An adaptive clinical trials procedure for a sensitive subgroup examined in the multiple sclerosis context

Constructing treatment selection rules based on an estimated treatment effect function: different approaches to take stochastic uncertainty into account have a substantial effect on performance

BACKGROUND

Today we are often interested in the predictive value of a continuous marker with respect to the expected difference in outcome between a new treatment and a standard treatment. We can investigate this in a randomized control trial, allowing us to assess interactions between treatment and marker and to construct a treatment selection rule. A first step is often to estimate the treatment effect as a function of the marker value. A variety of approaches have been suggested for the second step to define explicitly the rule to select the treatment, varying in the way to take uncertainty into account. Little is known about the merits of the different approaches.

METHODS

Four construction principles for the second step are compared. They are based on the root of the estimated function, on confidence intervals for the root, or on pointwise or simultaneous confidence bands. All of them have been used implicitly or explicitly in the literature. As performance characteristics we consider the probability to select at least some patients, the probability to classify patients with and without a benefit correctly, and the gain in expected outcome at the population level. These characteristics are investigated in a simulation study.

RESULTS

As to be expected confidence interval/band based approaches reduce the risk to select patients who do not benefit from the new treatment, but they tend to overlook patients who can benefit. Simply using positivity of the estimated treatment effect function for selection implies often a larger gain in expected outcome.

CONCLUSIONS

The use of 95% confidence intervals/bands in constructing treatment selection rules is a rather conservative approach. There is a need for better construction principles for treatment selection rules aiming to maximize the gain in expected outcome at the population level. Choosing a confidence level of 80% may be a first step in this direction.

Baseline EDSS proportions in MS clinical trials affect the overall outcome and power: A cautionary note

BACKGROUND

In randomized clinical trials, when treatments do not work equally effectively across stratifications of participants, observed event rates may differ from those hypothesized leading to deviations in estimated power.

OBJECTIVES

To investigate the effect of distributions of baseline Expanded Disability Status Scale (EDSS) proportions in relapsing-remitting multiple sclerosis (RRMS) on the trial outcome, confirmed disability progression rate (CDPR), and power.

METHODS

We reported CDPRs in the CombiRx trial by baseline EDSS and by groups (1st (0, 1), 2nd (1.5, 2), 3rd (2.5, 3), and 4th (⩾3.5)) and investigated the effect of different combinations of baseline EDSS proportions on the trial outcome and power.

RESULTS

There were 244 (25.4%) participants in the 1st group, 368 (38.4%) in the 2nd group, 223 (23.3%) in the 3rd group, and 124 (12.9%) in the 4th group with CDPRs of 40.1%, 13.9%, 11.2%, and 16.9%, respectively. Both CDPR and power increased when the proportion of the 1st group increased in hypothetical trials with equal sample sizes in each arm, and a 10% increase in the 1st group led to a 5% increase in power.

CONCLUSION

Various baseline EDSS proportions yielded different CDPRs and power, suggesting caution in interpretation of treatment effects across trials that enrolled participants with different proportions of baseline EDSS.

Clinical trial designs incorporating predictive biomarkers

Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.