All doctors treating patients with Breast Cancer know which key variables indicate a good prognosis and which values decrease the chances of surviving. However because of complex interactions between the variables and survival doctors cannot give an individualized prognosis to a patient. The...

Missing imputation (MI) is a method to deal with missing at random (MAR) data. It is a Monte Carlo procedure where missing values are replaced by several (usually less than 10) simulated versions. It consists of three steps (Shafer, 1999): i. generation of the imputed values for the missing data;...

Through the use of user-written programs, primarily mim (Carlin, Galati, and Royston, 2008), Stata users can analyze multiply imputed (MI) datasets. Among other capabilities, mim allows the user to estimate a range of regression models and to perform a multi-parameter hypothesis tests after...

Analyzing survey data is different from analyzing data generated by experiments in several important ways. These differences will be discussed using the NHANES III adult data set as an example. Topics will include specifying the survey elements, analysis of subpopulations, model diagnostics and...

Stata is a very good tool to analyze survey data. It is able to consider many important aspects of complex survey design and the availability of alternative variance estimation methods. Through the use of matrix and macro language it also allows the user to store and manage output results...

This presentation focuses on predicted probabilities for multilevel models for dichotomous or ordinal responses. In a three-level model, for instance with patients nested in doctors nested in hospitals, predictions for patients could be for new or existing doctors and, in the latter case, for...

The Statistical Consulting Group provides a variety of resources to Stata users on campus, from walk-in and email consulting to an extensive website on materials related to Stata. This presentation will explain how the group offers such services and will discuss the three major components of the...

Classification errors, selection bias, and uncontrolled confounding are likely to be present in most epidemiological studies, but the uncertainty introduced by this type of biases is seldom quantified. The authors present a simple yet easy-to-use method to adjust the relative risk of a disease...