Abstract
MotivationIn clinical trials, individuals are matched using demographic criteria, paired and then randomly assigned to treatment and control groups to determine a drug’s efficacy. A chief cause for the irreproducibility of results across pilot to Phase-III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups.ResultsPair Matcher (PaM) addresses stratification bias by optimizing pairing assignments a priori and/or a posteriori to the trial using both genetic and demographic criteria. Using simulated and real datasets, we show that PaM identifies ideal and near-ideal pairs that are more genetically homogeneous than those identified based on competing methods, including the commonly used principal component analysis (PCA). Homogenizing the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the trial or genetic study. PaM’s ancestral inferences also allow characterizing responders and developing a precision medicine approach to treatment.Availability and implementation PaM is freely available via Rhttps://github.com/eelhaik/PAM and a web-interface at http://elhaik-matcher.sheffield.ac.uk/ElhaikLab/.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
It is well recognized that pharmaceutical research and development (R&D) is in crisis
We first evaluated the performances of PaMsimple without a threshold and with a threshold of 7 across all simulated datasets
We evaluated the homogeneity of the pairs inferred by Pair Matcher (PaM) and principal component analysis (PCA) using both geographic and GDs
Summary
It is well recognized that pharmaceutical research and development (R&D) is in crisis. The number of new drugs approved per billion US dollars spent on R&D has halved roughly every 9 years since 1950 (Scannell et al, 2012) as spending in the industry has inflated to an average of $$5.8 billion per drug in 2011 compared to $1.3 billion per drug in 2005 (Roy, 2012) The latter phases of clinical trials test the drug’s efficacy compared to a placebo or other treatments in a randomized trial setting and require assessing tens, hundreds (Phase II trials) and eventually tens of thousands (Phase-III trials) of volunteers over a long period of time to prove that there is substantial evidence of a clinical benefit of the drug. As the regulatory environment is unlikely to relax (Scannell et al, 2012), it is important to understand why randomized control trials may be more successful in smaller trials
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