RRApp, a robust randomization app, for clinical and translational research

Chengcheng Tu, Emma K T Benn, Chengcheng Tu, Emma K T Benn

Abstract

While junior clinical researchers at academic medical institutions across the US often desire to be actively engaged in randomized-clinical trials, they often lack adequate resources and research capacity to design and implement them. This insufficiency hinders their ability to generate a rigorous randomization scheme to minimize selection bias and yield comparable groups. Moreover, there are limited online user-friendly randomization tools. Thus, we developed a free robust randomization app (RRApp). RRApp incorporates 6 major randomization techniques: simple randomization, stratified randomization, block randomization, permuted block randomization, stratified block randomization, and stratified permuted block randomization. The design phase has been completed, including robust server scripts and a straightforward user-interface using the "shiny" package in R. Randomization schemes generated in RRApp can be input directly into the Research Electronic Data Capture (REDCap) system. RRApp has been evaluated by biostatisticians and junior clinical faculty at the Icahn School of Medicine at Mount Sinai. Constructive feedback regarding the quality and functionality of RRApp was also provided by attendees of the 2016 Association for Clinical and Translational Statisticians Annual Meeting. RRApp aims to educate early stage clinical trialists about the importance of randomization, while simultaneously assisting them, in a user-friendly fashion, to generate reproducible randomization schemes.

Keywords: Clinical trials; RCT; biostatistics; randomization; reproducibility.

Figures

Fig. 1
Fig. 1
(a) Main panel of user interface for robust randomization app (RRApp) and (b) RRApp side panel for data entry.
Fig. 2
Fig. 2
Four steps to generate a randomization scheme in robust randomization app (RRApp).

References

    1. Armitage P. The role of randomization in clinical trials. Statistics in Medicine 1982; 1: 345–352.
    1. Suresh KP. An overview of randomization techniques: an unbiased assessment of outcome in clinical research. Journal of Human Reproductive Sciences 2011; 4: 8.
    1. Lee KC, El-Ibiary SY, Hudmon KS. Evaluation of research training and productivity among junior pharmacy practice faculty in the United States. Journal of Pharmacy Practice 2010; 23: 553–559.
    1. Maas ML, et al. Increasing nursing faculty research: the Iowa gerontological nursing research and regional research consortium strategies. Journal of Nursing Scholarship 2009; 41: 411–419.
    1. Ringel SP, et al. Training clinical researchers in neurology: we must do better. Neurology 2001; 57: 388–392.
    1. Morice V. RandoWeb, an online randomization tool for clinical trials. Computer Methods and Programs in Biomedicine 2012; 107: 308–314.
    1. Schrimpf D, Haag M, Pilz LR. Possible combinations of electronic data capture and randomization systems. Methods of Information in Medicine 2014; 53: 202–207.
    1. Xiao L, et al. An easily accessible web-based minimization random allocation system for clinical trials. Journal of Medical Internet Research 2013; 15: e139.
    1. McPherson G, Campbell M. Methods of randomization Pharmaceutical Sciences Encyclopedia, 2010.
    1. Cleophas TJ, Zwinderman AH. Randomized clinical trials, history, designs. Understanding Clinical Data Analysis 2017: 33–60.
    1. Altman DG, Bland JM. How to randomise. BMJ 1999; 319: 703–704.
    1. Kang M, Ragan BG, Park J-H. Issues in outcomes research: an overview of randomization techniques for clinical trials. Journal of Athletic Training 2008; 43: 215–221.
    1. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2014. ()
    1. Chang W, et al. shiny: web application framework for R. R package version 0.13.1 [Internet], 2016 [cited June 30, 2017]. ()
    1. Urbanek S. rJava: low-level R to Java interface [Internet], 2017 [cited June 30, 2017]. ()
    1. Dragulescu A. xlsx: read, write, format Excel 2007 and Excel 97/2000/XP/2003 files [Internet], 2014 [cited June 30, 2017]. ()
    1. Dragulescu AA. xlsxjars: package required POI jars for the xlsx package [Internet], 2014 [cited June 30, 2017]. ()
    1. GmbH MS. XLConnect: Excel connector for R. R package version 0.2-13 [Internet], 2017 [cited June 30, 2017]. ()
    1. GmbH MS. XLConnectJars: JAR dependencies for the XLConnect package R package version 0.2-13 [Internet], 2017 [cited June 30, 2017]. ()
    1. Harris PA, et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics 2009; 42: 377–381.
    1. Perron BE, Stearns AG. A review of a presentation technology: Prezi. Research on Social Work Practice 2011; 21: 376–377.

Source: PubMed

3
Iratkozz fel