Housekeeping

Introduction

The goal of this lab is to reinforce concepts we have already covered in class. In class, we skipped through some of the minor details of the hierarchical models we have looked at, such as careful interpretation of results and basic model assessment. In this lab, the TA will walk you through some of those details using the same hospital data you started working on in the previous lab.

The Data

We will again consider data from the Centers for Medicare and Medicaid Services on hospital costs and profit from the 2014 fiscal year.

Our interest is in examining variability of net hospital income across states. In particular, we would like to rank the states by net income of hospitals.

It is important to control for the potential influence of hospital ownership (in the variable called control) and of the number of beds (a proxy for hospital size). The ownership categories include Voluntary Nonprofit-Church, Voluntary Nonprofit-Other, Proprietary-Individual, Proprietary-Corporation, Proprietary-Partnership, Proprietary-Other, Governmental-Federal, Governmental-City-County, Governmental-County, Governmental-State, Governmental-Hospital District, Governmental-City, and Governmental-Other.

Exercises

Describe a frequentist hierarchical normal model for capturing the association between ownership type and income, while controlling for the effect of the number of beds and the hierarchical structure of the data, through the states.

The TA will walk you through how one would approach each of the following.

• Exploratory data analysis investigating the relationship between the response and each predictor.
• Investigating whether the inclusion of random intercepts and/or random slopes are necessary.
• Clear specification of a final model and model fitting in R.
• Basic model assessment (no need to check for leverage or influential points).
• Uncertainty quantification around effect estimates of interest (no point estimates without uncertainty!).
• Clear description of results in language accessible to a general scientifically-savvy reader.

No submissions are required at the end of this lab.