Owen Yang
Dear Chat GPT, what is an instrument variable (IV)?
Instrumental variables (IV) is a statistical technique used to address confounding bias in observational studies. In medical research, observational studies are frequently used to investigate the effectiveness of treatments, interventions or procedures. However, unlike randomized controlled trials (RCTs), observational studies do not randomly allocate participants to different groups. Instead, researchers rely on statistical techniques to adjust for differences between groups and potential confounders that might influence the outcome of interest.
The problem with confounding bias is that it can distort the relationship between the treatment and the outcome, leading to erroneous conclusions. For example, a study that compares the effectiveness of two different treatments for heart disease may find that one treatment is superior to the other. However, if the two groups differ in other important characteristics (e.g. age, sex, severity of the disease), these differences may be the real cause of the observed effect, rather than the treatment itself. Instrumental variables provide a way to deal with this problem.
An instrumental variable is a variable that satisfies two conditions: first, it is associated with the treatment (i.e. the variable that is being investigated); second, it is not associated with the outcome of interest, except through its association with the treatment. The idea is that the instrumental variable acts as a natural experiment, mimicking the random allocation of participants in RCTs, by providing a source of variation in the treatment that is unrelated to the confounding factors that may affect the outcome.
For example, let’s consider a study that investigates the effect of a new drug on blood pressure. One potential confounder in this study is the patient’s adherence to the medication. Patients who are more adherent to the treatment may have better outcomes than those who are less adherent, even if the medication itself is ineffective. To account for this potential confounding factor, the researchers might use an instrumental variable, such as distance to the nearest pharmacy, as a proxy for adherence. The reasoning is that patients who live closer to a pharmacy are more likely to fill their prescriptions and be adherent to the medication, regardless of whether the medication works or not. Therefore, the distance to the nearest pharmacy is associated with the treatment (i.e. the medication), but not with the outcome of interest (blood pressure), except through its association with the treatment.
The use of instrumental variables requires careful selection and validation. The instrumental variable should have a strong association with the treatment of interest, but a weak or no association with the outcome of interest, except through the treatment. The validity of the instrumental variable can be assessed using statistical tests, such as the F-statistic or the Durbin-Wu-Hausman test, which examine the relationship between the instrumental variable and the treatment, and the relationship between the instrumental variable and the outcome. If the instrumental variable is valid, then the estimates obtained using the instrumental variable method can provide unbiased estimates of the treatment effect.
Another important consideration in the use of instrumental variables is the potential for overfitting or model misspecification. Instrumental variables require complex statistical models, and the choice of model specification can affect the validity and reliability of the estimates. Therefore, researchers must carefully select the functional form of the instrumental variable and the outcome model, as well as include appropriate covariates to account for other potential confounders.
In conclusion, instrumental variables are a valuable statistical technique for addressing confounding bias in observational studies. They provide a way to mimic the random allocation of participants in RCTs, by using a natural experiment to vary the treatment in a way that is not confounded by other factors that might influence the outcome. However, the selection and validation of instrumental variables require careful consideration of their association with the treatment and outcome, as well as appropriate model specification to avoid overfitting or misspecification.
