Journal of Biometrics & Biostatistics

Objectives: This present study aimed to select the best count distributions for missing teeth in elders and to investigate the relationship between missing teeth and the predictors.

Materials and methods: Data were extracted from the biennial survey of 2015-2016 the U. S. National Health and Nutrition Examination Survey. Only adults aged 60 years or over who completed oral health examination and demographics interview were included. Descriptive statistics were used to demonstrate the basic information of this studied population. The performances of four different count regression models (Poisson regression, negative binomial regression with linear variance function (NB1), negative binomial regression with quadratic variance function (NB2), and zero-inflated negative binomial regression) were compared through different approaches including the values of model fit test statistics such as Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC), the magnitude of standard errors and a visual graph on the performances of fitted models.

Results: The disparities on missing teeth existed in old adults by poverty and educational level and race/ethnicity. More missing teeth were found in participants who are Blacks (mean=13.89), with less education (​​<12years) (mean: 13.11). Significance of t-test for “α”  indicated that Poisson distribution is not appropriate for missing teeth due to overdispersion. NB1 is the best model with the smallest AIC and BIC and the smallest standard errors of parameter estimates compared to other three candidate models.

Conclusion: The negative binomial distribution with linear variance function is the best distribution. Due to the fact of missing teeth which ranged from 0 to 28, the caution should be given when we interpreted the fitted model using NB1 as the missing teeth are close to 0 and 28.

This paper is an application of randomization test for clinical, four ways cross over, trials. The response variable was the proportion of nights with hypoglycaemic episode i.e., lowering the concentration of sugar in the blood. The hypothetical data has been used to examine how persuasively the probability of an episode depends on doses. We also observed how the power, of nonparametric randomization test, was affected when data possessed missing observations with varying sample sizes at 5% level of significance. One consequence in case of missing observations was the reduction of the power of the test, due to the reduction in the actual sample size. As a remedial mean imputation approach used, dose wise and found better power results.