Phase II trial of mapatumumab, a fully human agonist monoclonal antibody to tumor necrosis factor-related apoptosis-inducing ligand receptor 1 (TRAIL-R1), in combination with paclitaxel and carboplatin in patients with advanced non-small-cell lung cancer.

BACKGROUND: This phase II study examined the efficacy of mapatumumab in combination with paclitaxel and carboplatin in patients with non-small-cell lung cancer (NSCLC). PATIENTS AND METHODS: Patients with stage IIIB or stage IV advanced primary NSCLC were randomly assigned (1:1:1) to receive up to 6 courses of standard-dose paclitaxel and carboplatin or a combination of paclitaxel, carboplatin, and mapatumumab (10 mg/kg or 30 mg/kg). Primary efficacy end points were overall response rate and median progression-free survival (PFS). Secondary efficacy end points included disease control rate, overall survival (OS), time to response, and duration of response. Exploratory studies included evaluation of historical biopsy materials for TRAIL-R1 expression by immunohistochemical analysis and serum levels of M30, a marker of apoptosis, before and after the first 2 doses of mapatumumab. Safety parameters, including adverse events (AEs), laboratory tests, and immunogenicity, were assessed. RESULTS: The majority of patients had stage IV disease (79%) and an Eastern Cooperative Oncology Group (ECOG) performance status of 0 (58%); baseline characteristics were similar across treatment arms. No improvements in response or disease control rates, PFS, or OS were gained from the addition of mapatumumab. Adverse events in the mapatumumab arms were generally consistent with toxicities seen in the carboplatin and paclitaxel control arm. Levels of M30 were highly variable, and consistent patterns were not seen across treatment arms. CONCLUSION: This study showed no clinical benefit from adding mapatumumab to carboplatin and paclitaxel in unselected patients with NSCLC. The combination was generally well tolerated. The possibility of subgroups sensitive to mapatumumab is discussed.

Power calculation for overall hypothesis testing with high-dimensional commensurate outcomes.

The complexity of system biology means that any metabolic, genetic, or proteomic pathway typically includes so many components (e.g., molecules) that statistical methods specialized for overall testing of high-dimensional and commensurate outcomes are required. While many overall tests have been proposed, very few have power and sample size methods. We develop accurate power and sample size methods and software to facilitate study planning for high-dimensional pathway analysis. With an account of any complex correlation structure between high-dimensional outcomes, the new methods allow power calculation even when the sample size is less than the number of variables. We derive the exact (finite-sample) and approximate non-null distributions of the 'univariate' approach to repeated measures test statistic, as well as power-equivalent scenarios useful to generalize our numerical evaluations. Extensive simulations of group comparisons support the accuracy of the approximations even when the ratio of number of variables to sample size is large. We derive a minimum set of constants and parameters sufficient and practical for power calculation. Using the new methods and specifying the minimum set to determine power for a study of metabolic consequences of vitamin B6 deficiency helps illustrate the practical value of the new results. Free software implementing the power and sample size methods applies to a wide range of designs, including one group pre-intervention and post-intervention comparisons, multiple parallel group comparisons with one-way or factorial designs, and the adjustment and evaluation of covariate effects.

Confidence regions for repeated measures ANOVA power curves based on estimated covariance.

BACKGROUND: Using covariance or mean estimates from previous data introduces randomness into each power value in a power curve. Creating confidence intervals about the power estimates improves study planning by allowing scientists to account for the uncertainty in the power estimates. Driving examples arise in many imaging applications. METHODS: We use both analytical and Monte Carlo simulation methods. Our analytical derivations apply to power for tests with the univariate approach to repeated measures (UNIREP). Approximate confidence intervals and regions for power based on an estimated covariance matrix and fixed means are described. Extensive simulations are used to examine the properties of the approximations. RESULTS: Closed-form expressions are given for approximate power and confidence intervals and regions. Monte Carlo simulations support the accuracy of the approximations for practical ranges of sample size, rank of the design matrix, error degrees of freedom, and the amount of deviation from sphericity. The new methods provide accurate coverage probabilities for all four UNIREP tests, even for small sample sizes. Accuracy is higher for higher power values than for lower power values, making the methods especially useful in practical research conditions. The new techniques allow the plotting of power confidence regions around an estimated power curve, an approach that has been well received by researchers. Free software makes the new methods readily available. CONCLUSIONS: The new techniques allow a convenient way to account for the uncertainty of using an estimated covariance matrix in choosing a sample size for a repeated measures ANOVA design. Medical imaging and many other types of healthcare research often use repeated measures ANOVA.

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models.

The POWERLIBSAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts.

Analytic, Computational, and Approximate Forms for Ratios of Noncentral and Central Gaussian Quadratic Forms.

Many useful statistics equal the ratio of a possibly noncentral chi-square to a quadratic form in Gaussian variables with all positive weights. Expressing the density and distribution function as positively weighted sums of corresponding F functions has many advantages. The mixture forms have analytic value when embedded within a more complex problem. The mixture forms also have computational value. The expansions work well with quadratic forms having few components and small degrees of freedom. A more general algorithm from earlier literature can take longer or fail to converge in the same setting. Many approximations have been suggested for the problem. a positively weighted noncentral quadratic form can always have two moments matched to a noncentral chi-square. For a single quadratic form, the noncentral form performs neither uniformly more or less accurately than older approximations. The approach also gives a noncentral F approximation for any ratio of a positively weighted noncentral form to a positively weighted central quadratic form. The method provides better accuracy for noncentral ratios than approximations based on a single chi-square. The accuracy suffices for many practical applications, such as power analysis, even with few degrees of freedom. Naturally the approximation proves much faster and simpler to compute than any exact method. Embedding the approximation in analytic expressions provides simple forms which correctly guarantee only positive values have nonzero probabilities, and also automatically reduce to partially or fully exact results when either quadratic form has only one term.