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Statistical Methods for Binary Outcomes Adjusting for Outcome Dependent Sampling in Longitudinal Studies with

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Summary

Informed sampling strategies (ISS) can select informative subsets from clinical trial biospecimens. This study expands ISS to address nonignorable dropout, preventing biased results in longitudinal studies.

Keywords:
Ascertainment-corrected likelihoodBUILD R packageGeneralized linear mixed modelsInformed sampling strategiesMixture modelsNonignorable dropoutOutcome-dependent samplingWeighted likelihoodbiosamples

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Area of Science:

  • Biomedical Research
  • Clinical Trials
  • Longitudinal Studies

Background:

  • Longitudinal studies collect clinical data and biospecimens.
  • Leveraging existing biospecimens addresses new research questions.
  • Assaying all samples may be costly or infeasible.

Purpose of the Study:

  • To expand informed sampling strategies (ISS) for longitudinal studies.
  • To address nonignorable dropout, which can bias results.
  • To provide a framework for cost-effective biospecimen utilization.

Main Methods:

  • Modified mixture models to adjust for nonignorable dropout.
  • Integrated mixture models with ISS frameworks.
  • Developed analytical corrections for selected subsamples.

Main Results:

  • Proposed ISS framework accounts for nonignorable dropout.
  • Modified mixture models accommodate ISS data.
  • Methods implemented in the BUILD R package.

Conclusions:

  • Expanded ISS framework prevents bias from nonignorable dropout.
  • New methods enable cost-effective analysis of biospecimens.
  • Facilitates robust research using longitudinal clinical data.