Consider a dataset consisting of 610 males involved in a study of coronary heart disease. The outcome variable is CHD status (1 = case, 0 = noncase), the exposure variable of interest is CAT which is a dichotomous variable that indicates high (coded 1) or normal (coded 0) catecholamine level. The only other variables recorded in the data set are AGE (1 = age > 55, 0 = age ≤ 55) and ECG (1 = abnormal, 0 = normal). The dataset involving the above variables is given as follows: a) Is data listing described above in events/trials format or in subject-specific format? Explain briefly. (b) Show that the saturated model yields the same probability of having CHD for those with covariate profile AGE = 1, CAT = 0, and ECG = 1 as that computed directly from the data. (c) Conduct an appropriate goodness-of-fit test to determine if the model adequately fits the data. As given in the SAS code above, the model is a full model with all main effects and interactions (both two way and three way interactions). A main effect model can be obtained from SAS by this model statement – model cases/total = AGE CAT ECG / cl;​Perform a hypothesis test to see if the interactions (including all of the two-way and the three-way​Interactions) help with the model using a likelihood ratio test (LRT) to compare the full model and the main effect model using alpha of 0.05.

Consider a dataset consisting of 610 males involved in a study of coronary heart disease. The outcome variable is CHD status (1 = case, 0 = noncase), the exposure variable of interest is CAT which is a dichotomous variable that indicates high (coded 1) or normal (coded 0) catecholamine level. The only other variables recorded in the data set are AGE (1 = age > 55, 0 = age ≤ 55) and ECG (1 = abnormal, 0 = normal). The dataset involving the above variables is given as follows: a) Is data listing described above in events/trials format or in subject-specific format? Explain briefly. (b) Show that the saturated model yields the same probability of having CHD for those with covariate profile AGE = 1, CAT = 0, and ECG = 1 as that computed directly from the data. (c) Conduct an appropriate goodness-of-fit test to determine if the model adequately fits the data. As given in the SAS code above, the model is a full model with all main effects and interactions (both two way and three way interactions). A main effect model can be obtained from SAS by this model statement – model cases/total = AGE CAT ECG / cl; ​Perform a hypothesis test to see if the interactions (including all of the two-way and the three-way ​Interactions) help with the model using a likelihood ratio test (LRT) to compare the full model and the main effect model using alpha of 0.05.