Two‐Stage Residual Inclusion: An Overview

Often times, researchers want to measure the effect of certain interventions in the real-world. Doing this in practice is often difficult.  For instance, consider measuring health outcomes among individuals who visit doctors compared to those who don’t.  Inevitably, individuals who visit doctors will have worse outcomes.  Why?  Are doctors killing patients?   This is clearly a…

Nested g-computation procedure

What is the difference in health care cost when two different treatments are used?  This question is challenging because cumulative health care cost is often censored either by death or lack of continuous enrollment.  Lin (2000) addressed this issue in his 2000 paper (see paper and my blog write-up). The problem with this approach, however,…

Dealing with time-censored cost data

We health economists deal with medical cost data all the time.  One challenge we all face is that the medical cost data is often censored.  The censoring may occur because the patient dies.  If you are using administrative health insurance claims data, censoring may occur because people switch their health plan and leave your sample.…

The problem with odds ratios

Many researchers use logit models to estimate the effect of specific variables on a binary (i.e., 0 or 1) outcome.  How are these models derived?  How are odds ratios calculated?  What are the problems with odds ratios?  I answer all these questions in this post, following a lovely summary by Norton and Dowd (2018). Deriving…

Which inflation index should I use?

Many studies use data on health care costs from multiple time periods.  To make costs comparable over time, researchers often use an inflation index to translate previous years costs to current dollars.  The first question is, what inflation indices are available to make this adjustment.  A paper by Dunn et al. (2018) reviews the potential…