On a tobit–Birnbaum–Saunders model with an application to medical data

Impacts of COVID-19 vaccine boosters on clinical outcomes associated with the Omicron variant in China: A cross-sectional survey

Objective

To investigate the real-world effectiveness of COVID-19 vaccine boosters during China's Omicron wave.

Methods

In January 2023, we surveyed Shenzhen, China residents via online questionnaires to investigate their COVID-19 symptoms and vaccination history. The outcomes of interest included fever, other COVID-19-related symptoms, severity of symptoms, whether early onset (before December 23, 2022) and duration. Respondents were categorized as no booster, one booster 6mo ago, one booster within 6mo, or two boosters based on dose count and vaccination timing. We used multivariable logistic regressions and Tobit models to assess COVID-19 vaccine booster impacts.

Results

Compared to the no booster group, two booster recipients had a lower fever risk (OR = 0.35, 95 %CI = 0.16-0.76) but not lower risks of COVID-19-related symptoms (OR = 0.74, 95 %CI = 0.26-2.06) and self-reported severe symptoms (OR = 0.47, 95 %CI = 0.19-1.15). Nor did the two booster recipients had a shorter illness duration (marginal effect = -0.79 days, 95 %CI = -1.65-0.07) and a lower risk of symptom onset delay (OR = 0.48, 95 %CI = 0.19-1.23). Compared to the no booster group, both one booster within six months (OR = 2.17, 95 %CI = 1.34-3.52) and one booster six months ago (OR = 1.30, 95 %CI = 0.92-1.82) did not reduce the risks of fever and symptoms (one booster within six months: OR = 1.57, 95 %CI = 0.84-2.90; one booster six months ago: OR = 1.23, 95 %CI = 0.79-1.93). Regardless of timing, one booster did not reduce illness duration (within six months: marginal effect = 0.25 days, 95 %CI = -0.20-0.70; six months ago: marginal effect = 0.27 days, 95 %CI = -0.08-0.62). However, receiving one booster within six months delayed symptom onset (OR = 0.54, 95 %CI = 0.34-0.86), while one booster six months ago did not (OR = 1.03, 95 %CI = 0.74-1.44).

Conclusions

Receiving two booster doses reduced the onset of fever during the Omicron outbreak in mainland China.

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making. In this work, a methodology of data-influence analytics based on mixed-effects logistic regression models is proposed for detecting potentially influential observations which can affect the quality of these models. Global and local influence diagnostic techniques are used simultaneously in this detection, which are often used separately. In addition, predictive performance measures are considered for this analytics. A study with children and adolescent asthma real data, collected from a public hospital of São Paulo, Brazil, is conducted to illustrate the proposed methodology. The results show that the influence diagnostic methodology is helpful for obtaining an accurate predictive model that provides scientific evidence when data-driven medical decision-making.

A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess

In this paper, we consider skew-normal distributions for constructing new a distribution which allows us to model proportions and rates with zero/one inflation as an alternative to the inflated beta distributions. The new distribution is a mixture between a Bernoulli distribution for explaining the zero/one excess and a censored skew-normal distribution for the continuous variable. The maximum likelihood method is used for parameter estimation. Observed and expected Fisher information matrices are derived to conduct likelihood-based inference in this new type skew-normal distribution. Given the flexibility of the new distributions, we are able to show, in real data scenarios, the good performance of our proposal.