An R package for implementing simulations for seamless phase II/III clinical trials using early outcomes for treatment selection
Computational Statistics & Data Analysis, ISSN: 0167-9473, Vol: 56, Issue: 5, Page: 1150-1160
2012
- 13Citations
- 41Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Adaptive seamless phase II/III clinical trial designs allowing treatment selection at an interim analysis have gained much attention because of their potential benefits compared to more conventional drug development programmes with separate trials for individual phases. A scenario of particular interest is that in which the final outcome in the trial is based on long-term follow-up, but the interim analysis can only realistically be based on early (short-term) outcomes. A new software package ( asd ) for the statistical software R implements simulations for designs of this type, in addition to the simpler scenario where treatment selection is based on the definitive (final) outcome. The methodology is briefly described and two examples of proposed trial designs in progressive multiple sclerosis are provided, with R code to illustrate application of the methodology.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0167947310004159; http://dx.doi.org/10.1016/j.csda.2010.10.027; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84857658991&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0167947310004159; https://api.elsevier.com/content/article/PII:S0167947310004159?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0167947310004159?httpAccept=text/plain; https://dx.doi.org/10.1016/j.csda.2010.10.027
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know