Some Additional Topics

Confirmatory Trials and Meta-Analyses

Because of the planning, infrastructure, cost, and time required, Phase III trials are carefully designed to avoid equivocal findings, and thus provide definitive evidence for or against changes in therapy standards. Nonetheless, questions regarding the treatments under evaluation will invariably remain unanswered, and thus there is an important role for replicated (referred to as confirmatory) trials and the synthesis of similar trials into a coherent body of evidence. This is particularly important for clinical decision making, where evidence from trials must be weighed in relation to an individual patient's utility for various treatment options. For the small to moderate benefits seen for most cancer treatment advances, these confirmatory trials may in fact be necessary to effect change.71,72 Although costly to obtain, such corroborating evidence can contribute greatly to practice changes, as the qualitative value of multiple trials with similar findings cannot be understated. It is also not uncommon that replicated trials fail to obtain similar results, providing an opportunity for closer scrutiny of differences between trials and their potential breadth of applicability.

A more formal quantitative means of combining evidence from trials is by meta-analysis, a widely used analytic tool in many areas of social and medical science. Meta-analysis refers to a process whereby data from independent studies are combined to form a quantitative summary estimate of a given effect. Randomized trials are in fact more suited to meta-analysis than nearly all other research designs, as there is likely to be considerable similarity in disease definitions, study design features, classes of therapeutic agents or procedures, and endpoints, and this information is increasingly well documented in trial reports.73 Meta-analyses were initially carried out by extracting effect estimates from published literature and combining these into a single estimate using statistical techniques, but in medical meta-analysis studies, it is usually considered necessary to obtain patientlevel data from each trial, a laborious process that necessarily includes seeking data from unpublished trials to avoid the "publication bias" toward positive findings. Once these data are acquired and standardized for a common endpoint, the individual trial effect measures (i.e., hazard ratios) may be combined using an appropriate statistical model into a summary effect estimate. Results of each trial are also presented, as well as tests for evidence of significant heterogeneity among trials, in which case a single summary measure may be inappropriate and thus omitted. Often, a quality weight measure based on design features is assigned to each trial, providing a natural opportunity to evaluate trial quality.

A principal advantage of meta-analyses is the ability to evaluate consistency among trial findings and possibly uncover small but clinically meaningful treatment effects that were not statistically significant in any one trial (although the meta-analysis is generally not considered the equivalent of an adequately powered RCT). One disadvantage is that combined trial estimates based on heterogeneous treatment regimens may be able to illustrate only a proof of principle (for example, "multidrug chemotherapy is effective"), which may be of limited use for specific clinical questions, such as which regimen to use. Furthermore, comparisons of different regimens from data aggregated across trials are subject to all attendant limitations and biases of observational studies. Despite these and other limitations and pitfalls,74,75 meta-analyses are a valuable complementary research strategy that has been influential in cancer treatment. An excellent example of the methods and data summaries used in meta-analysis in oncology can be found in the reports of the Early Breast Cancer Trialists' Collaborative Group.76

Bayesian Approach

As in the case of Phase I and II trials, Bayesian statistical methodology is increasingly being applied to various aspects of Phase III trials. In study design, a Bayesian approach to formulating the sample size in terms of the questions (1) "what treatment effect might realistically be realized with a new regimen?" and (2) "what magnitude of effect would prompt the current standard to be supplanted?" provides a useful framework that more closely resembles clinical practice.77,78 In trial monitoring, the use of "skeptical" and "optimistic" prior distributions introduced earlier, considered in conjunction with accumulating trial results, provides a rational means to determine whether current results are sufficiently convincing to justify early stopping.79

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