PENDEKATAN FUNGSI DISKRIMINAN UNTUK PENAKSIRAN ODDS RATIO - , LAILATUL MASFUFAH ([Yogyakarta] : Universitas Gadjah Mada, 2012)
Assuming a binary outcome, Logistic Regression is the most common approach to estimating odds ratio corresponding to continous predictor. We revisit a method termed the discriminant Function Approach, which leads to closed-form estimators and coresponding standard errors. In its most appealing implication, we show that the approach suggests a multiple linier regression of the continuous predictor of interest on the outcome and other covariates, in place of the traditional logistic regression model. If standard diagnostic support the assumptions (including normality of errors) accompanying this linier regression model, the resulting estimator has demonstrable advantages over the usual maximum likelihood estimator via logistic regression. The include improvements in term of bias and efficiency based on unbiased estimator of the log odds ratio, as well as the availability of an estimate when logistic regression fails to converge due to a separation of data point. Use of the discriminant Function approach as described here for multivariable analysis requiresless stringent assumptions than those for which it was historically criticized, and is worth considering when odds ratio associated with particular continous predictor is of primary interest. Case studies illustrate these points.