100 Ideas That Changed Graphic Design Pdf.rar
Nichole Wernett
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We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.
Table 1 lists some well-recognised adaptations and examples of their use. Note that multiple adaptations may be used in a single trial, e.g. a group-sequential design may also feature mid-course sample size re-estimation and/or adaptive randomisation [6], and many multi-arm multi-stage (MAMS) designs are inherently seamless [7]. ADs can improve trials across all phases of clinical development, and seamless designs allow for a more rapid transition between phases I and II [8, 9] or phases II and III [10, 11].
Giles et al. conducted a randomised trial investigating three induction therapies for previously untreated, adverse karyotype, acute myeloid leukaemia in elderly patients [33]. Their goal was to compare the standard combination regimen of idarubicin and ara-C (IA) against two experimental combination regimens involving troxacitabine and either idarubicin or ara-C (TI and TA, respectively). The primary endpoint was complete remission without any non-haematological grade 4 toxicities by 50 days. The trial began with equal randomisation to the three arms but then used a response-adaptive randomisation (RAR) scheme that allowed changes to the randomisation probabilities, depending on observed outcomes: shifting the randomisation probabilities in favour of arms that showed promise during the course of the trial or stopping poorly performing arms altogether (i.e. effectively reducing their randomisation probability to zero). The probability of randomising to IA (the standard) was held constant at 1/3 as long as all three arms remained part of the trial. The RAR design was motivated by the desire to reduce the number of patients randomised to inferior treatment arms.
After 24 patients had been randomised, the probability of randomising to TI was just over 7%, so recruitment to this arm was terminated and the randomisation probabilities for IA and TA recalculated (Fig. 2). The trial was eventually stopped after 34 patients, when the probability of randomising to TA had dropped to 4%. The final success rates were 10/18 (56%) for IA, 3/11 (27%) for TA, and 0/5 (0%) for TI. Due to the RAR design, more than half of the patients (18/34) were treated with the standard of care (IA), which was the best of the three treatments on the basis of the observed outcome data, and the trial could be stopped after 34 patients, which was less than half of the planned maximum of 75. On the other hand, the randomisation probabilities were highly imbalanced in favour of the control arm towards the end, suggesting that recruitment to this trial could have been stopped even earlier (e.g. after patient 26).
Before a study can begin, funding to conduct it must be obtained. The first step is to convince the decision-making body that the design is appropriate (in addition to showing scientific merits and potential, as with any other study). This is sometimes more difficult with ADs than for traditional trial designs, as the decision makers might not be as familiar with the methods proposed, and committees can tend towards conservative decisions. To overcome this, it is helpful to ensure that the design is explained in non-technical terms while its advantages over (non-adaptive) alternatives and its limitations are highlighted. On occasion, it might also be helpful to involve a statistician with experience of ADs, either by recommending the expert to be a reviewer of the proposal or by including an independent assessment report when submitting the case.
Our final set of practical challenges relates to running the study. Once again, many aspects will be similar to traditional fixed designs, although additional considerations may be required for particular types of adaptations. For instance, drug supply for multi-arm studies is more complex as imbalances between centres can be larger and discontinuing arms will alter the drug demand in a difficult-to-predict manner. For trials that allow the ratio at which patients are allocated to each treatment to change once the trial is under way, it is especially important that there is a bespoke central system for randomisation. This will ensure that randomisation errors are minimised and that drug supply requirements can be communicated promptly to pharmacies dispensing study medication.
For some ADs, there are CIs that have the correct coverage level taking into account the design used [18, 19, 85, 86], including simple repeated CIs [87]. If a particular AD does not have a method that can be readily applied, then it is advisable to carry out simulations at the design stage to see whether the coverage of the naively found CIs deviates considerably from the planned level. In that case, a bootstrap procedure could be applied for a wide range of designs if this is not too computationally demanding.
While this paper focuses on frequentist (classical) statistical methods for trial design and analysis, there is also a wealth of Bayesian AD methods [100] that are increasingly being applied in clinical research [23]. Bayesian designs are much more common for early-phase dose escalation [101, 102] and adaptive randomisation [103] but are gaining popularity also in confirmatory settings [104], such as seamless phase II/III trials [105] and in umbrella or basket trials [106]. Bayesian statistics and adaptivity go very well together [4]. For instance, taking multiple looks at the data is (statistically) unproblematic as it does not have to be adjusted for separately in a Bayesian framework.
Prospective planning of an AD is important for credibility and regulatory considerations [41]. However, as in any other (non-AD) trial, some events not envisaged during the course of the trial may call for changes to the design that are outside the scope of a priori planned adaptations, or there may be a failure to implement planned adaptations. Questions may be raised regarding the implications of such unplanned ad hoc modifications. Is the planned statistical framework still valid? Were the changes driven by potential bias? Are the results still interpretable in relation to the original research question? Thus, any unplanned modifications must be stated clearly, with an explanation as to why they were implemented and how they may impact the interpretation of trial results.
We wrote this paper to encourage the wider use of ADs with pre-planned opportunities to make design changes in clinical trials. Although there are a few practical stumbling blocks on the way to a good AD trial, they can almost always be overcome with careful planning. We have highlighted some pivotal issues around funding, communication and implementation that occur in many AD trials. When in doubt about a particular design aspect, we recommend looking up and learning from examples of trials that have used similar designs. As AD methods are beginning to find their way into clinical research, more case studies will become available for a wider range of applications. Practitioners clearly need to publish more of their examples. Table 1 lists a very small selection.
Because RAR governs the CoR binding status of the heterodimer and, consequently, the ability of RXR to respond to its own ligand, the degree of CoR interaction is a crucial determinant on which pharmacological agents can act for modulating heterodimer activity. This postulation is supported by our data showing that the pharmacological profiles of retinoids are primarily determined by their impact on CoR interactions. Among these various types of modulators, RAR inverse agonists are defined by their ability to reinforce CoR interaction. AGN193109 was the first reported inverse agonist for RARs [48,80], but it exhibits a weaker inverse agonistic activity than BMS493 [21]. To our knowledge, the latter is the most powerful inverse agonist for all three RAR subtypes. The resolution of the crystallographic structure of the complex formed by the RARα LBD bound to BMS493 and CoRNR1 peptide of NCoR showed that the specificity of the interaction between RAR and CoR is conferred by an extended β-strand in RAR LBD forming an antiparallel β-sheet with CoR residues [23]. It also provided a structural basis for the increase of CoR affinity in the presence of an inverse agonist. Accordingly, RAR inverse agonists prevent agonist-bound RXR from interacting with CoA. In a striking contrast, CD2665 decreases the CoR interaction with RARα and allows liganded-RXR to associate with CoA which produces an active heterodimer. Structurally, bulky groups conferring the antagonistic nature of CD2665 and BMS493 are very different (Figure 1). One can reason that the particular structure of CD2665 impairs the formation of the extended β-strand required for CoR association without generating an optimal surface for CoA interaction. Other retinoids used in this study exhibit an intermediate functional profile such as AGN870. Interestingly, both EMSA and two-hybrid assays with RARα show that, when compared to CD2665, AGN870 is less efficient to release CoR but a little more effective for CoA recruitment. It is likely that the resulting co-regulator equilibriums promoted by the two molecules are similar as they can synergize in a similar manner with an RXR agonist.
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