The mathematics of drug dose individualization should be built with random-effects linear models

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.

"De-Shrinking" EBEs: The Solution for Bayesian Therapeutic Drug Monitoring

BACKGROUND

Therapeutic drug monitoring (TDM) aims at individualising a dosage regimen and is increasingly being performed by estimating individual pharmacokinetic parameters via empirical Bayes estimates (EBEs). However, EBEs suffer from shrinkage that makes them biased. This bias is a weakness for TDM and probably a barrier to the acceptance of drug dosage adjustments by prescribers.

OBJECTIVE

The aim of this article is to propose a methodology that allows a correction of EBE shrinkage and an improvement in their precision.

METHODS

As EBEs are defined, they can be seen as a special case of ridge estimators depending on a parameter usually denoted λ. After a bias correction depending on λ, we chose λ so that the individual pharmacokinetic estimations have minimal imprecision. Our estimate is by construction always better than EBE with respect to bias (i.e. shrinkage) and precision.

RESULTS

We illustrate the performance of this approach with two different drugs: iohexol and isavuconazole. Depending on the patient's actual pharmacokinetic parameter values, the improvement given by our approach ranged from 0 to 100%.

CONCLUSION

This innovative methodology is promising since, to the best of our knowledge, no other individual shrinkage correction has been proposed.

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.

A graphical approach to assess the goodness-of-fit of random-effects linear models when the goal is to measure individual benefits of medical treatments in severely ill patients

Background

Two-dimensional personalized medicine (2-PM) models are tools for measuring individual benefits of medical treatments for chronic diseases which have potential applications in personalized medicine. These models assume normality for the distribution of random effects. It is necessary to examine the appropriateness of this assumption. Here, we propose a graphical approach to assessing the goodness-of-fit of 2-PM models with continuous responses.

Methods

We propose benefit quantile-quantile (BQQ) plots which compare the empirical quantiles of individual benefits from a patient sample predicted through an empirical Bayes (EB) approach versus the quantiles of the theoretical distribution of individual benefits derived from the assumption of normality for the random effects. We examine the performance of the approach by conducting a simulation study that compared 2-PM models with non-normal distributions for the random effects versus models with comparable normal distributions. Cramer-von Mises discrepancies were used to quantify the performance of the approach. The approach was illustrated with data from a clinical trial of imipramine for patients with depression.

Results

Simulations showed that BQQ plots were able to capture deviations from the normality assumption for the random effects and did not show any asymmetric deviations from the y = x line when the random effects were normally distributed. For the depression data, the points of the BQQ plot were scattered around closely to the y = x line, without presenting any asymmetric deviations. This implied the adequacy of the normality assumption for the random effects and the goodness-of-fit of the 2-PM model for the imipramine data.

Conclusion

BQQ plots are sensitive to violations of the normality assumption for the random effects, suggesting that the approach is a useful tool for examining the goodness-of-fit of random-effects linear models when the goal is to measure individual treatment benefits.

The Effect of Body Weight Changes on Total Plasma Clozapine Concentrations Determined by Applying a Statistical Model to the Data From a Double-Blind Trial

PURPOSE/BACKGROUND

Some therapeutic drug monitoring studies suggest that increased weight is associated with small increases in clozapine concentrations. The goal of this study was to reanalyze a US double-blind study using a sophisticated statistical model to test whether weight gains from baseline or increases in percentage of body fat from baseline, computed from a published equation, are associated with increased total plasma clozapine concentrations after controlling for the effects of smoking and sex.

METHODS/PROCEDURES

Using data from a multidosage randomized double-blind US clozapine trial previously published, a random intercept linear model of steady-state total plasma clozapine concentrations was fitted to 424 concentrations from 47 patients.

FINDINGS/RESULTS

After adjusting for sex and smoking, (1) a 1-kg gain in body weight during clozapine treatment was significantly associated with a 1.4% increase in total plasma clozapine concentrations (95% confidence interval = 0.55 to 2.3) and (2) a 1-point increase in percentage of body fat during clozapine treatment was significantly associated with a 5.4% increase in total clozapine concentration (2.5 to 8.3) in females and 1.4% (-1.1 to 4.0) in males.

IMPLICATIONS/CONCLUSIONS

As hypothesized, weight increases during clozapine treatment, which probably reflect increases in fat tissue, were associated with increases in total plasma concentrations. Pending further replication in other samples, it seems likely that clozapine may deposit in body fat and that this may decrease clozapine clearance. This change may be small in most patients but may be clinically relevant in females with major gains in body fat.

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.

The Art of Pharmacotherapy: Reflections on Pharmacophobia

PURPOSE/BACKGROUND

This commentary deals with the neglected issue of the art of psychopharmacology by recounting the authors' journeys.

METHODS/PROCEDURES

First, a model of medical science situated within the history of medicine is described including (1) a limitation of the mathematical model of science, (2) the distinction between mechanistic science and mathematical science, (3) how this distinction is applied to medicine, and (4) how this distinction is applied to explain pharmacology to psychiatrists. Second, the neglected art of psychopharmacology is addressed by explaining (1) where the art of psychopharmacotherapy was hiding in the first author's psychopharmacology research, (2) how the Health Belief Model was applied to the art of medicine, (3) how the second author became interested in the Health Belief Model, and (4) his studies introducing the Health Belief Model in psychopharmacology. The authors' collaboration led to: (1) study of the effect of pharmacophobia on poor adherence and (2) reflection on the limits of the art of psychopharmacology.

FINDINGS/RESULTS

Low adherence was found in 45% (116/258) of psychiatric patients with pharmacophobia versus 22% (149/682) in those with no pharmacophobia, providing an odds ratio of 2.9 (95% confidence interval, 2.2-4.0) and an adjusted odds ratio of 2.5 (95% confidence interval, 1.8-3.5) after adjusting for other variables contributing to poor adherence.

IMPLICATIONS/CONCLUSIONS

Different cognitive patterns in different patients may contribute to poor adherence. Specific interventions targeting these varying cognitive styles may be needed in different patients to improve drug adherence.

Reviving Research on Medication Attitudes for Improving Pharmacotherapy: Focusing on Adherence

There is little current interest in research into patients' attitudes toward medications. In the 1960s, psychiatric researchers including Uhlenhuth, Rickels and Covi focused on this area, but this research topic needs to be revived in the 21st century. The Health Belief Model may hold potential for doing this. This model was initially developed by 2 health psychologists, Rosenstock and Becker, to explain why patients did not follow medical interventions. The application of this model to study medication adherence in psychiatric outpatients has provided multiple findings including the conclusions that adherence is associated with: (1) the balance between internal and external health control beliefs, (2) psychological reactance, (3) patients' attitudes toward prescribed drug treatment in general and (4) the balance between the necessity of taking medications versus the concerns derived from adverse drug reactions (ADRs). Poor adherence is associated with several cognitive styles of patients, including: (1) high internal and external health control beliefs (patients who feel that their health is controlled both by external factors and their own beliefs), (2) higher psychological reactance, (3) pharmacophobia (present in 1/6 patients) and (4) skepticism about medications (a high concern for ADRs and a low belief in the necessity of taking medications). All of these findings suggest that shared decision-making is particularly important in fostering adherence in psychiatric patients. Two wider applications of this article can be made: (1) opening psychiatry to advances in clinical psychology and (2) expanding studies on attitudes toward medications to other medical disciplines.

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.

The effects of antiepileptic inducers in neuropsychopharmacology, a neglected issue. Part I: A summary of the current state for clinicians

The literature on inducers in epilepsy and bipolar disorder is seriously contaminated by false negative findings. This is part i of a comprehensive review on antiepileptic drug (AED) inducers using both mechanistic pharmacological and evidence-based medicine to provide practical recommendations to neurologists and psychiatrists concerning how to control for them. Carbamazepine, phenobarbital and phenytoin, are clinically relevant AED inducers; correction factors were calculated for studied induced drugs. These correction factors are rough simplifications for orienting clinicians, since there is great variability in the population regarding inductive effects. As new information is published, the correction factors may need to be modified. Some of the correction factors are so high that the drugs (e.g., bupropion, quetiapine or lurasidone) should not co-prescribed with potent inducers. Clobazam, eslicarbazepine, felbamate, lamotrigine, oxcarbazepine, rufinamide, topiramate, vigabatrin and valproic acid are grouped as mild inducers which may (i)be inducers only in high doses; (ii)frequently combine with inhibitory properties; and (iii)take months to reach maximum effects or de-induction, definitively longer than the potent inducers. Potent inducers, definitively, and mild inducers, possibly, have relevant effects in the endogenous metabolism of (i)sexual hormones, (ii) vitamin D, (iii)thyroid hormones, (iv)lipid metabolism, and (v)folic acid.