Evaluation of non-linear-mixed-effect modeling to reduce the sample sizes of pediatric trials in type 2 diabetes mellitus

Improved Confidence in a Confirmatory Stage by Application of Item-Based Pharmacometrics Model: Illustration with a Phase III Active Comparator-Controlled Trial in COPD Patients

PURPOSE

The current study aimed to illustrate how a non-linear mixed effect (NLME) model-based analysis may improve confidence in a Phase III trial through more precise estimates of the drug effect.

METHODS

The FULFIL clinical trial was a Phase III study that compared 24 weeks of once daily inhaled triple therapy with twice daily inhaled dual therapy in patients with chronic obstructive pulmonary disease (COPD). Patient reported outcome data, obtained by using The Evaluating Respiratory Symptoms in COPD (E-RS:COPD) questionnaire, from the FULFIL study were analyzed using an NLME item-based response theory model (IRT). The change from baseline (CFB) in E-RS:COPD total score over 4-week intervals for each treatment arm was obtained using the IRT and compared with published results obtained with a mixed model repeated measures (MMRM) analysis.

RESULTS

The IRT included a graded response model characterizing item parameters and a Weibull function combined with an offset function to describe the COPD symptoms-time course in patients receiving either triple therapy (n = 907) or dual therapy (n = 894). The IRT improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of at least 3.64 times larger for the MMRM analysis to achieve the IRT precision in the CFB estimate.

CONCLUSION

This study shows the advantage of IRT over MMRM with a direct comparison of the same primary endpoint for the two analyses using the same observed clinical trial data, resulting in an increased confidence in Phase III.

Mathematical Modelling for Optimal Vaccine Dose Finding: Maximising Efficacy and Minimising Toxicity

Vaccination is a key tool to reduce global disease burden. Vaccine dose can affect vaccine efficacy and toxicity. Given the expense of developing vaccines, optimising vaccine dose is essential. Mathematical modelling has been suggested as an approach for optimising vaccine dose by quantitatively establishing the relationships between dose and efficacy/toxicity. In this work, we performed simulation studies to assess the performance of modelling approaches in determining optimal dose. We found that the ability of modelling approaches to determine optimal dose improved with trial size, particularly for studies with at least 30 trial participants, and that, generally, using a peaking or a weighted model-averaging-based dose–efficacy relationship was most effective in finding optimal dose. Most methods of trial dose selection were similarly effective for the purpose of determining optimal dose; however, including modelling to adapt doses during a trial may lead to more trial participants receiving a more optimal dose. Clinical trial dosing around the predicted optimal dose, rather than only at the predicted optimal dose, may improve final dose selection. This work suggests modelling can be used effectively for vaccine dose finding, prompting potential practical applications of these methods in accelerating effective vaccine development and saving lives.

Developing and Evaluating Behaviour Change Interventions for People with Younger-Onset Type 2 Diabetes: Lessons and Recommendations from Existing Programmes

People with younger-onset type 2 diabetes (YOT2D, diagnosis before 40 years of age) are at higher risk of morbidity and premature mortality compared with their similar-age type 1 diabetes and later-onset type 2 diabetes peers. Despite recommendations for targeted, behavioural, and psychosocial approaches to optimising health outcomes, there are few such interventions for this group. Furthermore, evaluations of health behaviour change interventions targeting this priority population have proven challenging to complete. Despite this, there is little guidance for future behavioural programme developers. The aims of this paper are to synthesise lessons learned and recommendations from published evaluations of YOT2D-focused health behaviour change interventions, and illustrate challenges and solutions using case studies from our own experience. A rapid review of the literature identified 11 trials of behavioural interventions for YOT2D (5 randomised controlled trials, 6 pre/post studies). We sourced related needs assessment and development papers to describe the life course of each programme. We identified two development and two evaluation-related themes impacting successful trial execution. Development recommendations include ensuring appropriate adaptation of existing interventions to the unique challenges and characteristics of the target group, use of theory or theoretical frameworks throughout, and involvement of the priority population and key stakeholders from inception. Evaluation recommendations include planning for meaningful evaluation and development of age-appropriate Core Outcomes Sets. Future programme developers would benefit from closer attention to intervention development guidelines and a focus on supporting those with YOT2D to achieve behaviour change and diabetes self-management goals, ahead of change to biomedical outcomes.

Improved Decision-Making Confidence Using Item-Based Pharmacometric Model: Illustration with a Phase II Placebo-Controlled Trial

This study aimed to illustrate how a new methodology to assess clinical trial outcome measures using a longitudinal item response theory–based model (IRM) could serve as an alternative to mixed model repeated measures (MMRM). Data from the EXACT (Exacerbation of chronic pulmonary disease tool) which is used to capture frequency, severity, and duration of exacerbations in COPD were analyzed using an IRM. The IRM included a graded response model characterizing item parameters and functions describing symptom-time course. Total scores were simulated (month 12) using uncertainty in parameter estimates. The 50th (2.5th, 97.5th) percentiles of the resulting simulated differences in average total score (drug minus placebo) represented the estimated drug effect (95%CI), which was compared with published MMRM results. Furthermore, differences in sample size, sensitivity, specificity, and type I and II errors between approaches were explored. Patients received either oral danirixin 75 mg twice daily (n = 45) or placebo (n = 48) on top of standard of care over 52 weeks. A step function best described the COPD symptoms-time course in both trial arms. The IRM improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of 2.5 times larger for the MMRM analysis to achieve the IRM precision. The IRM showed a higher probability of a positive predictive value (34%) than MMRM (22%). An item model–based analysis data gave more precise estimates of drug effect than MMRM analysis for the same endpoint in this one case study.

Sweet/Fat Preference Taste in Subjects Who are Lean, Obese and Very Obese

Purpose

This study assessed the perception of sweetness, creaminess, and pleasantness from a sweet/fat preference test in subjects who are lean (BMI: 19–25), obese (BMI: 30–33) or very obese (BMI: 34–40) using categorical modeling.

Methods

Subjects tasted 16 dairy solutions consisting of 0%, 3.5%, 11.3% and 37.5% fat and each containing 0%, 5%, 10%, or 20% sugar and rated them for sweetness, creaminess and pleasantness.

Results

A proportional odds model described the perception of sweetness using an Emax for the effect of sugar and a linear effect for fat. Perception of creaminess was dependent on the fat and sugar content and was described with proportional odds model with linear effects of sugar and fat. Perception of pleasantness increased with sugar and fat but decreased in solutions containing 37.5% fat. A differential odds model using an Emax model for fat and sugar with a negative interaction between them allowed the sugar content to be less than proportional and the fat content to be greater than proportional for pleasantness.

Conclusions

Application of modeling provided understanding of these complex interactions of sugar and fat on the perception of sweetness, creaminess, and pleasantness and provides a tool to investigate obesity and pharmacological intervention.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-020-02968-9.