Preclinical In Vivo Antitumor Activity Experiments: Methodological Pitfalls and a New Framework for their Design and Analysis

Porcu, Luca (2020). Preclinical In Vivo Antitumor Activity Experiments: Methodological Pitfalls and a New Framework for their Design and Analysis. PhD thesis The Open University.



Aims: Poorly designed, analyzed and reported preclinical in vivo experiments (inVivoExp) raise ethical as well as scientific concerns. It could be hypothesized that the recurring failure of apparently promising interventions to improve outcome in clinical trials has been partially caused by poor quality of statistical design and analysis (QoStat) of inVivoExp. This project aimed to assess and correlate QoStat with clinical activity, and to improve the statistical framework used in inVivoExp.

Methods: A systematic search of Medline and EMBASE databases was carried out to identify epithelial ovarian cancer clinical trials assessing the antitumor activity of candidate compounds (CC) as monotherapy. For each eligible CC, a systematic search was carried out to identify scientific papers reporting inVivoExp on rats and mice, in which the CC was administered as monotherapy. An ad hoc checklist was used to assess QoStat of inVivoExp. QoStat was correlated to the clinical activity.

Results: Fifty-two eligible CCs and 121 inVivoExp were identified. In 45 out of 120 (37.5%) inVivoExp the method of treatment assignment was not specified. The randomization type was specified in 3 out of 74 (4.1%) inVivoExp and sample size was justified in 9 (7.4%) inVivoExp. If the primary outcome was tumor volume, the antitumor activity endpoint was declared in 14 out of 106 (13.2%) inVivoExp. The length of follow-up was specified in 43 (35.5%) inVivoExp. Outcome assessor was blinded in 5 (4.1%) inVivoExp. Inefficient statistical methods were often applied to analyze tumor growth data. A new statistical framework based on the Mann-Whitney statistic was proposed and applied to a specific tumor model.

Conclusions: QoStat of inVivoExp was so poor that the correlation with clinical activity was impossible. The magnitude of the biological signal was poorly estimated. The new statistical framework should be considered for the design and analysis of in vivo tumor growth studies.

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