EVIDENCE meta-analysis: evaluating minimal residual disease as an intermediate clinical endpoint for multiple myeloma.

Estimating progression-free survival (PFS) and overall survival (OS) superiority during clinical trials of multiple myeloma (MM) has become increasingly challenging as novel therapeutics have improved patient outcomes. Thus, it is imperative to identify earlier endpoint surrogates that are predictive of long-term clinical benefit to expedite development of more effective therapies. Minimal residual disease (MRD)-negativity is a common intermediate endpoint that has shown prognostic value for clinical benefit in trials of patients with multiple myeloma (MM). This meta-analysis was based on the FDA guidance for considerations for a meta-analysis of MRD as a clinical endpoint and evaluates MRD-negativity as an early endpoint reasonably likely to predict long-term clinical benefit. Eligible studies were phase 2 or 3 randomized controlled clinical trials measuring MRD negativity as an endpoint in patients with MM, with follow-up of ≥6 months following an a priori defined time point of 12±3 months post-randomization. Eight newly diagnosed MM-(NDMM)-studies evaluating 4,907 patients were included. Trial-level associations between MRD-negativity and PFS were R2WLSiv (95% CI) 0.67 (0.43-0.91) and R2copula 0.84 (0.64->0.99) at the 12-month timepoint. The individual-level association between 12-month MRD negativity and PFS resulted in a global odds ratio of 4.02 (95% CI: 2.57-5.46). For relapse/refractory MM-(RRMM), there were four studies included, and the individual-level association between 12-month MRD negativity and PFS resulted in a global odds ratio of 7.67 (4.24-11.10). A clinical trial demonstrating a treatment effect on MRD is reasonably likely to eventually demonstrate a treatment effect on PFS, suggesting that MRD may be an early clinical endpoint reasonably likely to predict clinical benefit in MM, that may be used to support accelerated approval and thereby expedite the availability of new drugs to patients with MM.

Group sequential monitoring based on the maximum of weighted log-rank statistics with the Fleming-Harrington class of weights in oncology clinical trials.

Clinical trials in oncology often involve the statistical analysis of time-to-event data such as progression-free survival or overall survival to determine the benefit of a treatment or therapy. The log-rank test is commonly used to compare time-to-event data from two groups. The log-rank test is especially powerful when the two groups have proportional hazards. However, survival curves encountered in oncology studies that differ from one another do not always differ by having proportional hazards; in such instances, the log-rank test loses power, and the survival curves are said to have "non-proportional hazards". This non-proportional hazards situation occurs for immunotherapies in oncology; immunotherapies often have a delayed treatment effect when compared to chemotherapy or radiation therapy. To correctly identify and deliver efficacious treatments to patients, it is important in oncology studies to have available a statistical test that can detect the difference in survival curves even in a non-proportional hazards situation such as one caused by delayed treatment effect. An attempt to address this need was the "max-combo" test, which was originally described only for a single analysis timepoint; this article generalizes that test to preserve type I error when there are one or more interim analyses, enabling efficacious treatments to be identified and made available to patients more rapidly.

A comparison of two experimental design approaches in applying conjoint analysis in patient-centered outcomes research: a randomized trial.

BACKGROUND: While the application of conjoint analysis and discrete-choice experiments in health are now widely accepted, a healthy debate exists around competing approaches to experimental design. There remains, however, a paucity of experimental evidence comparing competing design approaches and their impact on the application of these methods in patient-centered outcomes research. OBJECTIVES: Our objectives were to directly compare the choice-model parameters and predictions of an orthogonal and a D-efficient experimental design using a randomized trial (i.e., an experiment on experiments) within an application of conjoint analysis studying patient-centered outcomes among outpatients diagnosed with schizophrenia in Germany. METHODS: Outpatients diagnosed with schizophrenia were surveyed and randomized to receive choice tasks developed using either an orthogonal or a D-efficient experimental design. The choice tasks elicited judgments from the respondents as to which of two patient profiles (varying across seven outcomes and process attributes) was preferable from their own perspective. The results from the two survey designs were analyzed using the multinomial logit model, and the resulting parameter estimates and their robust standard errors were compared across the two arms of the study (i.e., the orthogonal and D-efficient designs). The predictive performances of the two resulting models were also compared by computing their percentage of survey responses classified correctly, and the potential for variation in scale between the two designs of the experiments was tested statistically and explored graphically. RESULTS: The results of the two models were statistically identical. No difference was found using an overall chi-squared test of equality for the seven parameters (p = 0.69) or via uncorrected pairwise comparisons of the parameter estimates (p-values ranged from 0.30 to 0.98). The D-efficient design resulted in directionally smaller standard errors for six of the seven parameters, of which only two were statistically significant, and no differences were found in the observed D-efficiencies of their standard errors (p = 0.62). The D-efficient design resulted in poorer predictive performance, but this was not significant (p = 0.73); there was some evidence that the parameters of the D-efficient design were biased marginally towards the null. While no statistical difference in scale was detected between the two designs (p = 0.74), the D-efficient design had a higher relative scale (1.06). This could be observed when the parameters were explored graphically, as the D-efficient parameters were lower. CONCLUSIONS: Our results indicate that orthogonal and D-efficient experimental designs have produced results that are statistically equivalent. This said, we have identified several qualitative findings that speak to the potential differences in these results that may have been statistically identified in a larger sample. While more comparative studies focused on the statistical efficiency of competing design strategies are needed, a more pressing research problem is to document the impact the experimental design has on respondent efficiency.