Unbiased Analysis of Today's Healthcare Issues
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…
Is treatment A better than treatment B? This is a standard question in health economics. Typically, the answer is determined by a clinical trial. Treatment efficacy (as measured in a trial), however, may not match treatment effectiveness (as measured in the real world). For instance, patient adherence to treatment may be worse, if the treatment…
Healthcare database studies can be complicated. The data itself can be complex. Raw data needs to be transformed into useful variables. Then this analytic file needs to evaluated in an appropriate manner. Plus, the results need to be communicated clearly. What are the key aspects for describing this approach both among members of the research…
Let us say we observe a women without any symptoms for hemophilia. What are the odds she is a carrier for the disease? A few relevant points about the disease are helpful. First, hemophilia is a recessive, X-chromosome linked trait. Thus, males with hemophilia inherited the disease from their mothers via their X-chromosome and females…
Let’s say you are studying patients taking Drug A for Disease X. Further assume that you want to identify predictors that a patient is adherent to Drug A. Using health insurance claims data, you define your outcome–medication adherence–as whether the patient had proportion of days covered (PDC) is greater than or equal to 80%, the…
One popular technique for causal inference is difference-in-difference (DiD) estimation. Under DiD, there is one cohort of observations with both a pre and post intervention time period and another group that was not exposed to the intervention. The time trend of the group without an intervention in essence serves as a control group for the…
We must accept that there is subjectivity in every stage of scientific inquiry, but objectivity is nevertheless the fundamental goal. Therefore, we should base judgments on evidence and careful reasoning, and seek wherever possible to eliminate potential sources of bias.(Brownstein et al. 2018) In many cases–for instance building a cost effectiveness model–there are some parameters…
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…
To answer this question, one can examine how individuals choose health plans based on premiums, expected out of pocket cost, plan quality, and other factors. A paper by Abaluck and Gruber (2011) use data from 2006 and find that up to 70% of seniors appear to choose plans that are not optimal. Do these conclusions…