Designing a non-inferiority study in kidney transplantation: a case study

An open-label, randomized trial indicates that everolimus with tacrolimus or cyclosporine is comparable to standard immunosuppression in de novo kidney transplant patients

This is a randomized trial (ATHENA study) in de novo kidney transplant patients to compare everolimus versus mycophenolic acid (MPA) with similar tacrolimus exposure in both groups, or everolimus with concomitant tacrolimus or cyclosporine (CsA), in an unselected population. In this 12-month, multicenter, open-label study, de novo kidney transplant recipients were randomized to everolimus with tacrolimus (EVR/TAC), everolimus with CsA (EVR/CsA) or MPA with tacrolimus (MPA/TAC), with similar tacrolimus exposure in both groups. Non-inferiority of the primary end point (estimated glomerular filtration rate [eGFR] at month 12), assessed in the per-protocol population of 338 patients, was not shown for EVR/TAC or EVR/CsA versus MPA/TAC. In 123 patients with TAC levels within the protocol-specified range, eGFR outcomes were comparable between groups. The mean increase in eGFR during months 1 to 12 post-transplant, analyzed post hoc, was similar with EVR/TAC or EVR/CsA versus MPA/TAC. The incidence of treatment failure (biopsy proven acute rejection, graft loss or death) was not significant for EVR/TAC but significant for EVR/CsA versus MPA/TAC. Most biopsy-proven acute rejection events in this study were graded mild (BANFF IA). There were no differences in proteinuria between groups. Cytomegalovirus and BK virus infection were significantly more frequent with MPA/TAC. Thus, everolimus with TAC or CsA showed comparable efficacy to MPA/TAC in de novo kidney transplant patients. Non-inferiority of renal function, when pre-specified, was not shown, but the mean increase in eGFR from month 1 to 12 was comparable to MPA/TAC.

Efficacy and Safety of Everolimus Plus Low-Dose Tacrolimus Versus Mycophenolate Mofetil Plus Standard-Dose Tacrolimus in De Novo Renal Transplant Recipients: 12-Month Data

In this 12-month, multicenter, randomized, open-label, noninferiority study, de novo renal transplant recipients (RTxRs) were randomized (1:1) to receive everolimus plus low-dose tacrolimus (EVR+LTac) or mycophenolate mofetil plus standard-dose Tac (MMF+STac) with induction therapy (basiliximab or rabbit anti-thymocyte globulin). Noninferiority of composite efficacy failure rate (treated biopsy-proven acute rejection [tBPAR]/graft loss/death/loss to follow-up) in EVR+LTac versus MMF+STac was missed by 1.4%, considering the noninferiority margin of 10% (24.6% vs. 20.4%; 4.2% [-3.0, 11.4]). Incidence of tBPAR (19.1% vs. 11.2%; p < 0.05) was significantly higher, while graft loss (1.3% vs. 3.9%; p < 0.05) and composite of graft loss/death/lost to follow-up (6.1% vs. 10.5%, p = 0.05) were significantly lower in EVR+LTac versus MMF+STac groups, respectively. Mean estimated glomerular filtration rate was similar between EVR+LTac and MMF+STac groups (63.1 [22.0] vs. 63.1 [19.5] mL/min/1.73 m² ) and safety was comparable. In conclusion, EVR+LTac missed noninferiority versus MMF+STac based on the 10% noninferiority margin. Further studies evaluating optimal immunosuppression for improved efficacy will guide appropriate dosing and target levels of EVR and LTac in RTxRs.

Bayesian methods for the design and analysis of noninferiority trials

The gold standard for evaluating treatment efficacy of a medical product is a placebo-controlled trial. However, when the use of placebo is considered to be unethical or impractical, a viable alternative for evaluating treatment efficacy is through a noninferiority (NI) study where a test treatment is compared to an active control treatment. The minimal objective of such a study is to determine whether the test treatment is superior to placebo. An assumption is made that if the active control treatment remains efficacious, as was observed when it was compared against placebo, then a test treatment that has comparable efficacy with the active control, within a certain range, must also be superior to placebo. Because of this assumption, the design, implementation, and analysis of NI trials present challenges for sponsors and regulators. In designing and analyzing NI trials, substantial historical data are often required on the active control treatment and placebo. Bayesian approaches provide a natural framework for synthesizing the historical data in the form of prior distributions that can effectively be used in design and analysis of a NI clinical trial. Despite a flurry of recent research activities in the area of Bayesian approaches in medical product development, there are still substantial gaps in recognition and acceptance of Bayesian approaches in NI trial design and analysis. The Bayesian Scientific Working Group of the Drug Information Association provides a coordinated effort to target the education and implementation issues on Bayesian approaches for NI trials. In this article, we provide a review of both frequentist and Bayesian approaches in NI trials, and elaborate on the implementation for two common Bayesian methods including hierarchical prior method and meta-analytic-predictive approach. Simulations are conducted to investigate the properties of the Bayesian methods, and some real clinical trial examples are presented for illustration.

Robust meta-analytic-predictive priors in clinical trials with historical control information

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

The network meta-analytic-predictive approach to non-inferiority trials

In non-inferiority clinical trials, a test treatment is compared to an active-control rather than to placebo. Such designs are considered when placebo is unethical or not feasible. The critical question is whether the test treatment would have been superior to placebo, had placebo been used in the non-inferiority trial. This question can only be addressed indirectly, based on information from relevant historical trials with data on active-control and placebo. The network meta-analytic-predictive approach to non-inferiority trials is based on a network meta-analysis of the data from the historical trials and the non-inferiority trial, and the prediction of the putative test vs. placebo effect in the non-inferiority trial. The approach extends previous work by incorporating between-trial variability for all relevant parameters and focusing on the parameters in the non-inferiority trial rather than on population means. Two prominent examples with binary outcomes are used to illustrate the approach.