Literature DB >> 27631183

Standardization as a Tool for Causal Inference in Medical Research.

Safoora Gharibzadeh1, Kazem Mohammad1, Abbas Rahimiforoushani1, Atieh Amouzegar2, Mohammad Ali Mansournia1.   

Abstract

Traditional standardization methods have been used in medical research for a long time to standardize the effect of interest for one confounder such as age. Model-based standardization extension of these methods is used when we have more than one variable produces an effect which is the population average and has marginal causal interpretation. In this paper, we discuss the most traditional model-based standardization methods that are used to estimate the marginal causal effect of exposure. We applied these methods to data from Tehran Thyroid Study and estimated the standardized effect of exposure on outcome. Based on the simulation studies, covariate standardization is preferred except when 1) we have enough information about the mechanism of exposure or 2) the outcome is rare and exposure is frequent, so propensity score standardization is suggested.

Mesh:

Year:  2016        PMID: 27631183     DOI: 0161909/AIM.0011

Source DB:  PubMed          Journal:  Arch Iran Med        ISSN: 1029-2977            Impact factor:   1.354


  10 in total

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9.  Application of Standardization for Causal Inference in Observational Studies: A Step-by-step Tutorial for Analysis Using R Software.

Authors:  Sangwon Lee; Woojoo Lee
Journal:  J Prev Med Public Health       Date:  2022-02-11

10.  Is cardiovascular risk reduction therapy effective in South Asian, Chinese and other patients with diabetes? A population-based cohort study from Canada.

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  10 in total

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