patients who have renal artery occlusive disease develop complications that prolong hospitalization, our current methods of protecting the kidney during ischemia should be improved to potentially reduce this cause of morbidity.

Adult↗
PubMed1993

[Application of the concept of fuzzy logistic controller for treatment of hypertension during anesthesia].

The concept of Zadeh's fuzzy logistic controller was applied to control hypertension during anesthesia, and its clinical usefulness was examined in patients undergoing elective surgery. Arterial blood pressure was determined by use of an automatic blood pressure device. Nicardipine was used as hypotensive drug. Based on the fuzzy control rules, we developed state-action diagram so as to maintain systolic blood pressure at around 130 mmHg. Nicardipine was infused by using a digitally controlled infusion pump, and its infusion rate was changed according to this diagram. Although acute hypertension associated with endotracheal intubation was not significantly attenuated, hypertension associated with either skin incision or endotracheal extubation, where blood pressure increased slowly, was successfully controlled with our system. These results suggest that the application of the concept of fuzzy logistic controller is useful for treatment of hypertension during anesthesia, especially when blood pressure increases slowly.

Anesthesia↗
PubMed1993

[From multiple regression analysis to logistic model, proportional hazard model and log linear model. Its concept and application].

Recently logistic model, proportional hazard model and log linear model have been used frequently in the medical literatures. Here, each model is reviewed briefly from basics to its application, pointing out pitfalls in its application, some of which are common to any regression analysis. The logistic model is especially useful for the analysis of retrospective data where odds ratio is utilized to evaluate the outcome probability. On the other hand, proportional hazard model is useful when we analyze censored data, utilizing hazard function. Log linear model has been used where contingency table has more than three independent variables, the situation where its applicability in clinical medicine is wide. Familiarity with these statistical methods would enable us to evaluate data more effectively and efficiently and ultimately to read literature more easily.

Models, Statistical↗
PubMed1993

Immunocytochemistry in the differential diagnosis of effusions: use of logistic regression to select a panel of antibodies to distinguish adenocarcinomas from mesothelial proliferations.

Many studies have shown immunohistochemistry to be beneficial in discriminating between adenocarcinoma and mesothelial reactions in effusions. Although many of these studies suggest using a panel of antibodies, none of them used statistical methods to optimize their choice of assays. In the current study, stepwise logistic regression was applied to our data to select an appropriate panel of antibodies to differentiate between adenocarcinoma and all types of mesothelial proliferations. One hundred effusions (64 cases of adenocarcinoma, 27 cases of benign mesothelial proliferations, and 9 cases of malignant mesothelial proliferations) were analyzed for their reactivity with anti-EMA, anti-MFG, anti-CEA, Leu-M1, B72.3, and the newly described epithelial membrane marker BER-EP4. An abbreviated panel consisting of anti-CEA, EMA, and B72.3 was shown to be sufficient in over 95% of our cases to accurately characterize a given effusion. When all three assays are negative, a diagnosis of adenocarcinoma is extremely unlikely, while when two or three of the assays are positive a diagnosis of adenocarcinoma is almost certain. The use of stepwise logistic regression has proven useful in the design of antibody panels as an adjunct to the differential diagnosis of effusions and may be applicable to the selection of panels in other diagnostic problems.

Adenocarcinoma↗
PubMed1997

Random effects probit and logistic regression models for three-level data.

In analysis of binary data from clustered and longitudinal studies, random effect models have been recently developed to accommodate two-level problems such as subjects nested within clusters or repeated classifications within subjects. Unfortunately, these models cannot be applied to three-level problems that occur frequently in practice. For example, multicenter longitudinal clinical trials involve repeated assessments within individuals and individuals are nested within study centers. This combination of clustered and longitudinal data represents the classic three-level problem in biometry. Similarly, in prevention studies, various educational programs designed to minimize risk taking behavior (e.g., smoking prevention and cessation) may be compared where randomization to various design conditions is at the level of the school and the intervention is performed at the level of the classroom. Previous statistical approaches to the three-level problem for binary response data have either ignored one level of nesting, treated it as a fixed effect, or used first- and second-order Taylor series expansions of the logarithm of the conditional likelihood to linearize these models and estimate model parameters using more conventional procedures for measurement data. Recent studies indicate that these approximate solutions exhibit considerable bias and provide little advantage over use of traditional logistic regression analysis ignoring the hierarchical structure. In this paper, we generalize earlier results for two-level random effects probit and logistic regression models to the three-level case. Parameter estimation is based on full-information maximum marginal likelihood estimation (MMLE) using numerical quadrature to approximate the multiple random effects. The model is illustrated using data from 135 classrooms from 28 schools on the effects of two smoking cessation interventions.

Clinical Trials as Topic↗
PubMed1993

Hierarchical logistic regression models for imputation of unresolved enumeration status in undercount estimation.

"In this article we describe a logistic regression modeling approach for nonresponse in the [U.S.] Post-Enumeration Survey (PES) that has desirable theoretical properties and that has performed well in practice.... In the 1990 PES, interviews were not obtained from approximately 1.2% of households in the sample, and approximately 2.1% of the individuals in interviewed households were considered unresolved after follow-up....The missing binary enumeration statuses for these unresolved cases were replaced with probabilities estimated under a statistical model that incorporated covariate information observed for these cases. This article describes an approach to modeling missing binary outcomes when there are a large number of covariates."

Americas↗
PubMed1987

Method of fitting logistic curve.

"The paper presents a simple method of fitting the Logistic Curve by estimating the parameters in a different way than the methods known in the literature."

Logistic Models↗
PubMed1996

Malignant melanoma risk factors by anatomic site: a case-control study and polychotomous logistic regression analysis.

This population-based case-control study systematically examined reported malignant melanoma risk factors by anatomic site. Study subjects consisted of 548 invasive melanoma cases diagnosed in Connecticut during 1987-1989 and 494 randomly selected controls. Multivariate polychotomous logistic regression was used to determine whether risk factors differed across anatomic sites. Risk factors examined included demographic and pigmentary characteristics, sun exposure-related factors, anatomic site-specific sunburn, recreational water activity clothing habits and number of nevi. A pattern of site-specificity was observed for sunburn. A history of sunburn at an anatomic site was specifically related to the development of malignant melanoma at that site more so than at other sites. This site-specificity was consistent with a direct role for intense, intermittent sun exposure in the development of melanoma. Age and gender were the only risk factors that differed significantly in effect across anatomic sites. The age difference was explained by differences in histologic subtype across sites. The gender difference could not be explained by sex differences in anatomic site-specific sunburns or in recreational water activity clothing habits. Alternative explanations include sex differences in behavioral patterns of sun exposure that we did not measure and as yet unelucidated differences in susceptibility to melanoma according to sex and anatomic site.

Adult↗
PubMed1996

Predictive diagnostics for logistic models.

Novel methodology is implemented to assess the predictive power of covariate information associated with sequential binary events. Logistic models are first fitted on the basis of a subset of the observations and then evaluated sequentially on the rest. The probabilistic forecasts are compared to the outcomes via a scoring function, but as most validation samples are small, the usual reference distribution for the test statistics is inadequate. However, bootstrap-based distributions can easily be constructed. The first example pertains to the evaluation of screening tests for major depression. It illustrates that goodness-of-fit and predictive assessments lead to the selection of very different models. The second example deals with the prediction of a major event in the natural history of HIV-induced disease. It shows that this type of analysis can reveal features missed by other approaches.

Calibration↗
PubMedDate not supplied

Weighted likelihood, pseudo-likelihood and maximum likelihood methods for logistic regression analysis of two-stage data.

General approaches to the fitting of binary response models to data collected in two-stage and other stratified sampling designs include weighted likelihood, pseudo-likelihood and full maximum likelihood. In previous work the authors developed the large sample theory and methodology for fitting of logistic regression models to two-stage case-control data using full maximum likelihood. The present paper describes computational algorithms that permit efficient estimation of regression coefficients using weighted, pseudo- and full maximum likelihood. It also presents results of a simulation study involving continuous covariables where maximum likelihood clearly outperformed the other two methods and discusses the analysis of data from three bona fide case-control studies that illustrate some important relationships among the three methods. A concluding section discusses the application of two-stage methods to case-control studies with validation subsampling for control of measurement error.

Adult↗
PubMed1997

Interpretation of the odds ratio from logistic regression after a transformation of the covariate vector.

Logistic regression assumes a linear relationship between the logit and the continuous covariate. When the relationship is not linear, one can employ transformations of the covariate to satisfy the linearity assumption. We present an approach that allows interpretation of the odds ratio as it relates to the continuous covariate in its original metric. A numerical example illustrates the results.

Alcoholism↗