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…

Longitudinal Modelling of Healthcare Expenditures: Challenges and Solutions

Previous analyses–such as Basu and Manning 2009–have addressed the problem of mass of health care expenditures around $0. In typical economic analyses, we assume that the dependent variable is normally distributed. In the case of health care expenditures, however, a large number of people have $0 expenditures (i.e., healthy individuals). Further, among sick individuals that…

The gold standard of scientific evidence

That is the title of my latest article in Pharmaceutical Market Europe. An excerpt is below. Randomised controlled trials (RCTs) are regarded as the gold standard of scientific evidence, and for good reason. By randomizsing a treatment across study arms, RCTs eliminate patient-treamtent selection bias, resulting in reliable causal inference. In contrast, in the real…