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An R-Based Landscape Validation of a Competing Risk Model
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Assessing risk model calibration with missing covariates.

Yei Eun Shin1, Mitchell H Gail1, Ruth M Pfeiffer1

  • 1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA.

Biostatistics (Oxford, England)
|February 22, 2021
PubMed
Summary
This summary is machine-generated.

We developed efficient weighting methods to assess risk model calibration using auxiliary information, improving prediction accuracy for events like second primary thyroid cancer.

Keywords:
Case-cohort studyExternal validationMissingModel calibrationNested case–control studyPseudo-risk modelSurvey calibrationWeight adjustment

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Missing covariate data is common in cohort studies, complicating risk model validation.
  • Calibration assessment, using the observed (O) to expected (E) event ratio, is crucial for risk models.
  • Existing methods like inverse probability weighting (IPW) can be inefficient with missing data.

Purpose of the Study:

  • To propose efficient weighting methods for risk model calibration when predictors are missing.
  • To improve the estimation of the O/E ratio by incorporating auxiliary information.
  • To assess the calibration of an absolute risk model for second primary thyroid cancer.

Main Methods:

  • Adjusting inverse probability weights using survey calibration with auxiliary information.
  • Utilizing a pseudo-risk estimate as an auxiliary statistic for estimating expected events (E).
  • Deriving analytic variance formulas for the O/E ratio with adjusted weights.

Main Results:

  • Weight adjustment with pseudo-risk significantly improved efficiency compared to standard IPW.
  • The proposed method yielded consistent estimates even with imperfect pseudo-risk approximations.
  • Multiple imputation was efficient but prone to bias if the imputation model was misspecified.

Conclusions:

  • The novel weighting adjustment method enhances the efficiency and accuracy of risk model calibration.
  • This approach effectively addresses missing data challenges in independent cohort validation.
  • The methods were successfully applied to assess thyroid cancer risk model calibration.