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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification
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Multiple linear regression modeling with values below a lower limit of quantification - a statistical method

Lorena Hafermann1, Isao Yokota2, Linda Kalski3,4

  • 1Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.

BMC Medical Research Methodology
|January 17, 2026
PubMed
Summary
This summary is machine-generated.

This study compares statistical methods for handling laboratory measurements below the lower limit of quantification (LLOQ) in multiple linear regression. The two compartment model performed best for independent variables, while Tobit regression was optimal for dependent variables.

Keywords:
Left censoringLower limit of quantificationRegression modelStatistical method comparison

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Area of Science:

  • Biostatistics
  • Medical Data Analysis
  • Regression Modeling

Background:

  • Missing values are common in medical data, particularly laboratory measurements below the lower limit of quantification (LLOQ).
  • Handling these left-censored values is crucial for accurate multivariable linear regression analysis.
  • Limited comparative studies exist for methods addressing LLOQ data in regression.

Purpose of the Study:

  • To compare the performance of statistical methods for handling values below the LLOQ in multiple linear regression.
  • To evaluate methods when LLOQ data is an independent or dependent variable.
  • To provide guidance on selecting appropriate methods for left-censored data in regression.

Main Methods:

  • A simulation study was conducted to compare statistical methods.
  • Methods were evaluated for scenarios where LLOQ data served as independent or dependent variables.
  • Variations in data distributions, sample sizes, missing proportions, correlations, and linearity were explored.

Main Results:

  • The two compartment model demonstrated superior performance (bias, coverage) when LLOQ data was an independent variable without significant collinearity.
  • Tobit regression exhibited the lowest bias and highest coverage for LLOQ data as the dependent variable, up to 0.8 censoring proportions.

Conclusions:

  • Choosing an appropriate method for handling left-censored data below the LLOQ is essential in multiple linear regression.
  • This study offers guidance on the performance of established methods for such data challenges.