This study used count-data model, combined with random effects of the survey plots, to develop forest stand level tree mortality models and to investigate the factors influencing tree mortality. The aim of the study was to provide a baseline for forest health monitoring and resource management. The data were consists of forest inventory plots from the continuous inventory of the eastern Texas forests, USA. The data were randomly divided into a ratio of 4 ∶ 1 for training and validation. The generalized linear models were developed using count-data method with a mixed-effect model to analyze the factors affecting tree mortality, used site factors, stand factors, and climate factors as independent variables, with the number of dead trees as dependent variable. Three model evaluation metrics, including Akaike information criterion (AIC), Bayesian information criterion (BIC), and -2-fold log-likelihood function value (-2logL), were used to assess the fitting effect among models. Two more evaluation metrics, mean absolute error (MAE) and root mean squared error (RMSE), were used to assess the prediction effect in order to screen out the optimal forest stand level mortality models. The results showed significantly negative correlation with elevation (P<0.01) and positive correlation with slope (P<0.05) between site factors and the number of dead trees. This indicates that the number of dead trees decreases with increasing elevation and increases with increasing slope. For the stand factors, the number of dead trees was significantly positively correlated with both stand age (P<0.001) and tree basal area (P<0.001), while it was significantly negatively correlated with stand squared mean diameter at breast height (P<0.001) and stand density (P<0.05). This suggests that the number of dead trees increases with the increase in stand age and tree basal area, while decreases with an increase in stand squared mean diameter at breast height and stand density. As for climate factors, the number of dead trees had significantly negative correlation with standardized precipitation evapotranspiration index (P<0.05), drought length (P<0.001), mean annual temperature (P<0.001), and the mean summer precipitation (P<0.05), but showed significantly positive correlation with mean summer temperature (P<0.001).This implies that the number of dead trees increases as drought intensity and mean summer temperature rise, whereas the numbers decrease as drought length, mean annual temperature, and mean summer precipitation increase. Among all base-count models, the zero-inflated negative binomial (ZINB) model had the best fit. The fitting accuracy of the mixed-effects model was significantly improved by adding the sample random effect. Based on the comparison of all model simulation results, we concluded that the ZINB-mixed model was the optimal model for the stand-level mortality model in the east of Texas forests.