This paper investigates the occurrence of severe oral mucositis and associated factors in blood and solid cancer pediatric patients subjected to cancer treatment, using a survival analysis. A longitudinal, descriptive, observational and inductive study of 142 pediatric patients aged from 0 to 19 years was conducted from 2013 to 2017. Data were collected using a form to record the sociodemographic characteristics and health-related aspects of patients and the modified Oral Assessment Guide (OAG). Survival analysis was performed using the Kaplan–Meier method and Cox semiparametric model. The median times to occurrence of severe oral mucositis were 35.3 and 77.1 days for patients with hematologic malignancies and solid tumors, respectively. The Cox model showed that white cell changes and platelet counts as well as the use of natural chemotherapeutic agents are risk factors for the occurrence of oral mucositis among patients with hematologic malignancies. Nonetheless, among patients with solid tumors, the occurrence of oral mucositis was associated with female sex, mixed ethnicity, the presence of metastasis, abnormal creatinine levels, a combination of chemotherapy, radiotherapy, and surgery, and the administration of chemotherapeutic agents included in the miscellaneous group. The time to occurrence of severe oral mucositis and its associated factors varied according to cancer type.

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This paper provides a general framework for controlling quality characteristics related to control variables and limited to the intervals (0, 1], [0, 1), or [0, 1]. The proposed control chart is based on the inflated beta regression model considering a reparametrization of the inflated beta distribution indexed by the response mean, which is useful for modeling fractions and proportions. The contribution of the paper is twofold. First, we extend the inflated beta regression model by allowing a regression structure for the precision parameter. We also present closed-form expressions for the score vector and Fisher’s information matrix. Second, based on the proposed regression model, we introduce a new model-based control chart. The control limits are obtained considering the estimates of the inflated beta regression model parameters. We conduct a Monte Carlo simulation study to evaluate the performance of the proposed regression model estimators, and the performance of the proposed control chart is evaluated in terms of run length distribution. Finally, we present and discuss an empirical application to show the applicability of the proposed regression control chart.