Overrunning issue at the interim analysis for group sequential design

It is pretty common these days that clinical trials (especially the late phase, adequate, and well-controlled studies) employ interim analyses to determine if the efficacy results are too good so that the study should be stopped early for overwhelming efficacy, or if the efficacy results are not good so that the study should be stopped early for futility, or both. A study with formal interim analyses to look at the comparative efficacy is called 'group sequential design' even though the 'group sequential design' may not be formally used in the study protocol. Group sequential design is the most common type of adaptive design as described in the FDA guidance "adaptive designs for clinical trials". 

As mentioned in an early post "overrunning issues in adaptive design clinical trials", one of the issues with interim analyses in group sequential design is the overrunning issue. Overrunning consists of extra data, collected by investigators while awaiting results of the interim analysis (IA). Overrunning is the 
phenomenon that data will continue to accumulate after it is decided to stop a trial (Whitehead, 1992). In many cases there will be patients who have already been admitted to the trial but whose responses are not yet known. Also some extra patients will enter the trial because of the delay between the moment the data for the final interim analysis were retrieved, and the moment participating clinical centers receive instruction to stop recruitment.

EMA guidances "reflection paper on methodological issues in confirmatory clinical trials planned with an adaptive design" had a full paragraph discussing the overrunning issue: 

When planning for an interim analysis, the decision needs to be made about the timing of the interim analysis and what data is to be included in the interim analysis. For an event-driven study where the number of events is the endpoint, the timing of the interim analysis can be based on the percentage of the events, for example, the interim analysis can be performed when 50% of the total number of events are accrued. In other words, once 50% of the total number of events is achieved. For a longitudinal design, the endpoint is measured at various intervals. At any time during the study, there will be patients at different stages of the study (reaching the end of the study, reaching a specific duration of the study, or just being randomized into the study). It is more difficult to determine a good timing for interim analysis. Suppose that an interim analysis is planned when 50% of subjects reach the end of the study, by that time, there will be plenty of subjects in the various stage of the study already, just having not reached the end of study yet. If the study enrollment is pretty fast and the study endpoint is pretty long (for example 52 weeks), by the time 50% of subjects reach the study endpoint, the majority of subjects (if not all) have already been randomized into the study. 

If the interim analysis results trigger the recommendation of discontinuing the study early (either for efficacy or futility), the debate is whether the interim analysis needs to be re-run by including the overrunning subjects before adopting the recommendation to discontinue the study early. 

This exact issue about the handling of the overrunning subjects was discussed in Biogen's aducanumab clinical trials in Alzheimer's disease. As I discussed in a previous post "Futility Analysis and Conditional Power When Two Phase 3 Studies are Simultaneously Conducted", Biogen made the wrong decision and discontinued its pivotal studies (EMERGE and ENGAGE) where one of the studies was later found to have statistically significant treatment effects. The wrong decision was driven by two issues: 

• they calculated the conditional powers based on the pooled data from both studies (instead of calculating the conditional powers separately based on the data from individual studies) - this was discussed in the previous post. 
• they stopped the study early without re-running the interim analysis by including the overrunning subjects. 

The overrunning issue was mentioned in a recent article in the Wall Street Journal (Jan 4, 2022) "How Biogen Fumbled Its Alzheimer's Drug ---Once-promising Aduhelm is pricey and without proven efficacy"

"By evaluating data midstream in approval-seeking trials, companies can try to predict whether a drug will succeed if the trial continues. Stopping trials early for "futility," in industry parlance, can save millions of dollars and prevent patients from investing hope on an ineffective drug.

In a March 2019 meeting, Biogen executives on a small "senior decision team," as the company called it, concluded that the trials were doomed. Biogen pulled the plug and asked researchers around the world to shut down trials. It told more than 3,000 Alzheimer's patients who had volunteered that they would no longer receive treatment.

Biogen stock fell by nearly 30% the day of the announcement.

Biogen executives made errors in shutting down the trials. The trial plan called for analyzing data after half of patients completed the study treatment in late December 2018. By the time Biogen completed the analysis in March 2019, three more months of additional data were available -- but the decision team didn't scrutinize the additional data before the company halted the trials, Biogen has said.

A Biogen consultant in the summer of 2018 recommended to senior Biogen statisticians that they consider all available trial data, according to a person involved in the process. The consultant cautioned them that a plan to leave out consideration of additional trial data after the cutoff date -- and to leave out certain data from patients in the trial before the cutoff -- would open up Biogen to criticism and scrutiny, the person said. The statisticians didn't heed the consultant's advice, and it isn't clear whether the decision team or management considered the recommendation, the person said.

Biogen declined to comment on past discussions with its consultants but said it followed its pre-established statistical-analysis plan.

The decision not to consider the three months of additional data was a misstep, said some clinical-trial experts and statisticians. "Additional data after a study stops is called 'overrunning.' We plan for it," said Scott Emerson, a professor emeritus of biostatistics at the University of Washington who served on an FDA advisory committee that recommended against approving Aduhelm in November 2020. "In this case, the overrunning data was large."

The Biogen spokeswoman said: "Our decision to stop the trials, though clearly incorrect in hindsight, was based on putting patients at the forefront -- as it always should be. Cost was not considered in determining futility."

Only in the weeks after the trials stopped did Biogen scientists complete a preliminary analysis of the overrunning data and recognize their mistake, the Biogen spokeswoman said. The data seemed to show that one of the trials would have produced positive results, despite the likelihood of a negative outcome in the second trial. Initially, Biogen had analyzed combined data from both trials."

Even though the aducanumab was finally approved by FDA through the accelerated approval pathway, the approval was very controversial - the first drug and the only drug so far that was approved based on studies that had been stopped early for futility. There are strong pushback from academic about the use of aducanumab because of its unapproved efficacy and perhaps also because of the drama of resurrecting a drug that was declared failed. 

We all wonder: had these two pivotal studies not been stopped for futility by the sponsor, what would be the situation now for aducanumab?