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sleev: An R Package for Semiparametric Likelihood Estimation with Errors in Variables.

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This summary is machine-generated.

This study introduces the R package sleev for analyzing biomedical data with measurement errors. It efficiently implements the sieve maximum likelihood estimator (SMLE) for two-phase studies with error-prone outcomes or covariates.

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Area of Science:

  • Biomedical research
  • Statistical methodology
  • Data science

Background:

  • Routinely collected data in biomedical research often contain measurement errors in outcomes or covariates.
  • Two-phase study designs are common, where only a subsample of data is validated.
  • Analyzing error-prone data requires specialized statistical methods.

Purpose of the Study:

  • To address the need for computationally efficient and user-friendly tools for analyzing error-prone data in two-phase studies.
  • To introduce the R package `sleev` for implementing the sieve maximum likelihood estimator (SMLE).
  • To facilitate semiparametric likelihood-based inference for error-prone binary and continuous outcomes and covariates.

Main Methods:

  • Utilized the sieve maximum likelihood estimator (SMLE) approach.
  • Developed the R package `sleev` to implement SMLE for two-phase studies.
  • The package handles error-prone binary and continuous outcomes and covariates.

Main Results:

  • The R package `sleev` provides a user-friendly tool for applying SMLE.
  • Enables efficient and robust analysis of complex error-prone data.
  • Supports analysis for both binary and continuous outcomes with measurement error.

Conclusions:

  • The `sleev` R package effectively fills the gap for analyzing error-prone data in two-phase studies.
  • It enhances the accessibility and efficiency of using SMLE in biomedical research.
  • The tool supports a wide range of data types, including error-prone responses and covariates.