Pitfalls in the design and analysis of paediatric clinical trials: a case of a 'failed' multi-centre study, and potential solutions

Optimal sample size determination for single-arm trials in pediatric and rare populations with Bayesian borrowing

In many therapeutic areas with unmet medical needs, such as pediatric oncology and rare diseases, one of the deterrent factors for clinical trial interpretability is the limited sample size with less-than-ideal operating characteristics. Single arm is usually the only viable design due to feasibility and ethical concerns. For the trial results to be more interpretable and conclusive, the evaluation of operating characteristics, such as type I error rate and power, and the appropriate utilization of prior information for study design, shall be prespecified and fully investigated during the trial planning phase. So far, very few existing literature addressed optimal sample size determination issues for the planning of pediatric and rare population trials, with majority of research focusing on analysis perspective with focus on Bayesian borrowing. In practice, when a single-arm trial is designed for rare population, it is not uncommon that the only information available is from an earlier trial and/or a few clinical publications based on observational studies, often constituting mixed or uncertain conclusions. In light of this, an optimal Bayesian sample size determination method for single-arm trial with binary or continuous endpoint is proposed, where conflicting prior beliefs can be readily incorporated. Prior effective sample size can be calculated to assess the robustness as well as the prior information borrowed. Moreover, due to the lack of closed-form posterior distributions in general, an alternative approach for calculating Bayesian power is described. Simulation studies are provided to demonstrate the utility of the proposed methods. In addition, a case study in pediatric patients with leukemia is included to illustrate the proposed method with the existing approaches.

Rare disease registries: potential applications towards impact on development of new drug treatments

BACKGROUND

Low prevalence, lack of knowledge about the disease course, and phenotype heterogeneity hamper the development of drugs for rare diseases. Rare disease registries (RDRs) can be helpful by playing a role in understanding the course of the disease, and providing information necessary for clinical trial design, if designed and maintained properly. We describe the potential applications of a RDR and what type of information should be incorporated to support the design of clinical trials in the process of drug development, based on a broad inventory of registry experience. We evaluated two existing RDRs in more detail to check the completeness of these RDRs for trial design.

RESULTS

Before and during the application for regulatory approval a RDR can improve the efficiency and quality in clinical trial design by informing the sample size calculation and expected disease course. In exceptional circumstances information from RDRs has been used as historical controls for a one-armed clinical trial, and high quality RDRs may be used for registry-based randomized controlled trials. In the post marketing phase of (conditional) drug approval a disease-specific RDR is likely to provide more relevant information than a product-specific registry.

CONCLUSIONS

A RDR can be very helpful to improve the efficiency and quality of clinical trial design in several ways. To enable the applicability and optimal use of a RDR longitudinal data collection is indispensable, and specific data collection, prepared for repeated measurement, is needed. The developed checklist can help to define the appropriate variables to include. Attention should be paid to the inclusion of patient-relevant outcome measures in the RDR from the start. More research and experience is needed on the possibilities and limitations of combining RDR information with clinical trial data to maximize the availability of relevant evidence for regulatory decisions in rare diseases.

Comparison of nuisance parameters in pediatric versus adult randomized trials: a meta-epidemiologic empirical evaluation

Background

We wished to compare the nuisance parameters of pediatric vs. adult randomized-trials (RCTs) and determine if the latter can be used in sample size computations of the former.

Methods

In this meta-epidemiologic empirical evaluation we examined meta-analyses from the Cochrane Database of Systematic-Reviews, with at least one pediatric-RCT and at least one adult-RCT. Within each meta-analysis of binary efficacy-outcomes, we calculated the pooled-control-group event-rate (CER) across separately all pediatric and adult-trials, using random-effect models and subsequently calculated the control-group event-rate risk-ratio (CER-RR) of the pooled-pediatric-CERs vs. adult-CERs. Within each meta-analysis with continuous outcomes we calculated the pooled-control-group effect standard deviation (CE-SD) across separately all pediatric and adult-trials and subsequently calculated the CE-SD-ratio of the pooled-pediatric-CE-SDs vs. adult-CE-SDs. We then calculated across all meta-analyses the pooled-CER-RRs and pooled-CE-SD-ratios (primary endpoints) and the pooled-magnitude of effect-sizes of CER-RRs and CE-SD-ratios using REMs. A ratio < 1 indicates that pediatric trials have smaller nuisance parameters than adult trials.

Results

We analyzed 208 meta-analyses (135 for binary-outcomes, 73 for continuous-outcomes). For binary outcomes, pediatric-RCTs had on average 10% smaller CERs than adult-RCTs (summary-CE-RR: 0.90; 95% CI: 0.83, 0.98). For mortality outcomes the summary-CE-RR was 0.48 (95% CIs: 0.31, 0.74). For continuous outcomes, pediatric-RCTs had on average 26% smaller CE-SDs than adult-RCTs (summary-CE-SD-ratio: 0.74).

Conclusions

Clinically relevant differences in nuisance parameters between pediatric and adult trials were detected. These differences have implications for design of future studies. Extrapolation of nuisance parameters for sample-sizes calculations from adult-trials to pediatric-trials should be cautiously done.

Sample size calculations in pediatric clinical trials conducted in an ICU: a systematic review

At the design stage of a clinical trial, several assumptions have to be made. These usually include guesses about parameters that are not of direct interest but must be accounted for in the analysis of the treatment effect and also in the sample size calculation (nuisance parameters, e.g. the standard deviation or the control group event rate). We conducted a systematic review to investigate the impact of misspecification of nuisance parameters in pediatric randomized controlled trials conducted in intensive care units. We searched MEDLINE through PubMed. We included all publications concerning two-arm RCTs where efficacy assessment was the main objective. We included trials with pharmacological interventions. Only trials with a dichotomous or a continuous outcome were included. This led to the inclusion of 70 articles describing 71 trials. In 49 trial reports a sample size calculation was reported. Relative misspecification could be calculated for 28 trials, 22 with a dichotomous and 6 with a continuous primary outcome. The median [inter-quartile range (IQR)] overestimation was 6.9 [-12.1, 57.8]% for the control group event rate in trials with dichotomous outcomes and -1.5 [-15.3, 5.1]% for the standard deviation in trials with continuous outcomes. Our results show that there is room for improvement in the clear reporting of sample size calculations in pediatric clinical trials conducted in ICUs. Researchers should be aware of the importance of nuisance parameters in study design and in the interpretation of the results.