Design of Clinical Trials of Targeted Therapy

PL04-02 The development of the randomized clinical trial (RCT) is arguably the most important contribution to scientific medicine of the 20 th century. Beginning in the late 1940s, clinical trials rapidly became the gold standard method throughout the world for assessing the relative effects of therapies for diseases and medical conditions. By the end of the century and continuing into the 21 st century, thousands of trials have been and are being conducted world-wide in virtually all disease areas. Regulatory agencies now require properly designed and executed RCTs as the primary source of information on the safety and efficacy of therapies proposed for marketing approval in specified indications. The design of RCTs utilize sound scientific principles for the reduction of bias and for increasing the precision and controlling error rates in the testing of pre-specified statistical hypotheses. Powerful tools for controlling bias, including randomization of treatment assignment, blinded treatment assignments, blinded evaluation of outcome, and intent-to-treat analyses have become so widely accepted that they now seem almost self-evident requirements, but it is important to remember that these are relatively new developments in scientific medicine. The hallmark of modern RCTs, in contrast to studies prior to the time of wide-spread application of such trials, is that sufficient numbers of similar patients (those meeting pre-specified eligibility criteria) need to be treated alike following a written protocol covering all aspects of the treatment and the details of the trial. Assuming that the trial is well-designed and executed, the number of patients so treated is the primary determinant of the precision of the results and the reliability of the conclusions from the trial. This concept of moving from individualized treatment regimens given idiosyncratically to each patient based primarily on the prior experience of the treating physician to treating large numbers of patients in a similar fashion is central to the success of RCTs. Although the assumptions underlying the design of a RCT do not require that the patients are completely homogeneous, the primary conclusions are usually stated on a population basis. For example, the conclusion might be that the overall survival distribution of patients entered on study was significantly better for patients randomized to receive treatment A than for those randomized to receive treatment B. The difference is often characterized, sometimes misleadingly, by some simple summary of the overall distribution (e.g., the estimated median OS on treatment A was 18 months compared to 13 months on treatment B) although the commonly used test statistics compare the overall distributions (e.g., a log rank test of the OS distributions). Secondary exploratory analyses might be conducted to see if the treatment effect differs by certain patient characteristics, but the overall comparison is properly understood to represent the primary result of the trial. Recently, the efforts to develop therapies targeted to some specific biologic target has raised questions about the proper way to evaluate such therapies in RCTs. The oft-stated ultimate goal of such targeted therapy is individualized therapy based on a patient’s demographic (eg, age, race, gender, etc) and biologic characteristics (eg, biologic markers, genomic data, specific biologic pathway targets, etc). That is, the specific therapy given to each patient would be tailored to the particular patient being treated, designed to achieve the best possible outcome for that patient. Although this concept has strong intuitive appeal, the irony is that this brings us full circle back to the pre-clinical trials era of individualized therapy. A major difference, of course, is that in the new setting the individualization is based on solid scientific evidence of how to tailor the therapy. In the old setting, the basis for the individualization was at best not clear. Nevertheless, there are challenges in the design of clinical trials to examine the effectiveness of individualized, or at least more tailored, therapies. In this paper, some issues around the design of clinical trials of targeted therapies will be explored. Suppose we wish to compare, in a RCT, a regimen (T) involving some targeted therapy with a standard regimen (S) not involving the targeted therapy. Patients with the target in question, assumed in the simplest case to be a binary characteristic (i.e., either the target is present or not), are identified via a standardized assay applied to all patients prior to entering the trial. We are interested in the relative efficiency of different designs in this setting, where efficiency is measured in terms of the numbers of patients required on the trial, the duration of the trial, and the cost of the trial, including the costs of eligibility screening. The two most obvious choices of design are the traditional design in which all patients are entered regardless of the assay results - or perhaps the design in which the assay is not conducted at all (thus eliminating the assay costs), and a targeted design in which only patients with a positive assay are entered in the trial. In order to determine the relative efficiency of competing designs, certain assumptions or prior results are required. These assumptions are critical in determining the relative efficiency of the designs. Key assumptions include the accuracy of the assay, in particular the rates of false positives and false negatives; the effect of the targeted therapy both in patients with the target and in patients without the target; the prevalence of patients with the target; and, particularly for comparison with the design omitting the assay, the cost of the assay. In the simplest setting, in which the assay is perfect, yielding no false positives and no false negatives, and in which the favorable treatment effect is restricted to patients with the target, the relative efficiency of the targeted design in terms of the numbers of patients can be quite large. However, in cases where these assumptions are not met, the efficiency gains may be more modest; indeed, the targeted design can be less efficient in certain situations. If the assumptions required to achieve a major increase in efficiency are of uncertain validity, one alternative is to design trials which test for overall treatment effects as in the traditional designs while also including valid tests of effects limited to patients with the target. These trials require careful pre-specification of objectives and procedures which control the error rates, but provide a practical alternative design in this situation. Some possibilities along these lines will be presented.