Instrumental variables II: instrumental variable application-in 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalance

J Clin Epidemiol. 2009 Dec;62(12):1233-41. doi: 10.1016/j.jclinepi.2008.12.006. Epub 2009 Apr 5.


Objective: An instrumental variable (IV) is an unconfounded proxy for a study exposure that can be used to estimate a causal effect in the presence of unmeasured confounding. To provide reliably consistent estimates of effect, IVs should be both valid and reasonably strong. Physician prescribing preference (PPP) is an IV that uses variation in doctors' prescribing to predict drug treatment. As reduction in covariate imbalance may suggest increased IV validity, we sought to examine the covariate balance and instrument strength in 25 formulations of the PPP IV in two cohort studies.

Study design and setting: We applied the PPP IV to assess antipsychotic medication (APM) use and subsequent death among two cohorts of elderly patients. We varied the measurement of PPP, plus performed cohort restriction and stratification. We modeled risk differences with two-stage least square regression. First-stage partial r(2) values characterized the strength of the instrument. The Mahalanobis distance summarized balance across multiple covariates.

Results: Partial r(2) ranged from 0.028 to 0.099. PPP generally alleviated imbalances in nonpsychiatry-related patient characteristics, and the overall imbalance was reduced by an average of 36% (+/-40%) over the two cohorts.

Conclusion: In our study setting, most of the 25 formulations of the PPP IV were strong IVs and resulted in a strong reduction of imbalance in many variations. The association between strength and imbalance was mixed.

Publication types

  • Multicenter Study
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms
  • Antipsychotic Agents / administration & dosage*
  • Antipsychotic Agents / adverse effects
  • British Columbia / epidemiology
  • Confounding Factors, Epidemiologic
  • Epidemiologic Research Design
  • Humans
  • Mortality*
  • Pennsylvania / epidemiology
  • Practice Patterns, Physicians' / statistics & numerical data*


  • Antipsychotic Agents