Penalized regression with negative-unlabeled data: An approach to developing a long COVID research index

TL;DR

This paper develops and evaluates a penalized regression method to create a reliable PASC research index for long COVID, demonstrating high discriminatory power through theoretical analysis, simulations, and real data application.

Contribution

It formalizes and assesses a Lasso-penalized logistic regression approach for defining a PASC index, advancing methods for long COVID research.

Findings

The approach effectively identifies symptoms associated with PASC.

The resulting score has high discriminatory power for PASC detection.

Application to RECOVER cohort data illustrates practical utility.

Abstract

Moderate to severe post-acute sequelae of SARS-CoV-2 infection (PASC), also called long COVID, is estimated to impact as many as 10% of SARS-CoV-2 infected individuals, representing a chronic condition with a substantial global public health burden. An expansive literature has identified over 200 long-term and persistent symptoms associated with a history of SARS-CoV-2 infection; yet, there remains to be a clear consensus on a syndrome definition. Such a definition is a critical first step in future studies of risk and resiliency factors, mechanisms of disease, and interventions for both treatment and prevention. We recently applied a strategy for defining a PASC research index based on a Lasso-penalized logistic regression on history of SARS-CoV-2 infection. In the current paper we formalize and evaluate this approach through theoretical derivations and simulation studies. We demonstrate that this approach appropriately selects symptoms associated with PASC and results in a score that has high discriminatory power for detecting PASC. An application to data on participants enrolled in the RECOVER (Researching COVID to Enhance Recovery) Adult Cohort is presented to illustrate our findings.