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Abstract A Trial within Cohorts (TwiCs) study design is a trial design that uses the infrastructure of an observational cohort study to initiate a randomized trial. Upon cohort enrollment, the participants provide consent for being randomized in future studies without being informed. Once a new treatment is available, eligible cohort participants are randomly assigned to the treatment or standard of care. Patients randomized to the treatment arm are offered the new treatment, which they can choose to refuse. Patients who refuse will receive standard of care instead. Patients randomized to the standard of care arm receive no information about the trial and continue receiving standard of care as part of the cohort study. Standard cohort measures are used for outcome comparisons. The TwiCs study design aims to overcome some issues encountered in standard Randomized Controlled Trials (RCTs). An example of an issue in standard RCTs is the slow patient accrual. A TwiCs study aims to improve this by selecting patients using a cohort and only offering the intervention to patients in the intervention arm. In oncology, the TwiCs study design has gained increasing interest during the last decade. Despite its potential advantages over RCTs, the TwiCs study design has several methodological challenges that need careful consideration when planning a TwiCs study. In this article, we focus on these challenges and reflect on them using experiences from TwiCs studies initiated in oncology. Important methodological challenges that are discussed are the timing of randomization, the issue of non-compliance (refusal) after randomization in the intervention arm, and the definition of the intention-to-treat effect in a TwiCs study and how this effect is related to its counterpart in standard RCTs.

Abstract Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials – simulated trials based on three different mathematical models – to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design – sample size, endpoint, randomization rate, and interim analyses – we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.

Abstract Background Given the large genetic heterogeneity in amyotrophic lateral sclerosis (ALS), it seems likely that genetic subgroups may benefit differently from treatment. An exploratory meta-analysis identified that patients homozygous for the C-allele at SNP rs12608932, a single nucleotide polymorphism in the gene UNC13A, had a statistically significant survival benefit when treated with lithium carbonate. We aim to confirm the efficacy of lithium carbonate on the time to death or respiratory insufficiency in patients with ALS homozygous for the C-allele at SNP rs12608932 in UNC13A. Methods A randomized, group-sequential, event-driven, double-blind, placebo-controlled trial will be conducted in 15 sites across Europe and Australia. Patients will be genotyped for UNC13A; those homozygous for the C-allele at SNP rs12608932 will be eligible. Patients must have a diagnosis of ALS according to the revised El Escorial criteria, and a TRICALS risk-profile score between −6.0 and −2.0. An expected number of 1200 patients will be screened in order to enroll a target sample size of 171 patients. Patients will be randomly allocated in a 2:1 ratio to lithium carbonate or matching placebo, and treated for a maximum duration of 24 months. The primary endpoint is the time to death or respiratory insufficiency, whichever occurs first. Key secondary endpoints include functional decline, respiratory function, quality of life, tolerability, and safety. An interim analysis for futility and efficacy will be conducted after the occurrence of 41 events. Discussion Lithium carbonate has been proven to be safe and well-tolerated in patients with ALS. Given the favorable safety profile, the potential benefits are considered to outweigh the burden and risks associated with study participation. This study may provide conclusive evidence about the life-prolonging potential of lithium carbonate in a genetic ALS subgroup. Trial registration EudraCT number 2020-000579-19 . Registered on 29 March 2021.

Abstract Background Parkinson’s disease (PD) is a neurodegenerative disease, for which no disease-modifying therapies exist. Preclinical and clinical evidence suggest that hypoxia-based therapy might have short- and long-term benefits in PD. We present the contours of the first study to assess the safety, feasibility and physiological and symptomatic impact of hypoxia-based therapy in individuals with PD. Methods/Design In 20 individuals with PD, we will investigate the safety, tolerability and short-term symptomatic efficacy of continuous and intermittent hypoxia using individual, double-blind, randomized placebo-controlled N-of-1 trials. This design allows for dose finding and for including more individualized outcomes, as each individual serves as its own control. A wide range of exploratory outcomes is deployed, including the Movement Disorders Society Unified Parkinson’s Disease Rating scale (MDS-UPDRS) part III, Timed Up & Go Test, Mini Balance Evaluation Systems (MiniBES) test and wrist accelerometry. Also, self-reported impression of overall symptoms, motor and non-motor symptoms and urge to take dopaminergic medication will be assessed on a 10-point Likert scale. As part of a hypothesis-generating part of the study, we also deploy several exploratory outcomes to probe possible underlying mechanisms of action, including cortisol, erythropoietin and platelet-derived growth factor β. Efficacy will be assessed primarily by a Bayesian analysis. Discussion This evaluation of hypoxia therapy could provide insight in novel pathways that may be pursued for PD treatment. This trial also serves as a proof of concept for deploying an N-of-1 design and for including individualized outcomes in PD research, as a basis for personalized treatment approaches. Trial registration ClinicalTrials.gov Identifier: NCT05214287 (registered January 28, 2022).

Abstract Background Neonates with critical congenital heart disease (CCHD) undergoing cardiac surgery with cardiopulmonary bypass (CPB) are at risk of brain injury that may result in adverse neurodevelopment. To date, no therapy is available to improve long-term neurodevelopmental outcomes of CCHD neonates. Allopurinol, a xanthine oxidase inhibitor, prevents the formation of reactive oxygen and nitrogen species, thereby limiting cell damage during reperfusion and reoxygenation to the brain and heart. Animal and neonatal studies suggest that allopurinol reduces hypoxic-ischemic brain injury and is cardioprotective and safe. This trial aims to test the hypothesis that allopurinol administration in CCHD neonates will result in a 20% reduction in moderate to severe ischemic and hemorrhagic brain injury. Methods This is a phase III, randomized, quadruple-blinded, placebo-controlled, multicenter trial. Neonates with a prenatal or postnatal CCHD diagnosis requiring cardiac surgery with CPB in the first 4 weeks after birth are eligible to participate. Allopurinol or mannitol-placebo will be administered intravenously in 2 doses early postnatally in neonates diagnosed antenatally and 3 doses perioperatively of 20 mg/kg each in all neonates. The primary outcome is a composite endpoint of moderate/severe ischemic or hemorrhagic brain injury on early postoperative MRI, being too unstable for postoperative MRI, or mortality within 1 month following CPB. A total of 236 patients (n = 188 with prenatal diagnosis) is required to demonstrate a reduction of the primary outcome incidence by 20% in the prenatal group and by 9% in the postnatal group (power 80%; overall type 1 error controlled at 5%, two-sided), including 1 interim analysis at n = 118 (n = 94 with prenatal diagnosis) with the option to stop early for efficacy. Secondary outcomes include preoperative and postoperative brain injury severity, white matter injury volume (MRI), and cardiac function (echocardiography); postnatal and postoperative seizure activity (aEEG) and regional cerebral oxygen saturation (NIRS); neurodevelopment at 3 months (general movements); motor, cognitive, and language development and quality of life at 24 months; and safety and cost-effectiveness of allopurinol. Discussion This trial will investigate whether allopurinol administered directly after birth and around cardiac surgery reduces moderate/severe ischemic and hemorrhagic brain injury and improves cardiac function and neurodevelopmental outcome in CCHD neonates. Trial registration EudraCT 2017-004596-31. Registered on November 14, 2017. ClinicalTrials.gov NCT04217421. Registered on January 3, 2020

In-silico trials rely on virtual populations and interventions simulated using patient-specific models and may offer a solution to lower costs. Here, the authors present the flow diverter performance assessment in-silico trial, which models the treatment of intracranial aneurysms with a flow-diverting stent.

Despite recent and potent technological advances, the real-world implementation of remote digital health technology in the care and monitoring of patients with motor neuron disease has not yet been realized. Digital health technology may increase the accessibility to and personalization of care, whereas remote biosensors could optimize the collection of vital clinical parameters, irrespective of patients’ ability to visit the clinic. To facilitate the wide-scale adoption of digital health care technology and to align current initiatives, we outline a road map that will identify clinically relevant digital parameters; mediate the development of benefit-to-burden criteria for innovative technology; and direct the validation, harmonization, and adoption of digital health care technology in real-world settings. We define two key end products of the road map: (1) a set of reliable digital parameters to capture data collected under free-living conditions that reflect patient-centric measures and facilitate clinical decision making and (2) an integrated, open-source system that provides personalized feedback to patients, health care providers, clinical researchers, and caregivers and is linked to a flexible and adaptable platform that integrates patient data in real time. Given the ever-changing care needs of patients and the relentless progression rate of motor neuron disease, the adoption of digital health care technology will significantly benefit the delivery of care and accelerate the development of effective treatments.

Abstract 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.

Abstract 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.

Abstract Background When profiling multiple health care providers, adjustment for case-mix is essential to accurately classify the quality of providers. Unfortunately, misclassification of provider performance is not uncommon and can have grave implications. Propensity score (PS) methods have been proposed as viable alternatives to conventional multivariable regression. The objective was to assess the outlier classification performance of risk adjustment methods when profiling multiple providers. Methods In a simulation study based on empirical data, the classification performance of logistic regression (fixed and random effects), PS adjustment, and three PS weighting methods was evaluated when varying parameters such as the number of providers, the average incidence of the outcome, and the percentage of outliers. Traditional classification accuracy measures were considered, including sensitivity and specificity. Results Fixed effects logistic regression consistently had the highest sensitivity and negative predictive value, yet a low specificity and positive predictive value. Of the random effects methods, PS adjustment and random effects logistic regression performed equally well or better than all the remaining PS methods for all classification accuracy measures across the studied scenarios. Conclusions Of the evaluated PS methods, only PS adjustment can be considered a viable alternative to random effects logistic regression when profiling multiple providers in different scenarios.

Abstract Evidence-based treatment for pregnant women will ultimately require research conducted in the population of pregnant women. Currently, few scholars have addressed the issue of responsible inclusion of pregnant women in drug research. Because of additional risks associated with including pregnant women in drug research and the altered ways in which drugs are processed by the pregnant body, pregnant women cannot be treated as an ordinary subgroup in the various phases of drug development. Instead, responsible inclusion of pregnant women requires careful design and planning of research for pregnant women specifically. Knowledge about these aspects is virtually nonexistent. In this article, we present a practical framework for the responsible inclusion of pregnant women in drug development. We suggest that the framework consists of using a question-based approach with five key questions in combination with three prerequisites which should be addressed when considering inclusion of pregnant women in drug research. The five questions are:A. Can we consider the drug safe (enough) for first exposure in pregnant women and fetuses? B. In which dose range (potentially depending on gestational age) can the drug be considered to remain safe in pregnant women? C. At what dose (regimen, within the range considered safe) can we expect efficacy in pregnant women? D. Can efficacy be confirmed at the target dose, either similar to the initial population or different? E. Can clinical safety be confirmed at a sufficiently acceptable level at the target dose for pregnant women and fetuses, so as to conclude a positive benefit–risk ratio? Combining questions and prerequisites leads to a scheme for appropriate timing of responsible inclusion of pregnant women in drug research. Accordingly, we explore several research design options for including pregnant women in drug trials that are feasible within the framework. Ultimately, the framework may lead to (i) earlier inclusion of pregnant women in drug development, (ii) ensuring that key prerequisites, such as proper dosing, are addressed before more substantial numbers of pregnant women are included in trials, and (iii) optimal use of safety and efficacy data from the initial (nonpregnant) population throughout the drug development process.

Abstract Background Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data. Results Six raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes. Conclusion Gene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data.

Abstract Background It is well recognized that treatment effects may not be homogeneous across the study population. Subgroup analyses constitute a fundamental step in the assessment of evidence from confirmatory (Phase III) clinical trials, where conclusions for the overall study population might not hold. Subgroup analyses can have different and distinct purposes, requiring specific design and analysis solutions. It is relevant to evaluate methodological developments in subgroup analyses against these purposes to guide health care professionals and regulators as well as to identify gaps in current methodology. Methods We defined four purposes for subgroup analyses: (1) Investigate the consistency of treatment effects across subgroups of clinical importance, (2) Explore the treatment effect across different subgroups within an overall non-significant trial, (3) Evaluate safety profiles limited to one or a few subgroup(s), (4) Establish efficacy in the targeted subgroup when included in a confirmatory testing strategy of a single trial. We reviewed the methodology in line with this “purpose-based” framework. The review covered papers published between January 2005 and April 2015 and aimed to classify them in none, one or more of the aforementioned purposes. Results In total 1857 potentially eligible papers were identified. Forty-eight papers were selected and 20 additional relevant papers were identified from their references, leading to 68 papers in total. Nineteen were dedicated to purpose 1, 16 to purpose 4, one to purpose 2 and none to purpose 3. Seven papers were dedicated to more than one purpose, the 25 remaining could not be classified unambiguously. Purposes of the methods were often not specifically indicated, methods for subgroup analysis for safety purposes were almost absent and a multitude of diverse methods were developed for purpose (1). Conclusions It is important that researchers developing methodology for subgroup analysis explicitly clarify the objectives of their methods in terms that can be understood from a patient’s, health care provider’s and/or regulator’s perspective. A clear operational definition for consistency of treatment effects across subgroups is lacking, but is needed to improve the usability of subgroup analyses in this setting. Finally, methods to particularly explore benefit-risk systematically across subgroups need more research.

Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is used to evaluate whether the accumulated samples show enough evidence or more experiments should be initiated. We found 313 differentially expressed genes, based on the cumulative information of the experiments. SMA offers an alternative to existing methods in generating a gene list by evaluating the adequacy of the cumulative information.

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

BACKGROUND: A non-inferiority (NI) trial is intended to show that the effect of a new treatment is not worse than the comparator. We conducted a review to identify how NI trials were conducted and reported, and whether the standard requirements from the guidelines were followed. METHODOLOGY AND PRINCIPAL FINDINGS: From 300 randomly selected articles on NI trials registered in PubMed at 5 February 2009, we included 227 NI articles that referred to 232 trials. We excluded studies on bioequivalence, trials on healthy volunteers, non-drug trials, and articles of which the full-text version could not be retrieved. A large proportion of trials (34.0%) did not use blinding. The NI margin was reported in 97.8% of the trials, but only 45.7% of the trials reported the method to determine the margin. Most of the trials used either intention to treat (ITT) (34.9%) or per-protocol (PP) analysis (19.4%), while 41.8% of the trials used both methods. Less than 10% of the trials included a placebo arm to confirm the efficacy of the new drug and active comparator against placebo, and less than 5.0% were reporting the similarity of the current trial with the previous comparator's trials. In general, no difference was seen in the quality of reporting before and after the release of the CONSORT statement extension 2006 or between the high-impact and low-impact journals. CONCLUSION: The conduct and reporting of NI trials can be improved, particularly in terms of maximizing the use of blinding, the use of both ITT and PP analysis, reporting the similarity with the previous comparator's trials to guarantee a valid constancy assumption, and most importantly reporting the method to determine the NI margin.