Unbiased Analysis of Today's Healthcare Issues
Randomized controlled trials are the gold standard for evaluating treatment efficacy, but effectiveness in the real-world may vary. One reason for this is that clinical trials often have stricter inclusion criteria than is the case for the target treated population. Policymakers, payers, and clinicians may wonder how well the results from the narrower clinical trial…
TL;DR A paper by Ress and Wild (2024) provide the following recommendations in answering this question. When aiming to control for a large covariate set, consider using the superlearner to estimate nuisance parameters. When employing the superlearner to estimate nuisance parameters, consider using doubly robust estimation approaches, such as AIPW and TMLE. When faced with…
How do mental health issues impact the likelihood of food security? This question is difficult to answer empirically for (at a minimum) two primary reasons: Endogeneity/Unobserved factors. For instance, personal, family, and neighborhood characteristics (e.g., family stability, access to health care, exposure to violence) may impact both mental health and the likelihood of food insecurity.…
What is the impact of being insured on health outcomes? This is a difficult question to answer in part because of adverse selection (e.g., sicker patients may choose to be insured). But even absent adverse selection, the ability to diagnosis a disease may vary between the insurer and uninsured. Consider this example from Kaliski (2023):…
FDA draft guidance published this month says you should.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…