The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling

The present study investigated estimate biases in cross-classified random effect modeling (CCREM) and hierarchical linear modeling (HLM) when ignoring a crossed factor in CCREM considering the impact of the feeder and the magnitude of coefficients. There were six simulation factors: the magnitude of coefficient, the correlation between the level 2 residuals, the number of groups, the average number of individuals sampled from each group, the intra-unit correlation coefficient, and the number of feeders. The targeted interests of the coefficients were four fixed effects and two random effects. The results showed that ignoring a crossed factor in cross-classified data causes a parameter bias for the random effects of level 2 predictors and a standard error bias for the fixed effects of intercepts, level 1 predictors, and level 2 predictors. Bayesian information criteria generally outperformed Akaike information criteria in detecting the correct model.

Characteristics of PSA Bounce after Radiotherapy for Prostate Cancer: A Meta-Analysis

The rate and characteristics of prostate-specific antigen (PSA) bounce post-radiotherapy remain unclear. To address this issue, we performed a meta-analysis. Reports of PSA bounce post-radiotherapy with a cutoff of 0.2 ng/mL were searched by using Medline and Web of Science. The primary endpoint was the occurrence rate, and the secondary endpoints were bounce characteristics such as amplitude, time to occurrence, nadir value, and time to nadir. Radiotherapy modality, age, risk classification, androgen deprivation therapy, and the follow-up period were extracted as clinical variables. Meta-analysis and univariate meta-regression were performed with random-effect modeling. Among 290 search-positive studies, 50 reports including 26,258 patients were identified. The rate of bounce was 31%; amplitude was 1.3 ng/mL; time to occurrence was 18 months; nadir value was 0.5 ng/mL; time to nadir was 33 months. Univariate meta-regression analysis showed that radiotherapy modality (29.7%), age (20.2%), and risk classification (12.2%) were the major causes of heterogeneity in the rate of bounce. This is the first meta-analysis of PSA bounce post-radiotherapy. The results are useful for post-radiotherapy surveillance of prostate cancer patients.