Robert Moss

Confusing placebo effect with natural history in epilepsy: A big data approach.

For unknown reasons, placebos reduce seizures in clinical trials in many patients. It is also unclear why some drugs showing statistical superiority to placebo in one trial may fail to do so in another. Using Seizuretracker.com, a patient‐centered database of 684,825 seizures, we simulated “placebo” and “drug” trials. These simulations were employed to clarify the sources of placebo effects in epilepsy, and to identify methods of diminishing placebo effects. Simulation 1 included 9 trials with a 6‐week baseline and 6‐week test period, starting at time 0, 3, 6…24 months. Here, “placebo” reduced seizures regardless of study start time. Regression‐to‐the‐mean persisted only for 3 to 6 months. Simulation 2 comprised a 6‐week baseline and then 2 years of follow‐up. Seizure frequencies continued to improve throughout follow‐up. Although the group improved, individuals switched from improvement to worsening and back. Simulation 3 involved a placebo‐controlled “drug” trial, to explore methods of placebo response reduction. An efficacious “drug” failed to demonstrate a significant effect compared with “placebo” (p = 0.12), although modifications either in study start time (p = 0.025) or baseline population reduction (p = 0.0028) allowed the drug to achieve a statistically significant effect compared with placebo. In epilepsy clinical trials, some seizure reduction traditionally attributed to placebo effect may reflect the natural course of the disease itself. Understanding these dynamics will allow future investigations into optimal clinical trial design and may lead to identification of more effective therapies. Ann Neurol 2015;78:329–336

A big data approach to the development of mixed-effects models for seizure count data

Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure‐recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy.

Circadian and circaseptan rhythms in human epilepsy: a retrospective cohort study

BACKGROUND Epilepsy has long been suspected to be governed by cyclic rhythms, with seizure rates rising and falling periodically over weeks, months, or even years. The very long scales of seizure patterns seem to defy natural explanation and have sometimes been attributed to hormonal cycles or environmental factors. This study aimed to quantify the strength and prevalence of seizure cycles at multiple temporal scales across a large cohort of people with epilepsy. METHODS This retrospective cohort study used the two most comprehensive databases of human seizures (SeizureTracker [USA] and NeuroVista [Melbourne, VIC, Australia]) and analytic techniques from circular statistics to analyse patients with epilepsy for the presence and frequency of multitemporal cycles of seizure activity. NeuroVista patients were selected on the basis of having intractable focal epilepsy; data from patients with at least 30 clinical seizures were used. SeizureTracker participants are self selected and data do not adhere to any specific criteria; we used patients with a minimum of 100 seizures. The presence of seizure cycles over multiple time scales was measured using the mean resultant length (R value). The Rayleigh test and Hodges-Ajne test were used to test for circular uniformity. Monte-Carlo simulations were used to confirm the results of the Rayleigh test for seizure phase. FINDINGS We used data from 12 people from the NeuroVista study (data recorded from June 10, 2010, to Aug 22, 2012) and 1118 patients from the SeizureTracker database (data recorded from Jan 1, 2007, to Oct 19, 2015). At least 891 (80%) of 1118 patients in the SeizureTracker cohort and 11 (92%) of 12 patients in the NeuroVista cohort showed circadian (24 h) modulation of their seizure rates. In the NeuroVista cohort, patient 8 had a significant cycle at precisely 1 week. Two others (patients 1 and 7) also had approximately 1-week cycles. Patients 1 and 4 had 2-week cycles. In the SeizureTracker cohort, between 77 (7%) and 233 (21%) of the 1118 patients showed strong circaseptan (weekly) rhythms, with a clear 7-day period. Between 151 (14%) and 247 (22%) patients had significant seizure cycles that were longer than 3 weeks. Seizure cycles were equally prevalent in men and women, and peak seizure rates were evenly distributed across all days of the week. INTERPRETATION Our results suggest that seizure cycles are robust, patient specific, and more widespread than previously understood. They align with the accepted consensus that most epilepsies have some diurnal influence. Variations in seizure rate have important clinical implications. Detection and tracking of seizure cycles on a patient-specific basis should be standard in epilepsy management practices. FUNDING Australian National Health and Medical Research Council.

Is seizure frequency variance a predictable quantity

There is currently no formal method for predicting the range expected in an individuals seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting.

Monte Carlo simulations of randomized clinical trials in epilepsy

The placebo response in epilepsy randomized clinical trials (RCTs) has recently been shown to largely reflect underlying natural variability in seizure frequency. Based on this observation, we sought to explore the parameter space of RCT design to optimize trial efficiency and cost.

Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability

A fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk.

Characteristics of large patient-reported outcomes: Where can one million seizures get us?

To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.

Different as night and day: Patterns of isolated seizures, clusters, and status epilepticus

Using approximations based on presumed U.S. time zones, we characterized day and nighttime seizure patterns in a patient‐reported database, Seizure Tracker. A total of 632 995 seizures (9698 patients) were classified into 4 categories: isolated seizure event (ISE), cluster without status epilepticus (CWOS), cluster including status epilepticus (CIS), and status epilepticus (SE). We used a multinomial mixed‐effects logistic regression model to calculate odds ratios (ORs) to determine night/day ratios for the difference between seizure patterns: ISE versus SE, ISE versus CWOS, ISE versus CIS, and CWOS versus CIS. Ranges of OR values were reported across cluster definitions. In adults, ISE was more likely at night compared to CWOS (OR = 1.49, 95% adjusted confidence interval [CI] = 1.36‐1.63) and to CIS (OR = 1.61, 95% adjusted CI = 1.34‐1.88). The ORs for ISE versus SE and CWOS versus SE were not significantly different regardless of cluster definition. In children, ISE was less likely at night compared to SE (OR = 0.85, 95% adjusted CI = 0.79‐0.91). ISE was more likely at night compared to CWOS (OR = 1.35, 95% adjusted CI = 1.26‐1.44) and CIS (OR = 1.65, 95% adjusted CI = 1.44‐1.86). CWOS was more likely during the night compared to CIS (OR = 1.22, 95% adjusted CI = 1.05‐1.39). With the exception of SE in children, our data suggest that more severe patterns favor daytime. This suggests distinct day/night preferences for different seizure patterns in children and adults.

Common data elements for epilepsy mobile health systems

Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry‐wide standards for mHealth epilepsy data.