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Adaptive Clinical Trial Design James Rosenzweig and David Mcsorley WHITE PAPER Adaptive Clinical Trial Design James Rosenzweig and David McSorley Clinical trials in the pharmaceutical industry are incorporating adaptive design methods with greater frequency, as the economic resources needed for clinical research have expanded. These designs leverage accumulating information during a trial in real time and have the potential to reduce the costs and streamline the time frames for clinical trials in drug development, particularly in the earlier phases during proof of concept and dose selection. Adaptive design is usually defined as the According to the FDA guidelines, an adap- changes should be made during the course use of accumulating data obtained during tive clinical trial can involve: of the study. Adaptive designs use interim the conduct of the trial to decide how to • Analysis at decision points during the analyses of the accumulating data from modify aspects of the trial as it progresses, trial to stop or to adjust patient accrual within an ongoing study to modify various without affecting its validity and integrity aspects of the trial and then continue (Gallo et al., 2006). This form of research • Interim evaluations to determine if under the modifications. The different design involves a prospectively planned the trial should be stopped early—be- types of adaptations in study design have opportunity to modify the parameters of cause of a determination of success, often been put into categories, which the study based on analysis of interim data demonstration of futility, or finding of helps clarify the specific issues to be dealt while the study is being performed. The unacceptable harm to subjects with (Chow and Chang, 2008). However, prospective planning of key time points • Hypothesis reversal of noninferiority to categories may overlap and studies may for interim analysis, and the parameters for superiority or vice versa combine multiple strategies, as seen in the change in the study, have to be set before following: • Discontinuation of arms or doses, or the study is underway. the changing of doses while the trial is 1. Adaptive randomization Adaptive clinical trial designs were further underway These designs allow for changing the characterized by the US Food and Drug • Modification of the randomization rate randomization schedules of a study by ad- Administration (FDA) (FDA, 2010). to increase the probability that a patient justing the probability of treatment assign- The FDA divides adaptive designs into is allocated to the most appropriate arm ment based on prior assignments in order two categories—“well understood” and to either avoid an imbalance of important “less well understood.” Those considered Others have argued for a more liberal patient characteristics between treatment well understood have a record of being definition of adaptive design, which allows groups or to increase the likelihood of performed in the past, with established not only for prospective adaptations, but being assigned to a particular treatment statistical methods and familiarity with the for concurrent (ad hoc) and retrospec- group. Adaptive randomization schemes FDA from previously approved studies. tive adaptations (Chow, 2014). The use include treatment-adaptive (TA), covari- Less well understood designs fail to meet of Bayesian methodologies can enable ate-adaptive (CA), and response-adaptive these criteria. greater insight on which options for design (RA) randomization. Treatment-adaptive The power of knowledge. The value of understanding. WHITE PAPER Adaptive Clinical Trial Design and covariate-adaptive designs aim to An example of a group sequential design and education to implement this. A total balance the treatment groups with respect that employed adaptive elements was of 1,375 patients agreed to participate in to patient characteristics by changing the the Diabetes Control and Complications the EDIC study, the open-label follow-up way the subsequent patients are assigned to Trial (DCCT) (DCCT, 1993) and its to the DCCT, which continues to this day. a treatment group, whereas response-adap- follow-up, the Epidemiology of Diabetes The EDIC study continues to monitor the tive randomization aims to increase the Interventions and Complications (EDIC) DCCT patients for level of complications probability of being assigned to the treat- study (Nathan et al., 2005). The DCCT and cardiovascular events. Although the ment group with more favorable responses. was a multicenter, randomized clinical level of glycemic control in the two groups Response-adaptive schemes can therefore trial designed to determine whether or came together not long after the random- cause imbalances in patient characteristics not very tight control of blood glucose ized phase was completed, it was found that may require subsequent adjustment for (experimental therapy at the time) could that those in the intensive arm for only 6.5 these imbalances resulting in a combined reduce or prevent the microvascular and years continued to have fewer microvascular RACA design (Ning and Huang, 2010). macrovascular complications of type 1 complications for at least 30 years of fol- Adaptive randomization schemes have the diabetes, when compared with the standard low-up, and macrovascular benefits as well. most utility in small (n < 100), early-phase insulin treatment of the day. The study had trials—where equal probability randomiza- parallel arms consisting of subjects with 3. Flexible sample size re-estimation tion may not produce the desired balance no complications and those with very early This design enables the size of the sample in patient characteristics among treatment complications. The study was initiated in in the study to be changed or re-estimated groups—as the designs quickly become im- 1982. A feasibility phase with 278 subjects based on unblinded interim effect size data practical for large or longer-duration trials. was completed in 1985, and an interim and often may be included as one of the analysis determined that the experimen- adaptations in an adaptive group sequen- 2. Adaptive group sequential design tal intervention was safe and effective in tial design. In a fixed-sample study design, Classical group sequential methods use improving glycemic control by a sufficient the sample size is determined before the repeated significance testing on accu- margin to allow for full recruitment, which study and is based on prior estimates of the mulating groups of enrolled patients to expanded to 1,441 subjects. A specific clinically meaningful effect size between the decide whether to stop or continue a trial concern had been that the intervention treatment and control groups that can be based on established stopping boundar- to intensify glycemic control would be achieved for a specified power and type I er- ies for each test that maintain the overall accompanied by an unacceptably high risk ror rate. It is not uncommon for effect sizes type I and type II error rates across all of severe hypoglycemia; this was found not to be initially specified incorrectly, resulting tests. Type I error occurs when the null to be the case. An independent data, safety, in an underpowered design, especially if the hypothesis is true and type II error occurs and quality committee (DSQ) followed the variability turns out to be larger than initially when the null hypothesis is false but is results on a regular basis while the investi- specified. As a result, it may be desirable to not rejected. The concept of adaptive gators were blinded. adjust the sample sizes based on accrued design allows for additional changes to a data while a trial is underway. However, any study protocol as it progresses as a result The full randomized controlled clinical sample size re-estimation should be planned of analysis of interim data (Bauer and trial phase was stopped prematurely after in advance and done using appropriate Köhne, 1994). These include potential a mean follow-up time of 6.5 years, when group sequential methods so as to preserve modification, deletion or addition of treat- the benefits of intensive treatment were the type I error rate. ment arms, re-estimation of the sample found to be incontrovertible by the DSQ size, change of study endpoints, changing and not likely to be reversed over time. At 4. “Drop the losers” of dose and/or duration of treatment, that point, subjects on intensive control When multiple treatment arms are used, it and modification of randomization sched- were encouraged to continue and those is often helpful to have a multistage design ules. Adaptive group sequential designs originally assigned to conventional treat- to enable the investigators to drop arms combine the concepts of both early ment were advised to switch to intensive that are shown to be inferior to others. stopping and re-engineering of the design treatment. During the closeout phase of This design is sometimes referred to as based on the observed early results. the trial, they were provided the resources selection design or “pick up the winners,” The power of knowledge. The value of understanding. WHITE PAPER Adaptive Clinical Trial Design as it also allows adding additional arms decreased. If one subject experiences DLT, method of Branson and Whitehead (2002) (Chow, 2014). Typically, it is the first then three subjects are added. If only one gave accurate and consistent results, with an stage of a two-stage design, in which the of the six subjects experiences DLT, the advantage to the IPE method. inferior arms are dropped according to dose can be increased, and if two or more criteria specified in the beginning of the have DLT, then the dose is decreased. 7. Adaptive hypothesis design study. The winning treatment groups go It is possible to make potential changes to on to the next stage of the study. It is also Increasingly, dose-finding studies have the hypothesis of a study based on interim possible to use different analytic approach- utilized an iterative model-fitting process, data that is collected. This can be done by es (e.g., Bayesian predictive probabilities often called the continuous reassessment applying the closure principle (Marcus et vs.
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