Efficacy of Episil® in patients with hematologic malignancies: a comparative study.

BACKGROUND: Episil® is a nonabsorbable liquid medical material used to coat and protect the mucosa in patients with oral mucositis. A few studies have reported its efficacy in patients with head and neck cancer. However, reports on its use in patients with hematologic malignancies are scarce. Thus, we aimed to evaluate the efficacy of Episil for the treatment of oral mucositis in patients with acute myelogenous leukemia, malignant lymphoma, acute lymphocytic leukemia, multiple myeloma, and myelodysplastic syndrome. METHODS: Between May 2018 and March 2019, a total of thirty-seven patients with acute myelogenous leukemia, malignant lymphoma, acute lymphocytic leukemia, multiple myeloma, and myelodysplastic syndrome who received Episil® for the treatment of oral mucositis were included in this study. All patients were treated at the Hiroshima Red Cross and Atomic-bomb Surgery Hospital. To determine the severity of oral mucositis, 22 out of the 37 patients were interviewed and compared objectively using the Common Terminology Criteria for Adverse Events, version 3.0. In addition, subjective measures of the effects of oral mucositis were assessed using an original evaluation protocol (a unique evaluation chart specific to the Department of Oral Surgery, Hiroshima Red Cross & Atomic-bomb Survivors Hospital). RESULTS: Out of 37 participants recruited in the study, 31 (84%) described the sensation of Episil® as very good or good. Moreover, the severity of mucositis was found to decrease after the use of Episil® in seven patients out of 22 (19%), particularly in those with mucositis at multiple sites. Participants' evaluations revealed pain relief and improvement in speech and feeding functions. Participants with grade 3 mucositis reported a greater improvement in pain relief, speech, and feeding functions than those with grade 2 mucositis. CONCLUSIONS: This study suggests the efficacy of Episil® in treating oral mucositis in patients with hematologic malignancies, particularly in those with oral mucositis at multiple sites. In addition to pain relief, Episil® may improve speech and feeding functions.

Empirical Correction to the Likelihood Ratio Statistic for Structural Equation Modeling with Many Variables.

Survey data typically contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. The most widely used statistic for evaluating the adequacy of a SEM model is T ML, a slight modification to the likelihood ratio statistic. Under normality assumption, T ML approximately follows a chi-square distribution when the number of observations (N) is large and the number of items or variables (p) is small. However, in practice, p can be rather large while N is always limited due to not having enough participants. Even with a relatively large N, empirical results show that T ML rejects the correct model too often when p is not too small. Various corrections to T ML have been proposed, but they are mostly heuristic. Following the principle of the Bartlett correction, this paper proposes an empirical approach to correct T ML so that the mean of the resulting statistic approximately equals the degrees of freedom of the nominal chi-square distribution. Results show that empirically corrected statistics follow the nominal chi-square distribution much more closely than previously proposed corrections to T ML, and they control type I errors reasonably well whenever N ≥ max(50,2p). The formulations of the empirically corrected statistics are further used to predict type I errors of T ML as reported in the literature, and they perform well.

A mathematical estimation of true cancer incidence using data from population-based cancer registries.

BACKGROUND: Accurate cancer incidence data are needed to plan, monitor and evaluate national cancer control programs. In Japan, however, such information is not available owing to incomplete cancer registries. In order to attain incidence estimation adjusted to account for this incomplete information, we have developed a new method. METHODS: We developed a nonlinear regression model between observed incidence/mortality ratios and proportions of death certificate notification to observed incidence in various cancer registries. This model enables us to obtain the 'true incidence/mortality ratio', which, in the regression curve, is at zero point for the proportion of death certificate notifications. This is an ideal registration state without any missing cases. By multiplying it by the number of cancer mortalities from the National Vital Statistics, corrected cancer incidence can be estimated. RESULTS: Applying this method for the estimation of the Japanese cancer incidence in 1997, we obtained the 'true incidence/mortality ratios' of 2.074 for men and 2.587 for women. Cancer incidences in Japan for 1997 were thus estimated to be 346,000 for men and 280,000 for women. CONCLUSIONS: A new method is proposed to estimate the national cancer incidence after adjusting for completeness of cancer registries. This method enables us to more accurately estimate the cancer incidence in a country where several cancer registries exist with various degrees of completeness of registration.

A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis.

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given sample size, also provides more accurate results than those based on standard asymptotics. But the procedure needs a matrix to play the role of the population covariance matrix. The closer the matrix is to the true population covariance matrix, the more valid the bootstrap inference is. The current paper proposes a class of covariance matrices by combining theory and data. Thus, a proper matrix from this class is closer to the true population covariance matrix than those constructed by any existing methods. Each of the covariance matrices is easy to generate and also satisfies several desired properties. An example with nine cognitive variables and a confirmatory factor model illustrates the details for creating population covariance matrices with different misspecifications. When evaluating the substantive model, bootstrap or simulation procedures based on these matrices will lead to more accurate conclusion than that based on artificial covariance matrices.

Four improved statistics for contrasting means by correcting skewness and kurtosis.

This paper is concerned with removing the influence of non-normality in the classical t-statistic for contrasting means. Using higher-order expansion to quantify the effect of non-normality, four corrected statistics are provided. Two aim to correct the mean bias and two to correct the overall distribution. The classical t-statistic is also robust against non-normality when the observed variables satisfy certain structures. A special case is when the marginal distributions of the contrast are independent and identically distributed.