| Summary: | 碩士 === 國立臺灣大學 === 流行病學與預防醫學研究所 === 99 === This article focuses on establishing a regression model for a binary response variable and also on model diagnostics. The data were derived from a cross-sectional screening program conducted from 1999 to 2004 with 76545 screening subjects in a Taiwan county. The binary outcome was “the presence of low back pain”, and 23 independent variables included explanatory variables on epidemiological, biological, and medical factors. We use Akaine Information Criterion (AIC) for stepwise model selection, and finally identified 12 significantly correlated explanatory variables. Risk factors included older age, female gender, higher body stature, larger body mass index (BMI), menopaused women, and higher serum total cholesterol level. Protective factors included negative marriage history, women not yet reaching menopause, higher systolic blood pressure, impaired fasting glucose level, negative smoking history, and negative betel nuts consumption. The significant protective power of systolic blood pressure and impaired fasting glucose has never been published in previous research yet, and warrants further investigation.
Although all parameters showed significance influence, the predictive power of the model was only moderate with receiver operating characteristic (ROC) value 0.671. The model then underwent a series of model diagnostics for half normal probability plot with simulated envelope, linear predictor, potential variables (body weight, triglyceride level), explanatory variables transformation (body height, systolic blood pressure, body mass index), link function, outliers, and influential points. Though some modification could improve the model fitting, such as transformation of systolic blood pressure to its power of 2, transformation of body mass index to its power of -1.5, and changing link function to complimentary log-log function, the predictive power was still unsatisfactory. The overall model diagnostics reported that model fitting was fair, but ROC value could not be improved through model modification. Therefore, there might be still some other important explanatory variables that are necessary to be ruled in further study, such as social status, income, work satisfaction, etc.
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