Unbiased Analysis of Today's Healthcare Issues

“You can’t fix by analysis what you bungled by design”

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 […]

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The problem with instrumental variables

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 […]

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Falsification Test for Instrumental Variables

Should instrumental variables (IV) be used for real-world evaluation of the comparative effectiveness of different studies?  It depends on who you ask.Garabedian et al. (2014) state Although no observational method can completely eliminate confounding, we recommend against treating instrumental variable analysis as a solution to the inherent biases in observational CER studies. On the other hand, Glymour, […]

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Local Instrumental Variables

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, […]

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