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Main Authors:	de Villa, A. Ruiz, Badiella, Ll.
Format:	Preprint
Published:	2025
Subjects:	Methodology 62D20
Online Access:	https://arxiv.org/abs/2511.05939
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author	de Villa, A. Ruiz Badiella, Ll.
author_facet	de Villa, A. Ruiz Badiella, Ll.
contents	The analysis of randomized trials is often complicated by the occurrence of intercurrent events and missing values. Even though there are different strategies to address missing values it is still common to require missing values imputation. In the present article we explore the estimation of treatment effects in RCTs from a causal inference perspective under different missing data mechanisms with a particular emphasis on missings not at random (MNAR). By modelling the missingness process with directed acylcic graphs and patient-specific potential response variables, we present a new approach to obtain an unbiased estimation of treatment effects without needing to impute missing values. Additionally, we provide a formal that the average conditional log-odds ratio is a robust measure even under MNAR missing values if adjusted by sufficient confounders.
format	Preprint
id	arxiv_https___arxiv_org_abs_2511_05939
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Estimating Treatment Effects with Missings Not At Random in the Estimand Framework using Causal Inference de Villa, A. Ruiz Badiella, Ll. Methodology 62D20 The analysis of randomized trials is often complicated by the occurrence of intercurrent events and missing values. Even though there are different strategies to address missing values it is still common to require missing values imputation. In the present article we explore the estimation of treatment effects in RCTs from a causal inference perspective under different missing data mechanisms with a particular emphasis on missings not at random (MNAR). By modelling the missingness process with directed acylcic graphs and patient-specific potential response variables, we present a new approach to obtain an unbiased estimation of treatment effects without needing to impute missing values. Additionally, we provide a formal that the average conditional log-odds ratio is a robust measure even under MNAR missing values if adjusted by sufficient confounders.
title	Estimating Treatment Effects with Missings Not At Random in the Estimand Framework using Causal Inference
topic	Methodology 62D20
url	https://arxiv.org/abs/2511.05939

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