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Published on: January 8, 2020
Methods for using data abstracted from medical charts to impute longitudinal missing data in a clinical trial
Paul L Hebert1, Leslie T Taylor, Jason J Wang
1Department of Veterans Affairs, Northwest Center for Outcomes Research in Older Adults, VA Puget Sound Health Care System, Seattle, WA, USA. Paul.Hebert2@va.gov
Summary
This study presents an efficient method to impute missing blood pressure data in hypertension trials by utilizing medical chart data. This approach improves data accuracy for clinical trial analysis.
Area of Science:
- Clinical Trials
- Biostatistics
- Cardiovascular Research
Background:
- Missing data in clinical trials can bias results.
- Accurate blood pressure measurements are crucial for hypertension trials.
Purpose of the Study:
- To introduce a novel imputation method for missing blood pressure data in hypertension trials.
- To leverage medical chart data for improved data imputation.
Main Methods:
- A two-step imputation method was developed.
- Step 1: Estimated a longitudinal mixed-effects model using chart blood pressure data.
- Step 2: Used model-fitted values to impute missing trial blood pressure data.
Main Results:
- The chart-based imputation method yielded unbiased estimates with lower mean squared deviation for data missing at random.
- For data missing not at random, the method reduced but did not eliminate bias.
Conclusions:
- Integrating chart data via longitudinal mixed-effects models is an efficient imputation strategy for randomized trials.
- This method enhances the reliability of longitudinal data in clinical studies.
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