Comparable Pharmacokinetics, Safety, and Tolerability of Etrolizumab Administered by Prefilled Syringe or Autoinjector in a Randomized Trial in Healthy Volunteers.

INTRODUCTION: Etrolizumab is a novel, dual-action anti-ß7 integrin antibody studied in phase 3 trials in patients with inflammatory bowel disease. An autoinjector (AI) is being developed in parallel to complement the prefilled syringe with needle safety device (PFS-NSD) for subcutaneous (SC) administration in these trials. Here we demonstrate the comparable pharmacokinetics, tolerability, and safety of both devices. METHODS: This randomized, open-label, two-part study in healthy participants evaluated the comparability of etrolizumab exposure between the AI and the PFS-NSD. Part 1 (pilot) involved a small number of participants, and initial results were used to finalize the design of the larger part 2 (pivotal) study. In both parts, participants were randomly assigned to receive a single SC dose of etrolizumab 105 mg by AI or PFS-NSD. Randomization was stratified by body weight. Primary pharmacokinetic outcomes were Cmax, AUClast, and AUC0-inf. RESULTS: One hundred and eighty healthy participants (part 1, n = 30; part 2, n = 150) received a single SC dose of etrolizumab by AI or PFS-NSD. Primary pharmacokinetic results from part 1 supported modification of the part 2 study design. Results from part 2 demonstrated that etrolizumab exposure was equivalent between devices, with geometric mean ratios (GMRs) between AI and PFS-NSD of 102% (90% confidence interval [CI] 94.2-111) for Cmax, 98.0% (90% CI 89.3-107) for AUClast, and 97.6% (90% CI 88.6-107) for AUC0-inf. Median tmax and mean terminal t1/2 were also similar between devices. GMRs and 90% CIs of all primary pharmacokinetic parameters were fully contained within the predefined equivalence limits (80-125%). CONCLUSION: This pharmacokinetic study demonstrated that single SC injections of etrolizumab 105 mg using an AI or a PFS-NSD resulted in equivalent etrolizumab exposure and similar safety and tolerability in healthy participants. Taken together, these results support the use of an AI for etrolizumab administration. TRIAL REGISTRATION: NCT02996019.

Assessing Time-Varying Causal Effect Moderation in Mobile Health.

In mobile health interventions aimed at behavior change and maintenance, treatments are provided in real time to manage current or impending high risk situations or promote healthy behaviors in near real time. Currently there is great scientific interest in developing data analysis approaches to guide the development of mobile interventions. In particular data from mobile health studies might be used to examine effect moderators-individual characteristics, time-varying context or past treatment response that moderate the effect of current treatment on a subsequent response. This paper introduces a formal definition for moderated effects in terms of potential outcomes, a definition that is particularly suited to mobile interventions, where treatment occasions are numerous, individuals are not always available for treatment, and potential moderators might be influenced by past treatment. Methods for estimating moderated effects are developed and compared. The proposed approach is illustrated using BASICS-Mobile, a smartphone-based intervention designed to curb heavy drinking and smoking among college students.

A derived and validated score to predict prolonged mechanical ventilation in patients undergoing cardiac surgery.

OBJECTIVES: Prolonged mechanical ventilation after cardiac surgery imposes a significant burden on the patient in terms of morbidity as well as a financial burden on the hospital. We undertook a retrospective analysis of 2 prospectively collected databases developed in tertiary cardiac care centers to derive and validate a risk index predicting prolonged mechanical ventilation after cardiac surgery. METHODS: We studied a retrospective cohort of 32,045 patients undergoing cardiac surgery in 2 hospitals in Toronto, Canada. The development cohort consisted of 21,661 patients at Toronto General Hospital. Data Sunnybrook Health Sciences Centre, Toronto, Canada, with 10,384 patients, served as an institutional validation cohort. We operationally characterized prolonged mechanical ventilation as the duration from surgery completion to extubation exceeding 48 hours. RESULTS: Prolonged postoperative mechanical ventilation rates in the development and validation cohort were 6% and 7%, respectively. Multivariable regression in the development cohort showed that the following factors were strong predictors of prolonged mechanical ventilation after cardiac surgery: previous cardiac surgery, lower left ventricular ejection fraction, shock, surgery involving repair of congenital heart disease, and cardiopulmonary bypass time. The intraoperative multivariable model retained good discrimination in the validation cohort, achieving a c statistic of 0.787. CONCLUSIONS: Prolonged mechanical ventilation after cardiac surgery can be accurately predicted by readily available pre- and intraoperative information.

Sieve estimation in a Markov illness-death process under dual censoring.

Semiparametric methods are well established for the analysis of a progressive Markov illness-death process observed up to a noninformative right censoring time. However, often the intermediate and terminal events are censored in different ways, leading to a dual censoring scheme. In such settings, unbiased estimation of the cumulative transition intensity functions cannot be achieved without some degree of smoothing. To overcome this problem, we develop a sieve maximum likelihood approach for inference on the hazard ratio. A simulation study shows that the sieve estimator offers improved finite-sample performance over common imputation-based alternatives and is robust to some forms of dependent censoring. The proposed method is illustrated using data from cancer trials.

Microrandomized trials: An experimental design for developing just-in-time adaptive interventions.

OBJECTIVE: This article presents an experimental design, the microrandomized trial, developed to support optimization of just-in-time adaptive interventions (JITAIs). JITAIs are mHealth technologies that aim to deliver the right intervention components at the right times and locations to optimally support individuals' health behaviors. Microrandomized trials offer a way to optimize such interventions by enabling modeling of causal effects and time-varying effect moderation for individual intervention components within a JITAI. METHOD: The article describes the microrandomized trial design, enumerates research questions that this experimental design can help answer, and provides an overview of the data analyses that can be used to assess the causal effects of studied intervention components and investigate time-varying moderation of those effects. RESULTS: Microrandomized trials enable causal modeling of proximal effects of the randomized intervention components and assessment of time-varying moderation of those effects. CONCLUSION: Microrandomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions' effects, enabling creation of more effective JITAIs.

Bias in progression-free survival analysis due to intermittent assessment of progression.

Cancer clinical trials are routinely designed to assess the effect of treatment on disease progression and death, often in terms of a composite endpoint called progression-free survival. When progression status is known only at periodic assessment times, the progression time is interval censored, and complications arise in the analysis of progression-free survival. Despite the advances in methods for dealing with interval-censored data, naive methods such as right-endpoint imputation are widely adopted in this setting. We examine the asymptotic and empirical properties of estimators of the marginal progression-free survival functions and associated treatment effects under this scheme. Specifically, we explore the determinants of the asymptotic bias and point out that there is typically a loss in power of tests for treatment effects.