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Two Stage Least Squares with Time-Varying Instruments: An Application to an Evaluation of Treatment Intensification for Type-2 Diabetes

Author

Listed:
  • Tompsett, D
  • Vansteelandt, S
  • Grieve, R
  • Petersen, I Author Name: Gomes, M

Abstract

As longitudinal data becomes more available in many settings, policy makers are increasingly interested in the effect of time-varying treatments (e.g. sustained treatment strategies). In settings such as this, the preferred analysis techniques are the g-methods, however these require the untestable assumption of no unmeasured confounding. Instrumental variable analyses can minimise bias through unmeasured confounding. Of these methods, the Two Stage Least Squares technique is one of the most well used in Econometrics, but it has not been fully extended, and evaluated, in full time-varying settings. This paper proposes a robust two stage least squares method for the econometric evaluation of time-varying treatment. Using a simulation study we found that, unlike standard two stage least squares, it performs relatively well across a wide range of circumstances, including model misspecification. It compares well with recent time-varying instrument approaches via g-estimation. We illustrate the methods in an evaluation of treatment intensification for Type-2 Diabetes Mellitus, exploring the exogeneity in prescribing preferences to operationalise a time-varying instrument.

Suggested Citation

  • Tompsett, D & Vansteelandt, S & Grieve, R & Petersen, I Author Name: Gomes, M, 2024. "Two Stage Least Squares with Time-Varying Instruments: An Application to an Evaluation of Treatment Intensification for Type-2 Diabetes," Health, Econometrics and Data Group (HEDG) Working Papers 24/19, HEDG, c/o Department of Economics, University of York.
    • Handle: RePEc:yor:hectdg:24/19
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    References listed on IDEAS

    as
    1. Shuxiao Chen & Bo Zhang, 2021. "Estimating and Improving Dynamic Treatment Regimes With a Time-Varying Instrumental Variable," Papers 2104.07822, arXiv.org.
    2. Baltagi, Badi H & Griffin, James M, 1988. "A Generalized Error Component Model with Heteroscedastic Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(4), pages 745-753, November.
    3. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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    Keywords

    instrumental variable; time-varying; two stage least squares; physician preference; diabetes;
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