Modified inverse propensity weighting method to alleviate estimation errors in the model with multiple endogenous variables

Bhubaneswor Dhakal; Geraldine F H McLeod; Andrea Insch; Joseph M Boden

doi:10.1016/j.mex.2023.102513

Modified inverse propensity weighting method to alleviate estimation errors in the model with multiple endogenous variables

MethodsX. 2023 Dec 20:12:102513. doi: 10.1016/j.mex.2023.102513. eCollection 2024 Jun.

Authors

Bhubaneswor Dhakal¹, Geraldine F H McLeod¹, Andrea Insch², Joseph M Boden¹

Affiliations

¹ Department of Psychological Medicine, University of Otago, PO Box 4345, Christchurch 8140, New Zealand.
² Department of Marketing, University of Otago, Dunedin, New Zealand.

Abstract

Multiple mental health disorders affect on decisions of people. The disorders are also outcomes of other factors. Health studies commonly follow an inverse propensity weight (IPW) method to address estimation errors associated with the presence of one confounder or covariate number exceeding the recommended sample size. However, approaches of IPW appropriate to alleviate the estimation error associated with multiple confounders distributed unequally in the study samples were not explained in our search literature. This study used longitudinal cohort data from Christchurch Health and Development Study and demonstrated IPW approach to address two confounders with similar natures in terms of etiological process. In our sample, some individuals had no mental health disorder at all, while others had either one of depression or anxiety or both. The methodological step to evaluate a new IPW approach include * Estimated IPWs from all possible combinations of the major depression and anxiety disorder: (a) IPW based on anxiety factor only assuming both mental health problems resulted from the same etiological processes; (b) IPW based on major depression factor only assuming both mental health problems resulted from the same etiological processes; (c) IPW assuming three (independent) categories of etiological processes: neither; either; both of major depression or anxiety disorder, (d) IPW assuming four (independent) categories of etiological processes: neither; major depression only; any anxiety disorder only; both. (e) No IPW or control model (no confounding problem.•Estimated outcome model with one each IPW at a time and one without IPw (control model).•Compared fit statistics of all estimated models.•The IPW derived assuming four categories of etiological processes produced the robust based fit statistics criteria. The study showed significant effects of both mental health problems on investment but the anxiety revealed a stronger effect than that of major depression.

Keywords: Anxiety-disorder; Covariates-exceed-sample-size; Double-confounders; Major-depression; Methodological-study; Methods-addressing-endogeneity-problem; Modified inverse propensity score weighting; Panel-data.