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.

This study analyzes the factors contributing to the duration of severe oral mucositis in oncopediatric patients. A longitudinal study was conducted in the pediatric department of a cancer referral hospital between 2013 and 2017. Seventy-three patients diagnosed with cancer undergoing chemotherapy protocols were analyzed. Oral evaluations were performed using the Modified Oral Assessment Guide criteria, and the data were collected from the patients’ records. The Kaplan-Meier method was used to estimate survival curves. Most patients were males (52.1%), of mixed race (“pardo”) (49.3%), with a mean age of 7.56 years (±5.34). There was a predominance of patients diagnosed with solid tumors (52.1%), with no metastasis (86.3%), using natural product chemotherapeutics (56.2%), who had not undergone a bone marrow transplant (97.3%); amputation was observed in 35.6% of patients, while death rates were as high as 8.2%. The survival analysis estimated a mean time of 30.6 days until complete remission of severe oral mucositis. The regression analysis showed that patients over 10 years old had a median mucositis duration 1.4 times greater than those at the age of 10 years or younger. Patients without metastasis had a median mucositis duration 1.7 times greater than those with metastasis (p-value ≤ 0.10). Increasing age and the absence of metastasis were conditions that prolonged the duration of severe oral mucositis.

The beta prime regression model proposed by Bourguignon, Santos-Neto, and de Castro is an alternative to the generalized linear models and useful to model positive asymmetric data. In this paper, we propose two general misspecification tests based on the RESET test for beta prime regression models with varying precision. In the first test, we add the testing variable in the mean submodel, whereas the second test focuses on adding the testing variables in all submodels. We conduct an extensive Monte Carlo simulation study to evaluate the performance of the proposed tests in finite sample size in terms of their sizes and powers, thus obtaining information about the best combination of test statistics and testing variables to perform the proposed tests. We also present and discuss two empirical applications to show the applicability and importance of the proposed tests.

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.