The original ALIAS trial
ALIAS is a randomized, parallel-group, double-blind, phase 3 trial testing the treatment superiority of 25% albumin over saline in improving the outcome of acute ischemic stroke subjects [15]. The primary endpoint is favorable outcome, defined as either a modified Rankin scale score of 0 or 1, or an NIHSS score of 0 or 1, or both, at 90 days. With a fixed equal allocation, 422 and 419 subjects were randomized to the albumin (treatment) group and the saline (control) group respectively. Scheduled interim analyses were conducted at n=275 and n=550. The Data and Safety Monitoring Board requested an additional analysis at n=732, and stopped the trial for futility after 841 participants were randomized. The primary outcome did not differ by treatment assignment (albumin, 44.1%; saline, 44.2%. 95% confidence interval: 0.84 - 1.10 adjusting for baseline covariates). O’Brien and Fleming-type stopping guidelines were adopted for both efficacy and futility assessments. Unexpectedly, the response rate in the control arm increased over time while the response rate in the treatment arm remained stable across the trial. To visualize the time-trend of response rates, subjects are divided into 28 stages according to the enrollment sequence (i.e. every 30 subjects at each stage). The weighted least square (WLS) regression technique is applied to these data while taking into account the variation of arm size within each stage (Figure 1). The graph demonstrates a clear upward trend in the control arm’s response rate; with the slope parameter β=0.012, p-value<0.0005. Meanwhile, the treatment arm’s response rate is quite stable; with the slope parameter β=0.00043, p-value=0.90).
Simulation study design
The goal of this study design is to apply three different BRAR methods in redesigning of the ALIAS trial. The BRAR methods examined are the probability-weighted allocation approach (BRAR (1/2)) [16], the natural lead-in allocation (BRAR(n/2N)) [17], and the information-weighted allocation (BRAR(1/2, σ2)) [18,19]. Information on the enrollment sequence, treatment assignment, and response outcome of the 841 subjects in the original ALIAS trial are used for this simulation study with BRAR. Interim analyses are conducted at n=275, n=550, and n=732, and the final analysis at n=841. Equal allocation is applied to the first 275 subjects. Response adaptive randomization probabilities are updated concurrently with each interim analysis. For each of the 4 enrollment segments (1-275, 276-550, 551-732, and 733-841), numbers of subjects in each arm are calculated based on the corresponding allocation probabilities. The trial will be stopped for efficacy if the posterior success probability for either arm being superior to the other exceeds the efficacy boundary of 0.995. The trial will be terminated for futility when the chance that posterior success probability for either arm being superior to the other is different from each other is less than 5%. Table 1 lists implementation parameters of the simulation study.
Simulation study dataset generation
Simulation study datasets, including enrollment sequence, treatment arm, and outcome, are generated by sampling from the ALIAS trial. For the treatment arm (without time-trend), simulation subjects are randomly selected from the entire ALIAS treatment arm with replacement. For the control arm (with time-trend), the ith simulation subject is randomly selected from a group of 7 ALIAS control arm subjects with enrollment sequence in the range of (i-3, i+3). For example, the 10th simulation subject in the control arm will be randomly selected from the 7 ALIAS subjects with enrollment sequence from 7 to 13 in the control arm.
BRAR algorithm
In this simulation study, we evaluated the operation characteristics of the trial under two burn-in period lengths (275 and 350) and three BRAR algorithms [13,14]. Burn-in period uses a fixed equal allocation for subject randomization. During the adaptive randomization phases, for each subject, the treatment assignment is generated based on the current treatment allocation ratio, which is obtained from the selected BRAR algorithm and responses of subjects enrolled prior to the current allocation ratio update. The fixed equal randomization is incorporated as a comparator.
The simulation study is re-executed 1000 times. Detailed information can be found in Figure 2 below. Since the goal of this study is to evaluate the impact of time trend on the performance of BRAR design, there is no safety monitoring procedures involved. The R, version 3.2.5 software (R Foundation for Statistical Computing) [20] was used for the analysis.