Unbiased Analysis of Today's Healthcare Issues
An interesting recent paper by Moler-Zapata, Grieve, Basu, and O’Neill (2023) compares local instrumental variables (LIV) with two-stage least squares (2SLS) to IV. Local instrumental variable (LIV) approaches use continuous/multi-valued instrumental variables (IV) to generate consistent estimates of average treatment effects (ATEs) and Conditional Average Treatment Effects (CATEs). There is little evidence on how LIV…
What is better: propensity score matching or instrumental variables? How about both? That is basically what is proposed using a near/far matching approach as described in Baiocchi et al. (2012). In this paper, they use a two step approach to examine the causal affect of adopting a new treatment–carotid arterial stents (CAS)–versus and older treatment–carotid…
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…
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…
This is a quote from Light, Singer, and Willet (1990) and is mentioned on a recent commentary by Stephen B. Soumerai and Ross Koppel in Health Services Research. The commentary focuses on the use of instrumental variables when conducting health care effectiveness reserach. They define instrumental variables concisely as: An instrumental variable (IV) is a variable, generally found in…
When using real-world data, researchers must always deal with a key issue: selection bias. To get around this bias, many health care researchers use an instrumental variable that can predict the explanatory variable of interest (e.g., receipt of a specific treatment) but is not correlated with patient outcomes (e.g., mortality). A commonly used IV is…
Traditional instrumental variables (IV) econometric methodologies often fail to take into account response heterogeneity. Response heterogeneity based on characteristics not observed by the researcher can create a heterogeneity in the self-selection process. For instance, one group of people who elect to receive surgery may have knowledge of a family history where surgery is typically successful,…