TY - CHAP
T1 - Matching Methods for Large Observational Studies
AU - Yu, Ruoqi
N1 - Publisher Copyright:
© 2023 selection and editorial matter, José Zubizarreta, Elizabeth A. Stuart, Dylan S. Small, Paul R. Rosenbaum; individual chapters, the contributors.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - As technologies have rapidly developed in recent decades, data sets have grown in size and become more accessible for analysis, e.g., electronic health records, medical claims data, educational databases, and social media data. This chapter focuses on size-scalable matching techniques for the common case of optimal pair matching. It is devoted to incorporating other matching techniques when the sample size is large. The chapter discusses several different matching designs – matching with multiple controls, matching without pairing, full matching, and coarsened exact matching. The idea of splitting into subpopulations based on observed covariates is practical and reasonable, but it can restrict the possible matches in undesirable ways. Caliper matching is a common matching technique to improve the balance of a covariate. Several important matching techniques can be adapted for use in sparsified networks. The first class of techniques focuses on excluding the candidate matches that are far apart and do not deserved to be included in the matched data.
AB - As technologies have rapidly developed in recent decades, data sets have grown in size and become more accessible for analysis, e.g., electronic health records, medical claims data, educational databases, and social media data. This chapter focuses on size-scalable matching techniques for the common case of optimal pair matching. It is devoted to incorporating other matching techniques when the sample size is large. The chapter discusses several different matching designs – matching with multiple controls, matching without pairing, full matching, and coarsened exact matching. The idea of splitting into subpopulations based on observed covariates is practical and reasonable, but it can restrict the possible matches in undesirable ways. Caliper matching is a common matching technique to improve the balance of a covariate. Several important matching techniques can be adapted for use in sparsified networks. The first class of techniques focuses on excluding the candidate matches that are far apart and do not deserved to be included in the matched data.
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U2 - 10.1201/9781003102670-13
DO - 10.1201/9781003102670-13
M3 - Chapter
AN - SCOPUS:85163448214
SN - 9780367609528
T3 - Chapman & Hall/CRC Handbooks of Modern Statistical Methods
SP - 239
EP - 260
BT - Handbook of Matching and Weighting Adjustments for Causal Inference
A2 - Zubizarreta, José R
A2 - Stuart, Elizabeth A
A2 - Small, Dylan S
A2 - Rosenbaum, Paul R
PB - CRC Press
ER -