Balancing Covariates in Epidemiology: The Role of Matching
Matching is a crucial preprocessing technique in epidemiological research, aiming to balance covariates between different study groups to facilitate valid comparisons and strengthen causal inference. Common support and balance are key factors in successful matching, with types such as individual-level and frequency matching. While matching aims for exchangeability similar to randomized experiments, it involves trade-offs like potential data loss and increased study complexity. Evaluating the effectiveness of matching compared to traditional methods like OLS regression requires careful consideration of bias-variance trade-offs.
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Presentation Transcript
SERious SERious Summaries Summaries Season 1 Matching Episode 16 Summary prepared by Dr. Hailey Banack
Finding the Perfect Match Requires Finding the Perfect Match Requires Common Support: Matching with Dr. Common Support: Matching with Dr. Anusha Anusha Vable Vable Matching is something we learn about in our intro to epidemiology classes and yet we probably spend little time thinking about it after that, we just do it. But when should we match and when does it help us and when does it hurt us? What do we need to consider before we match? Dr. Anusha Vable joins us to help us understand matching in detail.
S1 E16 Episode Notes Episode Notes
Matching is a preprocessing technique used to balance the distribution of confounders between treated and control groups before conducting analysis. \
What is common What is common support? support? Matching Matching Ensuring overlap in covariate distributions between exposed and unexposed groups **crucial in matching to ensure valid comparisons** Goal of matching is to create a smaller, more balanced dataset where treatment assignment is less likely to be influenced by measured covariates, thus strengthening causal inference Types of matching: Types of matching: 1. Individual-level matching: pairing participants based on specific characteristics 2. Frequency matching: matching groups based on distributions of characteristics
Matching aims to achieve exchangeability akin to randomized experiments by balancing measured covariates between groups to facilitate causal inference Matching involves trade-offs such as potential data loss and increased complexity in study design and interpretation. The matching literature is diverse and spread across disciplines, posing challenges in understanding and applying different techniques effectively in epidemiological research
-Common support and balance are both essential for successful matching Simulation studies have shown that in cases where ordinary least squares regression (OLS) estimates are unbiased, matching may not provide additional benefits and could increase variance. Common support: requires overlap in covariates Balance: ensures similar covariate distributions between groups -Evaluating whether matching improves inference compared to traditional methods like OLS requires careful consideration of bias- variance trade-offs. -Matching can simulate balanced distributions akin to randomized trials, enhancing interpretability but potentially at the cost of wider confidence intervals.
Propensity score matching (PSM) and coarsened exact matching (CEM) are methods discussed for preprocessing data to improve analytic samples. -PSM matches based on the predicted probability of exposure, using regression models to estimate propensity scores -Coarsened exact matching categorizes continuous variables into broader groups for better matching. -Different matching strategies include nearest neighbor matching and matching with replacement, each offering distinct advantages based on study objectives -Choosing between matching methods involves considerations of data structure, implementation ease, and desired balance between bias and variance.
References discussed in this episode: References discussed in this episode: 1. Ho, D., Imai, K., King, G., & Stuart, E. (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, 15(3), 199-236. doi:10.1093/pan/mpl013 2. Stuart EA. Matching methods for causal inference: A review and a look forward. Stat Sci. 2010 Feb 1;25(1):1-21. doi: 10.1214/09-STS313. PMID: 20871802; PMCID: PMC2943670. 3. Vable AM, Kiang MV, Glymour MM, Rigdon J, Drabo EF, Basu S. Performance of Matching Methods as Compared With Unmatched Ordinary Least Squares Regression Under Constant Effects. Am J Epidemiol. 2019 Jul 1;188(7):1345-1354. doi: 10.1093/aje/kwz093. PMID: 30995301; PMCID: PMC6601529. 4.Iacus, S., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. doi:10.1093/pan/mpr013
Acknowledgements Co Co- -hosts: hosts: Hailey Banack, Matt Fox Special guest: Special guest: Anusha Vable Mahrukh Abid (UofT) Sue Bevan (SER)
