Bayesian sample size calculations for comparing two strategies in SMART studies.

In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have grown in popularity as they offer a more individualized approach. As a result, sequential multiple assignment randomized trials (SMARTs) have gained attention as the most suitable clinical trial design to formalize the study of these strategies. While the number of SMARTs has increased in recent years, sample size and design considerations have generally been carried out in frequentist settings. However, standard frequentist formulae require assumptions on interim response rates and variance components. Misspecifying these can lead to incorrect sample size calculations and correspondingly inadequate levels of power. The Bayesian framework offers a straightforward path to alleviate some of these concerns. In this paper, we provide calculations in a Bayesian setting to allow more realistic and robust estimates that account for uncertainty in inputs through the 'two priors' approach. Additionally, compared to the standard frequentist formulae, this methodology allows us to rely on fewer assumptions, integrate pre-trial knowledge, and switch the focus from the standardized effect size to the MDD. The proposed methodology is evaluated in a thorough simulation study and is implemented to estimate the sample size for a full-scale SMART of an internet-based adaptive stress management intervention on cardiovascular disease patients using data from its pilot study conducted in two Canadian provinces.

A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies.

Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson process. This approach is based on the modeling of enrollments as a Poisson process where the recruitment rates are assumed to be constant over time and to follow a common Gamma prior distribution. However, the constant-rate assumption is a restrictive limitation that is rarely appropriate for applications in real studies. In this paper, we illustrate a flexible generalization of this methodology which allows the enrollment rates to vary over time by modeling them through B-splines. We show the suitability of this approach for a wide range of recruitment behaviors in a simulation study and by estimating the recruitment progression of the Canadian Co-infection Cohort.

The time-dependent Poisson-gamma model in practice: Recruitment forecasting in HIV trials.

Despite a growing body of literature in the area of recruitment modeling for multicenter studies, in practice, statistical models to predict enrollments are rarely used and when they are, they often rely on unrealistic assumptions. The time-dependent Poisson-Gamma model (tPG) is a recently developed flexible methodology which allows analysts to predict recruitments in an ongoing multicenter trial, and its performance has been validated on data from a cohort study. In this article, we illustrate and further validate the tPG model on recruitment data from randomized controlled trials. Additionally, in the appendix, we provide a practical and easy to follow guide to its implementation via the tPG R package. To validate the model, we show the predictive performance of the proposed methodology in forecasting the recruitment process of two HIV vaccine trials conducted by the HIV Vaccine Trials Network in multiple Sub-Saharan countries.

Kidney Outcomes and Preservation of Kidney Function With Obinutuzumab in Patients With Lupus Nephritis: A Post Hoc Analysis of the NOBILITY Trial.

OBJECTIVE: To determine whether adding obinutuzumab to standard-of-care lupus nephritis (LN) therapy could improve the likelihood of long-term preservation of kidney function and do so with less glucocorticoids. METHODS: Post hoc analyses of the phase II NOBILITY trial were performed. Time to unfavorable kidney outcome (a composite of treatment failure, doubling of serum creatinine, or death), LN flare, first 30% and 40% declines in estimated glomerular filtration rate (eGFR) from baseline, and chronic eGFR slope during the trial were compared between patients with active LN who were randomized to take obinutuzumab (n = 63) or placebo (n = 62) in combination with mycophenolate mofetil and glucocorticoids. The number of patients who achieved complete renal response (CRR) on 7.5 mg or less per day of prednisone was also determined. RESULTS: Obinutuzumab reduced the risk of developing the composite kidney outcome by 60%, LN flare by 57%, and first eGFR decline of 30% or 40% by 80% and 91%, respectively. Patients receiving obinutuzumab had a significantly slower decline in eGFR than patients receiving placebo, with an annualized eGFR slope advantage of 4.1 ml/min/1.73 m2 /year (95% confidence interval 0.14-8.08). Overall, 38% of patients receiving obinutuzumab compared with 16% of patients receiving placebo achieved CRR at week 76 while receiving 7.5 mg or less per day of prednisone (P < 0.01); at week 104, the difference did not achieve significance (38% vs 22%; P = 0.06). CONCLUSION: Post hoc analyses of NOBILITY demonstrated that compared with standard-of-care therapy, obinutuzumab treatment resulted in superior preservation of kidney function and prevention of LN flares. More patients achieved CRR at week 76 with less glucocorticoid use in the obinutuzumab group.