Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling

BMC Medical Research Methodology - Tập 16 - Trang 1-10 - 2016
Claire Burny1,2,3,4, Muriel Rabilloud1,2,3,4, François Golfier2,3,5,6, Jérôme Massardier6,7, Touria Hajri2,3,6,8, Anne-Marie Schott2,3,8, Fabien Subtil1,2,3,4
1Service de Biostatistique, Hospices Civils de Lyon, Lyon, France
2Université de Lyon, Lyon, France
3Université Lyon 1, Villeurbanne, France
4CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé, Villeurbanne, France
5Department of Gynaecological and Oncological Surgery-Obstetrics, Lyon Sud University Hospital, Lyon, France
6French Trophoblastic Disease Reference Centre, Lyon Sud University Hospital, Lyon, France
7Department of Obstetrics, University Hospital Femme-Mère-Enfant, Lyon, France
8Pôle Information Médicale Evaluation Recherche, Equipe d’Accueil 4129, Hospices Civils de Lyon, Lyon, France

Tóm tắt

In randomized clinical trials or observational studies, it is common to collect biomarker values longitudinally on a cohort of individuals. The investigators may be interested in grouping individuals that share similar changes of biomarker values and use these groups for diagnosis or therapeutic purposes. However, most classical model-based classification methods rely mainly on empirical models such as splines or polynomials and do not reflect the physiological processes. A model-based classification method was developed for longitudinal biomarker measurements through a pharmacokinetic model that describes biomarker changes over time. The method is illustrated using data on human Chorionic Gonadotrophic Hormone measurements after curettage of hydatidiform moles. The resulting classification was linked to the evolution toward gestational trophoblastic neoplasia and may be used as a tool for early diagnosis. The diagnostic accuracy of the pharmacokinetic model was more reproducible than the one of a purely mathematical model that did not take into account the biological processes. The use of pharmacokinetic models in model-based classification approaches can lead to clinically useful classifications.

Tài liệu tham khảo

Subtil F, Rabilloud M. Robust non-linear mixed modelling of longitudinal PSA levels after prostate cancer treatment. Stat Med. 2010;29:573–87.

Josephson MA. Monitoring and managing graft health in the kidney transplant recipient. Clin J Am Soc Nephrol. 2011;6:1774–80.

Fraley C, Raftery AE. Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc. 2002;97:611–31.

Genolini C, Falissard B. KmL: a package to cluster longitudinal data. Comput Methods Programs Biomed. 2011;104:e112–21.

Giacofci M, Lambert-Lacroix S, Marot G, Picard F. Wavelet-based clustering for mixed-effects functional models in high dimension. Biometrics. 2013;69:31–40.

Sheiner LB, Steimer JL. Pharmacokinetic/pharmacodynamic modeling in drug development. Annu Rev Pharmacol Toxicol. 2000;40:67–95.

Soper JT. Gestational trophoblastic disease. Obstet Gynecol. 2006;108:176–87.

Schmitt C, Doret M, Massardier J, Hajri T, Schott AM, Raudrant D, et al. Risk of gestational trophoblastic neoplasia after hCG normalisation according to hydatidiform mole type. Gynecol Oncol. 2013;130:86–9.

Wolfberg AJ, Feltmate C, Goldstein DP, Berkowitz RS, Lieberman E. Low risk of relapse after achieving undetectable HCG levels in women with complete molar pregnancy. Obstet Gynecol. 2004;104:551–4.

Kohorn EI. The new FIGO 2000 staging and risk factor scoring system for gestational trophoblastic disease: Description and critical assessment. Int J Gynecol Cancer. 2001;11:73–7.

Schoeberl MR. A model for the behavior of beta-hCG after evacuation of hydatidiform moles. Gynecol Oncol. 2007;105:776–9.

You B, Pollet-Villard M, Fronton L, Labrousse C, Schott AM, Hajri T, et al. Predictive values of hCG clearance for risk of methotrexate resistance in low-risk gestational trophoblastic neoplasias. Ann Oncol. 2010;21:1643–50.

Zagars GK, Pollack A. The fall and rise of prostate-specific antigen. Kinetics of serum prostate-specific antigen levels after radiation therapy for prostate cancer. Cancer. 1993;72:832–42.

Taylor JM, Yu M, Sandler HM. Individualized predictions of disease progression following radiation therapy for prostate cancer. J Clin Oncol. 2005;23:816–25.

Bellera CA, Hanley JA, Joseph L, Albertsen PC. Hierarchical changepoint models for biochemical markers illustrated by tracking postradiotherapy prostate-specific antigen series in men with prostate cancer. Ann Epidemiol. 2008;18:270–82.

Symons MJ. Clustering Criteria and Multivariate Normal Mixtures. Biometrics. 1981;37:35–43.

Celeux G, Govaert G. A classification EM algorithm for clustering and two stochastic versions. Comput Stat Data Anal. 1992;14:315–32.

Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B Stat Methodol. 1977;39:1–38.

Biernacki C, Celeux G, Govaert G, Langrognet F. Model-based cluster and discriminant analysis with the MIXMOD software. Comput Stat Data Anal. 2006;51:587–600.

Biernacki C, Celeux G, Govaert G. Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Comput Stat Data Anal. 2003;41:561–75.

R Development Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2012.

Petzold L. Automatic Selection of Methods for Solving Stiff and Nonstiff Systems of Ordinary Differential Equations. SIAM J Sci Comput. 1983;4:136–48.

Pereyra V. Iterative Methods for Solving Nonlinear Least Squares Problems. SIAM J Numer Anal. 1967;4:27–36.

Bates DM, Watts DG. Nonlinear Regression Analysis and Its Applications. New York: Wiley-Interscience; 2007.

Biernacki C, Celeux G, Govaert G. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans Pattern Anal Mach Intell. 2000;22:719–25.

Stone M. Cross-Validatory Choice and Assessment of Statistical Predictions. J R Stat Soc Series B Stat Methodol. 1974;36:111–47.

Smith GC, Seaman SR, Wood AM, Royston P, White IR. Correcting for optimistic prediction in small data sets. Am J Epidemiol. 2014;180:318–24.

Tornøe CW, Agersø H, Jonsson EN, Madsen H, Nielsen HA. Non-linear mixed-effects pharmacokinetic/pharmacodynamic modelling in NLME using differential equations. Comput Methods Programs Biomed. 2004;76:31–40.

Muthén B. Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In: Kaplan D, editor. The SAGE Handbook of Quantitative Methodology for the Social Sciences. Thousand Oaks: SAGE Publications; 2004.

Proust-Lima C, Letenneur L, Jacqmin-Gadda H. A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome. Stat Med. 2007;26:2229–45.

Neelon B, Swamy GK, Burgette LF, Miranda ML. A Bayesian growth mixture model to examine maternal hypertension and birth outcomes. Stat Med. 2011;30:2721–35.

Muthén B, Shedden K. Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics. 1999;55:463–9.

Lavielle M, Mbogning C. An improved SAEM algorithm for maximum likelihood estimation in mixtures of non linear mixed effects models. Stat Comput. 2013;24:1–15.

Lu X, Huang Y. Bayesian analysis of nonlinear mixed-effects mixture models for longitudinal data with heterogeneity and skewness. Stat Med. 2014;33:2830–49.

Loh W-Y, Zheng W. Regression trees for longitudinal and multiresponse data. Ann Appl Stat. 2013;7:495–522.

Nagin DS. Group-Based Modeling of Development. Cambridge: Harvard University Press; 2005.

Milligan GW, Cooper MC. An examination of procedures for determining the number of clusters in a data set. Psychometrika. 1985;50:159–79.

Shim Y, Chung J, Choi I. A comparison study of cluster validity indices using a nonhierarchical clustering algorithm. In: Proceedings of the Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce. Vienna: IEEE; 2005. p. 199–204.

Everitt B, Landau S, Leese M. Cluster Analysis. 4th ed. London: Arnold; 2001.

Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38.

Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Modeling. 2007;4:535–69.

Marquardt DW. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM J Numer Anal. 1963;11:431–41.

Thông tin
Thông tin xuất bản

BMC Medical Research Methodology

Tập 16

1-10

Thông tin tác giả