Objective    To investigate the risk factors for early relapse after curative resection of Siewert type II/III adenocarcinoma of the esophagogastric junction (AEG) and to construct a visual predictive model.     Methods    A retrospective analysis was conducted on the clinicopathological data of patients diagnosed with Siewert type II/III AEG who underwent curative resection at the Second Hospital of Lanzhou University from January 2016 to March 2021. The samples were randomly divided into a training group and a validation group in a 7:3 ratio. The LASSO-logistic regression method was used to select variables predictive of early recurrence of Siewert type II/III AEG and to construct a predictive model for early recurrence. The model was validated through 1000 bootstrap resampling. Receiver operating characteristic (ROC) curves， area under the curve (AUC)， calibration curves， and decision curve analysis (DCA) were used to evaluate the model's stability.     Results    According to the inclusion and exclusion criteria of this study， a total of 320 Siewert type II/III AEG patients were included， with 122 experiencing recurrence within two years and 198 without recurrence within the same timeframe. Lasso-logistic regression analysis revealed AJCC staging， degree of differentiation， CA199， CEA， NLR， and tumor maximum diameter as independent predictive factors for early recurrence of Siewert type II/III AEG. A predictive model was constructed with these factors and depicted as a nomogram. The AUC of the ROC curve for the training group was 0.836， 95%CI (0.785–0.887)， with a sensitivity of 81.4% and a specificity of 85.6%； for the validation group， the AUC was 0.812， 95%CI (0.711–0.912)， with a sensitivity of 80.6% and a specificity of 87.7%. Calibration curves for both the training and validation groups displayed curves close to the reference line， indicating high model stability. The DCA curve showed the model provided a good net benefit with threshold probabilities between 0.05 and 0.75.     Conclusions    A multivariate model to predict early relapse in patients with Siewert type II/III AEG was developed using LASSO-logistic regression， which may be instrumental in assessing patient prognoses and in guiding postoperative surveillance and management for patients with Siewert type II/III AEG.

Missing data commonly occur in clinical trials due to various reasons. The occurrence of missing data will cause information loss of the original data， and reduce the robustness and validity of the research results. Therefore， how to adequately deal with missing data should be considered with caution in clinical trials. This article introduces the causes and types of missing data， as well as several common methodological approaches to addressing missing data， aiming to promote researchers’ understanding and improve the quality of handling missing data in trials. Nevertheless， the best way to deal with missing data is to prevent or reduce the occurrence of data being missing in clinical trials， rather than relying on post hoc statistical analyses.