Optimal individualized dosing strategies: A pharmacologic approach to developing dynamic treatment regimens for continuous‐valued treatments

There have been considerable advances in the methodology for estimating dynamic treatment regimens, and for the design of sequential trials that can be used to collect unconfounded data to inform such regimens. However, relatively little attention has been paid to how such methodology could be used to advance understanding of optimal treatment strategies in a continuous dose setting, even though it is often the case that considerable patient heterogeneity in drug response along with a narrow therapeutic window may necessitate the tailoring of dosing over time. Such is the case with warfarin, a common oral anticoagulant. We propose novel, realistic simulation models based on pharmacokinetic‐pharmacodynamic properties of the drug that can be used to evaluate potentially optimal dosing strategies. Our results suggest that this methodology can lead to a dosing strategy that performs well both within and across populations with different pharmacokinetic characteristics, and may assist in the design of randomized trials by narrowing the list of potential dosing strategies to those which are most promising.     

A cure‐rate model for Q‐learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients

Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft‐versus‐host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two stages of treatment may lead to a cure has not yet been considered. Using a sample of 9563 patients treated for blood and bone cancers by allogeneic hematopoietic cell transplantation drawn from the Center for Blood and Marrow Transplant Research database, we provide a case study of a novel approach to Q‐learning for survival data in the presence of a potentially curative treatment, and demonstrate the results differ substantially from an implementation of Q‐learning that fails to account for the cure‐rate.     

A characterization of missingness at random in a generalized shared‐parameter joint modeling framework for longitudinal and time‐to‐event data,and sensitivity analysis

We consider a conceptual correspondence between the missing data setting, and joint modeling of longitudinal and time‐to‐event outcomes. Based on this, we formulate an extended shared random effects joint model. Based on this, we provide a characterization of missing at random, which is in line with that in the missing data setting. The ideas are illustrated using data from a study on liver cirrhosis, contrasting the new framework with conventional joint models.     

Identifyability measures to select the parameters to be estimated in a solid‐state fermentation distributed parameter model

Process modeling can lead to of advantages such as helping in process control, reducing process costs and product quality improvement. This work proposes a solid‐state fermentation distributed parameter model composed by seven differential equations with seventeen parameters to represent the process. Also, parameters estimation with a parameters identifyability analysis (PIA) is performed to build an accurate model with optimum parameters. Statistical tests were made to verify the model accuracy with the estimated parameters considering different assumptions. The results have shown that the model assuming substrate inhibition better represents the process. It was also shown that eight from the seventeen original model parameters were nonidentifiable and better results were obtained with the removal of these parameters from the estimation procedure. Therefore, PIA can be useful to estimation procedure, since it may reduce the number of parameters that can be evaluated. Further, PIA improved the model results, showing to be an important procedure to be taken. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:905–917, 2016     

Time‐to‐event continual reassessment method incorporating treatment cycle information with application to an oncology phase I trial

Delayed dose limiting toxicities (i.e. beyond first cycle of treatment) is a challenge for phase I trials. The time‐to‐event continual reassessment method (TITE‐CRM) is a Bayesian dose‐finding design to address the issue of long observation time and early patient drop‐out. It uses a weighted binomial likelihood with weights assigned to observations by the unknown time‐to‐toxicity distribution, and is open to accrual continually. To avoid dosing at overly toxic levels while retaining accuracy and efficiency for DLT evaluation that involves multiple cycles, we propose an adaptive weight function by incorporating cyclical data of the experimental treatment with parameters updated continually. This provides a reasonable estimate for the time‐to‐toxicity distribution by accounting for inter‐cycle variability and maintains the statistical properties of consistency and coherence. A case study of a First‐in‐Human trial in cancer for an experimental biologic is presented using the proposed design. Design calibrations for the clinical and statistical parameters are conducted to ensure good operating characteristics. Simulation results show that the proposed TITE‐CRM design with adaptive weight function yields significantly shorter trial duration, does not expose patients to additional risk, is competitive against the existing weighting methods, and possesses some desirable properties.     

Effects of prenatal exposure to a 50‐Hz magnetic field on one‐trial passive avoidance learning in 1‐day‐old chicks

We investigated memory impairment in newly hatched chicks following in ovo exposure to a 50‐Hz magnetic field (MF) of 2 mT (60 min/day) on embryonic days 12–18. Isolated and paired chicks were used to test the effect of stress during training, and memory retention was tested at 10, 30, and 120 min, following exposure to a bitter‐tasting bead (100% methylanthranilate). Results showed that memory was intact at 10 min in both isolated and paired chicks with or without MF exposure. However, while isolated chicks had good memory retention levels at 30 and 120 min, those exposed to MF did not. The results suggest a potential disruption of memory formation following in ovo exposure to MF, with this effect only evident in the more stressed, isolated chicks. Bioelectromagnetics 31:150–155, 2010. © 2009 Wiley‐Liss, Inc.     

Adding psychotherapy to antidepressant medication in depression and anxiety disorders: a meta‐analysis

We conducted a meta‐analysis of randomized trials in which the effects of treatment with antidepressant medication were compared to the effects of combined pharmacotherapy and psychotherapy in adults with a diagnosed depressive or anxiety disorder. A total of 52 studies (with 3,623 patients) met inclusion criteria, 32 on depressive disorders and 21 on anxiety disorders (one on both depressive and anxiety disorders). The overall difference between pharmacotherapy and combined treatment was Hedges' g = 0.43 (95% CI: 0.31‐0.56), indicating a moderately large effect and clinically meaningful difference in favor of combined treatment, which corresponds to a number needed to treat (NNT) of 4.20. There was sufficient evidence that combined treatment is superior for major depression, panic disorder, and obsessive‐compulsive disorder (OCD). The effects of combined treatment compared with placebo only were about twice as large as those of pharmacotherapy compared with placebo only, underscoring the clinical advantage of combined treatment. The results also suggest that the effects of pharmacotherapy and those of psychotherapy are largely independent from each other, with both contributing about equally to the effects of combined treatment. We conclude that combined treatment appears to be more effective than treatment with antidepressant medication alone in major depression, panic disorder, and OCD. These effects remain strong and significant up to two years after treatment. Monotherapy with psychotropic medication may not constitute optimal care for common mental disorders.     

From food‐dependent statistics to metabolic parameters,a practical guide to the use of dynamic energy budget theory

The standard model of the dynamic energy budget theory for metabolic organisation has variables and parameters that can be quantified using indirect methods only. We present new methods (and software) to extract food‐independent parameter values of the energy budget from food‐dependent quantities that are easy to observe, and so facilitate the practical application of the theory to enhance predictability and extrapolation. A natural sequence of 10 steps is discussed to obtain some compound parameters first, then the primary parameters, then the composition parameters and finally the thermodynamic parameters; this sequence matches a sequence of required data of increasing complexity which is discussed in detail. Many applications do not require knowledge of all parameters, and we discuss methods to extrapolate parameters from one species to another. The conversion of mass, volume and energy measures of biomass is discussed; these conversions are not trivial because biomass can change in chemical composition in particular ways thanks to different forms of homeostasis. We solve problems like “What would be the ultimate reproduction rate and the von Bertalanffy growth rate at a specific food level, given that we have measured these statistics at abundant food?” and “What would be the maximum incubation time, given the parameters of the von Bertalanffy growth curve?”. We propose a new non‐destructive method for quantifying the chemical potential and entropy of living reserve and structure, that can potentially change our ideas on the thermodynamic properties of life. We illustrate the methods using data on daphnids and molluscs.     

Perinatal nutrition interacts with genetic background to alter behavior in a parent‐of‐origin‐dependent manner in adult Collaborative Cross mice

Previous studies in animal models and humans have shown that exposure to nutritional deficiencies in the perinatal period increases the risk of psychiatric disease. Less well understood is how such effects are modulated by the combination of genetic background and parent‐of‐origin (PO). To explore this, we exposed female mice from 20 Collaborative Cross (CC) strains to protein deficient, vitamin D deficient, methyl donor enriched or standard diet during the perinatal period. These CC females were then crossed to a male from a different CC strain to produce reciprocal F1 hybrid females comprising 10 distinct genetic backgrounds. The adult F1 females were then tested in the open field, light/dark, stress‐induced hyperthermia, forced swim and restraint stress assays. Our experimental design allowed us to estimate effects of genetic background, perinatal diet, PO and their interactions on behavior. Genetic background significantly affected all assessed phenotypes. Perinatal diet exposure interacted with genetic background to affect body weight, basal body temperature, anxiety‐like behavior and stress response. In 8 of 9 genetic backgrounds, PO effects were observed on multiple phenotypes. Additionally, we identified a small number of diet‐by‐PO effects on body weight, stress response, anxiety‐ and depressive‐like behavior. Our data show that rodent behaviors that model psychiatric disorders are affected by genetic background, PO and perinatal diet, as well as interactions among these factors.     

Log‐gamma linear‐mixed effects models for multiple outcomes with application to a longitudinal glaucoma study

Glaucoma is a progressive disease due to damage in the optic nerve with associated functional losses. Although the relationship between structural and functional progression in glaucoma is well established, there is disagreement on how this association evolves over time. In addressing this issue, we propose a new class of non‐Gaussian linear‐mixed models to estimate the correlations among subject‐specific effects in multivariate longitudinal studies with a skewed distribution of random effects, to be used in a study of glaucoma. This class provides an efficient estimation of subject‐specific effects by modeling the skewed random effects through the log‐gamma distribution. It also provides more reliable estimates of the correlations between the random effects. To validate the log‐gamma assumption against the usual normality assumption of the random effects, we propose a lack‐of‐fit test using the profile likelihood function of the shape parameter. We apply this method to data from a prospective observation study, the Diagnostic Innovations in Glaucoma Study, to present a statistically significant association between structural and functional change rates that leads to a better understanding of the progression of glaucoma over time.     

Accurate mean comparisons for paired samples with missing data: An application to a smoking‐cessation trial

In this paper, we consider mean comparisons for paired samples in which a certain portion of the observations are missing. This type of data commonly arises in medical researches where the outcomes are assessed at two time points after the application of treatments. New methods for statistical inference are proposed by making finiteness correction based on asymptotic expansions of some intuitive statistics. The comparison methods naturally extend to the two‐group case after some suitable manipulations. Simulation study is carried out to demonstrate the numerical accuracy of the proposed methods. Data from a smoking‐cessation trial are used to illustrate the application of the methods.