Optimized design and analysis of preclinical intervention studies in vivo

Teemu D. Laajala, Mikael Jumppanen, Riikka Huhtaniemi, Vidal Fey, Amanpreet Kaur, Matias Knuuttila, Eija Aho, Riikka Oksala, Jukka Westermarck, Sari Mäkelä, Matti Poutanen, Tero Aittokallio

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Recent reports have called into question the reproducibility, validity and translatability of the preclinical animal studies due to limitations in their experimental design and statistical analysis. To this end, we implemented a matching-based modelling approach for optimal intervention group allocation, randomization and power calculations, which takes full account of the complex animal characteristics at baseline prior to interventions. In prostate cancer xenograft studies, the method effectively normalized the confounding baseline variability, and resulted in animal allocations which were supported by RNA-seq profiling of the individual tumours. The matching information increased the statistical power to detect true treatment effects at smaller sample sizes in two castration-resistant prostate cancer models, thereby leading to saving of both animal lives and research costs. The novel modelling approach and its open-source and web-based software implementations enable the researchers to conduct adequately-powered and fully-blinded preclinical intervention studies, with the aim to accelerate the discovery of new therapeutic interventions.
Original languageEnglish
Article number30723
JournalScientific Reports
Volume6
DOIs
Publication statusPublished - 2 Aug 2016
Externally publishedYes
Publication typeA1 Journal article-refereed

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