The analysis--hierarchical models: past, present and future

Dropout in Online Education: A Longitudinal Multilevel Analysis of Elementary Students' Extracurricular English Course Engagement in China

While high dropout rates hinder the success of online education, little is known about the patterns of dropout beyond adult education, particularly regarding time-related variables. This study aims to address this gap by analyzing data from 219 online extracurricular English courses, involving approximately 300,000 Chinese elementary students. We employed a multilevel modeling technique and found that the dropout rate increased throughout the semester and peaked at chapter transitions. Furthermore, we also found that the dropout rate varied with grades and semesters. Younger students and those in the second semester were more likely to drop out from online education and exhibited a higher dropout rate during chapter transitions. These findings highlight the temporal dynamics of dropout in elementary online education in China and the variations across grade and semester, providing valuable implications for educators in designing more effective online learning environments. Future studies should further explore the generalizability of these patterns in other educational and cultural contexts.

Primary healthcare providers' knowledge, practices and beliefs relating to preventive sexual and reproductive health care for women from refugee and asylum-seeking backgrounds in Australia: a national cross-sectional survey

Background Many refugee women and women seeking asylum arrive in high-income countries with unmet preventive sexual and reproductive health (SRH) care needs. Primary healthcare providers (HCPs) are usually refugee and asylum seekers' first point of care. This study aimed to identify HCP characteristics associated with initiating conversations and discussing SRH opportunistically during other health interactions. Methods An anonymous online survey was distributed nationally to representatives of health professional organisations and Primary Health Networks. Hierarchical logistic regression analysed factors including HCP demographics, knowledge and awareness, perceived need for training and professional experience with refugee women were included in the models. Results Among 163 HCPs, those initiating conversations ranged from 27.3% (contraceptive care) to 35.2% (cervical screening). Opportunistic discussions ranged from 26.9% (breast screening) to 40.3% (contraceptive care). Positively associated factors included offering care to refugee women or women seeking asylum at least once every 2months 7.64 (95% CI 2.41;24.22, P P P P P P Conclusions Direct professional experience, frequency of service provision, years of practice, and part time work positively influence HCPs' SRH care practices. Enhancing bilingual health worker programs, outreach, education, and support for SRH and cultural competency training are essential to improving the preventive SRH care of refugee women and women seeking asylum.

Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers

A hierarchical machine learning (HML) framework is presented that uses a small dataset to learn and predict the dominant build parameters necessary to print high-fidelity 3D features of alginate hydrogels. We examine the 3D printing of soft hydrogel forms printed with the freeform reversible embedding of suspended hydrogel method based on a CAD file that isolated the single-strand diameter and shape fidelity of printed alginate. Combinations of system variables ranging from print speed, flow rate, ink concentration to nozzle diameter were systematically varied to generate a small dataset of 48 prints. Prints were imaged and scored according to their dimensional similarity to the CAD file, and high print fidelity was defined as prints with less than 10% error from the CAD file. As a part of the HML framework, statistical inference was performed, using the least absolute shrinkage and selection operator to find the dominant variables that drive the error in the final prints. Model fit between the system parameters and print score was elucidated and improved by a parameterized middle layer of variable relationships which showed good performance between the predicted and observed data (R² = 0.643). Optimization allowed for the prediction of build parameters that gave rise to high-fidelity prints of the measured features. A trade-off was identified when optimizing for the fidelity of different features printed within the same construct, showing the need for complex predictive design tools. A combination of known and discovered relationships was used to generate process maps for the 3D bioprinting designer that show error minimums based on the chosen input variables. Our approach offers a promising pathway toward scaling 3D bioprinting by optimizing print fidelity via learned build parameters that reduce the need for iterative testing.

Culling from the herd's perspective-Exploring herd-level management factors and culling rates in Québec dairy herds

The relationship between cows' health, reproductive performance or disorders and their longevity is well demonstrated in the literature. However these associations at the cow level might not hold true at the herd level, and herd-level variables can modify cow-level outcomes independently of the cows' characteristics. The interaction between cow-level and herd-level variables is a relevant issue for understanding the culling of dairy cows. However it requires the appropriate group-level variables to assess any contextual effect. Based on 10 years of health and production data, the objectives of this paper are:(a) to quantify the culling rates of dairy herds in Québec; (b) to determine the profiles of the herds based on herd-level factors, such as demographics, reproduction, production and health indicators, and whether these profiles can be related to herd culling rates for use as potential contextual variables in multilevel modelling of culling risk. A retrospective longitudinal study was conducted on data from dairy herds in Québec, Canada, by extracting health information events from the dairy herd health management software used by most Québec producers and their veterinarians. Data were extracted for all lactations taking place between January 1st, 2001 and December 31st, 2010. A total of 432,733 lactations from 156,409 cows out of 763 herds were available for analysis. Thirty cow-level variables were aggregated for each herd and years of follow-up, and their relationship was investigated by Multiple Factor Analysis (MFA). The overall annual culling rate was 32%, with a 95% confidence interval (CI) of [31.6%,32.5%]. The dairy sale rate by 60 days in milk (DIM) was 3.2% [2.8%,3.6%]. The annual culling rate within 60 DIM was 8.2% [7.9%,8.4%]. The explained variance for each axis from the MFA was very low: 14.8% for the first axis and 13.1% for the second. From the MFA results, we conclude there is no relationship between the groups of herd-level indicators, demonstrating the heterogeneity among herds for their demographics, reproduction and production performance, and health status. However, based on Principal Component Analysis (PCA), the profiles of herds could be determined according to specific, single, herd-level indicators independently. The relationships between culling rates and specific herd-level variables within factors were limited to livestock sales, proportion of first lactation cows, herd size, proportion of calvings occurring in the fall, longer calving intervals and reduced 21-day pregnancy rates, increased days to first service, average age at first calving, and reduced milk fever incidence. The indicators found could be considered as contextual variables in multilevel model-building strategies to investigate cow culling risk.

Contextual herd factors associated with cow culling risk in Québec dairy herds: A multilevel analysis

Several health disorders, such as milk fever, displaced abomasum, and mastitis, as well as impaired reproductive performance, are known risk factors for the removal of affected cows from a dairy herd. While cow-level risk factors are well documented in the literature, herd-level associations have been less frequently investigated. The objective of this study was to investigate the effect of cow- and herd-level determinants on variations in culling risk in Québec dairy herds: whether herd influences a cow's culling risk. For this, we assessed the influence of herd membership on cow culling risk according to displaced abomasum, milk fever, and retained placenta. A retrospective longitudinal study was conducted on data from dairy herds in the Province of Québec, Canada, by extracting health information events from the dairy herd health management software used by most Québec dairy producers and their veterinarians. Data were extracted for all lactations starting between January 1st and December 31st, 2010. Using multilevel logistic regression, we analysed a total of 10,529 cows from 201 herds that met the inclusion criteria. Milk fever and displaced abomasum were demonstrated to increase the cow culling risk. A minor general herd effect was found for the culling risk (i.e. an intra-class correlation of 1.0% and median odds ratio [MOR] of 1.20). The proportion of first lactation cows was responsible for this significant, but weak herd effect on individual cow culling risk, after taking into account the cow-level factors. On the other hand, the herd's average milk production was a protective factor. The planning and management of forthcoming replacement animals has to be taken into consideration when assessing cow culling risks and herd culling rates.

Can hierarchical modeling improve our understanding of bovine abortion due to Neospora caninum infection?

The purpose of this study was to use mixed logistic regression to evaluate the association between Neospora caninum serostatus and abortion, accounting for the effects of farms. The main objective was to explore the interpretation of this model and discuss the contribution of this analysis to our understanding of the disease's epidemiology. A mixed-effects logistic model using farms as a random effect and the serostatus for N. caninum, age of cattle and farm location as fixed effects was performed. The data from 1256 cows over 15 months of age from 60 farms were used, and the abortion information was obtained from farm records. A significant association between N. caninum serostatus and abortion was found (p<0.0001). Seropositive cows had 6.63 times greater odds of having a history of previous abortion (95% CI: 4.35-13.37). There was remarkable variability across farms in the probability of a cow having a history of an abortion. Including the effects of the farms in the regression, it was possible to estimate an intraclass correlation coefficient (ICC) of 16%. That means that 16% of the variation in abortion occurrence that was not explained by the fixed effects was due to farms. In practical terms, this variation means that while there are farms with several seropositive cows and no/few abortion cases, the opposite is also true, with a high number of abortions in farms with low/medium seroprevalence. The remaining variability (84%) was not explained by the effect of N. caninum, age, region, and the effect of farms, i.e., it is due to unknown factors that are causing abortions. The results of this study reinforce the importance of N. caninum as a cause of abortions and demonstrate the significant heterogeneity in the probability of a cow having a history of an abortion that cannot be explained completely by N. caninum serostatus, age or location. Including the hierarchical structure of the population along with correct interpretation of the model estimates helps us understand the effect of the farms, i.e., the probability of a cow abortion conditional to the farms, which also contributes to our understanding of the epidemiology of abortions caused by neosporosis. The use of hierarchical models and reporting the ICC should be encouraged.

Game-Related Performance Factors in four European Men's Professional Volleyball Championships

The present study was designed to assess the relevance of game-related performance factors as outcome predictors in high-level volleyball. To carry out the analysis, the official box scores of 399 matches played by 47 different teams in four different European male professional volleyball leagues (Italy, Poland, Germany and Turkey) during the 2013-14 regular season were analyzed. A logistic mixed model was performed to determine the effects of different variables in matches’ outcomes. According to the multivariate analysis the following factors were significantly associated with winning matches: the number of scorers (OR = 1.32; CI: 1.09 – 1.59), service errors (OR = 0.91; CI: 0.87 – 0.95), service points (OR = 1.25; CI: 1.15 -1.36), reception errors (OR = 0.79; CI: 0.74 – 0.84), the percentage of positive receptions (OR = 1.02; CI: 1.00 -1.04) and blocked balls (OR = 1.17; CI: 1.11 – 1.26). Team category 2 (OR = 0.39; CI: 0.24 – 0.63) and team category 3 (OR = 0.15; CI: 0.09 – 0.25) were significantly associated with losing matches. These findings can contribute to a better understanding of performance indicators in professional volleyball, helping coaches and decision makers to better determine the importance of particular game factors.

Investigation of the association between the test day milk fat-protein ratio and clinical mastitis using a Poisson regression approach for analysis of time-to-event data

The objective of the present study was to investigate the association between the milk fat-protein ratio and the incidence rate of clinical mastitis including repeated cases of clinical mastitis to determine the usefulness of this association to monitor metabolic disorders as risk factors for udder health. Herd records from 10 dairy herds of Holstein cows in Saxony, Germany, from September 2005-2011 (36,827 lactations of 17,657 cows) were used for statistical analysis. A mixed Poisson regression model with the weekly incidence rate of clinical mastitis as outcome variable was fitted. The model included repeated events of the outcome, time-varying covariates and multilevel clustering. Because the recording of clinical mastitis might have been imperfect, a probabilistic bias analysis was conducted to assess the impact of the misclassification of clinical mastitis on the conventional results. The lactational incidence of clinical mastitis was 38.2%. In 36.2% and 34.9% of the lactations, there was at least one dairy herd test day with a fat-protein ratio of <1.0 or >1.5, respectively. Misclassification of clinical mastitis was assumed to have resulted in bias towards the null. A clinical mastitis case increased the incidence rate of following cases of the same cow. Fat-protein ratios of <1.0 and >1.5 were associated with higher incidence rates of clinical mastitis depending on week in milk. The effect of a fat-protein ratio >1.5 on the incidence rate of clinical mastitis increased considerably over the course of lactation, whereas the effect of a fat-protein ratio <1.0 decreased. Fat-protein ratios <1.0 or >1.5 on the precedent test days of all cows irrespective of their time in milk seemed to be better predictors for clinical mastitis than the first test day results per lactation.

Determinants of End-of-Life Expenditures in Patients with Oral Cancer in Taiwan: A Population-Based Study

BACKGROUND

To investigate the association of basic demographic data, socioeconomic status, medical services, and hospital characteristics with end-of-life expenditure in patients with oral cancer in Taiwan who died between 2009 to 2011.

METHODS

This nationwide population-based, retrospective cohort study identified 5,386 patients who died from oral cancer. We evaluated medical cost in the last month of life by universal health insurance. The impact of each variable on the end-of-life expenditure was examined by hierarchical generalized linear model (HGLM) using a hospital-level random-intercept model.

RESULTS

The mean medical cost in the last six months of life was $2,611±3,329 (U.S. dollars). In HGLM using a random-intercept model, we found that patients younger than 65 years had an additional cost of $819 over those aged ≥65 years. Patients who had a high Charlson Comorbidity Index Score (CCIS) had an additional $616 cost over those with a low CCIS. Those who survived post-diagnosis less than 6 months had an additional $659 in expenses over those who survived more than 24 months. Medical cost was $249 more for patients who had medium to high individual SES, and $319 more for those who were treated by non-oncologists.

CONCLUSION

This study provides useful information for decision makers in understanding end-of-life expenditure in oral cancer. We found significantly increased end-of-life expenditure in patients if they were younger than 65 years or treated by non-oncologists, or had high CCIS, medium to high individual SES, and survival of less than 6 months after diagnosis.

Methodological quality and reporting of generalized linear mixed models in clinical medicine (2000-2012): a systematic review

BACKGROUND

Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixed Models (GLMMs) in medicine. This article presents a systematic review of the application and quality of results and information reported from GLMMs in the field of clinical medicine.

METHODS

A search using the Web of Science database was performed for published original articles in medical journals from 2000 to 2012. The search strategy included the topic "generalized linear mixed models","hierarchical generalized linear models", "multilevel generalized linear model" and as a research domain we refined by science technology. Papers reporting methodological considerations without application, and those that were not involved in clinical medicine or written in English were excluded.

RESULTS

A total of 443 articles were detected, with an increase over time in the number of articles. In total, 108 articles fit the inclusion criteria. Of these, 54.6% were declared to be longitudinal studies, whereas 58.3% and 26.9% were defined as repeated measurements and multilevel design, respectively. Twenty-two articles belonged to environmental and occupational public health, 10 articles to clinical neurology, 8 to oncology, and 7 to infectious diseases and pediatrics. The distribution of the response variable was reported in 88% of the articles, predominantly Binomial (n = 64) or Poisson (n = 22). Most of the useful information about GLMMs was not reported in most cases. Variance estimates of random effects were described in only 8 articles (9.2%). The model validation, the method of covariate selection and the method of goodness of fit were only reported in 8.0%, 36.8% and 14.9% of the articles, respectively.

CONCLUSIONS

During recent years, the use of GLMMs in medical literature has increased to take into account the correlation of data when modeling qualitative data or counts. According to the current recommendations, the quality of reporting has room for improvement regarding the characteristics of the analysis, estimation method, validation, and selection of the model.