Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/104981
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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Lei, Y. | - |
dc.contributor.author | Carlson, S. | - |
dc.contributor.author | Yelland, L. | - |
dc.contributor.author | Makrides, M. | - |
dc.contributor.author | Gibson, R. | - |
dc.contributor.author | Gajewski, B. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Journal of Applied Statistics, 2017; 44(8):1466-1478 | - |
dc.identifier.issn | 0266-4763 | - |
dc.identifier.issn | 1360-0532 | - |
dc.identifier.uri | http://hdl.handle.net/2440/104981 | - |
dc.description | Accepted 14 July 2016 | - |
dc.description.abstract | This research was motivated by our goal to design an efficient clinical trial to compare two doses of docosahexaenoic acid supplementation for reducing the rate of earliest preterm births (ePTB) and/or preterm births (PTB). Dichotomizing continuous gestational age (GA) data using a classic binomial distribution will result in a loss of information and reduced power. A distributional approach is an improved strategy to retain statistical power from the continuous distribution. However, appropriate distributions that fit the data properly, particularly in the tails, must be chosen, especially when the data are skewed. A recent study proposed a skew-normal method. We propose a three-component normal mixture model and introduce separate treatment effects at different components of GA. We evaluate operating characteristics of mixture model, beta-binomial model, and skew-normal model through simulation. We also apply these three methods to data from two completed clinical trials from the USA and Australia. Finite mixture models are shown to have favorable properties in PTB analysis but minimal benefit for ePTB analysis. Normal models on log-transformed data have the largest bias. Therefore we recommend finite mixture model for PTB study. Either finite mixture model or beta-binomial model is acceptable for ePTB study. | - |
dc.description.statementofresponsibility | Yang Lei, Susan Carlson, Lisa N. Yelland, Maria Makrides, Robert Gibson and Byron J. Gajewski | - |
dc.language.iso | en | - |
dc.publisher | Taylor & Francis | - |
dc.rights | © 2016 Informa UK Limited, trading as Taylor & Francis Group | - |
dc.source.uri | http://dx.doi.org/10.1080/02664763.2016.1214244 | - |
dc.subject | Bayesian; normal mixture model; simulation; dichotomization; preterm birth | - |
dc.title | Comparison of dichotomized and distributional approaches in rare event clinical trial design: a fixed Bayesian design | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1080/02664763.2016.1214244 | - |
dc.relation.grant | http://purl.org/au-research/grants/nhmrc/1052388 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Yelland, L. [0000-0003-3803-8728] | - |
dc.identifier.orcid | Makrides, M. [0000-0003-3832-541X] | - |
dc.identifier.orcid | Gibson, R. [0000-0002-8750-525X] | - |
Appears in Collections: | Aurora harvest 3 Public Health publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_104981.pdf | Accepted version | 566.87 kB | Adobe PDF | View/Open |
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