ﻻ يوجد ملخص باللغة العربية
Background and objective: The stepped wedge cluster randomized trial is a study design increasingly used for public health intervention evaluations. Most previous literature focuses on power calculations for this particular type of cluster randomized trials for continuous outcomes, along with an approximation to this approach for binary outcomes. Although not accurate for binary outcomes, it has been widely used. To improve the approximation for binary outcomes, two new methods for stepped wedge designs (SWDs) of binary outcomes have recently been published. However, these new methods have not been implemented in publicly available software. The objective of this paper is to present power calculation software for SWDs in various settings for both continuous and binary outcomes. Methods: We have developed a SAS macro %swdpwr and an R package swdpwr for power calculation in SWDs. Different scenarios including cross-sectional and cohort designs, binary and continuous outcomes, marginal and conditional models, three link functions, with and without time effects are accommodated in this software. Results: swdpwr provides an efficient tool to support investigators in the design and analysis of stepped wedge cluster randomized trails. swdpwr addresses the implementation gap between newly proposed methodology and their application to obtain more accurate power calculations in SWDs. Conclusions: This user-friendly software makes the new methods more accessible and incorporates as many variations as currently available, which were not supported in other related packages. swdpwr is implemented under two platforms: SAS and R, satisfying the needs of investigators from various backgrounds.
Modeling the diameter distribution of trees in forest stands is a common forestry task that supports key biologically and economically relevant management decisions. The choice of model used to represent the diameter distribution and how to estimate
In network analysis, many community detection algorithms have been developed, however, their implementation leaves unaddressed the question of the statistical validation of the results. Here we present robin(ROBustness In Network), an R package to as
In this article, we develop methods for sample size and power calculations in four-level intervention studies when intervention assignment is carried out at any level, with a particular focus on cluster randomized trials (CRTs). CRTs involving four l
Pooled testing (also known as group testing), where diagnostic tests are performed on pooled samples, has broad applications in the surveillance of diseases in animals and humans. An increasingly common use case is molecular xenomonitoring (MX), wher
We introduce and illustrate through numerical examples the R package texttt{SIHR} which handles the statistical inference for (1) linear and quadratic functionals in the high-dimensional linear regression and (2) linear functional in the high-dimensi