Adaptive approaches have gained widespread acceptance at all stages of clinical development. Mediana has developed the following types of adaptive designs in early-stage and late-stage clinical trials:
Dose-escalation designs in Phase I oncology trials: Adaptive designs based on the modified toxicity probability interval (mTPI) method and Bayesian logistic regression models (BLRM) in trials with a single agent and combination of two agents.
Adaptive designs with sample size re-estimation in confirmatory Phase III trials: Adaptive designs that support an option to update the total number of patients or target number of events based on the interim analysis findings.
Adaptive designs with arm or population selection in confirmatory Phase III trials: Adaptive designs that enable the trial’s sponsor to select the most promising trial arms or patient populations based on the interim analysis findings.
For more information on adaptive designs in early-stage and late-stage clinical trials, see the list of case studies based on recent consulting and software development projects.
Most Phase III trials are designed to address multiple clinical objectives. These objectives are often formulated in terms of evaluating an experimental treatment according to multiple endpoints, to compare multiple dose levels of a treatment to a common control, etc.
Mediana has set up powerful methods for addressing complex multiplicity issues arising in late-stage clinical trials:
Phase III trials with a single source of multiplicity: Trials with multiple endpoints.
Phase III trials with two sources of multiplicity: Trials with multiple endpoints evaluated at several doses of a novel treatment or multiple endpoints evaluated in several patient populations.
Phase III trials with three sources of multiplicity: Trials with multiple endpoints evaluated at several doses of a novel treatment in group-sequential or adaptive trials with multiple decision points (interim and final analyses).
For more information on applications of advanced multiplicity adjustment methods, see the list of case studies based on recent consulting and software development projects.
Subgroup analysis deals with evaluation of treatment effects in clinical trials with respect to the primary and key secondary endpoints in selected patient subgroups. The subgroups are defined based on baseline characteristics, including demographic, clinical, genetic and other variables.
Mediana has successfully applied advanced methods of confirmatory subgroup analysis and exploratory subgroup analysis in late-stage clinical trials:
Confirmatory subgroup analysis is performed in confirmatory Phase III trials with one or more prospectively defined subpopulations of patients that are more likely to benefit from the treatment than patients in the overall population. Analysis of the subgroups may result in a modified regulatory claim or additional regulatory claims.
Exploratory subgroup analysis is commonly utilized in Phase III trials and relies on a post-hoc subgroup search. Exploratory subgroup analysis can be performed in Phase III trials with a positive efficacy signal in the overall patient population to evaluate the benefit-risk profile of multiple subgroups. Based on the results of these evaluations, a subgroup may be excluded from a trial if the treatment mainly benefits patients in the complementary subgroup. Alternatively, if safety issues are detected in the overall population, a subgroup with an acceptable safety profile can be selected. Similar analyses can be also performed in failed Phase III trials to help identify one or more subgroups with a beneficial effect.
For more information on applications of confirmatory and exploratory subgroup analysis methods in late-stage clinical trials, see the list of case studies based on recent consulting and software development projects.