Estimating personal temporal symptom networks for childhood cancer survivors

Childhood cancer survivors experience persistent and evolving symptom burden post-therapy. Network analysis can help uncover the complex symptom patterns. However, current network analyses often rely on cross-sectional data and focus on average symptom patterns among survivors, overlooking individual heterogeneities.

We introduced an autoregressive logistic model with covariates to account for individual heterogeneities in network estimation and to construct personal temporal symptom networks. Simulation experiments were conducted to validate the robustness of this method in constructing personal temporal symptom networks. We also applied the autoregressive logistic model with covariates to longitudinal symptom data from a random sample of 2000 adult survivors of childhood cancer in the St. Jude Lifetime Cohort Study (SJLIFE).

Simulation studies demonstrate that the proposed method reliably recovers personal temporal symptom network structures under various conditions. In the real data application, older age, female sex, lower educational attainment, annual personal income <$20,000, and receipt of chemotherapy and/or radiation therapy are associated with stronger connections between symptoms at baseline and the first follow-up.

We demonstrate that the logistic autoregressive model with covariates effectively estimates personal temporal symptom networks for childhood cancer survivors, enabling personalized symptom monitoring and informing tailored symptom management strategies.