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Stat Med. Author manuscript; available in PMC 2015 October 15.

Published in final edited form as:

PMCID: PMC4159417

NIHMSID: NIHMS594536

Department of Public Health Science, Medical University of South Carolina, 135 Cannon Street, Charleston, SC 29425, USA

Correspondence to: Wenle Zhao, Department of Public Health Science, Medical University of South Carolina, 135 Cannon Street, Suite 305H, Charleston, SC, 29425, USA. Email: ude.csum@woahz

See other articles in PMC that cite the published article.

It is well known that competing demands exist between the control of important covariate imbalance and protection of treatment allocation randomness in confirmative clinical trials. When implementing a response-adaptive randomization algorithm in confirmative clinical trials designed under a frequentist framework, additional competing demands emerge between the shift of the treatment allocation ratio and the preservation of the power. Based on a large multicenter phase III stroke trial, we present a patient randomization scheme that manages these competing demands by applying a newly developed minimal sufficient balancing design for baseline covariates and a cap on the treatment allocation ratio shift in order to protect the allocation randomness and the power. Statistical properties of this randomization plan are studied by computer simulation. Trial operation characteristics, such as patient enrollment rate and primary outcome response delay, are also incorporated into the randomization plan.

The use of response-adaptive randomization (RAR) in clinical trials has received increasing attention in the last two decades [1, 2]. With RAR, the treatment allocation probabilities for the current patient to be randomized can be altered based on observed response data from previously randomized patients within the trial. As a component of Bayesian adaptive methods, RAR has gained noticeable application in trials designed under a Bayesian framework [3–5]. However, so far, very few confirmative trials designed under a frequentist framework using RAR have been reported. This is important to explore since under a frequentist framework, there is in most cases a loss in power when the allocation ratio is not balanced.

Motivations for using RAR in clinical trial treatment allocation mainly come from efficiency and ethics considerations [3]. If the purpose of the RAR is to maximize the efficiency (in terms of power), the increase in power of RAR over a fixed equal allocation randomization for large confirmative trials designed under a frequentist framework will be minimal. Therefore, the ethical benefit in this setting becomes the primary motivation for using RAR, in which, RAR is proposed as assigning a higher proportion of patients to the so-far better performing arm. However, the shift of the treatment allocation ratio from a balanced to an unbalanced allocation can result in a loss of power and creates competing demands between the ethical and efficiency considerations.

In addition to the competing demands between ethics and efficiency, the randomization scheme needs to consider the balance between treatment assignment randomness and baseline covariate balancing. The impacts of the treatment allocation ratio and the preservation of assignment randomness have been highlighted in the Michigan extracorporeal membrane oxygenation (ECMO) trial [6]. Using the randomized play-the-winner (RPW) rule [7], the Michigan ECMO trial ended with all 11 patients assigned to the ECMO arm surviving, and one patient assigned to the conventional treatment arm who died. This trial has been met with strong controversy [8, 9], partially because of the 11:1 final allocation ratio as the result of the RAR algorithm. The doubly adaptive biased coin (DABC) design was developed to control the variation of the treatment allocation ratio [10–12]. Recently, a variety of RAR designs with optimized target allocation ratios have been proposed with the aim of either maximizing the power or minimizing the total failures [13–15]. However, these two goals may compete with each other as it has been shown that they may not be achieved simultaneously [16]. Meanwhile, covariate-adjusted response adaptive (CARA) algorithms have been developed, with the aim of controlling imbalance in baseline prognostic factors during the RAR process [17–22]. Some of these studies used computer simulations to mimic real clinical trials in order to study the statistical and operational characteristics of the proposed RAR algorithm [22, 23]. However, there is a paucity of literature on the implementation of RAR in actual phase III trials designed under a frequentist framework. The competing demands under RAR are valid concerns and until these concerns are addressed, the actual value of using RAR in large phase III clinical trials will continue to be challenged [24–26].

In this paper, we examine the competing demands of ethics, allocation randomness and power when using RAR with covariate balancing. We implement an approach to manage these demands in the setting of an existing large multi-center phase III acute stroke trial with a binary outcome designed under a frequentist framework. Section 2 describes the setting of the trial and the primary goals of the randomization procedure. Section 3 presents the design of the randomization algorithm and its statistical properties. The implementation of the randomization algorithm in the trial is covered in Section 4, followed by a discussion on the benefits and costs of RAR in Section 5.

The Stroke Hyperglycemia Insulin Network Effort (SHINE) is an ongoing multicenter phase III clinical trial in acute stroke funded by the National Institute of Neurological Diseases and Stroke (NCT01369069) [27, 28]. In this study, approximately 1400 acute ischemic stroke patients with elevated blood glucose levels are being recruited from approximately 60 clinical sites in the US and will be randomized to either a sliding scale insulin treatment arm or the experimental insulin infusion arm. Investigators in charge of treating the enrolled patients will be unblinded. The primary efficacy outcome is Success or Failure as determined by the 90-day modified Rankin scale (mRS) and the baseline stroke severity measured by the National Institutes of Health Stroke Scale (NIHSS) [29]. Success outcome is defined as mRS = 0 for patients with mild stroke (NIHSS 3–7), mRS ≤ 1 for moderate stroke patients (NIHSS 8–14), and mRS ≤ 2 for severe stroke patients (NIHSS 15–22). Three baseline covariates are believed to have important impact on the primary outcome and therefore require balancing. They are the baseline stroke severity category (mild, moderate and severe), the use of thrombolysis (yes, no), and clinical site.

The patient randomization procedure in the SHINE trial is implemented in three stages and has three primary goals. The first goal is to protect the randomness of treatment allocation in order to prevent potential selection biases. The second goal is to prevent serious imbalances in the distributions of the three important baseline covariates in order to ensure the compatibility of the two treatment groups. The third goal is to promote a response-adaptive treatment allocation ratio, so that patients will have a higher than 50% chance to be assigned to the better performing treatment arm if a difference in the success rates between the two arms is observed from previously enrolled patients. When trying to achieve these goals, it is also required that the power reduction due to the unbalanced allocation ratio under RAR is minimal. The randomization algorithm is implemented in three stages as described in detail in Section 4. The rationale for the three stages is based on the specific trial setting. The SHINE trial has an anticipated enrollment rate of approximately 25 patients per month and a primary outcome that is measured 90 days post randomization. Although this may be considered a relatively short-term outcome, a burn-in period is required in order to obtain the necessary information on the covariates as well as the primary outcome that will be used specifically for the pre-defined adaptation of the allocation ratio.

Randomization in patient treatment allocation serves as a basis for frequentist inferences in clinical trials [30, 31]. The randomness of treatment allocation reduces selection bias. This benefit is maximized when the assignment is purely random (i.e., 50% chance of being assigned to either arm in a two-arm trial). When covariate balancing is included in the randomization algorithm in order to reduce accidental biases, the randomness of treatment assignment will be reduced [32].

Although the necessity of balancing important prognostic variables in clinical trials remains a topic of debate [33], balanced baseline variables between treatment arms remains at the top of the randomization objective list for many researchers. The absolute difference between the sizes of two treatment arms has been used as the imbalance control limit for the permuted block design [34], the biased coin design [35], the big stick design [36], and the maximal procedure design [37]. Some other randomization designs, such as the urn design [38], use the ratio of the absolute arm size difference to the total sample size as the imbalance measurement. A small *p*-value, i.e. less than 0.05, for a baseline covariate balance test often triggers suspicion of selection bias or wrong doing [39]. The recently published minimal sufficient balancing strategy aims to maximize the treatment allocation randomness while containing the baseline covariate imbalances within a pre-specified limit [40]. Because the baseline covariate balance is more likely to be challenged based on the *p*-value of the imbalance test [39], we use the imbalance test *p*-value as the imbalance measure and set the control limit (*p**) to range from 0.1 to 0.3 depending on the importance of the covariate.

Let *i* = 1, 2, and 3 refer to baseline covariates NIHSS, thrombolysis, and site respectively, and *n _{ijk}* be the observed number of patients in the

$${\chi}_{\mathit{NIHSS}}^{2}={\sum}_{j=1}^{3}\left[{\left({n}_{1jA}-{E}_{1jA}\right)}^{2}/{E}_{1jA}+{\left({n}_{1jB}-{E}_{1jB}\right)}^{2}/{E}_{1,jB}\right],\phantom{\rule{0.38889em}{0ex}}(df=2)$$

(1)

$${\chi}_{\mathit{tPA}}^{2}={\sum}_{j=1}^{2}\left[{\left({n}_{2jA}-{E}_{2jA}\right)}^{2}/{E}_{2jA}+{\left({n}_{2jB}-{E}_{2jB}\right)}^{2}/{E}_{2jB}\right],\phantom{\rule{0.38889em}{0ex}}(df=1)$$

(2)

Where:

$${E}_{1jk}=\frac{{n}_{1j}\times {n}_{k}}{{n}_{A}+{n}_{B}},\phantom{\rule{0.38889em}{0ex}}\phantom{\rule{0.38889em}{0ex}}\phantom{\rule{0.38889em}{0ex}}(j=1,2,3;k=A,B)$$

(3)

$${E}_{2jk}=\frac{{n}_{2j}\times {n}_{k}}{{n}_{A}+{n}_{B}},\phantom{\rule{0.38889em}{0ex}}\phantom{\rule{0.38889em}{0ex}}\phantom{\rule{0.38889em}{0ex}}(j=1,2;k=A,B)$$

(4)

The *p*-values for these two tests, *p _{NIHSS}* and

When the number of sites is large, the chi square test for the *R*×*C* contingency table will not work because many cells will be smaller than 6 during the trial [41]. Therefore, the imbalance within a site is measured by the one-sample test for a binomial proportion based on the difference between the observed allocation ratio within the site and the observed overall or target allocation ratio. Assume the current patient is in site *j*. Let *n _{j}* =

$${z}_{\mathit{Site}}=\frac{\left(\frac{{n}_{3jA}}{{n}_{j}}-\frac{{n}_{A}}{n}\right)}{\sqrt{\frac{{n}_{A}}{n}\times \frac{{n}_{B}}{n}\times \frac{1}{{n}_{j}}}}$$

(5)

The *p*-value for this test, *p _{Site}*, can be obtained based on the standard normal distribution. When

$${p}_{\mathit{Site}}=\{\begin{array}{cc}2{\sum}_{i=0}^{{n}_{3\phantom{\rule{0.16667em}{0ex}}jA}}\left(\begin{array}{c}{n}_{j}\\ i\end{array}\right)\times {\left(\frac{{n}_{A}}{n}\right)}^{i}\times {\left(\frac{{n}_{B}}{n}\right)}^{{n}_{3\phantom{\rule{0.16667em}{0ex}}jA}-i}& \text{if}\phantom{\rule{0.16667em}{0ex}}\frac{{n}_{3\phantom{\rule{0.16667em}{0ex}}jA}}{{n}_{j}}\le \frac{{n}_{A}}{n}\\ 2{\sum}_{i={n}_{3\phantom{\rule{0.16667em}{0ex}}jA}}^{{n}_{j}}\left(\begin{array}{c}{n}_{j}\\ i\end{array}\right)\times {\left(\frac{{n}_{A}}{n}\right)}^{i}\times {\left(\frac{{n}_{B}}{n}\right)}^{{n}_{3\phantom{\rule{0.16667em}{0ex}}jA}-i}& \text{if}\phantom{\rule{0.16667em}{0ex}}\frac{{n}_{3\phantom{\rule{0.16667em}{0ex}}jA}}{{n}_{j}}>\frac{{n}_{A}}{n}\end{array}$$

(6)

Unlike the minimization methods proposed by Pocock and Simon [42] and Taves [43], which have been criticized for the lack of randomness [44], the minimal sufficient balancing strategy uses biased coin assignments only when imbalances exceed a pre-specified control limit (*p**). If the *p*-value of the imbalance test is less than its control limit, a vote is registered toward the treatment arm aimed to reduce the imbalance. Otherwise, no vote is submitted. Based on the results from each test (NIHSS, tPA and site), if one arm receives more votes than the other arm, a biased coin assignment is applied. Otherwise, the target allocation ratio is used for the calculation of the treatment allocation probability.

A treatment assignment is considered deterministic if its conditional allocation probability is 1 for one arm and 0 for the other. The randomness of treatment assignments can be quantified by several different measurements, including the probability of deterministic assignments, the probability of purely random assignments, and the probability of correct guess defined by Blackwell and Hodges [45]. The values of these properties are affected by the number of covariates, the imbalance control limit *p*^{*}, and the biased coin probability *p _{bc}*. Shown in Figure 1 are computer simulation results of the proportion of purely random assignments under minimal sufficient balancing for a two-arm trial with three categorical covariates, balanced allocation and a sample size of 500. Based on input from clinical investigators, the distribution of NIHSS among the three levels was set at 0.3 (mild), 0.4 (moderate), and 0.3 (severe); and the proportion of tPA use was set at 0.4 (no) and 0.6 (yes). The imbalance control limit

As *p _{bc}* increases, the probability of making a correct guess for those biased coin assignments increases, however the overall number of biased coin assignments decreases. The average chance of making a correct guess for treatment assignments is 52.8% when

Alternatively, should the original version of the Pocock and Simon minimization method be used in the above setting, computer simulation shows that 49.0% of assignments will be purely random. If a threshold of 3 is included in the minimization algorithm so that a deterministic assignment will be applied only when the imbalance sum is 3 or more, the expected proportion of purely random assignments will increase to 62.8%. When a biased coin probability of 0.85 is used to replace the deterministic assignments, the expected proportion of purely random assignments will be reduced to 53.8%.

Primary motivations of using response-adaptive randomization to shift treatment allocation ratios in clinical trials are potential advantages in ethics, efficiency, and economics [11, 47–48]. Similar motivations also have been used to justify the use of fixed unequal randomization ratios in clinical trial practices. While the use of a fixed unequal allocation ratio is justified based on observed response information from previous studies or the need to acquire more secondary information from one arm (i.e, safety of experimental treatment), the use of RAR allows the randomization algorithm to dynamically adjust the treatment allocation ratio based on response information obtained within the study. For this reason, RAR could be a better choice over fixed unequal randomization. However, the potential reduction in power caused by the allocation ratio shifting away from a balanced ratio remains the same for both fixed unequal randomization and RAR.

Consider a trial comparing two independent binomial proportions testing the hypothesis *H*_{0} : *p _{A}* =

$$\mathit{Power}=\mathrm{\Phi}\phantom{\rule{0.16667em}{0ex}}\left[\frac{\mid {p}_{A}-{p}_{B}\mid -{z}_{1-\frac{\alpha}{2}}\sqrt{\overline{pq}\left(\frac{1}{{n}_{A}}+\frac{1}{{n}_{B}}\right)}}{\sqrt{\frac{{p}_{A}{q}_{A}}{{n}_{A}}+\frac{{p}_{B}{q}_{B}}{{n}_{B}}}}\right]$$

(7)

Where, *q _{A}* = 1 −

$$\mathit{Power}=1-\beta =\mathrm{\Phi}\phantom{\rule{0.16667em}{0ex}}\left[\frac{\mid {p}_{A}-{p}_{B}\mid -{z}_{1-{\scriptstyle \frac{\alpha}{2}}}\sqrt{\frac{({rp}_{A}+{p}_{B})({rq}_{A}+{q}_{B})}{nr}}}{\sqrt{\left(\frac{{p}_{A}{q}_{A}}{r}+{p}_{B}{q}_{B}\right)\left(\frac{1+r}{n}\right)}}\right]$$

(8)

Based on formula (8), Figure 2 shows the sensitivity of the power on the changes of the randomization allocation ratio. It is clear that as the allocation ratio shifts toward the better performing arm (*r* >1), the power is lower than the power under balanced allocation (*r* =1).

It is important to ensure that this effect is either minimized or taken into account when estimating the sample size in the trial design stage. By fixing the power, the required sample size is:

$$n=\frac{(1+r)}{{({p}_{A}-{p}_{B})}^{2}}{\left[{z}_{1-{\scriptstyle \frac{\alpha}{2}}}\sqrt{\frac{({rp}_{A}+{p}_{B})({rq}_{A}+{q}_{B})}{r(1+r)}}+{z}_{1-\beta}\sqrt{\frac{{p}_{A}{q}_{A}}{r}+{p}_{B}{q}_{B}}\right]}^{2}$$

(9)

The expected number of total failures associated with this required sample size is:

$${n}_{\mathit{Failure}}=\overline{q}n=\frac{{rq}_{A}+{q}_{B}}{{({p}_{A}-{p}_{B})}^{2}}{\left[{z}_{1-{\scriptstyle \frac{\alpha}{2}}}\sqrt{\frac{({rp}_{A}+{p}_{B})({rq}_{A}+{q}_{B})}{r(1+r)}}+{z}_{1-\beta}\sqrt{\frac{{p}_{A}{q}_{A}}{r}+{p}_{B}{q}_{B}}\right]}^{2}$$

(10)

Figure 3 shows the impact of the randomization allocation ratio on the expected number of failures under two conditions, one with a fixed sample size and the other with a fixed power.

With a fixed sample size, there is a gradual decrease in the total number of failures as the allocation ratio increases. However, the seemingly ethical benefit of reducing the number of failures is at the cost of power as seen in Figure 2. Under the fixed power scenario, shifting the allocation ratio from 1:1 in either direction results in an increase in the required sample size, and therefore increases the total number of failures.

From this point of view, the expected ethical benefit of shifting treatment allocation ratio by either fixed unbalanced randomization or RAR is questionable. For example, in a trial to detect 7% difference between two independent binomial proportions (32% for the treatment arm and 25% for the control) with significance level of *α* = 0.05, the power will be 82.8% with 700 patients in each of the two treatment groups, and the expected number of total failures in the study will be 1001. Should the allocation ratio be shifted to 2:1, with 933 patients randomized in the better performing arm and 467 patients in the other arm, the expected number of total failures will be 985, and the power will be reduced to 77.6% at the same time. In order to keep the original power at 82.8%, an additional 188 patients will be required, leading to a total sample size of 1588. The expected number of total failures in this trial will be 1117, that is 116 higher than the original 1001 under the balanced allocation. This fact reflects the conflict between the potential individual ethics and collective ethics within a clinical trial. Let _{r}_{:1} and _{1:1} represent the average chance of success for each patient enrolled in the study under *r* :1 and1:1 allocation ratios respectively. The ethics benefit for the unequal allocation ratio can be measured as:

$${\overline{p}}_{r:1}-{\overline{p}}_{1:1}=\frac{{rp}_{A}+{p}_{B}}{1+r}-\frac{{p}_{A}+{p}_{B}}{2}=\frac{(r-1)({p}_{A}-{p}_{B})}{2(r+1)}$$

(11)

In the scenario discussed above with *r* = 2, formula (11) represents the benefit of a 1.17% increase in the trial’s average success rate, at the cost of enrolling an additional 188 patients and expecting 116 of them to have failure outcomes. This result makes the selection of the 2:1 allocation ratio more difficult to justify.

In RAR research, not only is the expected value of the target allocation ratio important, but also the variance of the allocation ratio. Hu and Zhang proposed a Doubly-adaptive Biased Coin Design (DBCD) procedure for trials comparing two treatments with binomial outcomes [12]. With DBCD, the probability of assigning subject *i* to treatment *A* is:

$$Pr({T}_{i}=A)=\frac{\rho {(\rho /x)}^{\gamma}}{\rho {(\rho /x)}^{\gamma}+(1-\rho ){((1-\rho )/(1-x))}^{\gamma}}$$

(12)

where
$x=\frac{{n}_{A}}{{n}_{A}+{n}_{B}}$ is the observed allocation proportion of arm *A*,
$\rho =\frac{{p}_{A}^{\lambda}}{{p}_{A}^{\lambda}+{p}_{B}^{\lambda}}$ is the target allocation proportion of arm *A*, *γ* is a nonnegative integer effecting the asymptotic variance of a RAR procedure, and *λ* is parameter effecting the target allocation ratio. The variance of the allocation proportion is:

$$\mathit{Var}(\rho )=\frac{{q}_{A}{q}_{B}}{{({q}_{A}+{q}_{B})}^{3}}\left({p}_{A}+{p}_{B}+\frac{2}{1+2\lambda}\right)$$

(13)

Suppose *p _{A}* = 0.32,

To manage the competing demands between power and ethics, we include two parameters in the RAR algorithm, an allocation ratio cap *r*^{*} >1to protect power, and a maximal effect size to adapt, *δ*^{*}. When the absolute difference between the two observed response rates exceeds *δ*^{*}, the probability of assigning patients to the better performing arm will be capped:

$$Pr({T}_{i}=A)=\{\begin{array}{ll}\frac{1}{2}+\frac{({r}^{\ast}-1)({p}_{A}-{p}_{B})}{2(1+{r}^{\ast}){\delta}^{\ast}}\hfill & \text{if}\mid {p}_{A}-{p}_{B}\mid \phantom{\rule{0.16667em}{0ex}}\le {\delta}^{\ast}\hfill \\ \frac{1}{2}+\frac{({r}^{\ast}-1)}{2(1+{r}^{\ast})}\mathit{sign}({p}_{A}-{p}_{B})\hfill & \text{if}\mid {p}_{A}-{p}_{B}\mid \phantom{\rule{0.16667em}{0ex}}>{\delta}^{\ast}\hfill \end{array}$$

(14)

Selection of *r*^{*} and *δ*^{*}can be made based on the desired power of the trial and the outcome response sensitivity. The allocation ratio cap used for the calculation of treatment allocation probabilities is not equal to the final allocation ratio, because the observed response rates of the two arms could change from time to time, and not all assignments are made based on the capped allocation ratio. However, with the allocation ratio cap for each assignment, the final allocation ratio will not exceed the cap. This ensures that the trial will maintain the desired power to detect the treatment effect if such an effect exists.

The strategies for managing competing demands between treatment allocation randomness and covariate balancing, and between patient ethics and power provide a general framework for implementing covariate adjusted response adaptive (CARA) randomization. In practice, two other issues must be taken into account when setting up a randomization plan for a large multicenter trial. One is the uneven patient accrual rate during the study period, and the other is the response delay. For trials involving a large number of sites with a multi-year long patient recruitment period, it is not uncommon for the trial to start with few sites, and gradually add sites during the first year. The slow early enrollment phase can involve amendments to the study protocol, study treatment procedure adjustments, case report form changes and individual site protocol learning curves. These changes often result in larger variability in patient outcomes during the initial stages of the trial. In addition, outcome delay is common in trials treating neurological diseases like stroke. For example, the primary outcome for the SHINE trial is the functional outcome at 3 months as measured by the modified Rankin Scale (mRS) Score. In order to have stable estimates on response rates, the RAR component of the randomization algorithm should begin once an adequate proportion of patients have been through the study protocol and have available outcome data.

Based on these considerations, the SHINE randomization plan is implemented in three periods. Period 1 begins the trial enrollment and since there is no available data to contribute to the RAR or covariate balancing, it functions as a ‘burn-in period’ and simply controls overall treatment assignment balance. Period 2 begins once there is sufficient accumulating information on the distribution of treatment assignment for the pre-specified prognostic variables. At this stage in the trial, there still may not be adequate outcome information so the RAR aspect of the randomization algorithm continues in a burn-in phase while the covariate balancing can commence and begin to focus on preventing serious imbalances in the specified baseline covariates. Period 3 begins once there is sufficient information on the primary outcome and incorporates a response adaptive allocation probability that is used when baseline covariate imbalances are under control. The actual timing of these periods is study dependent and should be planned based on expected enrollment rates and timing of the primary outcome.

In Period 1, patients are randomized with the block urn design (BUD) [49], which has the following conditional allocation probabilities:

$$Pr({T}_{i}=A)=\frac{\mathit{MTI}+min({n}_{i-1,A},{n}_{i-1,B})-{n}_{i-1,A}}{2\left(\mathit{MTI}+min({n}_{i-1,A},{n}_{i-1,B})\right)-(i-1)}$$

(15)

Here *n _{i}*

Period 2 begins controlling imbalances in the pre-specified baseline covariates with the minimal sufficient balancing method [35]. When an eligible patient is ready for randomization, imbalances in the distribution of the baseline NIHSS category, the thrombolysis category, and site corresponding to the current patient are checked with formulas (1–6). A biased coin probability of 0.7 is used for the treatment assignment for subject *i* in favor of reducing imbalances if the number of serious imbalances toward one treatment arm is more than the number of serious imbalances toward the other treatment arm. Based on computer simulation results, it is anticipated that three out of every four patients in this period will receive purely random assignments, and none of the three baseline covariates will have a serious imbalance indicated by a *p*-value less than 0.3 in the imbalance test.

When Period 3 begins, the system first checks the imbalances for the three baseline covariates. A biased coin assignment is used in order to reduce imbalances if it is deemed being required based on the minimal sufficient balancing strategy. Otherwise, the target allocation ratio, based on the observed response rates of the two treatment arms, is directly used as the conditional allocation ratio for the treatment assignments. A proper biased coin probability *p _{bc}*, such as 0.85 or 0.9, can be selected so that a sufficient majority of patients in this period will receive treatment assignments based on the RAR algorithm. When RAR is started, the target allocation ratio no longer equals 1:1. It is important to point out that, the target allocation ratio change does not affect the definition of baseline covariate imbalances. Formulas (1–6) are defined to assess the treatment distribution within a covariate subgroup to the overall treatment distribution. Therefore, during Period 3, the imbalance test is conducted in the same way as Period 2.

With this randomization plan, it is anticipated that the treatment allocation randomness will be well protected with an estimate of more than three-fourths of patients receiving treatment assignment based solely on the target allocation ratio. Imbalances with *p*-value less than 0.3 in the three baseline covariates will be prevented. The power of the trial will be protected, and those patients enrolled in Period 3 will have more than 50% chance of being assigned to the better performing arm.

Response adaptive randomization has rarely been implemented in large, multicenter confirmative trials. As its potential benefits for individual patients within the trial remain an attractive feature, other issues related to or affected by the randomization algorithm must be simultaneously considered. Among them, treatment allocation randomness and baseline covariate balance must be properly protected. It is also important to point out that the ethical benefit of using RAR in trials designed under a frequentist framework is obtained at the cost of power. Under the condition of fixed power, the overall ethical benefit of RAR measured by the reduction of expected total number of failures may not exist as shown above.

Impact on the type I error is another important issue associated with RAR [50]. Consider a trial with the primary outcome affected by a time-dependent factor, so that those patients enrolled in the early phase of the trial have a lower success rate than those enrolled in the later stage of the trial. With RAR, the treatment group showing a higher success rate will benefit more from those later patients than the other treatment group. This will trigger an inflation of the type I error. If a negative time trend exists in the outcome, so that the success rate is reduced over time, the use of RAR will decrease the type I error of the trial. Computer simulation under the trial setting described above shows that if the success rate starts at 25% at the beginning of the trial and linearly increases to 35% at the end, the type I error will be inflated from 0.05 to 0.06. On the other hand, if the success rate drops from 25% to 20%, the type I error will be deflated from 0.05 to 0.04. Therefore, when RAR is implemented and a strong monotonic increase or decrease pattern is observed in patient outcomes, the interpretation of the trial results may be biased. Further work is needed in this area.

The use of RAR could create additional complexities for trial operations. For example, the change in target allocation ratio triggered by RAR could pose difficulties in managing study drug distribution. Nevertheless, with so many concerns, we believe it is possible to implement RAR with covariate balancing in a real trial and maintain a balance between randomness and baseline covariate balancing and ethics and power. Lessons and experiences learned from the use of the proposed randomization scheme for the SHINE trial will provide valuable knowledge for future trialists.

This study is supported by two National Institute of Neurological Diseases and Stroke NINDS grants: SHINE Grant U01 NS069498; NETT Grant U01 NS059041. The authors thank Dr. Yanqiu Weng for his valuable input on the simulation programming code.

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