Unbiased Analysis of Today's Healthcare Issues
Medicare covers largely elderly individuals, the disabled, and individuals with end stage kidney disease. Medicaid covers low-income individuals. So who are dual eligibles, that qualify for both programs? The Medicaid and CHIP Payment and Access Commission (MACPAC) and the Medicare Payment Advisory Commission (MedPAC) put out a report last year providing a summary of the…
While randomized controlled trials (RCTs) are the gold standard of experimental research, there are some limitations. For instance, in some cases use of an RCT is not feasible (e.g., the sample size may be too small as in the case of rare disease), not ethical (e.g., if the previous standard of care is not effective)…
At first glance, one may think the answer is no. Randomization should insure that baseline characteristics are balanced across trial arms. In practice, however, sometimes baseline characteristics due differ somewhat by trial arm simply by chance, especially in smaller randomized controlled trials (RCTs). A JAMA Guide to Statistics and Methods by Holmberg et al. 2022…
What should you do when you want to conduct a cost-effectiveness analysis based on efficacy estimates from clinical trials but the trial has missing data. One common approach—known as complete case analysis (CCA)—is to discard the participants with incomplete observations. This approach is problematic as not only is there a loss in efficiency of the…
A paper by Holmberg et al. (2022) in JAMA provides a number of examples of how collider bias can lead to problematic causal inference. The term collider bias is often invoked when using directed acyclic graphs (DAGs) to map the causal pathway. Collider bias occurs when you aim to measure the impact of A on…
Clinical trials are typically of (relatively) short duration, but innovative treatments may impact patient survival over multiple years. Health economists and outcomes researchers often are faced with the challenge of extrapolating clinical trial survival curves to estimate long-term survival gains for the typical patient. These estimates may be done parameterically (e.g., exponential, Weibull, Gompertz, log-logistic,…
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…
Ultimately, individuals and households pay for health care. Whether payments are made directly to providers, via taxes or through commercial insurance, households are the sole source of health care financing. Further, all treatments are ultimately provided on behalf of individuals and households. A key question, however, is which types of individuals pay more for health…
My former colleague Devin Incerti has a nice summary of how to implement survival function estimation in R. Not only does he mathematically describe the probability density function (PDF), cumulative density function (CDF), and hazard rates for 8 commonly used parametric survival curves [see table below], he also describes how to implement these using the…
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…