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

## TL;DR

The sleev R package provides a user-friendly tool for analyzing error-prone biomedical data using a robust statistical method.

## Contribution

The sleev package introduces a computationally efficient and accessible implementation of the sieve maximum likelihood estimator for two-phase studies.

## Key findings

- The package supports semiparametric likelihood-based inference for error-prone data with binary or continuous outcomes.
- It enables analysis of data with error-prone covariates and responses using validated subsamples.
- The method is efficient and robust for biomedical research using routinely collected data.

## Abstract

Data with measurement error in the outcome, covariates, or both are not uncommon, particularly with the increased use of routinely collected data for biomedical research. With error-prone data, often only a subsample of study data is validated; such settings are known as two-phase studies. The sieve maximum likelihood estimator (SMLE), which combines the error-prone data on all records with the validated data on a subsample, is a highly efficient and robust method to analyze such data. However, given their complexity, a computationally efficient and user-friendly tool is needed to obtain the SMLEs. The R package sleev fills this gap by making semiparametric likelihood-based inference using the SMLEs for error-prone two-phase data in settings with binary and continuous outcomes. Functions from this package can be used to analyze data with error-prone binary or continuous responses and error-prone covariates.