Spatial Random Sampling: A Structure-Preserving Data Sketching Tool

Random column sampling is not guaranteed to yield data sketches that preserve the underlying structures of the data and may not sample sufficiently from less-populated data clusters. Also, adaptive sampling can often provide accurate low rank approximations, yet may fall short of producing descriptive data sketches, especially when the cluster centers are linearly dependent. Motivated by that, this paper introduces a novel randomized column sampling tool dubbed Spatial Random Sampling (SRS), in which data points are sampled based on their proximity to randomly sampled points on the unit sphere. The most compelling feature of SRS is that the corresponding probability of sampling from a given data cluster is proportional to the surface area the cluster occupies on the unit sphere, independently from the size of the cluster population. Although it is fully randomized, SRS is shown to provide descriptive and balanced data representations. The proposed idea addresses a pressing need in data science and holds potential to inspire many novel approaches for analysis of big data

Randomization-Based Confidence Intervals for Cluster Randomized Trials

In a cluster randomized trial (CRT), groups of people are randomly assigned to different interventions. Existing parametric and semiparametric methods for CRTs rely on distributional assumptions or a large number of clusters to maintain nominal confidence interval (CI) coverage. Randomization-based inference is an alternative approach that is distribution-free and does not require a large number of clusters to be valid. Although it is well-known that a CI can be obtained by inverting a randomization test, this requires randomization testing a non-zero null hypothesis, which is challenging with non-continuous and survival outcomes. In this paper, we propose a general method for randomization-based CIs using individual-level data from a CRT. This fast and flexible approach accommodates various outcome types, can account for design features such as matching or stratification, and employs a computationally efficient algorithm. We evaluate this method\u27s performance through simulations and apply it to the Botswana Combination Prevention Project, a large HIV prevention trial with an interval-censored time-to-event outcome

Cluster Randomized Trials

This chapter discusses randomization, analysis, sample size and power for cluster randomized trials. Both continuous and binary outcomes are covered. R code to implement the methods is provided

Rejoinder: Matched Pairs and the Future of Cluster-Randomized Experiments

Rejoinder to "The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation" [arXiv:0910.3752]Comment: Published in at http://dx.doi.org/10.1214/09-STS274REJ the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A participatory physical and psychosocial intervention for balancing the demands and resources among industrial workers (PIPPI): study protocol of a cluster-randomized controlled trial

Background: Need for recovery and work ability are strongly associated with high employee turnover, well-being and sickness absence. However, scientific knowledge on effective interventions to improve work ability and decrease need for recovery is scarce. Thus, the present study aims to describe the background, design and protocol of a cluster randomized controlled trial evaluating the effectiveness of an intervention to reduce need for recovery and improve work ability among industrial workers. Methods/Design: A two-year cluster randomized controlled design will be utilized, in which controls will also receive the intervention in year two. More than 400 workers from three companies in Denmark will be aimed to be cluster randomized into intervention and control groups with at least 200 workers (at least 9 work teams) in each group. An organizational resources audit and subsequent action planning workshop will be carried out to map the existing resources and act upon initiatives not functioning as intended. Workshops will be conducted to train leaders and health and safety representatives in supporting and facilitating the intervention activities. Group and individual level participatory visual mapping sessions will be carried out allowing team members to discuss current physical and psychosocial work demands and resources, and develop action plans to minimize strain and if possible, optimize the resources. At all levels, the intervention will be integrated into the existing organization of work schedules. An extensive process and effect evaluation on need for recovery and work ability will be carried out via questionnaires, observations, interviews and organizational data assessed at several time points throughout the intervention period. Discussion: This study primarily aims to develop, implement and evaluate an intervention based on the abovementioned features which may improve the work environment, available resources and health of industrial workers, and hence their need for recovery and work ability

Improving end-of-life care in acute geriatric hospital wards using the Care Programme for the Last Days of Life : study protocol for a phase 3 cluster randomized controlled trial

Background: The Care Programme for the Last Days of Life has been developed to improve the quality of end-of-life care in acute geriatric hospital wards. The programme is based on existing end-of-life care programmes but modeled to the acute geriatric care setting. There is a lack of evidence of the effectiveness of end-of-life care programmes and the effects that may be achieved in patients dying in an acute geriatric hospital setting are unknown. The aim of this paper is to describe the research protocol of a cluster randomized controlled trial to evaluate the effects of the Care Programme for the Last Days of Life. Methods and design: A cluster randomized controlled trial will be conducted. Ten hospitals with one or more acute geriatric wards will conduct a one-year baseline assessment during which care will be provided as usual. For each patient dying in the ward, a questionnaire will be filled in by a nurse, a physician and a family carer. At the end of the baseline assessment hospitals will be randomized to receive intervention (implementation of the Care Programme) or no intervention. Subsequently, the Care Programme will be implemented in the intervention hospitals over a six-month period. A one-year post-intervention assessment will be performed immediately after the baseline assessment in the control hospitals and after the implementation period in the intervention hospitals. Primary outcomes are symptom frequency and symptom burden of patients in the last 48 hours of life. Discussion: This will be the first cluster randomized controlled trial to evaluate the effect of the Care Programme for the Last Days of Life for the acute geriatric hospital setting. The results will enable us to evaluate whether implementation of the Care Programme has positive effects on end-of-life care during the last days of life in this patient population and which components of the Care Programme contribute to improving the quality of end-of-life care

Comment: The Essential Role of Pair Matching

Comment on "The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation" [arXiv:0910.3752]Comment: Published in at http://dx.doi.org/10.1214/09-STS274A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org