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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Updated: Jun 24, 2025

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Statistical analysis plan for the NU IMPACT stepped-wedge cluster randomized trial.

Denise M Scholtens1, Nicola Lancki1, Karla Hemming2

  • 1Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America.

Contemporary Clinical Trials
|June 9, 2024
PubMed
Summary
This summary is machine-generated.

The NU IMPACT trial updated its statistical analysis plan for evaluating an electronic health record (EHR)-based symptom monitoring system in cancer care. This ensures robust analysis of patient adoption and implementation outcomes.

Keywords:
Cancer symptom monitoringElectronic health recordImplementation outcomesStepped-wedge trial

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Area of Science:

  • Health Services Research
  • Clinical Informatics
  • Oncology

Background:

  • The NU IMPACT trial, part of the IMPACT Consortium, evaluates an electronic health record (EHR)-embedded symptom monitoring and management program in outpatient cancer care.
  • It employs a stepped-wedge cluster randomized design for implementation outcomes and a patient-level randomized trial for effectiveness outcomes.
  • Updates to the statistical analysis plan were driven by a need for advanced methodologies in stepped-wedge trials.

Purpose of the Study:

  • To detail the updated statistical analysis plan for implementation outcomes in the NU IMPACT trial.
  • To outline the primary and secondary implementation outcome measures and their analytical approaches.
  • To ensure a rigorous and standardized approach to analyzing the trial's implementation data.

Main Methods:

  • The primary implementation outcome, patient adoption (clinic-level monthly patient engagement with the EHR system), will be analyzed using generalized least squares linear regression.
  • The analysis incorporates auto-regressive errors and adjusts for cluster and time effects to account for underlying secular trends.
  • Secondary patient and provider implementation outcomes will utilize a similar analytical strategy.

Main Results:

  • The abstract does not contain specific results, as it focuses on the updated statistical analysis plan.
  • The plan specifies the methods for analyzing patient adoption and other implementation outcomes.
  • The methodology is designed to provide robust evaluation of the EHR-based symptom monitoring program.

Conclusions:

  • The analytic updates reflect collaborative efforts among statisticians, implementation scientists, and trial leads.
  • This revised statistical analysis plan provides the a priori specified approach for analyzing implementation outcomes in the NU IMPACT trial.
  • The updated plan ensures a rigorous foundation for evaluating the EHR-based symptom monitoring system's implementation.