Placebo responses in randomized trials of antiepileptic drugs

Guekht, Alla; Korczyn, Amos D.; Бондарева, И. Б.; Gusev, E I

doi:10.1016/j.yebeh.2009.10.007

Cited by 37 publications

(27 citation statements)

References 39 publications

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“…Similar time periods are used in many clinical trials for anti-epileptic drugs 17–20 . Inclusion criteria were similar to many clinical trials: patients were required to have at least seven seizures over the 49-day period including seven-day “pre-baseline” and six-week “baseline” 21 . Exclusion criteria included: patient diaries suggesting generalized epilepsy (see 9 for details), status epilepticus within the 49-day eligibility period, no reported seizures after the 12 week intervention period, and no reporting of age or gender.…”

Section: Methodsmentioning

confidence: 99%

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

Tharayil

Chiang

Moss³

et al. 2017

Epilepsia

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OBJECTIVE 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. METHODS Using data from SeizureTracker.com, a patient-reported seizure diary tool with over 1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian Information Criteria. Generalized linear mixed-effects models were used to account for demographic and etiological covariates and for autocorrelation structure. Hold-out cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS For both adults and children, we found that a negative binomial model with autocorrelation over one day was optimal. Using hold-out cross validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to 4 seizures per day. SIGNIFICANCE The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

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Section: Methodsmentioning

confidence: 99%

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

Tharayil

Chiang

Moss³

et al. 2017

Epilepsia

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“…SeizureTracker was selected due to the large size of the dataset, and because of the relative consistency of the predictions (Methods 2.3.3) across datasets. The trial parameters were similar to a typical RCT 17 : baseline-8 weeks, titration-4 weeks, and experimental phase-12 weeks. Analogous to a recent trial 18 , we required a minimum of three seizures per month and no 21-day seizure-free period during baseline, and at least one seizure after the entire trial duration.…”

Section: Methodsmentioning

confidence: 99%

Does accounting for seizure frequency variability increase clinical trial power?

Goldenholz

Moss³

et al. 2017

Epilepsy Research

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Objective Seizure frequency variability is associated with placebo responses in randomized controlled trials (RCT). Increased variability can result in drug misclassification and, hence, decreased statistical power. We investigated a new method that directly incorporated variability into RCT analysis, ZV. Methods Two models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, ZV. Each predicted seizure frequency upper and lower limits using prior seizures. Accuracy was defined as percentage of time-intervals when the observed seizure frequencies were within the predicted limits. First, we tested the ZV method on three datasets (SeizureTracker: n=3016, Human Epilepsy Project: n=107, and NeuroVista: n=15). An additional independent SeizureTracker validation dataset was used to generate a set of 200 simulated trials each for 5 different sample sizes (total N=100 to 500 by 100), assuming 20% dropout and 30% drug efficacy. “Power” was determined as the percentage of trials successfully distinguishing placebo from drug (p<0.05). Results Prediction accuracy across datasets was, ZV: 91–100%, RR50: 42–80%. Simulated RCT ZV analysis achieved >90% power at N=100 per arm while RR50 required N=200 per arm. Significance ZV may increase the statistical power of an RCT relative to the traditional RR50.

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“…Estimates of the placebo response magnitude (for 50%-responders) in drug trials range from 4–19% (Burneo et al, 2002; Cramer et al, 1999; Guekht et al, 2010; Rheims et al, 2008; Zaccara et al, 2015). …”

Section: Part 1: Observations About Placebos In Epilepsymentioning

confidence: 99%

Response to placebo in clinical epilepsy trials—Old ideas and new insights

Goldenholz

Goldenholz²

2016

Epilepsy Research

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Randomized placebo controlled trials are a mainstay of modern clinical epilepsy research; the success or failure of innovative therapies depends on proving superiority to a placebo. Consequently, understanding what drives response to placebo (including the “placebo effect”) may facilitate evaluation of new therapies. In this review, part one will explore observations about placebos specific to epilepsy, including the relatively higher placebo response in children, apparent increase in placebo response over the past several decades, geographic variation in placebo effect, relationship to baseline epilepsy characteristics, influence of nocebo on clinical trials, the possible increase in (SUDEP) in placebo arms of trials, and patterns that placebo responses appear to follow in individual patients. Part two will discuss the principal causes of placebo responses, including regression to the mean, anticipation, classical conditioning, the Hawthorne effect, expectations from symbols, and the natural history of disease. Included in part two will be a brief overview of recent advances using simulations from large datasets that have afforded new insights into causes of epilepsy related placebo responses. In part three, new developments in study design will be explored, including sequential parallel comparison, two-way enriched design, time to pre-randomization, delayed start, and cohort reduction techniques.

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Placebo responses in randomized trials of antiepileptic drugs

Cited by 37 publications

References 39 publications

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

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

Does accounting for seizure frequency variability increase clinical trial power?

Response to placebo in clinical epilepsy trials—Old ideas and new insights

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