Random-Effects Linear Modeling and Sample Size Tables for Two Special Crossover Designs of Average Bioequivalence Studies: The Four-Period, Two-Sequence, Two-Formulation and Six-Period, Three-Sequence, Three-Formulation Designs

Measuring the individualization potential of treatment individualization rules: Application to rules built with a new parametric interaction model for parallel-group clinical trials

For personalized medicine, we propose a general method of evaluating the potential performance of an individualized treatment rule in future clinical applications with new patients. We focus on rules that choose the most beneficial treatment for the patient out of two active (nonplacebo) treatments, which the clinician will prescribe regularly to the patient after the decision. We develop a measure of the individualization potential (IP) of a rule. The IP compares the expected effectiveness of the rule in a future clinical individualization setting versus the effectiveness of not trying individualization. We illustrate our evaluation method by explaining how to measure the IP of a useful type of individualized rules calculated through a new parametric interaction model of data from parallel-group clinical trials with continuous responses. Our interaction model implies a structural equation model we use to estimate the rule and its IP. We examine the IP both theoretically and with simulations when the estimated individualized rule is put into practice in new patients. Our individualization approach was superior to outcome-weighted machine learning according to simulations. We also show connections with crossover and N-of-1 trials. As a real data application, we estimate a rule for the individualization of treatments for diabetic macular edema and evaluate its IP.

Semiparametric generalized estimating equations for repeated measurements in cross-over designs

A model for cross-over designs with repeated measures within each period was developed. It was obtained using an extension of generalized estimating equations that includes a parametric component to model treatment effects and a non-parametric component to model time and carry-over effects; the estimation approach for the non-parametric component is based on splines. A simulation study was carried out to explore the model properties. Thus, when there is a carry-over effect or a functional temporal effect, the proposed model presents better results than the standard models. Among the theoretical properties, the solution is found to be analogous to weighted least squares. Therefore, model diagnostics can be made by adapting the results from a multiple regression. The proposed methodology was implemented in the data sets of the cross-over experiments that motivated the approach of this work: systolic blood pressure and insulin in rabbits.

Measuring Individual Benefits of Medical Treatments Using Longitudinal Hospital Data with Non-Ignorable Missing Responses Caused by Patient Discharge: Application to the Study of Benefits of Pain Management Post Spinal Fusion

Electronic health records (EHR) provide valuable resources for longitudinal studies and understanding risk factors associated with poor clinical outcomes. However, they may not contain complete follow-ups, and the missing data may not be at random since hospital discharge may depend in part on expected but unrecorded clinical outcomes that occur after patient discharge. These non-ignorable missing data requires appropriate analysis methods. Here, we are interested in measuring and analyzing individual treatment benefits of medical treatments in patients recorded in EHR databases. We present a method for predicting individual benefits that handles non-ignorable missingness due to hospital discharge. The longitudinal clinical outcome of interest is modeled simultaneously with the hospital length of stay using a joint mixed-effects model, and individual benefits are predicted through a frequentist approach: the empirical Bayesian approach. We illustrate our approach by assessing individual pain management benefits to patients who underwent spinal fusion surgery. By calculating sample percentiles of empirical Bayes predictors of individual benefits, we examine the evolution of individual benefits over time. We additionally compare these percentiles with percentiles calculated with a Monte Carlo approach. We showed that empirical Bayes predictors of individual benefits do not only allow examining benefits in specific patients but also reflect overall population trends reliably.

Using population crossover trials to improve the decision process regarding treatment individualization in N-of-1 trials

Healthcare researchers are showing renewed interest in the utilization of N-of-1 clinical trials for the individualization of pharmacological treatments. Here, we propose a frequentist approach to conducting treatment individualization in N-of-1 trials that we call "partial empirical Bayes." We infer the most beneficial treatment for the patient from combining the information provided by a previously conducted population crossover trial with individual patient data. We propose a method for estimating an optimal number of treatment cycles and investigate the statistical conditions under which N-of-1 trials are more beneficial than traditional clinical approaches. We represent the patient population with a random-coefficients linear model and calculate estimators of posttreatment individual disease severities. We show the estimators' consistency under the most common N-of-1 designs and examine their prediction errors and performance with small numbers of patient's responses. We demonstrate by simulating new patients that our approach is equivalent or superior to both the common clinical practice of recommending the on-average best treatment for all patients and the common individualization method that simply compares average responses to the tested treatments. We conclude that some situations exist in which individualization with N-of-1 trials is highly beneficial while other situations exist in which individualization may be unfruitful.

Coffee and cigarette smoking interactions with lamotrigine

The objective of this analysis was to determine possible interactions between lamotrigine (LTG) and coffee or cigarette use. As part of the statistical analysis of factors influencing LTG pharmacokinetics (PK) in the Equigen chronic dose study, we collected prospective data from enrolled patients on their use of coffee and cigarettes. Subjects were part of a crossover replication study of generic LTG products with rigorous blood sampling and were instructed to not change their typical consumption of these products for the duration of the study. A total of 35 subjects were enrolled, with 33 subjects having sufficient data for analysis. Higher consumption of coffee was associated with a significantly lower area under the curve (AUC) and maximum concentration (Cmax) of lamotrigine (LTG). Higher cigarette use did not result in a significant change in AUC or Cmax. Coffee, but not cigarette use, either induces LTG metabolism or inhibits LTG absorption.

Construction of the Design Matrix for Generalized Linear Mixed-Effects Models in the Context of Clinical Trials of Treatment Sequences

The estimation of carry-over effects is a difficult problem in the design and analysis of clinical trials of treatment sequences including cross-over trials. Except for simple designs, carry-over effects are usually unidentifiable and therefore nonestimable. Solutions such as imposing parameter constraints are often unjustified and produce differing carry-over estimates depending on the constraint imposed. Generalized inverses or treatment-balancing often allow estimating main treatment effects, but the problem of estimating the carry-over contribution of a treatment sequence remains open in these approaches. Moreover, washout periods are not always feasible or ethical. A common feature of designs with unidentifiable parameters is that they do not have design matrices of full rank. Thus, we propose approaches to the construction of design matrices of full rank, without imposing artificial constraints on the carry-over effects. Our approaches are applicable within the framework of generalized linear mixed-effects models. We present a new model for the design and analysis of clinical trials of treatment sequences, called Antichronic System, and introduce some special sequences called Skip Sequences. We show that carry-over effects are identifiable only if appropriate Skip Sequences are used in the design and/or data analysis of the clinical trial. We explain how Skip Sequences can be implemented in practice, and present a method of computing the appropriate Skip Sequences. We show applications to the design of a cross-over study with 3 treatments and 3 periods, and to the data analysis of the STAR*D study of sequences of treatments for depression.

Estimating individual benefits of medical or behavioral treatments in severely ill patients

There is a need for statistical methods appropriate for the analysis of clinical trials from a personalized-medicine viewpoint as opposed to the common statistical practice that simply examines average treatment effects. This article proposes an approach to quantifying, reporting and analyzing individual benefits of medical or behavioral treatments to severely ill patients with chronic conditions, using data from clinical trials. The approach is a new development of a published framework for measuring the severity of a chronic disease and the benefits treatments provide to individuals, which utilizes regression models with random coefficients. Here, a patient is considered to be severely ill if the patient’s basal severity is close to one. This allows the derivation of a very flexible family of probability distributions of individual benefits that depend on treatment duration and the covariates included in the regression model. Our approach may enrich the statistical analysis of clinical trials of severely ill patients because it allows investigating the probability distribution of individual benefits in the patient population and the variables that influence it, and we can also measure the benefits achieved in specific patients including new patients. We illustrate our approach using data from a clinical trial of the anti-depressant imipramine.

Bioequivalence Between Generic and Branded Lamotrigine in People With Epilepsy: The EQUIGEN Randomized Clinical Trial

Importance

Switching between generic antiepileptic drugs is a highly debated issue that affects both clinical care and overall health care costs.

Objective

To evaluate the single-dose pharmacokinetic bioequivalence of 3 (1 branded and 2 generic drugs) on-market, immediate-release lamotrigine drug products.

Design, Setting, and Participants

The Equivalence Among Antiepileptic Drug Generic and Brand Products in People With Epilepsy (EQUIGEN) single-dose study is a crossover, prospective, sequence-randomized, replicate pharmacokinetic study conducted at 5 US academic epilepsy centers. Fifty adults (≥18 years) with epilepsy who were taking concomitant antiepileptic drugs and not currently receiving lamotrigine were enrolled between July 18, 2013, and January 19, 2015. Every participant was randomly assigned to 1 of 3 equivalent sequences, each comprising 6 study periods, during which they had blood draws before and after medication administration. Forty-nine participants were included in intention-to-treat analyses.

Interventions

Participants received a single 25-mg dose of immediate-release lamotrigine at the start of each period, with the branded and the 2 most disparate generic products each studied twice. Lamotrigine was selected as the antiepileptic drug of interest because of its wide use, publications indicating problems with generic switches, and complaints to the US Food and Drug Administration regarding generic products. Both participants and study personnel were blinded to the specific generic products selected.

Main Outcomes and Measures

The primary outcome was bioequivalence between products. Maximum plasma concentration (Cmax) and area under the concentration-time curve (AUC) were compared, and average bioequivalence (ABE) was established if the 90% CIs of the ratios of the 2 products were within equivalence limits (80%-125%).

Results

Of the 50 randomized participants, 49 (98%) received all 3 lamotrigine products and completed at least 3 pharmacokinetic assessments and 46 (92%) completed all 6 pharmacokinetic assessments. Among the 49 participants, 28 (57%) were men and 21 (43%) were women, 42 (86%) self-identified as white, and 46 (16) years was the mean (SD) age. The 3 drug products were considered bioequivalent because the 90% CIs were within equivalence limits (lowest and highest CI limits for Cmax, 92.6% and 110.4%; for AUC0-96, 96.9% and 101.9%). Replicate testing demonstrated no significant differences in within-subject variability across the 3 products (likelihood ratios, χ22 for log-transformed variables: AUC0-96, 2.58; Cmax, 0.64; and AUC0-∞, 4.05; P ≥ .13) and that the 3 products were also bioequivalent according to scaled ABE and individual bioequivalence criteria with no subject × formulation interaction (Cmax, 0.00; AUC0-96, 0.54; and AUC0-∞, 0.36; P ≥ .76).

Conclusions and Relevance

This study provides evidence that the disparate lamotrigine products studied are bioequivalent when tested in people with epilepsy taking concomitant antiepileptic drugs.

Trial Registration

clinicaltrials.gov Identifier: NCT01733394.

Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed-effects models

We propose statistical definitions of the individual benefit of a medical or behavioral treatment and of the severity of a chronic illness. These definitions are used to develop a graphical method that can be used by statisticians and clinicians in the data analysis of clinical trials from the perspective of personalized medicine. The method focuses on assessing and comparing individual effects of treatments rather than average effects and can be used with continuous and discrete responses, including dichotomous and count responses. The method is based on new developments in generalized linear mixed-effects models, which are introduced in this article. To illustrate, analyses of data from the Sequenced Treatment Alternatives to Relieve Depression clinical trial of sequences of treatments for depression and data from a clinical trial of respiratory treatments are presented. The estimation of individual benefits is also explained. Copyright © 2016 John Wiley & Sons, Ltd.

Generic-to-generic lamotrigine switches in people with epilepsy: the randomised controlled EQUIGEN trial

BACKGROUND

Patients and clinicians share concerns that generic drug substitution might lead to loss of efficacy or emergence of adverse events. In this trial, we assessed US Food and Drug Administration (FDA) bioequivalence standards by studying the effects of switching between two disparate generic immediate-release lamotrigine products in patients with epilepsy.

METHODS

The Equivalence among Generic Antiepileptic Drugs (EQUIGEN) chronic-dose study was a randomised, double-blind, crossover study that enrolled adults (aged ≥18 years) with epilepsy from six epilepsy centres at academic institutions across the USA who were receiving immediate-release lamotrigine dosed at 100 mg, 200 mg, 300 mg, or 400 mg twice daily. Eligible patients were randomly allocated (1:1) to one of two treatment sequences (sequence 1 or sequence 2), comprising four study periods of 14 days each. During each 14-day treatment period, patients received balanced doses of an oral generic lamotrigine product every 12 h (200-800 mg total, identical to lamotrigine dose prior to study enrolment); after each 14-day period, patients were crossed over to receive the other generic product. Computer-based randomisation was done using random permuted blocks of size two or four for each site to prevent sequence predictability. Both patients and study personnel were masked to the generic products selected, their predicted exposure (ie, "high" vs "low"), and their group allocation. The primary outcome of this trial was bioequivalence between the generic products, which was assessed at the end of the study through a comparison of maximum plasma concentration (Cmax) and area under the concentration-time curve (AUC) for each product in the analysis population (all patients who completed all four treatment periods). Bioequivalence was established if the 90% CIs of the ratios of these two parameters for the two products were within equivalence limits (80-125%) in the analysis population. This study is registered with ClinicalTrials.gov\, number NCT01713777.

FINDINGS

Between April 25, 2013, and Aug 12, 2014, 35 eligible patients were enrolled and randomly assigned to treatment sequence 1 (n=15) or treatment sequence 2 (n=20). 33 patients completed all four treatment periods and were included in the primary outcome analysis. The 90% CIs of the ratios of both Cmax and AUC were within equivalence limits (AUC 90% CI 98-103, Cmax 90% CI 99-105), showing that lamotrigine exposures were equivalent between the generic products. No significant changes in seizure frequency or adverse events were recorded. No deaths, study-related serious adverse events, or changes in clinical laboratory values or vital signs occurred during this study.

INTERPRETATION

Disparate generic lamotrigine products in patients with epilepsy showed bioequivalence with no detectable difference in clinical effects, confirming that US Food and Drug Administration bioequivalence standards are appropriate.

FUNDING

American Epilepsy Society, Epilepsy Foundation, and US Food and Drug Administration.