Dealing with missing data
Abstract
This chapter focuses on estimating treatment effects while dealing with missing data. We start with a brief introduction to some missing data concepts. Then, we discuss common methods to handle missing data, including both traditional approaches and those based on machine learning and deep learning approaches. Next, we highlight important factors to consider when analyzing datasets with missing information and the potential risk of confounding bias. To better understand the concepts discussed in this chapter, we use a simulated dataset to assess the impact of treatment interventions on patients with multiple sclerosis.
Fields of Study & Subjects
Advanced Causal Inference Techniques
Statistical Methods and Bayesian Inference
Statistical Methods in Clinical Trials
Publication details
- Journal / source
- Chapman & Hall/CRC eBooks
- Pages
- 165–202
- Publication date
- 11 March 2026
- Language
- EN
- Document type
- Book chapter