Estimation of Personal Symptom Networks Using the Ising Model for Adult Survivors of Childhood Cancer: A Simulation Study with Real-World Data Application

References

• Hong HC, Min A, Kim YM. A systematic review and pooled prevalence of symptoms among childhood and adolescent and young adult cancer survivors. J Clin Nurs. 2023;32(9–10):1768–1794. doi:10.1111/jocn.16201
   PubMed Web of Science ®Google Scholar
• Shin H, Dudley WN, Bhakta N, et al. Associations of symptom clusters and health outcomes in adult survivors of childhood cancer: a report from the St Jude Lifetime Cohort Study. J Clin Oncol. 2023;41(3):497–507. doi:10.1200/JCO.22.00361
   PubMed Web of Science ®Google Scholar
• Huang I-C, Brinkman TM, Kenzik K, et al. Association between the prevalence of symptoms and health-related quality of life in adult survivors of childhood cancer: a report from the St Jude Lifetime Cohort study. J Clin Oncol. 2013;31(33):4242. doi:10.1200/JCO.2012.47.8867
   PubMed Web of Science ®Google Scholar
• McNally RJ. Can network analysis transform psychopathology? Behav Res Therap. 2016;86:95–104. doi:10.1016/j.brat.2016.06.006
   PubMed Web of Science ®Google Scholar
• Barzel B, Barabasi AL. Universality in network dynamics. Nat Phys. 2013;9:673–681. doi:10.1038/nphys2741
   PubMed Web of Science ®Google Scholar
• Liu YY, Slotine JJ, Barabasi AL. Controllability of complex networks. Nature. 2011;473(7346):167–173. doi:10.1038/nature10011
   PubMed Web of Science ®Google Scholar
• Strogatz SH. Exploring complex networks. nature. 2001;410(6825):268–276. doi:10.1038/35065725
   PubMed Web of Science ®Google Scholar
• Dorogovtsev SN, Mendes JF. Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford university press; 2003.
   Google Scholar
• Albert R, Barabási A-L. Statistical mechanics of complex networks. Rev Mod Phys. 2002;74(1):47. doi:10.1103/RevModPhys.74.47
   Web of Science ®Google Scholar
• Papachristou N, Barnaghi P, Cooper B, et al. Network analysis of the multidimensional symptom experience of oncology. Sci Rep. 2019;9(1):2258. doi:10.1038/s41598-018-36973-1
   PubMedGoogle Scholar
• Cramer AO, Waldorp LJ, van der Maas HLJ, et al. Comorbidity: a network perspective. Behav Brain Sci. 2010;33(2–3):137–150. doi:10.1017/S0140525X09991567
   PubMed Web of Science ®Google Scholar
• Borsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Annual Rev Clin Psych. 2013;9:91–121. doi:10.1146/annurev-clinpsy-050212-185608
   PubMed Web of Science ®Google Scholar
• Borsboom D. Psychometric perspectives on diagnostic systems. J Clin Psych. 2008;64(9):1089–1108. doi:10.1002/jclp.20503
   PubMed Web of Science ®Google Scholar
• Borgatti SP, Mehra A, Brass DJ, et al. Network analysis in the social sciences. science. 2009;323(5916):892–895. doi:10.1126/science.1165821
   PubMed Web of Science ®Google Scholar
• Van Der Maas HL, Dolan CV, Grasman RPPP, et al. A dynamical model of general intelligence: the positive manifold of intelligence by mutualism. Psychol Rev. 2006;113(4):842. doi:10.1037/0033-295X.113.4.842
   PubMed Web of Science ®Google Scholar
• Shim EJ, Ha H, Suh Y-S, et al. Network analyses of associations between cancer‐related physical and psychological symptoms and quality of life in gastric cancer patients. Psycho‐Oncology. 2021;30(6):946–953. doi:10.1002/pon.5681
   PubMed Web of Science ®Google Scholar
• Kalantari E, Kouchaki S, Miaskowski C, et al. Network analysis to identify symptoms clusters and temporal interconnections in oncology patients. Sci Rep. 2022;12(1):17052. doi:10.1038/s41598-022-21140-4
   PubMed Web of Science ®Google Scholar
• Oeffinger KC, Mertens AC, Sklar CA, et al. Chronic health conditions in adult survivors of childhood cancer. N Engl J Med. 2006;355(15):1572–1582. doi:10.1056/NEJMsa060185
   PubMed Web of Science ®Google Scholar
• Cheng J, Levina E, Wang P, et al. A sparse Ising model with covariates. Biometrics. 2014;70(4):943–953. doi:10.1111/biom.12202
   PubMed Web of Science ®Google Scholar
• Haslbeck J. Estimating group differences in network models using moderation analysis. PsyArXiv. 2020.
   Google Scholar
• Haslbeck JM, Borsboom D, Waldorp LJ. Moderated network models. Multivariate Behavioral Research. 2021;56(2):256–287. doi:10.1080/00273171.2019.1677207
   PubMed Web of Science ®Google Scholar
• Qian M, Murphy SA. Performance guarantees for individualized treatment rules. Ann Stat. 2011;39(2):1180. doi:10.1214/10-AOS864
   PubMed Web of Science ®Google Scholar
• Molenaar PC. A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Measurement. 2004;2(4):201–218.
   Google Scholar
• Epskamp S. Psychometric network models from time-series and panel data. Psychometrika. 2020;85(1):206–231. doi:10.1007/s11336-020-09697-3
   PubMed Web of Science ®Google Scholar
• Epskamp S, Hoekstra RH, Burger J, et al. Longitudinal Design choices: Relating data to analysis. In: Network Psychometrics with R. Routledge; 2022:157–168.
   Google Scholar
• Foygel R, Drton M. Extended bayesian information criteria for gaussian graphical models. Adv Neural Inform Processi Syst. 2010;23:23.
   Google Scholar
• Barber RF, Drton M, High-dimensional Ising model selection with Bayesian information criteria. 2015.
   Google Scholar
• Van Borkulo CD, Borsboom D, Epskamp S, et al. A new method for constructing networks from binary data. Sci Rep. 2014;4(1):5918. doi:10.1038/srep05918
   PubMedGoogle Scholar
• Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Meth. 2018;50:195–212. doi:10.3758/s13428-017-0862-1
   PubMed Web of Science ®Google Scholar
• Koller D, Friedman N. Probabilistic Graphical Models: Principles and Techniques. MIT press; 2009.
   Google Scholar
• Loh P-L, Wainwright MJ. Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. Adv Neural Inform Process Syst. 2012;25.
   PubMedGoogle Scholar
• Ravikumar P, Wainwright MJ, Lafferty JD, High-dimensional Ising model selection using ℓ 1-regularized logistic regression. 2010.
   Google Scholar
• Marsman M, Huth K, Waldorp LJ, et al. Objective Bayesian edge screening and structure selection for ising networks. psychometrika. 2022;87(1):47–82. doi:10.1007/s11336-022-09848-8
   PubMed Web of Science ®Google Scholar
• Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychological Methods. 2018;23(4):617. doi:10.1037/met0000167
   PubMed Web of Science ®Google Scholar
• Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Societ Series B. 1996;58(1):267–288. doi:10.1111/j.2517-6161.1996.tb02080.x
   Google Scholar
• Chen J, Chen Z. Extended Bayesian information criteria for model selection with large model spaces. Biometrika. 2008;95(3):759–771. doi:10.1093/biomet/asn034
   Web of Science ®Google Scholar
• Meinshausen N, Bühlmann P, High-dimensional graphs and variable selection with the lasso. 2006.
   Google Scholar
• Rinaldo A, Wasserman L, G’Sell M, Bootstrapping and sample splitting for high-dimensional, assumption-lean inference. 2019.
   Google Scholar
• Howell CR, Bjornard KL, Ness KK, et al. Cohort profile: The St. Jude lifetime cohort study (SJLIFE) for paediatric cancer survivors. Int J Epidemiol. 2021;50(1):39–49. doi:10.1093/ije/dyaa203
   PubMed Web of Science ®Google Scholar
• Finnemann A, Borsboom D, Epskamp S, et al. The theoretical and statistical Ising model: a practical guide in R. Psych. 2021;3(4):593–617. doi:10.3390/psych3040039
   Google Scholar