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Business Analytics and Grammatical Evolution for the Prediction of Patient Recruitment in Multicentre Clinical Trials

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Handbook of Grammatical Evolution

Abstract

For a drug to be approved for human use, its safety and efficacy need to be evidenced through clinical trials. Optimisation of patient recruitment is an active area of business interest for pharma and contract research organisations (CRO) conducting clinical trials. The healthcare industry and CROs are gradually starting to adapt business analytics techniques to improve processes and help boost performance. Development of methods able to predict at the outset which prospective investigators/sites will succeed in patient recruitment can provide powerful tools for this business problem. In this chapter we describe the application of Grammatical Evolution to the prediction of patient recruitment in multicentre clinical trials.

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References

  1. P. Aegerter , N. Bendersky , T.C. Tran , J. Ropers , N. Taright , G. Chatellier, The use of DRG for identifying clinical trials centers with high recruitment potential: a feasibility study. Stud. Health Technol. Inform. 205, 783–787 (2014)

    Google Scholar 

  2. V.V. Anisimov, Statistical modeling of clinical trials (recruitment and randomization). Comput. Stand. Theory Methods 40(19–20), 3684–3699 (2011)

    Article  MathSciNet  Google Scholar 

  3. V.V. Anisimov, V.V. Fedorov, Modelling, prediction and adaptive adjustment of recruitment in multicentre trials. Stat. Med. 26(27), 4958–4975 (2007)

    Article  MathSciNet  Google Scholar 

  4. S. Bernard, C. Chatelain, S. Adam, R. Sabourin, The multiclass roc front method for cost-sensitive classification. Pattern Recogn. 52(C), 46–60 (2016)

    Article  Google Scholar 

  5. U. Bhowan, M. Johnston, M. Zhang, Developing new fitness functions in genetic programming for classification with unbalanced data. IEEE Trans. Syst. Man Cybern. B Cybern. 42(2), 406–421 (2012)

    Article  Google Scholar 

  6. U. Bhowan, M. Johnston, M. Zhang, X. Yao, Evolving diverse ensembles using genetic programming for classification with unbalanced data. Trans. Evol. Comput. 17(3), 368–386 (2013)

    Article  Google Scholar 

  7. G. Borlikova, M. Phillips, L. Smith, M. O’Neill, Evolving classification models for prediction of patient recruitment in multicentre clinical trials using grammatical evolution, in Applications of Evolutionary Computation. EvoApplications 2016, ed. by G. Squillero, P. Burelli. Lecture Notes in Computer Science, vol. 9597 (Springer International Publishing, Berlin, 2016), pp. 46–57

    Google Scholar 

  8. G. Borlikova, M. Phillips, L. Smith, M. Nicolau, M. O’Neill, Alternative fitness functions in the development of models for prediction of patient recruitment in multicentre clinical trials, in Operations Research Proceedings 2016, ed. by A. Fink, A. Fügenschuh, M.J. Geiger, vol. 9597 (Springer International Publishing, Berlin, 2018), pp. 375–381

    Google Scholar 

  9. A. Brabazon, M. O’Neill, Diagnosing corporate stability using grammatical evolution. Int. J. Appl. Math. Comput. Sci. 14(3), 363–374 (2004)

    MATH  Google Scholar 

  10. A. Brabazon, M. O’Neill, Credit classification using grammatical evolution. Informatica 30(3), 325–335 (2006)

    MATH  Google Scholar 

  11. A.P. Bradley, The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)

    Article  Google Scholar 

  12. C. Chatelain, S. Adam, Y. Lecourtier, L. Heutte, T. Paquet, A multi-model selection framework for unknown and/or evolutive misclassification cost problems. Pattern Recogn. 43(3), 815–823 (2010)

    Article  Google Scholar 

  13. I. Dempsey, M. O’Neill, A. Brabazon, Foundations in Grammatical Evolution for Dynamic Environments. Studies in Computational Intelligence, vol. 194 (Springer, Berlin, 2009)

    Google Scholar 

  14. R. Dilão, D. Muraro, M. Nicolau, M. Schoenauer, Validation of a morphogenesis model of drosophila early development by a multi-objective evolutionary optimization algorithm, in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2009, ed. by C. Pizzuti, M.D. Ritchie, M. Giacobini. Lecture Notes in Computer Science, vol. 5483 (Springer, Berlin, 2009), pp. 176–190

    Google Scholar 

  15. C. Elkan, The foundations of cost-sensitive learning, in Proceedings of the Seventeenth International Joint Conference of Artificial Intelligence, Seattle, Washington (2001), pp. 973–978

    Google Scholar 

  16. M. Fenton, J. McDermott, D. Fagan, S. Forstenlechner, E. Hemberg, M. O’Neill, Ponyge2: grammatical evolution in python, in Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO ‘17 (ACM, New York, 2017), pp. 1194–1201

    Google Scholar 

  17. C. Ferri, J. Hernandez-Orallo, R. Modroiu, An experimental comparison of performance measures for classification. Pattern Recogn. Lett. 30(1), 27–38 (2009)

    Article  Google Scholar 

  18. F. Kopcke, D. Lubgan, R. Fietkau, A. Scholler, C. Nau, M. Sturzl, R. Croner, H.U. Prokosch, D. Toddenroth, Evaluating predictive modeling algorithms to assess patient eligibility for clinical trials from routine data. BMC Med. Inform. Decis. Mak. 13, 134 (2013)

    Article  Google Scholar 

  19. M. Kuhn, Building predictive models in R using the caret package. J. Stat. Softw. 28(5), 1–26 (2008)

    Article  Google Scholar 

  20. M. Kuhn, K. Johnson, Applied Predictive Modeling (Springer, New York, 2013)

    Book  Google Scholar 

  21. J.C. Levesque, A. Durand, C. Gagne, R. Sabourin, Multi-objective evolutionary optimization for generating ensembles of classifiers in the roc space, in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation. GECCO ‘12 (ACM, New York, 2012), pp. 879–886

    Google Scholar 

  22. L. Marks, E. Power, Using technology to address recruitment issues in the clinical trial process. Trends Biotechnol. 20(3), 105–109 (2002)

    Article  Google Scholar 

  23. R.I. McKay, N.X. Hoai, P.A. Whigham, Y. Shan, M. O’Neill, Grammar-based genetic programming: a survey. Genet. Program Evolvable Mach. 11(3/4), 365–396 (2010)

    Article  Google Scholar 

  24. Y. Ni, A.F. Beck, R. Taylor, J. Dyas, I. Solti, J. Grupp-Phelan, J.W. Dexheimer, Will they participate? predicting patients’ response to clinical trial invitations in a pediatric emergency department. J. Am. Med. Inform. Assoc. 23(4), 671–680 (2016)

    Article  Google Scholar 

  25. M. Nicolau, M. Saunders, M. O’Neill, B. Osborne, A. Brabazon, Evolving interpolating models of net ecosystem co2 exchange using grammatical evolution, in Genetic Programming. Lecture Notes in Computer Science, vol. 7244 (Springer Berlin, 2012), pp. 134–145

    Google Scholar 

  26. M. O’Neill, C. Ryan, Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language, Genetic Programming, vol. 4 (Kluwer Academic Publishers, Boston 2003)

    Book  Google Scholar 

  27. D.M. Powers, Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)

    Google Scholar 

  28. F. Provost, T. Fawcett, Robust classification for imprecise environments. Mach. Learn. 42(3), 203–231 (2001)

    Article  Google Scholar 

  29. F. Provost, T. Fawcett, Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking (O’Reilly Media, Inc., Sebastopol, 2013)

    Google Scholar 

  30. G. Santafe, I.n. Inza, J.A. Lozano, Dealing with the evaluation of supervised classification algorithms. Artif. Intell. Rev. 44(4), 467–508 (2015)

    Google Scholar 

  31. P. Schuler, B. Buckley, Re-engineering Clinical Trials: Best Practices for Streamlining the Development Process (Academic, Amsterdam, 2014)

    Google Scholar 

  32. M. Sokolova, N. Japkowicz, S. Szpakowicz, Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation, in Australasian Joint Conference on Artificial Intelligence (Springer, Berlin, 2006), pp. 1015–1021

    Google Scholar 

  33. S. Treweek, E. Mitchell, M. Pitkethly, J. Cook, M. Kjeldstrøm, M. Johansen, T.K. Taskila, F. Sullivan, S. Wilson, C. Jackson, R. Jones, P. Lockhart, Strategies to improve recruitment to randomised controlled trials. Cochrane Database Syst. Rev. 14(4), MR000013 (2010)

    Google Scholar 

  34. Tufts: Csdd impact report - 89% of trials meet enrolment, but timelines slip, half of sites under-enrol, vol. 15(1) (2013). http://csdd.tufts.edu/files/uploads/jan-feb_2013_ir_summary.pdf

  35. C. Tuite, A. Agapitos, M. O’Neill, A. Brabazon, A preliminary investigation of overfitting in evolutionary driven model induction: implications for financial modelling, in Applications of Evolutionary Computation, ed. by C. Di Chio, A. Brabazon, G. Di Caro, R. Drechsler, M. Farooq, J. Grahl, G. Greenfield, C. Prins, J. Romero, G. Squillero, E. Tarantino, A.B. Tettamanzi, N. Urquhart, A.Ş.a. Uyar. Lecture Notes in Computer Science, vol. 6625 (Springer, Berlin, 2011), pp. 120–130

    Google Scholar 

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Acknowledgements

This research is based upon work supported by ICON Plc.

Author information

Authors and Affiliations

  1. Natural Computing Research & Applications Group, School of Business, University College Dublin, Dublin, Ireland

    Gilyana Borlikova & Michael O’Neill

  2. ICON plc, Dublin, Ireland

    Louis Smith & Michael Phillips

Authors
  1. Gilyana Borlikova
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  2. Louis Smith
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  3. Michael Phillips
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  4. Michael O’Neill
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Corresponding author

Correspondence to Gilyana Borlikova .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    Conor Ryan

  2. School of Business, University College Dublin, Dublin, Ireland

    Michael O'Neill

  3. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    JJ Collins

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Borlikova, G., Smith, L., Phillips, M., O’Neill, M. (2018). Business Analytics and Grammatical Evolution for the Prediction of Patient Recruitment in Multicentre Clinical Trials. In: Ryan, C., O'Neill, M., Collins, J. (eds) Handbook of Grammatical Evolution. Springer, Cham. https://doi.org/10.1007/978-3-319-78717-6_19

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  • DOI: https://doi.org/10.1007/978-3-319-78717-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78716-9

  • Online ISBN: 978-3-319-78717-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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