Unbiased Analysis of Today's Healthcare Issues
Does a treatment improve patient health? Does a policy intervention improve quality of life? Does more education increase income? These are fundamental questions that are difficult to answer with standard observational approaches. The reason? Selection bias. Patients who are sick take medicine; patients who are sicker may take more medicine. Thus, one could observe that…
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…
Bayesian analysis is increasingly common in health economic research. To apply Bayesian models, however, you need to select a prior distribution. How do you select your prior? Andrew Gelman (of the excellent Statistical Modeling, Causal Inference, and Social Science blog) provides some advice on selecting a prior on the stan-dev GitHub website. I review some…
So you want to compare two treatments–for instance Treatment A and Treatment B–in order to see which one has largest impact on health outcomes. Sure you could do a randomized controlled trial, but let’s say you don’t have the funding for that. You could use real world data to conduct just such an analysis. Let’s…
Devin Incerti does not blog much; only about 7 times in 4 years. When he does, however, it’s worth paying attention. For instance, he has put together a super helpful blog post describing how to do probabilistic sensitivity analysis (PSA) in R. A key feature of any PSA is determining the distribution from which one…
Earlier this year, I reviewed one definition of causality: the Bradford Hill criteria. Now I move from a medical definition causality to one developed by an economist (in fact, an economist from my alma matter). Granger causality basically identifies a variable as causal if: (i) it occurs the outcome of interest and (ii) including past…
Let’s say we are interested in determining whether a particular treatment improves quality of life. Common measures of quality of life include EQ-5D, SF-12, and SF-36, among others. However, a systematic review of 237 randomized controlled trials found that only 43% collected SF-12 or SF-36 measures. If you are measuring a treatment’s effect on quality…
If you observe two things occuring, how can you know whether event A causes event B. For instance, consider the case of patients who use a given treatment and finding that they have better health outcomes. While this relationship could be causal in nature, it may not be. For instance, if only people with higher…
We all know that there are two types of statistics: frequentist and Bayesian. Frequentist approaches treat parameters to be estimated as fixed quantities and the data we observe to come from a data generating process based on these quantities. The Bayesian approach assumes that we don’t want to just estimate the parameters as fixed points…