Sequential designs with small samples: Evaluation and recommendations for normal responses

Two-stage designs with small sample sizes

When applying group-sequential designs in clinical trials with normally distributed outcomes, approximate critical values are often applied. Here, normally distributed test statistics are assumed which, however, are in fact t-distributed. For small sample sizes, the approximation may lead to a serious inflation of the type I error rate. Recently, a method for computing the exact critical boundaries assuring type I error rate control was proposed and the critical boundaries for Pocock- and O'Brien-Fleming-like group-sequential designs were provided. For designs with one interim analysis, we present six alternative designs, which also control the type I error rate and in addition allow flexible design modifications. We compare the characteristics of these 6 two-stage designs. It is shown that considerable sample size savings can be achieved by including futility stopping and by optimizing the designs. Therefore, for clinical trials with small sample sizes as, for example, in the area of rare diseases, optimal two-stage designs with futility stopping may be a valuable alternative to classical group-sequential designs.

Exact critical values for group sequential designs with small sample sizes

Group sequential clinical trial designs allow the sequential hypothesis testing as data is accumulated over time, while ensuring the control of type-1 error rate. These designs vary in how they split the overall type-1 error among analyses, but practically, all assume that: 1. The underlying data is normal or approximately so, and 2. the sample sizes are large, so the individual test statistics are sufficiently normal rather than Student's t. These two assumptions lead to the reliance on the multivariate normal distribution for calculation of the critical values. Several publications have pointed out that for small sample sizes, such an approach leads to an inflated type-1 error and proposed different sets of critical values from either simulations or by an ad-hoc adjustment to the asymptotic critical values. In this paper, we develop the exact joint distribution of the test statistics for any sample size. We show how to calculate exact critical values that conform to some well-known alpha-spending functions, such as the O'Brien-Fleming and Pocock critical values. We also compare the resulting type-1 error of these critical values with the asymptotic, as well as with other methods that have been proposed for small sample sizes.

Applicability and added value of novel methods to improve drug development in rare diseases

BACKGROUND

The ASTERIX project developed a number of novel methods suited to study small populations. The objective of this exercise was to evaluate the applicability and added value of novel methods to improve drug development in small populations, using real world drug development programmes as reported in European Public Assessment Reports.

METHODS

The applicability and added value of thirteen novel methods developed within ASTERIX were evaluated using data from 26 European Public Assessment Reports (EPARs) for orphan medicinal products, representative of rare medical conditions as predefined through six clusters. The novel methods included were 'innovative trial designs' (six methods), 'level of evidence' (one method), 'study endpoints and statistical analysis' (four methods), and 'meta-analysis' (two methods) and they were selected from the methods developed within ASTERIX based on their novelty; methods that discussed already available and applied strategies were not included for the purpose of this validation exercise. Pre-requisites for application in a study were systematized for each method, and for each main study in the selected EPARs it was assessed if all pre-requisites were met. This direct applicability using the actual study design was firstly assessed. Secondary, applicability and added value were explored allowing changes to study objectives and design, but without deviating from the context of the drug development plan. We evaluated whether differences in applicability and added value could be observed between the six predefined condition clusters.

RESULTS AND DISCUSSION

Direct applicability of novel methods appeared to be limited to specific selected cases. The applicability and added value of novel methods increased substantially when changes to the study setting within the context of drug development were allowed. In this setting, novel methods for extrapolation, sample size re-assessment, multi-armed trials, optimal sequential design for small sample sizes, Bayesian sample size re-estimation, dynamic borrowing through power priors and fall-back tests for co-primary endpoints showed most promise - applicable in more than 40% of evaluated EPARs in all clusters. Most of the novel methods were applicable to conditions in the cluster of chronic and progressive conditions, involving multiple systems/organs. Relatively fewer methods were applicable to acute conditions with single episodes. For the chronic clusters, Goal Attainment Scaling was found to be particularly applicable as opposed to other (non-chronic) clusters.

CONCLUSION

Novel methods as developed in ASTERIX can improve drug development programs. Achieving optimal added value of these novel methods often requires consideration of the entire drug development program, rather than reconsideration of methods for a specific trial. The novel methods tested were mostly applicable in chronic conditions, and acute conditions with recurrent episodes.

Increasing the efficiency of clinical trials in neurodegenerative disorders using group sequential trial designs

OBJECTIVES

Clinical trials in neurodegenerative disorders are facing high futility rates and rising development costs. We aim to review and exemplify the value of group sequential trial designs (i.e., designs with one or more prospectively planned interim analyses) within the field of amyotrophic lateral sclerosis.

STUDY DESIGN AND SETTING

We reviewed the literature to identify sequentially conducted trials. Subsequently, we reanalyzed the dexpramipexole trial (EMPOWER), a classically designed and conducted trial involving 942 participants, by sequentially monitoring the functional questionnaire and survival endpoint. Finally, we simulated the performance of the sequential methodology under different treatment effects.

RESULTS

Only six (12%) randomized, placebo-controlled trials incorporated stopping rules for both futility and superiority. Despite its high enrollment rate, sequential reanalysis of the EMPOWER study reduced the total trial duration with 140 days (23.4%, 95% confidence interval [CI] 13.2-34.4%), the number of follow-ups with 2,688 visits (23.6%, 95% CI 11.3-38.6%), and the total drug exposure time with 73,377 days (20.6%, 95% CI 9.8-35.9%). The functional questionnaire considerably increased the heterogeneity in the test statistics, which may negatively affect sequential monitoring.

CONCLUSION

Group sequential trials can result in important reductions in the trial duration, which could make clinical trials more ethical by reducing the patients' exposure to noneffective treatments or by limiting their time on placebo.

A systematic literature review of evidence-based clinical practice for rare diseases: what are the perceived and real barriers for improving the evidence and how can they be overcome?

BACKGROUND

Evidence-based clinical practice is challenging in all fields, but poses special barriers in the field of rare diseases. The present paper summarises the main barriers faced by clinical research in rare diseases, and highlights opportunities for improvement.

METHODS

Systematic literature searches without meta-analyses and internal European Clinical Research Infrastructure Network (ECRIN) communications during face-to-face meetings and telephone conferences from 2013 to 2017 within the context of the ECRIN Integrating Activity (ECRIN-IA) project.

RESULTS

Barriers specific to rare diseases comprise the difficulty to recruit participants because of rarity, scattering of patients, limited knowledge on natural history of diseases, difficulties to achieve accurate diagnosis and identify patients in health information systems, and difficulties choosing clinically relevant outcomes.

CONCLUSIONS

Evidence-based clinical practice for rare diseases should start by collecting clinical data in databases and registries; defining measurable patient-centred outcomes; and selecting appropriate study designs adapted to small study populations. Rare diseases constitute one of the most paradigmatic fields in which multi-stakeholder engagement, especially from patients, is needed for success. Clinical research infrastructures and expertise networks offer opportunities for establishing evidence-based clinical practice within rare diseases.