Unbiased Analysis of Today's Healthcare Issues
FDA draft guidance published this month says you should. In most cases, adjusting for covariates is not necessary. Randomization generally insurers that covariates are balanced across clinical trial arms. Randomization, however, may not always result in perfectly balanced trial arms. In these cases, the FDA notes that covariate adjustment is perfectly acceptable. There are some…
Let’s say you want to measure the causal impact of a specific treatment on patient outcomes from real-world data. Many statistical approaches discuss the appropriate way to estimate this causal effect assuming you know the true data generating function. In practice, however, which variables to include in any regression model may be unclear. One approach…
You have data. You see that when people receive an intervention, the outcomes differ. Is this effect causal? For this to be the case you need 4 critical assumptions. Stable Unit Treatment Value Assumption (SUTVA). This assumes that each treated unit does not affect other units. For instance, consider the case where you imposed a…
If you work in research, you may have heard that you need to worry about confounding. But what is confounding? And how can you address the problems it causes. What is confounding? An example Let us say that you are interested in the effect of an Ivy League education on a child’s long-term income. You…
A few months ago, I wrote about what marginal structural models (MSM) were and why one should consider using them in cases where the treatment of interest or confounders vary over time. I linked to how to implement this with statistical programming languages as well. The original paper that developed MSM actually has a very…
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…
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,…
Let’s say you have a test and it is composed of a bunch of different questions. Or you have a set of statistics–say statistics measuring how good a basketball player is–and you want to know how closely these statistics reliably measure the same thing. What you are looking for is a way to measure test…
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.…
IV methods are usually implemented using a two‐stage approach where the first stage estimates an expectation of the endogenous variable conditional on measured confounders and one or more IV. The second stage model then predicts outcomes as a function of the estimated treatment values from the first stage, measured confounders, and potentially other control variables.…