Optimizing Exclusion Criteria for Clinical Trials of Persistent Lyme Disease Using Real-World Data

This study demonstrates the importance of using RWD to optimize eligibility criteria in PLD clinical trials. By providing insights into the balance between sample attrition and scientific justification, researchers can enhance trial feasibility, generalizability, and robustness. Our RWD sample demonstrates that researchers could substantially increase the sample yield from 10% to 64% by loosening restrictions on coinfections and misdiagnoses of chronic fatigue syndrome, fibromyalgia syndrome, and psychiatric conditions.

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Publication Details

Journal
Healthcare
Published
2024-12-25
DOI
https://doi.org/10.3390/healthcare13010020
Citations
2
Primary Topic
Vector-borne infectious diseases
Type
article
Field-Weighted Citation Impact
1.03

Funders

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article

Optimizing Exclusion Criteria for Clinical Trials of Persistent Lyme Disease Using Real-World Data

Lorraine Johnson, Deanna Needell, Mira Shapiro, Raphael B. Stricker
2 citations
Healthcare
Vector-borne infectious diseases
1.03
article

Optimizing Exclusion Criteria for Clinical Trials of Persistent Lyme Disease Using Real-World Data

Lorraine Johnson, Deanna Needell, Mira Shapiro, Raphael B. Stricker
article en
2 citations

Abstract

This study demonstrates the importance of using RWD to optimize eligibility criteria in PLD clinical trials. By providing insights into the balance between sample attrition and scientific justification, researchers can enhance trial feasibility, generalizability, and robustness. Our RWD sample demonstrates that researchers could substantially increase the sample yield from 10% to 64% by loosening restrictions on coinfections and misdiagnoses of chronic fatigue syndrome, fibromyalgia syndrome, and psychiatric conditions.

HealthcareVol. 13(1)
University of California, Los Angeles (US), Medical Research Associates (US)
National Science Foundation, Bay Area Lyme Foundation, Division of Mathematical Sciences
Reduced inequalities
Openalex Percentile: Top 22%
Vector-borne infectious diseases
1.03
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