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Approaches using IPTW are based on two steps: (i) estimation of the individual propensity scores (PS), and (ii) estimation of the treatment effect by applying PS weights. Thus, a variance estimator that accounts for both steps is crucial for correct inference. Using a variance estimator which ignores the first step leads to overestimated variance when the estimand is the average treatment effect (ATE), and to under or overestimated estimates when targeting the average treatment effect on the treated (ATT). In this article, we emphasize the importance of using an IPTW variance estimator that correctly considers the uncertainty in PS estimation. We present a comprehensive tutorial to obtain unbiased variance estimates, by proposing and applying a unifying formula for different types of PS weights (ATE, ATT, matching and overlap weights). This can be derived either via the linearization approach or M\u2010estimation. Extensive R code is provided along with the corresponding large\u2010sample theory. We perform simulation studies to illustrate the behavior of the estimators under different treatment and outcome prevalences and demonstrate appropriate behavior of the analytical variance estimator. We also use a reproducible analysis of observational lung cancer data as an illustrative example, estimating the effect of receiving a PET\u2010CT scan on the receipt of surgery.<\/jats:p>","DOI":"10.1002\/sim.10078","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T22:17:43Z","timestamp":1713219463000},"page":"2672-2694","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["On variance estimation of the inverse probability\u2010of\u2010treatment weighting estimator: A tutorial for different types of propensity score weights"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4536-8987","authenticated-orcid":false,"given":"Andriana","family":"Kostouraki","sequence":"first","affiliation":[{"name":"Inequalities in Cancer Outcomes Network, Department of Non\u2010Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health London 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