A two-stage group-sequential design for delayed treatment responses with the possibility of trial restart.

Common statistical theory applicable to confirmatory phase III trial designs usually assumes that patients are enrolled simultaneously and there is no time gap between enrollment and outcome observation. However, in practice, patients are enrolled successively and there is a lag between the enrollment of a patient and the measurement of the primary outcome. For single-stage designs, the difference between theory and practice only impacts on the trial duration but not on the statistical analysis and its interpretation. For designs with interim analyses, however, the number of patients already enrolled into the trial and the number of patients with available outcome measurements differ, which can cause issues regarding the statistical analyses of the data. The main issue is that current methodologies either imply that at the time of the interim analysis there are so-called pipeline patients whose data are not used to make a statistical decision (like stopping early for efficacy) or the enrollment into the trial needs to be at least paused for interim analysis to avoid pipeline patients. There are methods for delayed responses available that introduced error-spending stopping boundaries for the enrollment of patients followed by critical values to reject the null hypothesis in case the stopping boundaries have been crossed beforehand. Here, we will discuss other solutions, considering different boundary determination algorithms using conditional power and introducing a design allowing for recruitment restart while keeping the type I error rate controlled.

Should the two-trial paradigm still be the gold standard in drug assessment?

Two significant pivotal trials are usually required for a new drug approval by a regulatory agency. This standard requirement is known as the two-trial paradigm. However, several authors have questioned why we need exactly two pivotal trials, what statistical error the regulators are trying to protect against, and potential alternative approaches. Therefore, it is important to investigate these questions to better understand the regulatory decision-making in the assessment of drugs' effectiveness. It is common that two identically designed trials are run solely to adhere to the two-trial rule. Previous work showed that combining the data from the two trials into a single trial (one-trial paradigm) would increase the power while ensuring the same level of type I error protection as the two-trial paradigm. However, this is true only under a specific scenario and there is little investigation on the type I error protection over the whole null region. In this article, we compare the two paradigms by considering scenarios in which the two trials are conducted in identical or different populations as well as with equal or unequal size. With identical populations, the results show that a single trial provides better type I error protection and higher power. Conversely, with different populations, although the one-trial rule is more powerful in some cases, it does not always protect against the type I error. Hence, there is the need for appropriate flexibility around the two-trial paradigm and the appropriate approach should be chosen based on the questions we are interested in.

Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue?

Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.

An alternative method to analyse the biomarker-strategy design.

Recent developments in genomics and proteomics enable the discovery of biomarkers that allow identification of subgroups of patients responding well to a treatment. One currently used clinical trial design incorporating a predictive biomarker is the so-called biomarker strategy design (or marker-based strategy design). Conventionally, the results from this design are analysed by comparing the mean of the biomarker-led arm with the mean of the randomised arm. Several problems regarding the analysis of the data obtained from this design have been identified in the literature. In this paper, we show how these problems can be resolved if the sample sizes in the subgroups fulfil the specified orthogonality condition. We also propose a different analysis strategy that allows definition of test statistics for the biomarker-by-treatment interaction effect as well as for the classical treatment effect and the biomarker effect. We derive equations for the sample size calculation for the case of perfect and imperfect biomarker assays. We also show that the often used 1:1 randomisation does not necessarily lead to the smallest sample size. In addition, we provide point estimators and confidence intervals for the treatment effects in the subgroups. Application of our method is illustrated using a real data example.

Flexible selection of a single treatment incorporating short-term endpoint information in a phase II/III clinical trial.

Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim analysis have been the focus of much recent research interest. Many of the methods proposed are based on the group sequential approach. This paper considers designs of this type in which the treatment selection can be based on short-term endpoint information for more patients than have primary endpoint data available. We show that in such a case, the familywise type I error rate may be inflated if previously proposed group sequential methods are used and the treatment selection rule is not specified in advance. A method is proposed to avoid this inflation by considering the treatment selection that maximises the conditional error given the data available at the interim analysis. A simulation study is reported that illustrates the type I error rate inflation and compares the power of the new approach with two other methods: a combination testing approach and a group sequential method that does not use the short-term endpoint data, both of which also strongly control the type I error rate. The new method is also illustrated through application to a study in Alzheimer's disease.

A comparison of methods for treatment selection in seamless phase II/III clinical trials incorporating information on short-term endpoints.

In an adaptive seamless phase II/III clinical trial interim analysis, data are used for treatment selection, enabling resources to be focused on comparison of more effective treatment(s) with a control. In this paper, we compare two methods recently proposed to enable use of short-term endpoint data for decision-making at the interim analysis. The comparison focuses on the power and the probability of correctly identifying the most promising treatment. We show that the choice of method depends on how well short-term data predict the best treatment, which may be measured by the correlation between treatment effects on short- and long-term endpoints.

Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data.

Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods.

The group-based social skills training SOSTA-FRA in children and adolescents with high functioning autism spectrum disorder--study protocol of the randomised, multi-centre controlled SOSTA--net trial.

BACKGROUND: Group-based social skills training (SST) has repeatedly been recommended as treatment of choice in high-functioning autism spectrum disorder (HFASD). To date, no sufficiently powered randomised controlled trial has been performed to establish efficacy and safety of SST in children and adolescents with HFASD. In this randomised, multi-centre, controlled trial with 220 children and adolescents with HFASD it is hypothesized, that add-on group-based SST using the 12 weeks manualised SOSTA-FRA program will result in improved social responsiveness (measured by the parent rated social responsiveness scale, SRS) compared to treatment as usual (TAU). It is further expected, that parent and self reported anxiety and depressive symptoms will decline and pro-social behaviour will increase in the treatment group. A neurophysiological study in the Frankfurt HFASD subgroup will be performed pre- and post treatment to assess changes in neural function induced by SST versus TAU. METHODS/DESIGN: The SOSTA - net trial is designed as a prospective, randomised, multi-centre, controlled trial with two parallel groups. The primary outcome is change in SRS score directly after the intervention and at 3 months follow-up. Several secondary outcome measures are also obtained. The target sample consists of 220 individuals with ASD, included at the six study centres. DISCUSSION: This study is currently one of the largest trials on SST in children and adolescents with HFASD worldwide. Compared to recent randomised controlled studies, our study shows several advantages with regard to in- and exclusion criteria, study methods, and the therapeutic approach chosen, which can be easily implemented in non-university-based clinical settings. TRIAL REGISTRATION: ISRCTN94863788--SOSTA--net: Group-based social skills training in children and adolescents with high functioning autism spectrum disorder.

Direct comparison of the FibroScan XL and M probes for assessment of liver fibrosis in obese and nonobese patients.

BACKGROUND: A novel Fibroscan XL probe has recently been introduced and validated for obese patients, and has a diagnostic accuracy comparable with that of the standard M probe. The aim of this study was to analyze and understand the differences between these two probes in nonobese patients, to identify underlying causes for these differences, and to develop a practical algorithm to translate results for the XL probe to those for the M probe. METHODS AND RESULTS: Both probes were directly compared first in copolymer phantoms of varying stiffness (4.8, 11, and 40 kPa) and then in 371 obese and nonobese patients (body mass index, range 17.2-72.4) from German (n = 129) and Canadian (n = 242) centers. Liver stiffness values for both probes correlated better in phantoms than in patients (r = 0.98 versus 0.82, P < 0.001). Significantly more patients could be measured successfully using the XL probe than the M probe (98.4% versus 85.2%, respectively, P < 0.001) while the M probe produced a smaller interquartile range (21% versus 32%). Failure of the M probe to measure liver stiffness was not only observed in patients with a high body mass index and long skin-liver capsule distance but also in some nonobese patients (n = 10) due to quenching of the signal from subcutaneous fat tissue. In contrast with the phantoms, the XL probe consistently produced approximately 20% lower liver stiffness values in humans compared with the M probe. A long skin-liver capsule distance and a high degree of steatosis were responsible for this discordance. Adjustment of cutoff values for the XL probe (<5.5, 5.5-7, 7-10, and >10 kPa for F0, F1-2, F3, and F4 fibrosis, respectively) significantly improved agreement between the two probes from r = 0.655 to 0.679. CONCLUSION: Liver stiffness can be measured in significantly more obese and nonobese patients using the XL probe than the M probe. However, the XL probe is less accurate and adjusted cutoff values are required.

Estimation of secondary endpoints in two-stage phase II oncology trials.

In the development of a new treatment in oncology, phase II trials play a key role. On the basis of the data obtained during phase II, it is decided whether the treatment should be studied further. Therefore, the decision to be made on the basis of the data of a phase II trial must be as accurate as possible. For ethical and economic reasons, phase II trials are usually performed with a planned interim analysis. Furthermore, the decision about stopping or continuing the study is usually based on a short-term outcome like tumor response, whereas secondary endpoints comprise stable disease, progressive disease, toxicity, and/or overall survival. The data obtained in a phase II trial are often analyzed and interpreted by applying the maximum likelihood estimator (MLE) without taking into account the sequential nature of the trial. However, this approach provides biased results and may therefore lead to wrong conclusions. Whereas unbiased estimators for two-stage designs have been derived for the primary endpoint, such estimators are currently not available for secondary endpoints. We present uniformly minimum variance unbiased estimators (UMVUE) for secondary endpoints in two-stage designs that allow stopping for futility (and efficacy). We compare the mean squared error of the UMVUE and the MLE and investigate the efficiency of the UMVUE. A clinical trial example illustrates the application.

Patterns of multimorbidity in primary care patients at high risk of future hospitalization.

Care management is seen as a promising approach to address the complex care needs of patients with multimorbidity. Predictive modeling based on insurance claims data is an emerging concept to identify patients likely to benefit from care management interventions. We aimed to identify and explore patterns of multimorbidity in primary care patients with high predicted risk of future hospitalizations in order to develop a primary care-based care management intervention. We conducted a retrospective cohort study to assess insurance claims data of 6026 patients from 10 primary care practices in Germany. We stratified the population by the predicted likelihood of hospitalization (LOH) using a diagnostic cost group-based case-finding software. Co-occurrence of chronic conditions in multimorbid patients with an upper-quartile LOH score was explored by extraction of mutually exclusive patterns. Predictive modeling identified multimorbid elderly patients with a high number of co-occurring chronic conditions (mean number 7.8 [SD 3.1]). Assessing co-occurrence of highly prevalent chronic conditions in 1407 multimorbid patients with upper-quartile LOH revealed 471 mutually exclusive patterns with low single frequencies. The observed prevalence significantly exceeded expected prevalence for patterns with causal comorbidity. Additionally, chronic pain (related to osteoarthritis) or depression could be identified as discordant co-occurring conditions in 80% (12/15) of the most common multimorbidity patterns. High-risk primary care patients suffer from heterogeneous individual patterns of co-occurring chronic conditions. Care management interventions will have to account for discordant co-occurring conditions such as osteoarthritis and depression.

Health related quality of life and comorbidity. A descriptive analysis comparing EQ-5D dimensions of patients in the German disease management program for type 2 diabetes and patients in routine care.

BACKGROUND: The co-occurance of multiple medical conditions has a negative impact on health related quality of life (HRQoL) for patients with type 2 diabetes. These patients demand for intensified care programs. Participation in a disease management program (DMP) for type 2 diabetes has shown to counterbalance this effect. However, it remains unclear which dimensions of HRQoL are influenced by the DMP. The aim of this study was to explore the HRQoL dimensions of patients with type 2 diabetes in the German DMP and patients in routine care (RC). METHODS: This analysis is part of a comparative evaluation of the German DMP for patients with type 2 diabetes. A questionnaire, including the HRQoL measure EQ-5D, was mailed to a random sample of 3,546 patients with type 2 diabetes (59.3% female). The EQ-5D dimensions were analyzed by grouping patients according to their participation in the German DMP for diabetes into DMP and RC. RESULTS: Compared to patients in DMP, patients in RC reported more problems for the dimensions mobility (P < 0.05), self care (P < 0.05) and performing usual activities (P < 0.01). Depending on the number of other conditions, remarkable differences for reporting "no problems" exist for patients with six or more comorbid conditions regarding the dimensions mobility (RC = 8.7%, DMP = 32.3%), self care (RC = 43.5%, DMP = 64.5%), usual activities (RC = 13.0%, DMP = 33.9%) and anxiety or depression (RC = 37.0%, DMP = 48.4%). CONCLUSION: Patients participating in the German DMP for type 2 diabetes mellitus show significantly higher ratings of their HRQoL in the dimensions mobility, self care and performing usual activities compared to patients in RC. This difference can also be observed in patients with significant comorbidities. As these dimensions are known to be essential for diabetes care, the German DMP may contribute to improved care even for comorbid diabetes patients.