For example, a variable that records whether or not someone has ever been diagnosed with a health condition like lung cancer could be measured in two categories, yes and no. 10 A dependent variable in a binary logistic regression has two levels. The main characteristic that differentiates binary logistic regression from other generalised linear models is the type of dependent (or outcome) variable. The binary logistic regression model is part of a family of statistical models called generalised linear models. Given the high percentage of FMCH publications that include binary logistic regression, understanding this method is important for FMCH authors and reviewers. 7–9 In the Family Medicine and Community Health (FMCH) journal, 35 out of the 142 (24.6%) peer-reviewed published original research papers between 20 reported using binary logistic regression as one of the analytical methods. 6īinary logistic regression is one method that is particularly appropriate for analysing survey data in the widely used cross-sectional and case–control research designs. 6 Among the recommendations for improving family medicine research is strengthening the use of appropriate research methods. 2 As a result, there have been numerous calls in recent years 3 for family and community medicine practitioners around the world 4 5 to become more involved in research. 1 However, research has an important place in family medicine to improve quality, responsiveness and innovation in patient care. Complete model reporting for binary logistic regression includes descriptive statistics, a statement on whether assumptions were checked and met, ORs and CIs for each predictor, overall model significance and overall model fit.įrom its inception, the field of family medicine has prioritised patient care over research. Related to the count- are model sensitivity-the percentage of those with the outcome who were correctly predicted to have the outcome-and specificity-the percentage of those without the outcome who were correctly predicted to not have the outcome. One model fit measure is the count-, which is the percentage of observations where the model correctly predicted the outcome variable value. Model significance quantifies whether the model is better than the baseline value (ie, the percentage of people with the outcome) at explaining or predicting whether the observed cases in the data set have the outcome. The model produces ORs, which suggest increased, decreased or no change in odds of being in one category of the outcome with an increase in the value of the predictor. The binary logistic regression model relies on assumptions including independent observations, no perfect multicollinearity and linearity. Binary logistic regression is one method frequently used in family medicine research to classify, explain or predict the values of some characteristic, behaviour or outcome. ![]() ![]() However, recent recommendations to strengthen family medicine include calls to focus more on research including improving research methods used in the field. Family medicine has traditionally prioritised patient care over research.
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