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Regularity of a renewal process estimated from binary data.

John D Rice1, Robert L Strawderman1, Brent A Johnson1

  • 1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, U.S.A.

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Summary
This summary is machine-generated.

This study developed methods to assess event regularity when exact times are unknown, finding text message reminders improved HIV self-testing regularity but not frequency.

Keywords:
Estimating equationsGamma distributionLongitudinal binary dataRecurrent eventsRenewal processes

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Area of Science:

  • Biostatistics
  • Public Health
  • Epidemiology

Background:

  • Assessing event regularity over time is crucial for clinical decisions and public health.
  • The study addresses challenges in measuring HIV self-testing regularity among high-risk individuals due to unrecorded exact testing times.

Purpose of the Study:

  • To develop statistical methods for estimating distributional parameters of renewal processes with partially observed event data.
  • To quantify and estimate the regularity of event occurrences using the coefficient of variation (CV) of interevent times.

Main Methods:

  • Proposed a likelihood-based discrete survival model using time to first event.
  • Introduced a quasi-likelihood approach utilizing forward recurrence time for potentially greater efficiency.
  • Focused on the gamma renewal process and evaluated methods via simulation studies.

Main Results:

  • Both proposed methods demonstrated ability to estimate parameters for event regularity.
  • Text message reminders were found to significantly enhance the regularity of HIV self-testing behavior.
  • No significant impact of text message reminders on the frequency of self-testing was observed.

Conclusions:

  • The developed methods are suitable for analyzing event regularity with incomplete time data.
  • Interventions like text message reminders can improve behavioral regularity in public health contexts.
  • Further research can explore advanced statistical modeling for complex event patterns.