Accounting for Residual Confounding in the Analysis

CHAPTER SECTIONS

Analysis Plan


Section 4

Accounting for Residual Confounding in the Analysis

Contributors

Despite incorporating design-based control for confounding—such as stratification, pair matching, or constrained randomization—it is sometimes advisable to also include in the analysis covariates that might still be unbalanced across the arms of the study. Depending on the goals of the study, these covariates might be at the cluster level or even at the individual level. However, depending on the sample size, the number of covariates might be limited.

Key Question: Residual Confouding

When the number of clusters is small, permutation tests might be recommended. Again, the study statistician, in collaboration with the investigators, will determine the appropriate design and analysis methods.

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SECTIONS

CHAPTER SECTIONS

sections

  1. Introduction
  2. Intraclass Correlation
  3. Unequal Cluster Sizes
  4. Accounting for Residual Confounding in the Analysis
  5. Missing Data and Intention-to-Treat Analyses
  6. Electronic Health Record Data Extraction
  7. Unanticipated Changes
  8. Interim Reassessment of Sample Size in Cluster Randomized Trials
  9. Case Study: STOP CRC Trial

Resources

Pragmatic and Group-Randomized Trials in Public Health and Medicine—Part 3. Analysis Approaches
Online course From the NIH Office of Disease Prevention


Version History

June 23, 2022: Updated the name of the NIH Collaboratory in the contributors list and made nonsubstantive changes to the text as part of the annual content update (changes made by D. Seils).

July 2, 2020: Minor corrections to layout and formatting (changes made by D. Seils).

May 27, 2020: Added Heagerty to the contributors list and reordered the sections of this chapter as part the annual content update (changes made by D. Seils).

May 1, 2020: Added a Resources sidebar with a link to online course material as part of the annual content update (changes made by D. Seils).

January 16, 2019: Added a “key question” image, and made nonsubstantive changes to the text as part of the annual content update (changes made by D. Seils).

Published August 25, 2017

current section :

Accounting for Residual Confounding in the Analysis

  1. Introduction
  2. Intraclass Correlation
  3. Unequal Cluster Sizes
  4. Accounting for Residual Confounding in the Analysis
  5. Missing Data and Intention-to-Treat Analyses
  6. Electronic Health Record Data Extraction
  7. Unanticipated Changes
  8. Interim Reassessment of Sample Size in Cluster Randomized Trials
  9. Case Study: STOP CRC Trial

Citation:

Heagerty PJ, DeLong ER; for the NIH Health Care Systems Research Collaboratory Biostatistics and Study Design Core. Analysis Plan: Accounting for Residual Confounding in the Analysis. In: Rethinking Clinical Trials: A Living Textbook of Pragmatic Clinical Trials. Bethesda, MD: NIH Pragmatic Trials Collaboratory. Available at: https://rethinkingclinicaltrials.org/chapters/design/analysis-plan-top/accounting-for-residual-confounding-in-the-analysis/. Updated June 23, 2022. DOI: 10.28929/018.