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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption
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Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical

Selene Yue Xu1, Sandahl Nelson2,3, Jacqueline Kerr3,4,5

  • 11 Department of Mathematics, UC San Diego, La Jolla, USA.

Statistical Methods in Medical Research
|July 14, 2016
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Summary

Missing accelerometer data from physical activity monitors can lead to inaccurate results. New statistical methods for handling missing data significantly improve the precision of regression analyses in overweight breast cancer survivors.

Keywords:
Accelerometer datalinear mixed effects modelmissing dataphysical activityweighted regression

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Area of Science:

  • Biomedical Engineering
  • Public Health
  • Epidemiology

Background:

  • Physical inactivity is a major risk factor for chronic diseases.
  • Accelerometers objectively measure physical activity but often have missing data due to nonwear.
  • Missing data can compromise the reliability of accelerometer-based physical activity research.

Purpose of the Study:

  • To examine missing data patterns in accelerometer outputs among overweight postmenopausal breast cancer survivors.
  • To develop and evaluate statistical methods for addressing missing accelerometer data.
  • To improve the accuracy and precision of regression analyses using accelerometer data.

Main Methods:

  • Utilized a cohort of 333 overweight postmenopausal breast cancer survivors.
  • Created pseudo-simulated datasets reflecting realistic missing data patterns.
  • Developed and compared statistical imputation and variance weighting algorithms.
  • Evaluated bias and precision of different methods for handling missing data.

Main Results:

  • Ignoring missing accelerometer data resulted in unstable regression estimates.
  • Variance weighting and subject-level imputation improved analysis precision by over 50%.
  • Statistical methods effectively accounted for missing data effects.

Conclusions:

  • Accounting for missing accelerometer data is crucial for reliable analysis.
  • Imputation and variance weighting are effective and easy-to-implement tools.
  • These methods enhance the analysis of physical activity data from accelerometers.