Chevret, S.

Gasparini, M.

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04 August 2020POLITECNICO DI TORINORepository ISTITUZIONALEIntensive care medicine in 2050: clinical trials designs / Gasparini, M.; Chevret, S.. - In: INTENSIVE CARE MEDICINE. -ISSN 0342-4642. - (2018).OriginalIntensive care medicine in 2050: clinical trials designsspringerPublisher:PublishedDOI:10.1007/s00134-018-5364-9Terms of use:openAccessPublisher copyrightCopyright Springer. The final publication is available at link.springer.com(Article begins on next page)This article is made available under terms and conditions as specified in the  corresponding bibliographic description inthe repositoryAvailability:This version is available at: 11583/2724788 since: 2019-02-15T12:53:03ZSpringer Verlag1  Intensive Care Medicine in 2050: Clinical trials designs 1  2 M Gasparini and S Chevret 3  4  5 1.Department of Mathematical Sciences, Politecnico di Torino, Torino, Italy 6 2. AP-HP, Saint Louis Hospital, Paris, France; Paris 7 University; Inserm ECSTRA 7 Team, UMR1153, Paris, France 8  9  10 Corresponding author 11 Prof Sylvie Chevret 12 AP-HP, Saint Louis Hospital, Medical Intensive Care Unit, 13 Paris, France; 14 Paris 7 University; 15 Inserm ECSTRA Team, UMR1153, Paris, France 16  17 Competing interests: none 18 Word count: 1,000  19 References: 15 20 1 table 21  22 2  For a long time, clinical trials have been designed in a fairly standard way. In 1 particular, in confirmatory randomized clinical trials (RCTs), widely considered the 2 top of the evidence pyramid, each patient typically has a 1:1 chance of being 3 allocated to the experimental or the control treatment. Such scheme involves a large 4 number of patients, due to the often modest expected benefits (“effect size”), while 5 the statistical  requirements to control misconclusions are quite rigid: the type I 6 error rate of false positive findings is consensually fixed at 5% and that of type II 7 error rate of false negative findings fixed at most at 20%. Indeed, the effect size is 8 the factor of greatest impact on both sample size and power computations, which 9 explain the failure of most RCTs in critical care medicine to demonstrate the desired 10 effect size (10.1% on average), often largely above the observed one (1.4% on 11 average, [1]). 12 If RCT must be characterized ethically by the principle of equipoise, that is, of some 13 genuine uncertainty over whether a treatment will be beneficial, then it has been 14 argued that such negative and likely underpowered trials are unethical [1]: 15 participants may be called to sacrifice their own best interests for the benefit of 16 future patients. Lastly, RCTs are also faced with feasibility issues, when dealing with 17 interventions that could not be easily controlled and quantified such as ICU 18 admission and mechanical ventilatory support. 19 For all these reasons, it has been claimed that effectiveness of clinical trials should 20 be improved by adopting a more integrated model that increases flexibility and 21 maximizes the use of accumulated knowledge. Novel tools include the smart use of 22 supplementary evidence, adaptive designs, and Bayesian designs. 23 Using supplementary evidence for precision medicine 24 First, merging the strength of randomized clinical trials on homogeneous 25 populations (carefully selected through inclusion/exclusion criteria) and 26 observational studies could be promising.  In this regard, mixed randomized trials 27 that allocate patients first to trial arm and then to treatment group have been 28 proposed [2]. This solution seems to be mostly applicable to population-based 29 screening or interventions that appear far from the ICU setting. 30 In the ICU, the complexity of critical illness syndromes is a fundamental justification 31 for the adoption of a personalized approach to research [3]. Thus, identifying more 32 effectively the patients who will benefit from treatment, by refining critical illness 33 3  (pheno)types of patients, has been the motivation for innovative proposals of the 1 so-called “precision medicine”. The change of paradigm has been mostly beneficial 2 in the oncology setting, where widespread changes in clinical practice for diagnosis 3 and treatment have been increasingly based on genomic features [4].  4 To increase our knowledge on the population that should be targeted when 5 designing a particular trial, Bioinformatics and Machine Learning have provided 6 useful tools for the exploration of the huge amount of data derived from new 7 genomic platforms, physiologic waveforms, RCTs and electronic medical records. 8 This was exemplified since the early 2000s with the development of the MIMIC II 9 (Multiparameter Intelligent Monitoring in Intensive Care) databases that contain 10 physiologic signals and vital signs time series captured from ICU patients [5]. To 11 take full advantage of these big data, prediction models should be validated 12 rigorously given their potential to influence decision making [6]. 13 In the light of what has been done in oncology, providing precise information about 14 ICU phenotypes should lead to targeted treatments or interventions in pre-specified 15 subpopulations. Pivotal clinical trials of such therapies will then naturally be based 16 on innovative adaptive and/or Bayesian designs. 17 Adaptive designs 18 Adaptive designs can make clinical trials more flexible by utilising results 19 accumulating in the trial to modify the trial course in accordance with pre-specified 20 rules, aiming at improving the study power and reducing sample size and trial cost. 21 First proposed in oncology to assess many treatments and biomarkers, they have 22 raised many controversial discussions from the beginning [7], and are still 23 underused [8] and surrounded by misconceptions [9]. Nevertheless, they appear to 24 provide a possible blueprint for therapeutic development in the ICU. 25 Many innovative adaptive designs have been proposed, including enrichment 26 designs, marker-stratified designs, and marker strategy designs (umbrella trials, 27 basket trials) (Table). Most of these designs aim at treating more patients with more 28 effective treatments, or identifying efficacious drugs for specific subgroups of 29 patients. Such “enrichment” adaptive designs give investigators the ability to study 30 treatment approaches in multiple patient phenotypes within a single trial, while 31 maintaining a reasonable overall sample size, based on their biomarker profiles 32 including omics [10], and shortening the time for drug development. Conversely, 33 4  treatments found to be ineffective can be dropped from a study for selected patient 1 subpopulations. 2 Bayesian designs 3 Bayesian statistics and adaptive designs go often hand in hand. For instance, taking 4 multiple looks at the data is (statistically) not a problem, since in a Bayesian 5 framework such operation does not have to be adjusted for in any special way. 6 Thus, many adaptive designs have been proposed in this framework. They include 7 Bayesian adaptive biomarker/enrichment designs or randomization-adaptive 8 designs that update random allocation probabilities, so that more patients are 9 allocated to the most promising strategy as evidence accumulate [11]. 10 Bayesian designs can compare multiple active treatment strategies in real-world 11 settings by allowing for the evaluation of more than one new agent at the same time 12 and by dropping/adding arms when a sufficient level of evidence is reached [12]. 13 Such a Multi-Arm Multi-Stage (MAMS) design has been proposed in sepsis-like 14 patients [13]. 15 Conclusions   16 Adaptive and Bayesian designs are a methodologically sound way to improve clinical 17 trials in critical care but they add significant complexity [14]. First, outcomes should 18 be available soon enough to permit adaptation of the trial design. Furthermore, 19 design is impacted by the accumulated data. This requires statisticians to be 20 engaged both in the planning phase and in the conduct phase of the trial, which 21 may delay its large use in ICU as observed in other settings [15]. However, 22 multidisciplinary collaborations and team science including experts from Genetics, 23 Bioinformatics and Statistics appear a key to the success of these new design 24 strategies in ICU. 25 References 26 1. Aberegg SK, Richards DR, O’Brien JM (2010). Delta inflation: a bias in the 27 design of randomized controlled trials in critical care medicine 14:R77. doi: 28 10.1186/cc8990. 29 2. Alsop J, Scott M, Archey W (2016). The mixed randomized trial: combining 30 randomized, pragmatic and observational clinical trial designs. J Comp Eff 31 Res. 5(6):569-579. doi:10.2217/cer-2016-0034 32 5  3. Maslove DM, Lamontagne F, Marshall JC, Heyland DK (2017). A path to 1 precision in the ICU. Critical Care 21:79. doi: 10.1186/s13054-017-1653-x. 2 4. Renfro LA, An MW, Mandrekar SJ. Precision Oncology: A New Era of Cancer 3 Clinical Trials (2017). Cancer Lett. 387: 121–126. doi: 4 10.1016/j.canlet.2016.03.015. 5 5. Scott DJ, Lee J, Silva I, Park S, Moody GB, Celi LA, Mark RG (2013). Accessing 6 the public MIMIC-II intensive care relational database for clinical research. 7 BMC Med Inform Decis Mak. doi: 10.1186/1472-6947-13-9. 8 6. Shah ND, Steyerberg EW, Kent DM (2018). Big data and predictive analytics. 9 Recalibrating expectations. JAMA. doi: 10.1001/jama.2018.5602 10 7. Bauer P, Bretz F, Dragalin V, König F, Wassmer G (2016). Twenty-five years of 11 confirmatory adaptive designs: opportunities and pitfalls. Stat Med. 12 35(3):325-47. doi: 10.1002/sim.6472. 13 8. Hatfield I, Allison A, Flight L, Julious SA, Dimairo M (2016). Adaptive designs 14 undertaken in clinical research: a review of registered clinical trials. Trials 15 17(1):150. doi: 10.1186/s13063-016-1273-9. 16 9. Pallmann P, Bedding AW, Choodari-Oskooei B, Dimairo M, Flight L, Hampson 17 LV, Holmes J, Mander AP, Odondi L, Sydes MR, Villar SS, Wason JMS, Weir CJ1, 18 Wheeler GM, Yap C1, Jaki T (2018). Adaptive designs in clinical trials: why use 19 them, and how to run and report them. BMC Med. 16(1):29. doi: 20 10.1186/s12916-018-1017-7. 21 10. Simon R. Genomic Alteration–Driven Clinical Trial Designs in Oncology 22 (2016).  Ann Intern Med 165:270-278. doi:10.7326/M15-2413 23 11. Ondra T, Dmitrienko A, Friede T, Graf A, Miller F, Stallard N, et al (2016). 24 Methods for identification and confirmation of targeted subgroups in clinical 25 trials: a systematic review. Journal of Biopharmaceutical Statistics 26(1):99-26 119. doi: 10.1080/10543406.2015.1092034. 27 12. Sydes MR, Parmar MK, Mason MD, Clarke NW, Amos C, Anderson J, et al. 28 (2012). Flexible trial design in practice - stopping arms for lack-of-benefit 29 and adding research arms mid-trial in STAMPEDE: a multi-arm multi-stage 30 randomized controlled trial. Trials 13(1):168. 31 6  13. Magaret A, Angus DC, Adhikari  NJK, Banura  P, Kissoon  N, Lawler  JV, Jacob 1 ST (2016). Design of a multi-arm randomized clinical trial with no control 2 arm. Contemporary Clinical Trials 46: 12–17. doi: 10.1016/j.cct.2015.11.003. 3 14. Bhatt DL, Mehta C. Adaptive designs for clinical trials (2016). N Engl J Med. 4 375(1):65-74. doi: 10.1056/NEJMra1510061. 5 15. Thorlund K, Haggstrom J, Park JJH, Mills EJ (2018). Key design considerations 6 for adaptive clinical trials: a primer for clinicians. BMJ 360:k698. doi: 7 10.1136/bmj.k698.  8 7  Table: Schematization of proposed biomarker-based adaptive designs  1  2  Enrichment or targeted designs Denomination Basket trial Umbrella trial Platform trials Biomarker-based Bayesian adaptive trial Main setting Single treatment, single biomarker, different subsets of patients One subset of patients, different biomarkers, Different drugs  Multiple biomarkers and multiple drugs  Response-adaptive randomization  Enrollment All the subsets are enrolled  One drug for one biomarker  (separate enrichment design for each biomarker) Randomization between strata (allocation probabilities modified to favor assignment of drugs with higher within-stratum response rate)   Modified allocation probabilities within each of biomarker-based treatment Advantages Access to targeted agents for patients in various subsets  Conclusions specific to the patient subset  More patients allocated to the best treatment Incorporate external information, and report based on probabilities on effect size  Drawbacks Rely on the assumption that profiling based on biomarker is enough Feasibility, notably for rare diseases (poor accrual and slow trial progress) Increased samples and heterogeneity Larger complexity and the involvement of statisticians  3 

Intensive care medicine in 2050: clinical trials designs

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Intensive care medicine in 2050: clinical trials designs

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