Optimizing matching and analysis combinations for estimating causal effects

Details

• Alternative Title:
  Sci Rep
• Personal Author:
  Colson, K. Ellicott ; Rudolph, Kara E. ; Zimmerman, Scott C. ; Goin, Dana E. ; Stuart, Elizabeth A. ; Laan, Mark van der ; Ahern, Jennifer
• Description:
  Matching methods are common in studies across many disciplines. However, there is limited evidence on how to optimally combine matching with subsequent analysis approaches to minimize bias and maximize efficiency for the quantity of interest. We conducted simulations to compare the performance of a wide variety of matching methods and analysis approaches in terms of bias, variance, and mean squared error (MSE). We then compared these approaches in an applied example of an employment training program. The results indicate that combining full matching with double robust analysis performed best in both the simulations and the applied example, particularly when combined with machine learning estimation methods. To reduce bias, current guidelines advise researchers to select the technique with the best post-matching covariate balance, but this work finds that such an approach does not always minimize mean squared error (MSE). These findings have important implications for future research utilizing matching. To minimize MSE, investigators should consider additional diagnostics, and use of simulations tailored to the study of interest to identify the optimal matching and analysis combination.
• Subjects:
  Bias Computer Simulation Data Interpretation, Statistical Education Employment Humans Models, Statistical Propensity Score Quality Improvement
• Source:
  Sci Rep. 2016; 6.
• Pubmed ID:
  26980444
• Pubmed Central ID:
  PMC4793248
• Document Type:
  Journal Article
• Funding:
  5R01AI074345-07/AI/NIAID NIH HHS/United States ; DP2 HD080350/DP/NCCDPHP CDC HHS/United States ; DP2 HD080350/HD/NICHD NIH HHS/United States ; R01MH099010/MH/NIMH NIH HHS/United States
• Volume:
  6
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:90bbb476642255853d49b5e193cfad1609dbc915839c94d1465454509551b19d
• Download URL:
  https://stacks.cdc.gov/view/cdc/41050/cdc_41050_DS1.pdf
• File Type:
  [PDF - 722.04 KB ]