GLIMMPSE is a free, web-based software tool that calculates power and sample size for the general linear multivariate model with Gaussian errors (http://glimmpse.SampleSizeShop.org/). GLIMMPSE provides a user-friendly interface for the computation of power and sample size. We consider models with fixed predictors, and models with fixed predictors and a single Gaussian covariate. Validation experiments demonstrate that GLIMMPSE matches the accuracy of previously published results, and performs well against simulations. We provide several online tutorials based on research in head and neck cancer. The tutorials demonstrate the use of GLIMMPSE to calculate power and sample size.
We used theoretical and simulation-based approaches to study Type I error rates for one-stage and two-stage analytic methods for cluster-randomized designs. The one-stage approach uses the observed data as outcomes, and accounts for within cluster correlation using a general linear mixed model. The two-stage model uses the cluster specific means as the outcomes in a general linear univariate model. We demonstrate analytically that both one-stage and two-stage models achieve exact Type I error rates when cluster sizes are equal. With unbalanced data, an exact size α test does not exist and Type I error inflation may occur. Via simulation, we compare the Type I error rates for four one-stage and six two-stage hypothesis testing approaches for unbalanced data. With unbalanced data, the two-stage model, weighted by the inverse of the estimated theoretical variance of the cluster means, and with variance constrained to be positive, provided the best Type I error control for studies having at least 6 clusters per arm. The one-stage model with Kenward-Roger degrees of freedom and unconstrained variance performed well for studies having at least 14 clusters per arm. The popular analytic method of using a one-stage model with denominator degrees of freedom appropriate for balanced data performed poorly for small sample sizes and low intracluster correlation. Since small sample sizes and low intracluster correlation are common features of cluster-randomized trials, the Kenward-Roger method is the preferred one-stage approach.
Purpose
Metabolic response to treatment measured by FDG PET has prognostic implications in many cancers. This study investigated the association between survival and early changes on FDG PET/CT for patients with BRAF-mutant melanoma receiving combined BRAF and MEK inhibition therapy.
Material/Methods
24 patients with advanced BRAF-mutant melanoma were included. Patients were treated with a BRAF inhibitor (Vemurafenib or Dabrafenib) and a MEK inhibitor (Cobimetinib or Trametinib) and were imaged at baseline, and shortly thereafter with FDG PET/CT. Each scan yielded two values of SUVmax: one for the most metabolically active focus and one for the least responsive focus. Short-term treatment response was assessed by evaluating the target lesions using EROTC criteria. A Cox proportional hazards model was used to examine associations between overall survival (OS) and progression-free survival (PFS) and changes in SUVmax.
Results
Mean time to follow-up FDG PET/CT was 26 days. At follow-up, 2 patients had a complete response. For the most metabolically active focus, 22 patients had a partial response. For the least responsive focus, 18 patients had a partial response, 2 had stable disease, and 2 had progressive disease.
16 patients were living at the end of the study. For the most metabolically active tumor, no association was observed between changes in SUVmax and OS (p=0.73) or PFS (p=0.17). For the least responsive tumor, change in SUVmax was associated with PFS (HR=1.34, 95% CI: 1.06 to 1.71, p=0.01) but not OS (p=0.52). ECOG score was associated with OS (HR=11.81, 95% CI: 1.42 to 97.60, p=0.02) and PFS (HR=24.72, 95% CI: 3.23 to 189.42, p=0.002).
Conclusion
Change in SUVmax for the least responsive tumor and baseline functional performance may be useful prognostic indicators for progression-free survival in patients with BRAF-mutant melanoma.
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