A Bayesian shared parameter model for joint modeling of longitudinal continuous and binary outcomes

2020 ◽  
pp. 1-18
Author(s):  
T. Baghfalaki ◽  
M. Ganjali ◽  
A. Kabir ◽  
A. Pazouki
Keyword(s):  
Joint Modeling ◽  
Binary Outcomes

scholarly journals Accuracy Measure of Separate and Joint Modelling for a Correlated Binary Outcome: The Case Study of Mother Education and Immunization in Bangladesh

2021 ◽  
Keyword(s):  
Urban Areas ◽  
Maternal Education ◽  
Joint Modeling ◽  
Correlated Data ◽  
Binary Outcomes ◽  
Joint Modelling ◽  
Logistic Regression Models ◽  
Accuracy Measure ◽  
Mother Education ◽  
Different Response

Joint modelling is a statistical approach that is used to analyze correlated data when two or more outcome variables are correlated. By joint modeling, we refer to the simultaneous analysis of two or more different response variables from the same individual. But in a separate model, it is unable to measure the effect of covariate simultaneously. This article focuses on separate and joint modelling for correlated discrete data, including logistic regression models for binary outcomes. Since most of the women are illiterate in Bangladesh and most of the people are living in urban areas, as a result, most of the women are not aware of immunization. But an educated mother is always aware of her child's health which is dependent on immunization. Therefore, mother education and immunization are interdependent. We jointly address the effect of maternal education and immunization. Joint modeling of these two outcomes is appropriate because mother education helps raise awareness of the child's health and immunization is the prevention of various diseases for the child's health. We also identified factors influencing maternal education and immunization among women in Bangladesh. By jointly modelling we found the correlation between maternal education and immunization and the most important contributing factor. The joint model removes a less significant impact of covariates as opposed to separate models. These findings further suggested that the simultaneous impact of correlated outcomes can be adequately addressed between different responses, which is overestimated or underestimated when examined separately.

Translocation Relative to Range

Methodology ◽  
2008 ◽  
Vol 4 (3) ◽  
pp. 132-138 ◽  
Author(s):  
Michael Höfler
Keyword(s):  
Risk Difference ◽  
Binary Outcomes ◽  
Number Needed To Treat ◽  
Likert Scales ◽  
Psychological Science ◽  
The Absolute ◽  
The Difference ◽  
Variance Parameters ◽  
Standardized Index ◽  
Maximum Possible Magnitude

A standardized index for effect intensity, the translocation relative to range (TRR), is discussed. TRR is defined as the difference between the expectations of an outcome under two conditions (the absolute increment) divided by the maximum possible amount for that difference. TRR measures the shift caused by a factor relative to the maximum possible magnitude of that shift. For binary outcomes, TRR simply equals the risk difference, also known as the inverse number needed to treat. TRR ranges from –1 to 1 but is – unlike a correlation coefficient – a measure for effect intensity, because it does not rely on variance parameters in a certain population as do effect size measures (e.g., correlations, Cohen’s d). However, the use of TRR is restricted on outcomes with fixed and meaningful endpoints given, for instance, for meaningful psychological questionnaires or Likert scales. The use of TRR vs. Cohen’s d is illustrated with three examples from Psychological Science 2006 (issues 5 through 8). It is argued that, whenever TRR applies, it should complement Cohen’s d to avoid the problems related to the latter. In any case, the absolute increment should complement d.

Sociodemographic inequalities in lifetime treatment seeking and receipt for common mental health problems

2020 ◽  
Author(s):  
Christopher Rayner ◽  
Jonathan Richard Iain Coleman ◽  
Kirstin Lee Purves ◽  
Ewan Carr ◽  
Rosa Cheesman ◽  
...  
Keyword(s):  
Depressive Disorders ◽  
Mental Health Problems ◽  
Sociodemographic Factors ◽  
Treatment Seeking ◽  
Binary Outcomes ◽  
Treatment Access ◽  
Sociodemographic Correlates ◽  
Common Mental Health Problems ◽  
Poor Outcomes ◽  
Lifetime Treatment

Background: Anxiety and depressive disorders can be chronic and disabling, and are associated with poor outcomes. Whilst there are effective treatments, access to these is variable and only a fraction of those in need receive treatment. Aims: The primary aim was to investigate sociodemographic correlates of lifetime treatment access and unpick the relationships between socioeconomic features and treatment inequalities. As such, we aimed to identify groups at greatest risk of never accessing treatment and targets for intervention. Methods: We tested for sociodemographic factors associated with treatment access in UK Biobank participants with lifetime generalised anxiety or major depressive disorder, performing multivariable logistic regressions on two binary outcomes: treatment-seeking (n=33,704) and treatment receipt (n=28,940). Results: Treatment access was less likely in those who were male, from a UK ethnic minority background and who self-medicated with alcohol or drugs. Treatment access was more likely in those who reported use of self-help strategies, with lower income (<£30,000) and greater neighbourhood deprivation, as well as those with a university degree. Conclusion: This work on lifetime treatment seeking and receipt replicates known correlates of treatment receipt during time of treatment need. Our focus on treatment-seeking and receipt highlights two targets for improving treatment access. More work is required to understand the psychosocial barriers to treatment, which mediate the associations observed here.

Video Object Segmentation Research Based on Features Joint Modeling

2014 ◽  
Vol 36 (11) ◽  
pp. 2356-2363
Author(s):  
Zong-Min LI ◽  
Xu-Chao GONG ◽  
Yu-Jie LIU
Keyword(s):  
Object Segmentation ◽  
Joint Modeling ◽  
Video Object Segmentation ◽  
Video Object

scholarly journals COVID-19 Pandemic Optimism and Vaccine Willingness among an Online Sample of US Gay, Bisexual, and Other Men Who Have Sex with Men

Vaccines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 745
Author(s):  
Rob Stephenson ◽  
Stephen P. Sullivan ◽  
Renee A. Pitter ◽  
Alexis S. Hunter ◽  
Tanaka MD Chavanduka
Keyword(s):  
Public Health ◽  
Three Dimensions ◽  
Vaccine Hesitancy ◽  
Binary Outcomes ◽  
Sex Partners ◽  
Black African ◽  
Factors Associated ◽  
Public Health Messages ◽  
Sex With Men ◽  
Living With Hiv

This paper presents data from an online sample of U.S gay, bisexual, and other men who have sex with men (GBMSM), to explore the factors associated with three dimensions of vaccine beliefs: perception of the likelihood of a COVID-19 vaccine becoming available, perception of when a COVID-19 vaccine would become available, and the likelihood of taking a COVID-19 vaccine. Data are taken from the Love and Sex in the Time of COVID-19 study, collected from November 2020 to January 2021. A sample of 290 GBMSM is analyzed, modeling three binary outcomes: belief that there will be a COVID-19 vaccine, belief that the COVID-19 vaccine will be available in 6 months, and being very likely to take the COVID-19 vaccine. In contrast to other studies, Black/African Americans and GBMSM living with HIV had higher levels of pandemic optimism and were more likely to be willing to accept a vaccine. Men who perceived a higher prevalence of COVID-19 among their friends and sex partners, and those who had reduced their sex partners, were more likely to be willing to take a COVID-19 vaccine. There remained a small percentage of participants (14%) who did not think the pandemic would end, that there would not be a vaccine and were unlikely to take a vaccine. To reach the levels of vaccination necessary to control the pandemic, it is imperative to understand the characteristics of those experiencing vaccine hesitancy and then tailor public health messages to their unique set of barriers and motivations.

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