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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Sample size determination for longitudinal designs with binary response.

Kush Kapur1, Runa Bhaumik, X Charlene Tang

  • 1Clinical Research Center and Department of Neurology, Boston Children's Hospital, Harvard Medical School, 21 Autumn St., Boston, MA 02215, U.S.A.

Statistics in Medicine
|May 14, 2014
PubMed
Summary
This summary is machine-generated.

This study presents statistical methods for calculating sample sizes in intervention studies with repeated binary outcomes, considering attrition and hierarchical designs. The methods ensure accurate power analysis even with missing data, aiding clinical trial planning.

Keywords:
Fisher information matrixGaussian quadraturelogistic mixed-effects modelpowertype I error rate

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Health Services Research

Background:

  • Determining appropriate sample size is crucial for the statistical power of clinical trials.
  • Repeated binary outcomes and hierarchical designs present unique challenges in sample size calculations.
  • Attrition rates can significantly impact the precision and power of study results.

Purpose of the Study:

  • To develop statistical methods for sample size determination in comparative efficacy studies with repeated binary outcomes.
  • To address the complexities of hierarchical designs and varying attrition rates in sample size calculations.
  • To provide practical guidelines for power analysis when variance components are unknown.

Main Methods:

  • Development of statistical methodologies for sample size calculation.
  • Incorporation of hierarchical data structures and time-varying attrition rates.
  • Exploration of the joint influence of between-subject variability and attrition on sample size.
  • Investigation of efficient estimation methods for power analysis.

Main Results:

  • Validated statistical methods for sample size determination in complex trial designs.
  • Demonstrated the impact of attrition rates and between-subject variability on required sample size.
  • Provided a practical approach for sample size calculation without individual variance component information.
  • Simulation studies confirmed the validity and efficiency of the proposed methods.

Conclusions:

  • The developed statistical methods accurately determine sample size for interventions with repeated binary outcomes, accounting for design complexity and attrition.
  • Efficient estimation and consideration of attrition are vital for robust power analysis in clinical trials.
  • The findings offer practical guidance for researchers planning randomized clinical trials, illustrated by examples in contraception and insomnia research.