Dietary sodium restriction prevents vascular endothelial growth factor inhibitor-induced hypertension.

Background: Vascular endothelial growth factor inhibitors (VEGFIs) are effective anticancer agents which often induce hypertension. VEGFI-induced hypertension is sodium-sensitive in animal studies. Therefore, the efficacy of dietary sodium restriction (DSR) to prevent VEGFI-induced hypertension in cancer patients was studied.

Direct Ex Vivo Observation of Homologous Recombination Defect Reversal After DNA-Damaging Chemotherapy in Patients With Metastatic Breast Cancer.

Purpose: Biomarkers that predict response to poly (ADP-ribose) polymerase inhibitors (PARPis) are required to detect PARPi sensitivity beyond germline -mutated (gBRCAm) cancers and PARPi resistance among reverted gBRCAm cancers. Therefore, we previously developed the Repair Capacity (RECAP) test, a functional homologous recombination (HR) assay that exploits the formation of RAD51 foci in proliferating cells after ex vivo irradiation of fresh primary breast cancer tissue. The aim of the current study was to validate the feasibility of this test on histologic biopsy specimens from metastatic breast cancer and to explore the utility of the RECAP test as a predictive tool for treatment with DNA-damaging agents, such as PARPis.

Proceedings of the First International Conference on Stepped Wedge Trial Design : York, UK, 10 March 2016.

I1 Introduction Mona Kanaan, Noreen Dadirai Mdege, Ada Keding O1 The HiSTORIC trial: a hybrid before-and-after and stepped wedge design RA Parker, N Mills, A Shah, F Strachan, C Keerie, CJ Weir O2 Stepped wedge trials with non-uniform correlation structure Andrew Forbes, Karla Hemming O3 Challenges and solutions for the operationalisation of the ENHANCE study: a pilot stepped wedge trial within a general practice setting Sarah A Lawton, Emma Healey, Martyn Lewis, Elaine Nicholls, Clare Jinks, Valerie Tan, Andrew Finney, Christian D Mallen, on behalf of the ENHANCE Study Team O4 Early lessons from the implementation of a stepped wedge trial design investigating the effectiveness of a training intervention in busy health care settings: the Thistle study Erik Lenguerrand, Graeme MacLennan, John Norrie, Siladitya Bhattacharya, Tim Draycott, on behalf of the Thistle group O5 Sample size calculation for longitudinal cluster randomised trials: a unified framework for closed cohort and repeated cross-section designs Richard Hooper, Steven Teerenstra, Esther de Hoop, Sandra Eldridge O6 Restricted randomisation schemes for stepped-wedge studies with a cluster-level covariate Alan Girling, Monica Taljaard O7 A flexible modelling of the time trend for the analysis of stepped wedge trials: results of a simulation study Gian Luca Di Tanna, Antonio Gasparrini P1 Tackling acute kidney injury – a UK stepped wedge clinical trial of hospital-level quality improvement interventions Anna Casula, Fergus Caskey, Erik Lenguerrand, Shona Methven, Stephanie MacNeill, Margaret May, Nicholas Selby P2 Sample size considerations for quantifying secondary bacterial transmission in a stepped wedge trial of influenza vaccine Leon Danon, Hannah Christensen, Adam Finn, Margaret May P3 Sample size calculation for time-to-event data in stepped wedge cluster randomised trials Fumihito Takanashi, Ada Keding, Simon Crouch, Mona Kanaan P4 Sample size calculations for stepped-wedge cluster randomised trials with unequal cluster sizes Caroline A. Kristunas, Karen L. Smith, Laura J.

Sample size calculation for stepped wedge and other longitudinal cluster randomised trials.

The sample size required for a cluster randomised trial is inflated compared with an individually randomised trial because outcomes of participants from the same cluster are correlated. Sample size calculations for longitudinal cluster randomised trials (including stepped wedge trials) need to take account of at least two levels of clustering: the clusters themselves and times within clusters. We derive formulae for sample size for repeated cross-section and closed cohort cluster randomised trials with normally distributed outcome measures, under a multilevel model allowing for variation between clusters and between times within clusters.

Simultaneous sequential monitoring of efficacy and safety led to masking of effects.

Objective: Usually, sequential designs for clinical trials are applied on the primary (=efficacy) outcome. In practice, other outcomes (e.g.

The need to balance merits and limitations from different disciplines when considering the stepped wedge cluster randomized trial design.

Background: Various papers have addressed pros and cons of the stepped wedge cluster randomized trial design (SWD). However, some issues have not or only limitedly been addressed. Our aim was to provide a comprehensive overview of all merits and limitations of the SWD to assist researchers, reviewers and medical ethics committees when deciding on the appropriateness of the SWD for a particular study.

The ethics of cluster-randomized trials requires further evaluation: a refinement of the Ottawa Statement.

Objectives: The Ottawa Statement is the first guidance document for the ethical and scientific conduct of cluster-randomized trials (CRTs). However, not all recommendations are straightforward to implement. In this paper we will reflect in particular on the recommendation on identifying human research subjects and the issue to what extent the randomization process should be disclosed if there is a risk of contamination.

Stepped wedge designs could reduce the required sample size in cluster randomized trials.

Objective: The stepped wedge design is increasingly being used in cluster randomized trials (CRTs). However, there is not much information available about the design and analysis strategies for these kinds of trials. Approaches to sample size and power calculations have been provided, but a simple sample size formula is lacking.

A simple sample size formula for analysis of covariance in cluster randomized trials.

For cluster randomized trials with a continuous outcome, the sample size is often calculated as if an analysis of the outcomes at the end of the treatment period (follow-up scores) would be performed. However, often a baseline measurement of the outcome is available or feasible to obtain. An analysis of covariance (ANCOVA) using both the baseline and follow-up score of the outcome will then have more power.

The "best balance" allocation led to optimal balance in cluster-controlled trials.

Objective: Balance of prognostic factors between treatment groups is desirable because it improves the accuracy, precision, and credibility of the results. In cluster-controlled trials, imbalance can easily occur by chance when the number of cluster is small. If all clusters are known at the start of the study, the "best balance" allocation method (BB) can be used to obtain optimal balance.