Document Type

Article

Publication Date

2021

DOI

10.1002/pst.2125

Publication Title

Pharmaceutical Statistics

Volume

20

Issue

6

Pages

1061-1073

Abstract

Before biomarkers can be used in clinical trials or patients' management, the laboratory assays that measure their levels have to go through development and analytical validation. One of the most critical performance metrics for validation of any assay is related to the minimum amount of values that can be detected and any value below this limit is referred to as below the limit of detection (LOD). Most of the existing approaches that model such biomarkers, restricted by LOD, are parametric in nature. These parametric models, however, heavily depend on the distributional assumptions, and can result in loss of precision under the model or the distributional misspecifications. Using an example from a prostate cancer clinical trial, we show how a critical relationship between serum androgen biomarker and a prognostic factor of overall survival is completely missed by the widely used parametric Tobit model. Motivated by this example, we implement a semiparametric approach, through a pseudo-value technique, that effectively captures the important relationship between the LOD restricted serum androgen and the prognostic factor. Our simulations show that the pseudo-value based semiparametric model outperforms a commonly used parametric model for modeling below LOD biomarkers by having lower mean square errors of estimation.

Comments

This is the peer reviewed version of the following article,

Dutta, S., & Halabi, S. (2021). A semiparametric modeling approach for analyzing clinical biomarkers restricted to limits of detection. Pharmaceutical Statistics, 20(6), 1061-1073.

which has been published in final form at https://doi.org/10.1002/pst.2125. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched, or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited."

Subject to an embargo period of 12 months.

Original Publication Citation

Dutta, S., & Halabi, S. (2021). A semiparametric modeling approach for analyzing clinical biomarkers restricted to limits of detection. Pharmaceutical Statistics, 20(6), 1061-1073. https://doi.org/10.1002/pst.2125

ORCID

0000-0002-7211-2752 (Dutta)

Available for download on Monday, November 21, 2022

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