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. 2018 Oct 12;6(4):124.
doi: 10.3390/healthcare6040124.

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

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Free PMC article

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

Lorraine Johnson et al. Healthcare (Basel). .
Free PMC article

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.

Keywords: Lyme disease; average treatment effect; big data; global rating of change scale; individualized care; patient registries; patient-centered research; patient-reported outcomes; real-world evidence; treatment heterogeneity.

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Preliminary sample, exclusions, and final sample size determination.
Figure 2
Figure 2
The majority of participants (51%) reported some improvement in their condition after treatment with antibiotics, with High Treatment Responders constituting 34% of participants. Approximately 37% reported their condition as unchanged. Only 12% reported their condition as worse. Slight deviation in the percentages in the figure from the text reflect rounding errors.
Figure 3
Figure 3
Average treatment effect, high responders, and nonresponders on global rating of change (GROC) scale shows heterogeneous treatment response among participants that average treatment effect masks.
Figure 4
Figure 4
Research in Lyme disease is sparse compared to other infectious diseases. (Derived from Goswami 2013 [10]).
Figure 5
Figure 5
(left) Treatment effects of nonrepresentative samples cannot be generalized to the full spectrum of disease; (right) In addition, within any given sample, individual patient treatment response varies from mean. (Derived from Kravitz 2004).

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