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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Statistical methods for testing carryover effects: A mixed effects model approach.

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  • 1Laboratory for Innovation Science at Harvard, 175 N. Harvard Street Suite 1350, Boston, MA, 02134, USA.

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|May 17, 2021
PubMed
Summary
This summary is machine-generated.

Carryover effects are often ignored, but this study introduces a new statistical method using linear mixed effect models to accurately estimate them. This approach addresses bias in noisy measurements and censored data, improving treatment effect analysis.

Keywords:
Blood pressureCensoringCholesterolDiabetesHypertensionLinear mixed modelLongitudinal dataMeasurement error

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Epidemiology

Background:

  • Carryover effects, or post-treatment influences, are often overlooked in statistical analyses.
  • Existing methods for testing carryover are biased when diagnosis relies on noisy measurements and subsequent open-label treatment.
  • The TROPHY trial highlighted issues with hypertension prevention studies using such designs.

Purpose of the Study:

  • To develop a valid statistical method for testing and estimating carryover effects.
  • To address bias introduced by noisy diagnostic measurements and open-label treatment.
  • To provide accurate estimates of treatment effects in the presence of censored data.

Main Methods:

  • Utilizing linear mixed effect models to directly model noisy diagnostic measurements.
  • Computing expected proportions over a diagnostic threshold to derive estimates.
  • Leveraging the concept of missing at random for data unavailable due to open-label treatment.

Main Results:

  • The proposed method provides valid statistical tests for carryover effects.
  • Consistent estimates of carryover effects are achieved, even with censored data.
  • Simulations based on real-world blood pressure data demonstrate the model's accuracy.

Conclusions:

  • Linear mixed effect models offer a robust approach to analyzing carryover effects in clinical trials.
  • This method corrects for bias in noisy measurements and censored data, enhancing study validity.
  • Accurate estimation of carryover effects is crucial for understanding treatment impacts, particularly in hypertension prevention.