A Proposal for Patient-Selected Controlled Trials: Good Science and Good Medicine


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Jim Omel, MD

Karl Schwartz, MFA

The novel type of controlled clinical trial that we are proposing is a hybrid system—one that we hope can help to reliably answer the study question, solve the enrollment problem, and also address the ethical concern of forcing patients to be randomly assigned, particularly for studies that lack equipoise. What good is a statistically perfect, well-designed randomized clinical trial if no one signs up?

—Jim Omel, MD, and Karl Schwartz, MFA

The Clinical Trials and Translational Research Advisory Committee (CTAC) of the National Cancer Institute (NCI) met for the 22nd time on March 12, 2014, in their ongoing effort to improve efficiency and effectiveness of cancer clinical trials. A significant portion of the meeting addressed lagging patient accrual numbers.

An important slide entitled “Analysis of Accrual for NCI Cooperative Group Phase III Trials Activated 2000-2010” explained that 254 such trials were activated during this period. Of this total, 51 are still open and accruing (with one-fifth of them < 90% accrued), and for 203 trials, accrual has stopped. Among the 203 trials that have stopped accruing patients, 119 met their goal by at least 90%, but 84 did not. Fifty-three trials (50 in adults and 3 in pediatric subjects) described by the committee as “the troublesome trials” were closed solely because of an inadequate accrual rate.

A second slide summarized the 254 trials during this period by stating that 24.4% of all adult cancer trials ended with < 90% accrual. There was significant discussion around the table regarding the human and financial costs of these failed trials as well as the reasons for the poor accrual. Trial arms with significantly different interventions (believed by patients and their doctors to lack equipoise) and randomization to very disparate arms were the two main reasons for the 24.4% accrual failure rate.

Current System Problems

Many reports have described a crisis in clinical research—ie, the very low enrollment rate in clinical trials—indicating that the system is not working for patients, researchers, or drug sponsors. Indeed, one study found that half of unpublished clinical trials have failed to accrue and reach endpoints, and another found that “of all NCI phase I, II, and III trials opened and closed between 2000 and 2007, only 50% to 60% achieved minimal stated accrual goals.”1

Our online survey of patients showed that randomization is the main reason for patients declining to participate in a study and that the recommendation of an oncologist is the primary reason for considering and participating in a study.2 As expected, the discussion of clinical trials with the patient’s oncologist was associated with the highest consideration (85%) and participation (53%) rates, suggesting a need to increase awareness of study protocols among treating physicians so that this discussion can become more routine and productive.

Patient issues and perceptions regarding randomization, study risk, eligibility, and tests and procedures provide opportunities to improve enrollment in clinical trials by focusing on these aspects of study design, paying special attention to the rationale of the protocol as a treatment decision.

During discussion of the patient accrual problems at the CTAC meeting, there seemed to be a sense of “nothing can be done; that’s just the way it is.” We, however, feel that something can be done. We propose that researchers explore the option of using patient-selected controlled trials.

A Novel Proposal

We are seeking your input on the patient-selected controlled trial as an additional tool to consider when comparing treatments for cancer, such as (but not limited to) when the compared interventions have very different risks, or when both treatment protocols can be used off-study.

The patient-selected controlled trial lets patients either choose to be randomly assigned or pick the study arm they want to be in. Their arm selection may be based on their own expectations, preference, or unique clinical risk factors. Such decisions will most often be guided by the patient’s physician.

One patient, for example, might prefer the study arm that would not put her fertility at risk. Another might prefer the treatment arm that appears to have the greater chance to achieve a cure. A third patient, having no opinion or expectation about which is better, might well choose to let a computer decide.

We want to underscore that we are not advocating for replacement of the randomized controlled trial design when it’s feasible and ethical to use it. We are proposing the patient-selected controlled trial as a way to conduct controlled trials when patients are unlikely to accept randomization—when clinical equipoise is lacking.

The novel type of controlled clinical trial that we are proposing is a hybrid system—one that we hope can help to reliably answer the study question, solve the enrollment problem, and also address the ethical concern of forcing patients to be randomly assigned, particularly for studies that lack equipoise.

Study Design Illustration

This design will lead to a mix of participants in terms of selection method (by patient-physician or computer) and in the number of patients assigned to each arm. Depending on the appeal of the study drug and the efficacy and risks of regular care, the study arms may be out of balance. For example:

  • 15% may choose the traditional treatment arm (patients will choose this if they want what is known and “safe”)
  • 55% may choose the study drug arm (patients will choose this when the traditional treatment is not very effective or is very toxic) or vice versa (depending on the preliminary evidence for the study drug, and the efficacy of the regular treatment)
  • 30% may choose to be randomized (these are the patients who truly do not care about which arm they are assigned to; they are also the true measure of equipoise of the trial)

In this example, 30% would receive the regular treatment, and 70% would receive the study drug. With this distribution, the statisticians can tell us how many participants are needed to get a reliable answer to the study question.

With this approach, distribution to the study arms objectively measures equipoise. It should be noted that clinical trials do not require an equal number of patients in each arm, and there are numerous examples of randomized trials with 2:1 enrollment in respective study arms.

We appreciate that there will be a need to provide incentives to participants who choose the established treatment in the study, if extra tests are required beyond standard care. To address this issue, it may be necessary to limit the number of tests required by participants choosing the standard treatment arm to only those needed to compare efficacy and toxicity. This will make their decision to participate in the study similar to choosing standard treatment off study.

These extra tests can be optional and also encouraged by modest financial incentives sufficient to offset the burden of study participation. It should be emphasized  (to enrolling patients) that such tests are done to help future patients or to more accurately monitor for response and toxicity.

Addressing Concerns

As with single-arm trials, the introduction of bias is the main weakness of the patient-selected controlled trial design. We believe that this shortcoming could be mitigated by the following factors:

  • The patient-selected controlled trial design will be more attractive to patients and referring physicians, allowing for larger studies and faster accrual.
  • Participating physicians have the greatest influence on which arm the patient will choose. This source of bias might be minimized by random selection of study doctors.
  • Participating physicians can agree to refer consecutive patients to the trial.
  • Because the patient-selected controlled trial design allows for patient choice, study doctors will likely feel there is less patient coercion compared to trials that force patients to be randomly assigned.
  • Study doctors can provide or capture the reason for choosing one arm or the other, thereby helping to interpret outcomes and to determine whether a larger study is needed.
  • When a patient is guided by a physician to choose a safer study arm because of a specific risk factor (such as retaining fertility), this improves the ethics of referring the patient to the trial (good medicine).
  • Prognostic indexes, biomarkers, and refined eligibility criteria can be used increasingly to counteract imbalances in the study arms (good science).
  • Statisticians may be able to predefine rules to censor outcomes based on prognostic factors or apply methods to achieve balance in the observational arms—such as by limiting accrual in the rapidly enrolling arms of the study.

Propensity Scoring Methods

Propensity scoring is a statistical technique that attempts to estimate the effect of an intervention by accounting for the covariates that predict receiving the treatment. Thus, propensity scoring anticipates and accounts for confounding variables in order to adjust for bias.3,4

Can the type of disease influence the reliability of propensity scoring to account for bias when comparing interventions? For example, heart disease could have many more variables that might influence the course of the disease, such as diet, quality of sleep, anxiety, and belief in the value of the chosen intervention. For lymphoma, prognostic indexes guide clinical decisions and risk stratification in trials (age, tumor bulk, comorbidities, stage, and so on).

For cancer, the variables that may guide a choice of study arms appear less formidable to account for. There is no placebo effect with cancer, and there is no observable influence of lifestyle. The main determinant of treatment resistance is the biology of disease, which is often unknown.

In addition, subset analysis—a “trial within a trial”—can evaluate whether attending physician or patient selection bias actually has any effect on treatment results. We invite suggestions or comments as to other influences that may need to be considered in interpreting the results of a patient-selected controlled trial.

Unmet Needs and Imperfect Options

Large single-arm studies evaluating approved drugs have helped to guide clinical practice, as have the outcomes of small randomized trials. Regulatory agencies have accepted single-arm studies as the basis for accelerated approval of agents that address an unmet need, and in most cases these approvals have been validated by further controlled study. Here the unmet need is to make the clinical trial system more efficient, inclusive, and sensitive to patient concerns, again, in select cases, where a randomized controlled trial is not feasible or ethical.

We should not let the perfect be “the enemy of the good”—that is, insisting on perfection can result in no improvement at all. The degree of study bias in a patient-selected controlled trial will depend in part on the distribution to the study arms, which can be mitigated by the methods described previously or by novel ideas not yet considered.

The natural history of the disease in the eligible population is another factor that can influence the need for a randomized control. For some cancers, the outcomes with the control are predictable, such as in the relapsed setting for patients with unfavorable prognostic markers. They will fail to respond and ultimately die from the toxicities of the ineffective treatment or of disease progression.

Our understanding is that there is no rule requiring randomization as the sole method of control in a pivotal clinical trial. Indeed, in a study of patients with disease that is refractory to standard treatments, each eligible patient is the control. What is critical is that the findings from the study are judged persuasive by expert consensus, and that the outcomes reasonably guide clinical practice or approval.

So, for example, the patient-selected controlled trial might be a used as an alternative to:

  • large phase II single-arm studies
  • a single-arm study for accelerated approval
  • the randomized comparison of two interventions with very different risks and approaches, such as when comparing biomarker-based targeted drugs to regular treatment, or when comparing oral drugs with low toxicity to high-dose myeloablative chemotherapy with stem cell transplantation
  • the randomized comparison of two interventions that can each be used off study (comparative effectiveness research)
  • a randomized controlled trial where equipoise is deficient (or controversial) for an otherwise valid study question, as an alternative to terminating a randomized controlled trial due to poor enrollment.

In Conclusion

We submit that the patient-selected controlled trial is clearly superior to any randomized clinical trial that is never started because it’s judged to be unfeasible, or to any randomized controlled trial that is terminated because of poor enrollment. What good is a statistically perfect well-designed randomized controlled trial if no one signs up? We hope and expect that the patient-selected controlled trial provides another way to do good science while practicing good medicine. ■

Disclosure: Dr. Omel and Mr. Schwartz reported no potential conflicts of interest.

References

1. Curt GA, Chabner BA: One in five cancer clinical trials is published: A terrible symptom—what’s the diagnosis? Oncologist 13:923-924, 2008.

2. Schwartz K: Interest, attitudes, and participation in clinical trials among lymphoma patients with online access. J Clin Oncol 27(15S):Abstract e19514, 2009.

3. Dahabreh IJ, Sheldrick RC, Paulus JK, et al: Do observational studies using propensity score methods agree with randomized trials? A systematic comparison of studies on acute coronary syndromes. Eur Heart J 33:1893-1901, 2012.

4. Pattanayak CW, Rubin DB, Zell ER: Propensity score methods for creating covariate balance in observational studies. Rev Esp Cardiol 64:897-903, 2011.

 

The authors invite comment and guidance, particularly from statisticians in this field. Write to us at karls@lymphomation.org or care of editor@ ASCOPost.com.


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