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

Abstract

Yiwang Zhou,1 Madeline R Horan,2 Samira Deshpande,1 Kirsten K Ness,2 Melissa M Hudson,3 I-Chan Huang,2,* Deokumar Srivastava1,* 1Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, USA; 2Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, USA; 3Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA*These authors contributed equally to this workCorrespondence: Yiwang Zhou, Department of Biostatistics, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Mail Stop 768, Memphis, Tennessee, TN 38105, USA, Tel [phone], Email [email protected]: Childhood cancer survivors experience interconnected symptoms, patterns of which can be elucidated by network analysis. However, current symptom networks are constructed based on the average survivors without considering individual heterogeneities. We propose to evaluate personal symptom network estimation using the Ising model with covariates through simulations and estimate personal symptom network for adult childhood cancer survivors.Patients and Methods: We adopted the Ising model with covariates to construct networks by employing logistic regressions for estimating associations between binary symptoms. Simulation experiments assessed the robustness of this method in constructing personal symptom network. Real-world data illustration included 1708 adult childhood cancer survivors from the St. Jude Lifetime Cohort Study (SJLIFE), a retrospective cohort study with prospective follow-up to characterize the etiology and late effects for childhood cancer survivors. Patients’ baseline symptoms in 10 domains (cardiac, pulmonary, sensation, nausea, movement, pain, memory, fatigue, anxiety, depression) and individual characteristics (age, sex, race/ethnicity, attained education, personal income, and marital status) were self-reported using survey. Treatment variables (any chemo or radiation therapy) were obtained from medical records. Personal symptom network of 10 domains was estimated using the Ising model, incorporating individual characteristics and treatment data.Results: Simulations confirmed the robustness of the Ising model with covariates in constructing personal symptom networks. Real-world data analysis identified age, sex, race/ethnicity, education, marital status, and treatment (any chemo and radiation therapy) as major factors influencing symptom co-occurrence. Older childhood cancer survivors showed stronger cardiac-fatigue associations. Survivors of racial/ethnic minorities had stronger pain-fatigue associations. Female survivors with above-college education demonstrated stronger pain-anxiety associations. Unmarried survivors who received radiation had stronger association between movement and memory problems.Conclusion: The Ising model with covariates accurately estimates personal symptom networks. Individual heterogeneities exist in symptom co-occurrence patterns for childhood cancer survivors. The estimated personal symptom network offers insights into interconnected symptom experiences.Keywords: bootstrap testing, individual heterogeneity, Ising model, network analysis, sociodemographic

Keywords

• bootstrap testing
• individual heterogeneity
• ising model
• network analysis
• sociodemographic.