https://www.mdpi.com/2227-9032/6/4/124

Healthcare 2018, 6(4), 124; https://doi.org/10.3390/healthcare6040124

Removing the Mask of Average Treatment Effects in Chronic Lyme Disease Research Using Big Data and Subgroup Analysis

1MyLymeData, Chico, CA 95927, USA
2Analytic Designers LLC., Bethesda, MD 20817, USA
3Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
Author to whom correspondence should be addressed.
Received: 4 September 2018 / Revised: 9 October 2018 / Accepted: 9 October 2018 / Published: 12 October 2018
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Abstract

Lyme disease is caused by the bacteria borrelia burgdorferi and is spread primarily through the bite of a tick. There is considerable uncertainty in the medical community regarding the best approach to treating patients with Lyme disease who do not respond fully to short-term antibiotic therapy. These patients have persistent Lyme disease symptoms resulting from lack of treatment, under-treatment, or lack of response to their antibiotic treatment protocol. In the past, treatment trials have used small restrictive samples and relied on average treatment effects as their measure of success and produced conflicting results. To provide individualized care, clinicians need information that reflects their patient population. Today, we have the ability to analyze large data bases, including patient registries, that reflect the broader range of patients more typically seen in clinical practice. This allows us to examine treatment variation within the sample and identify groups of patients that are most responsive to treatment. Using patient-reported outcome data from the MyLymeData online patient registry, we show that sub-group analysis techniques can unmask valuable information that is hidden if averages alone are used. In our analysis, this approach revealed treatment effectiveness for up to a third of patients with Lyme disease. This study is important because it can help open the door to more individualized patient care using patient-centered outcomes and real-world evidence. View Full-Text

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**Comment**
Great work here.  We need to start the coinfection discussion; however, as Lyme is just one beast of many and the involvement of other pathogens muddies the water considerably and needs addressing.  Many, many patients WILL NOT improve on a Lyme only treatment.