Biostatistics Research - Statistics, Uncertainty, Probability, Modeling

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Modified toxicity probability interval design: a safer and more reliable method than the 3 + 3 design for practical phase I trials.

Ji Y, Wang SJ

Center for Clinical and Research Informatics, NorthShore University HealthSystem, 1001 University Place, Evanston, IL 60201, USA. yji@northshore.org

The 3 + 3 design is the most common choice among clinicians for phase I dose-escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3 + 3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3 + 3 dose escalations in practice with virtually no logistic cost, and trial protocols based on the 3 + 3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3 + 3 design has rarely been compared with model-based designs in simulation studies with matched sample sizes. In the vast majority of statistical literature, the 3 + 3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the sample size required by the 3 + 3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with matched sample sizes, we demonstrate that the 3 + 3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity probability interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3 + 3 design does not yield higher probabilities in identifying the correct MTD, even when the sample size is matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-escalation studies whenever the 3 + 3 design is also considered. We provide free software to allow direct comparisons of the 3 + 3 design with other model-based designs in simulation studies with matched sample sizes.

Published 8 May 2013 in J Clin Oncol, 31(14): 1785-91.
Full-text of this article is available online (may require subscription).


Articles on Biostatistics published 27 March 2013:

Strategies for developing biostatistics resources in an academic health center.   Acad Med, 88(4): 454-60.

Biostatistics--the application of statistics to understanding health and biology-provides powerful tools for developing research questions, designing studies, refining measurements, analyzing data, and interpreting findings. Biostatistics plays an important role in health-related research, yet biostatistics resources are often fragmented, ad hoc, or oversubscribed within academic health centers (AHCs). Given the increasing complexity and quantity of health-related data, the emphasis on ... [Abstract] [Full-text]


Articles on Biostatistics published 21 January 2013:

Evaluation of student outcomes in online vs. campus biostatistics education in a graduate school of public health.   Prev Med, 56(2): 142-4.

[Abstract] [Full-text]


Articles on Biostatistics published 17 January 2013:

Understanding of statistical terms routinely used in meta-analyses: an international survey among researchers.   PLoS One, 8(1): e47229.

[Abstract] [Full-text]


Articles on Biostatistics published 18 December 2012:

Oral and maxillofacial surgery residents have poor understanding of biostatistics.   J Oral Maxillofac Surg, 71(1): 227-34.

[Abstract] [Full-text]


Articles on Biostatistics published 12 December 2012:

Characteristics of recent biostatistical methods adopted by researchers publishing in general/internal medicine journals.   Stat Med, 32(1): 1-10.

[Abstract] [Full-text]


Articles on Biostatistics published 12 October 2012:

Assessing group differences in biodiversity by simultaneously testing a user-defined selection of diversity indices.   Mol Ecol Resour, 12(6): 1068-78.

Comparing diversities between groups is a task biologists are frequently faced with, for example in ecological field trials or when dealing with metagenomics data. However, researchers often waver about which measure of diversity to choose as there is a multitude of approaches available. As Jost (2008, Molecular Ecology, 17, 4015) has pointed out, widely used measures such as the Shannon or Simpson index have undesirable properties which make them hard to compare and interpret. Many of the ... [Abstract] [Full-text]

|SE|S|AM|E| Barcode: NGS-oriented software for amplicon characterization--application to species and environmental barcoding.   Mol Ecol Resour, 12(6): 1151-7.

Progress in NGS technologies has opened up new opportunities for characterizing biodiversity, both for individual specimen identification and for environmental barcoding. Although the amount of data available to biologist is increasing, user-friendly tools to facilitate data analysis have yet to be developed. Our aim, with |SE|S|AM|E| Barcode, is to provide such support through a unified platform. The sequences are analysed through a pipeline that (i) processes NGS amplicon runs, filtering ... [Abstract] [Full-text]


Articles on Biostatistics published 11 October 2012:

A three-level mixed-effects location scale model with an application to ecological momentary assessment data.   Stat Med, 31(26): 3192-210.

In studies using ecological momentary assessment (EMA), or other intensive longitudinal data collection methods, interest frequently centers on changes in the variances, both within-subjects and between-subjects. For this, Hedeker et al. (Biometrics 2008; 64: 627-634) developed an extended two-level mixed-effects model that treats observations as being nested within subjects and allows covariates to influence both the within-subjects and between-subjects variance, beyond their influence on ... [Abstract] [Full-text]


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Appleton and Lange's Review of Epidemiology and Biostatistics for the USMLE

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