Logistic regression is often used to help make medical decisions with binary outcomes. Here we evaluate the use of several methods for selection of variables in logistic regression. We use a large dataset to predict the diagnosis of myocardial infarction in patients reporting to an emergency room with chest pain. Our results indicate that some of the examined methods are well suited for variabl...

To investigate the effects of certain factors on the rate of retained placenta, 2844 calving records from 1288 Holstein cows in a herd were used. These cows calved during year period of 2001 to 2007. A generalized statistical linear model was applied to analyze the data. Logistic regression model was applied as the statistical model. In the model, fixed effects of year, season (warm or cold) an...

©FSRH J Fam Plann Reprod Health Care 2008: 34(3) What is it? When a response variable has only two possible values (e.g. recurrence/not), binary logistic regression is commonly used to test or model the association between that response and a number of potential explanatory variables, with each association estimated in terms of an odds ratio (OR). Multinomial logistic regression is an extension...

Binary Logistic Regressions The classic example of a generalized linear model is when our response data y is binary, so that we can code it as zero/one. For example, one has the disease/does not have the disease, lives/dies, the device fails/device does not fail. To model binary data, a quite reasonable and very general approach is to use the predictor variables (the x’s) to estimate the probab...

background: the oncologic behavior of mucinous adenocarcinoma (ma) of colorectal differs from non-mucinous adenocarcinoma (nma). ma is more advanced at diagnosis and has a poorer prognosis than nma. we aimed to evaluate prognostic factors and survival rate in patients with ma compared with nma in western iran. methods: during 2008-2015 in a retrospective study, 83 patients with crc referred to ...

Logistic models are studied as a tool to convert output from numerical weather forecasting systems (deterministic and ensemble) into probability forecasts for binary events. A logistic model obtains by putting the logarithmic odds ratio equal to a linear combination of the inputs. As any statistical model, logistic models will suffer from over-fitting if the number of inputs is comparable to th...