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Pathophysiology of Fluorosis and Calcium Dose Prediction for Its Reversal in Children: Mathematical Modeling, Analysis, and Simulation of Three Clinical Case Studies
Published in P. Mereena Luke, K. R. Dhanya, Didier Rouxel, Nandakumar Kalarikkal, Sabu Thomas, Advanced Studies in Experimental and Clinical Medicine, 2021
Suja George, A. B. Gupta, Mayank Mehta, Akshara Goyal
Bivariate analysis examines the relationship between two variables, which may be continuous or categorical in nature. Bivariate analysis was used to verify for a couple of associations suggested by the pathophysiology of fluorosis [2], first between blood fluoride and serum calcium and second between serum calcium and serum alkaline phosphatase (SAP).
Causality Analysis of Climate and Ecosystem Time Series
Published in Vyacheslav Lyubchich, Yulia R. Gel, K. Halimeda Kilbourne, Thomas J. Miller, Nathaniel K. Newlands, Adam B. Smith, Evaluating Climate Change Impacts, 2020
Mohammad Gorji Sefidmazgi, Ali Gorji Sefidmazgi
The causality problem can be defined as detecting the cause-effect relationship between two variables. Generally, two types of bivariate causal structure can be found in real-world systems. In recursive schemes, the response variable of a system, A, acts as a stimulus variable of a system, B, that follows A in a chain. Here, we say that A is the cause of B or A → B (Figure 7.1(a)). In a feedback loop structure, there is also a regulation between B and A (Figure 7.1(b)). In this case, the causal link represented by A ↔ B is bidirectional and there is an interdependence between A and B (Barbieri, 2013). Such a bivariate analysis might lead to incorrect conclusions, because additional confounding factors that might exist in complex systems are not taken into account in the causality analysis. This bivariate definition should be extended to a multivariate case by considering additional variables.
Cognitive Function, Sosial Support and a Depression in Institutionalized Older Adults
Published in Teuku Tahlil, Hajjul Kamil, Asniar, Marthoenis, Challenges in Nursing Education and Research, 2020
Nurhasanah, Juanita, Jufrizal, Dara Febriana
The study used descriptive statistic to explore socio demographic data of the respondent. Also, correlation analysis which Chi-Square was used for investigating the bivariate relationship between research variables. Finally, logistic regression was used to evaluate the correlation of cognitive function and social support with depression.
Intra-regional variations and contextual effects on facility-based delivery in Bangladesh: A multi-level analysis
Published in Health Care for Women International, 2023
We employed three types of statistical analysis in this study: univariate, bivariate, and multivariate. In the univariate analysis, we have shown the percentage distributions of women by their background characteristics. At the bivariate level, simple cross-tabulation was done to examine the association between the outcome of interest and the contextual variables through Pearson’s chi-squared tests. At the multivariate level, we applied multilevel logistic regression analysis to examine the effects of the individual- and community-level characteristics on FBD to determine the extent to which the contextual factors explain regional variations in the institutional delivery of women in Bangladesh. The reasons behind the selection of multilevel logistic regression are as follows: (i) the outcome of interest, i.e. the dependent variable is binary – whether a woman used FBD for childbirth or not and (ii) the hierarchical structure of the dataset. The assumption is that women are nested within households and households are in turn nested within communities. This suggests that women may have different perception toward utilization of MHCSs utilization when residing in different communities with different characteristics.
The impact of obstacles to health and rehabilitation services on functioning and disability: a prospective survey on the 12-months after discharge from specialist rehabilitation for acquired brain injury
Published in Disability and Rehabilitation, 2022
Melissa Legg, Michele Foster, Rachel Jones, Melissa Kendall, Jennifer Fleming, Mandy Nielsen, Elizabeth Kendall, David Borg, Timothy Geraghty
Potential covariates were investigated as an initial step to building the moderated regression models. The choice of potential covariates to examine was guided by previous research on the sociodemographic and impairment-related variables associated with participation outcomes after brain injury [57,58]. Associations between the outcome variables and potential covariates were examined using pairwise Pearson’s or Spearman’s correlation coefficients, when appropriate, with a significance level of 5%. This was complemented by visual inspection of bivariate scatterplots. The categorical variables that were recorded as dummy variables prior to the analysis included: gender (1 male, 0 female); education (1 undergraduate university degree or above, 0 technical college or lower); marital status (1 married or defacto, 0 other); type of injury (1 traumatic, 0 non-traumatic); and lifetime funding status (1 funding, 0 no funding). Data screening and assumption checking for linear multiple regression were conducted using the post-regression tools in Stata (Version 13), as the final step in model specification. No extreme violations were noted in the final regression models including checks for linearity, normality of residuals, homogeneity of variance, multicollinearity of variables (i.e., variance inflation factors), and unusual or influential cases.
Pain and Loss of Pleasure in Receptive Anal Sex for Gay and Bisexual Men following Prostate Cancer Treatment: Results from the Restore-1 Study
Published in The Journal of Sex Research, 2022
Christopher W. Wheldon, Elizabeth J. Polter, B. R. Simon Rosser, Aditya Kapoor, Kristine M. C. Talley, Ryan Haggart, Nidhi Kohli, Badrinath R. Konety, Darryl Mitteldorf, Michael W. Ross, William West, Morgan Wright
This study should be considered exploratory given the small sample size and cross-sectional design. This was a secondary analysis of a study designed to assess the feasibility of an online sexual rehabilitation program for GBM with prostate cancer (Rosser et al., 2016a, 2018, 2020). The study was not powered to examine correlates of anodyspareunia so null findings should be interpreted with caution. There are likely confounding factors that were not controlled for in the bivariate analyses reported here. In addition, we do not have sufficient information to conclude that changes in sexual behavior or function were a direct result of prostate cancer treatment. Nor do we know the degree to which men in this sample met criteria for anodyspareunia prior to their prostate cancer treatment. Given the lack of a sampling frame for GBM with prostate cancer, it is unknown how these results may generalize beyond the sample reported here.