Optimized design and analysis of preclinical intervention studies in vivo

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Laajala, Teemu D ^a,b   Jumppanen, Mikael ^a   Huhtaniemi, Riikka ^a,c   Fey, Vidal ^a   Kaur, Amanpreet ^a,d   Knuuttila, Matias ^a   Aho, Eija ^c   Oksala, Riikka ^a,c   Westermarck, Jukka ^a   Mäkelä, Sari ^a   Poutanen, Matti ^a,e   Aittokallio, Tero ^a,b  

^a UNIVERSITY OF TURKU   (Finland)

^b UNIVERSITY OF HELSINKI   (Finland)

^c ORION CORPORATION   (Finland)

^d UNIVERSITY OF TURKU   (Finland)

^e UNIVERSITY OF GOTHENBURG   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL; CANCER TRANSPLANTATION; GENETICS; HUMAN; MALE; METHODOLOGY; MOUSE; PATHOLOGY; PROSTATE TUMOR; RANDOMIZATION; REPRODUCIBILITY; SAMPLE SIZE; SEQUENCE ANALYSIS; SOFTWARE; STATISTICAL MODEL;

ANIMALS; HUMANS; MALE; MICE; MODELS, STATISTICAL; NEOPLASM TRANSPLANTATION; PROSTATIC NEOPLASMS; RANDOM ALLOCATION; REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN; SAMPLE SIZE; SEQUENCE ANALYSIS, RNA; SOFTWARE;

EID: 84982712458     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep30723     Document Type: Article

References (48)

1. Collins, F. S. & Tabak, L. A. Policy: NIH plans to enhance reproducibility. Nature 505, 612-613, doi: 10. 1038/505612a (2014).
2. Henderson, V. C. et al. A meta-analysis of threats to valid clinical inference in preclinical research of sunitinib. Elife. 4, 1-13, doi: 10. 7554/eLife. 08351 (2015).
3. Begley, C. G. & Ellis, L. M. Drug development: Raise standards for preclinical cancer research. Nature 483, 531-533, doi: 10. 1038/483531a (2012).
4. Freedman, L. P., Cockburn, I. M. & Simcoe, T. S. The economics of reproducibility in preclinical research. PLoS Biol 13, e1002165, doi: 10. 1371/journal. pbio. 1002165 (2015).
5. Singh, M. & Ferrara, N. Modeling and predicting clinical efficacy for drugs targeting the tumor milieu. Nat Biotechnol 30, 648-657, doi: 10. 1038/nbt. 2286 (2012).
6. Couzin-Frankel, J. When mice mislead. Science 342, 922-925, doi: 10. 1126/science. 342. 6161. 922 (2013).
7. Perrin, S. Preclinical research: make mouse studies work. Nature 507, 423-425, doi: 10. 1038/507423a (2014).
8. Henderson, V. C., Kimmelman, J., Fergusson, D., Grimshaw, J. M. & Hackam, D. G. Threats to validity in the design and conduct of preclinical efficacy studies: A systematic review of guidelines for in vivo animal experiments. PLoS Med 10, e1001489, doi: 10. 1371/ journal. pmed. 1001489 (2013).
9. Cressey, D. UK funders demand strong statistics for animal studies. Nature 520, 271-272, doi: 10. 1038/520271a (2015).
10. Macleod, M. Why animal research needs to improve. Nature 477, 511, doi: 10. 1038/477511a (2011).
11. Muhlhausler, B. S., Bloomfield, F. H. & Gillman, M. W. Whole animal experiments should be more like human randomized controlled trials. PLoS Biol 11, e1001481, doi: 10. 1371/journal. pbio. 1001481 (2013).
12. Hildebrand, F. et al. Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice. Genome Biol 14, R4, doi: 10. 1186/gb-2013-14-1-r4 (2013).
13. Hasty, A. H. & Gutierrez, D. A. What have we really learned about macrophage recruitment to adipose tissue? Endocrinology 155, 12-14, doi: 10. 1210/en. 2013-2027 (2014).
14. Reardon, S. A mouse's house may ruin experiments. Nature 530, 264, doi: 10. 1038/nature. 2016. 19335 (2016).
15. Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. Improving bioscience research reporting: The ARRIVE guidelines for reporting animal research. PLoS Biol 8, e1000412, doi: 10. 1371/journal. pbio. 1000412 (2010).
16. Landis, S. C. et al. A call for transparent reporting to optimize the predictive value of preclinical research. Nature 490, 187-191, doi: 10. 1038/nature11556 (2011).
17. Baker, D., Lidster, K., Sottomayor, A. & Amor, S. Two years later: journals are not yet enforcing the ARRIVE guidelines on reporting standards for pre-clinical animal studies. PLoS Biol 12, e1001756, doi: 10. 1371/journal. pbio. 1001756 (2014).
18. Sugar, E., Pascoe, A. J. & Azad, N. Reporting of preclinical tumor-graft cancer therapeutic studies. Cancer Biol Ther 13, 1262-1268, doi: 10. 4161/cbt. 21782 (2012).
19. Eisen, J. A., Ganley, E. & MacCallum, C. J. Open science and reporting animal studies: who's accountable? PLoS Biol 12, e1001757, doi: 10. 1371/journal. pbio. 1001757 (2014).
20. Day, C. P., Merlino, G. & Van Dyke, T. Preclinical mouse cancer models: A maze of opportunities and challenges. Cell 163, 39-53, doi: 10. 1016/j. cell. 2015. 08. 068 (2015).
21. Su, Z. Optimal allocation of prognostic factors in randomized preclinical animal studies. Drug Inf J 45, 725-729, doi: 10. 1177/009286151104500508 (2011).
22. Kasturi, J. et al. IRINI: random group allocation of multiple prognostic factors. Contemp Clin Trials 32, 372-381, doi: 10. 1016/j. cct. 2010. 12. 009 (2011).
23. Knuuttila, M. et al. Castration induces up-regulation of intratumoral androgen biosynthesis and androgen receptor expression in an orthotopic VCaP human prostate cancer xenograft model. Am J Pathol 184, 2163-2173, doi: 10. 1016/j. ajpath. 2014. 04. 010 (2014).
24. Hutchinson, L. & Kirk, R. High drug attrition rates-where are we going wrong? Nat Re Clin Oncol 8, 189-190, doi: 10. 1038/ nrclinonc. 2011. 34 (2011).
25. Laajala, T. D. hamlet: hierarchical optimal matching and machine learning toolbox (R-package version 0. 9. 5). URL http://CRAN. Rproject. org/package= hamlet (2016).
26. Treasure, T. & MacRae, K. D. Minimisation: The platinum standard for trials? Randomisation doesn't guarantee similarity of groups; minimisation does. BMJ 317, 362-363 (1998).
27. Smith G. D. & Ebrahim S. Data dredging, bias, or confounding: They can all get you into the BMJ and the Friday papers. BMJ 325, 1437-1438 (2002).
28. Hoenig, J. M. & Heisey, D. M. The abuse of power: The pervasive fallacy of power calculations for data analysis. Am Stat 55, 19-24, doi: 10. 1198/000313001300339897 (2001).
29. Kimmelman, J., Mogil, J. S. & Dirnagl, U. Distinguishing between exploratory and confirmatory preclinical research will improve translation. PLoS Biol 12, e1001863, doi: 10. 1371/journal. pbio. 1001863 (2014).
30. van der Worp, H. B. et al. Can animal models of disease reliably inform human studies? PLoS Med 7, e1000245, doi: 10. 1371/journal. pmed. 1000245 (2010).
31. Seashore-Ludlow, B. et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov 5, 1210-1223, doi: 10. 1158/2159-8290. CD-15-0235 (2015).
32. Wessel, J. & Schork, N. J. Generalized genomic distance-based regression methodology for multilocus association analysis. Am J Hum Genet 79, 792-806, doi: 10. 1086/508346 (2006).
33. Pond, G. R., Tang, P. A., Welch, S. A. & Chen, E. X. Trends in the application of dynamic allocation methods in multi-arm cancer clinical trials. Clin Trials 7, 227-234, doi: 10. 1177/1740774510368301 (2010).
34. Greevy, R., Lu, B., Silver, J. H. & Rosenbaum, P. Optimal multivariate matching before randomization. Biostatistics 5, 263-275, doi: 10. 1093/biostatistics/5. 2. 263 (2004).
35. Lu, B., Greevy, R., Xu, X. & Beck, C. Optimal nonbipartite matching and its statistical applications. Am Stat 65, 21-30, doi: 10. 1198/ tast. 2011. 08294 (2011).
36. Pond, G. Statistical issues in the use of dynamic allocation methods for balancing baseline covariates. Br J Cancer 104, 1711-1715, doi: 10. 1038/bjc. 2011. 157 (2011).
37. Lachin, J. M., Matts, J. P. & Wei, L. J. Randomization in clinical trials: conclusions and recommendations. Control Clin Trials 9, 365-374 (1988).
38. Gower, J. C. A general coefficient of similarity and some of its properties. Biometrics 27, 857-871 (1971).
39. McCane, B. & Albert, M. Distance functions for categorical and mixed variables. Pattern Recognit Lett 29, 986-993, doi: 10. 1016/j. patrec. 2008. 01. 021 (2008).
40. Wilson, D. R. & Martinez, T. R. Improved heterogeneous distance functions. J Artif Intell Res 6, 1-34, doi: 10. 1613/jair. 346 (1997).
41. Guojun, G., Chaoqun, M. & Jianhong, W. Similarity and dissimilarity measures in Data clustering: Theory, algorithms, and applications (eds. Wells, M. T. et al.) Ch. 6, 67-106 (ASA-SIAM, 2007).
42. Zhao, L. et al. Bayesian hierarchical changepoint methods in modeling the tumor growth profiles in xenograft experiments. Clin Cancer Res 17, 1057-1064, doi: 10. 1158/1078-0432. CCR-10-1935 (2011).
43. Laajala, T. D. et al. Improved statistical modeling of tumor growth and treatment effect in preclinical animal studies with highly heterogeneous responses in vivo. Clin Cancer Res 18, 4385-4396, doi: 10. 1158/1078-0432. CCR-11-3215 (2012).
44. Bates, D. M., Maechler, M. & Bolker, B. lme4: Linear mixed-effects models using S4 classes (R-package version 1. 1-6). URL http://CRAN. R-project. org/package= lme4 (2014).
45. R Development Core Team. R: A language and environment for statistical computing (version 3. 2. 1). R Foundation for Statistical Computing, Vienna, Austria. URL http://www. R-project. org (2015).
46. Kuznetsova, A. lmerTest: Tests for random and fixed effects for linear mixed effect models (R-package version 2. 0-6). URL http://CRAN. R-project. org/package= lmerTest (2014).
47. Pinheiro, J. C. & Bates, D. M. Hypothesis tests and confidence intervals in Mixed effects models in S and S-PLUS (eds. Chambers, J. et al.) Ch. 2. 4, 82-96 (Springer-Verlag, 2000).
48. Gelman, A. & Hill, J. Sample size and power calculations in Data analysis using regression and multilevel/hierarchical models (eds. Alvarez, R. M. et al.) Ch. 20, 437-454 (Cambridge University Press, 2007).