A Bayesian Graphical and Probabilistic Proposal for Bias Analysis

2020 ◽  
pp. 69-78
Author(s):  
Claudia Ovalle ◽  
Danilo Alvares
Keyword(s):  
Bias Analysis

Item Bias Analysis of the Young Schema-Questionnaire for Psychopathology, Gender, and Educational Level

2011 ◽  
Vol 27 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Marleen M. Rijkeboer ◽  
Huub van den Bergh ◽  
Jan van den Bout
Keyword(s):  
Educational Level ◽  
Cross Validation ◽  
Item Bias ◽  
Response Patterns ◽  
Dutch Population ◽  
Bias Analysis ◽  
Young Schema Questionnaire ◽  
Item Functioning ◽  
Item Level ◽  
Schema Questionnaire

This study examines the construct validity of the Young Schema-Questionnaire at the item level in a Dutch population. Possible bias of items in relation to the presence or absence of psychopathology, gender, and educational level was analyzed, using a cross-validation design. None of the items of the YSQ exhibited differential item functioning (DIF) for gender, and only one item showed DIF for educational level. Furthermore, item bias analysis did not identify DIF for the presence or absence of psychopathology in as much as 195 of the 205 items comprising the YSQ. Ten items, however, spread over the questionnaire, were found to yield relatively inconsistent response patterns for patients and nonclinical participants.

2012 Workplace and Gender Relations Survey of Active Duty Members: Nonresponse Bias Analysis Report

2014 ◽  
Author(s):  
Fawzi Al-Nassir ◽  
Eric Falk ◽  
Owen Hung ◽  
Shoshana Magazine ◽  
Timothy Markheim ◽  
...  
Keyword(s):  
Gender Relations ◽  
Active Duty ◽  
Nonresponse Bias ◽  
Bias Analysis ◽  
And Gender

Effectiveness of Passion Fruit Peel Flour (Passiflora edulis L.) versus Turmeric Flour (Curcuma longa L.) on Glycemic Control: Systematic Review and Meta-Analysis

2020 ◽  
Vol 16 (5) ◽  
pp. 450-456
Author(s):  
Danilo F. Sousa ◽  
Vivian S. Veras ◽  
Vanessa E.C.S. Freire ◽  
Maria L. Paula ◽  
Maria A.A.O. Serra ◽  
...  
Keyword(s):  
Systematic Review ◽  
Glycemic Control ◽  
Curcuma Longa ◽  
Meta Analysis ◽  
Passion Fruit ◽  
Joint Analysis ◽  
Case Control Studies ◽  
Bias Analysis ◽  
Fruit Peel ◽  
Cross Sectional

Background:: It is undeniable that diabetes may cause several health complications for the population. Many of these complications are associated with poor glycemic control. Due to this, strategies to handle this problem are of great clinical importance and may contribute to reducing the various complications from diabetes. Objective: : The aim of this study was to compare the effectiveness of the passion fruit peel flour versus turmeric flour on glycemic control. Methods: This is a systematic review and meta-analysis following the PRISMA protocol. The following inclusion criteria were applied: (1) Case-control studies, cohort studies, and clinical trials, due to the improved statistical analysis and, in restrict cases, cross-sectional studies; (2) Articles published in any language. The databases used for the search were PubMed, Scopus, Web of Science, Cochrane, and LILACS. A bias analysis and a meta-analyses were undertaken using R Studio (version 3.3.1) using effect- size models. Results: : A total of 565 studies were identified from which 11 met the inclusion and exclusion criteria. Through isolated analysis, the effectiveness of turmeric flour on glycemic control was in the order of 0.73 CI (Confidence Interval) (from 0.68 to 0.79) and the effectiveness of passion fruit peel flour was 0.32 CI (0.23 to 0.45). The joint analysis resulted in 0.59 CI (0.52 to 0.68). The assessment of blood glucose was by glycated hemoglobin levels. All values were significant at a p < 0.05 level. Conclusion: Both interventions showed significant effects on glycemic control.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

scholarly journals Unsafe Injection Is Associated with Higher HIV Testing after Bayesian Adjustment for Unmeasured Confounding

2020 ◽  
Vol 23 (12) ◽  
pp. 848-855
Author(s):  
Soodabeh Navadeh ◽  
Ali Mirzazadeh ◽  
Willi McFarland ◽  
Phillip Coffin ◽  
Mohammad Chehrazi ◽  
...  
Keyword(s):  
Hiv Testing ◽  
San Francisco ◽  
Expert Opinion ◽  
Bias Analysis ◽  
Hiv Knowledge ◽  
Persons Who Inject Drugs ◽  
Adjusted Risk ◽  
Unmeasured Confounder ◽  
Unsafe Injection ◽  
Insufficient Knowledge

Background: To apply a novel method to adjust for HIV knowledge as an unmeasured confounder for the effect of unsafe injection on future HIV testing. Methods: The data were collected from 601 HIV-negative persons who inject drugs (PWID) from a cohort in San Francisco. The panel-data generalized estimating equations (GEE) technique was used to estimate the adjusted risk ratio (RR) for the effect of unsafe injection on not being tested (NBT) for HIV. Expert opinion quantified the bias parameters to adjust for insufficient knowledge about HIV transmission as an unmeasured confounder using Bayesian bias analysis. Results: Expert opinion estimated that 2.5%–40.0% of PWID with unsafe injection had insufficient HIV knowledge; whereas 1.0%–20.0% who practiced safe injection had insufficient knowledge. Experts also estimated the RR for the association between insufficient knowledge and NBT for HIV as 1.1-5.0. The RR estimate for the association between unsafe injection and NBT for HIV, adjusted for measured confounders, was 0.96 (95% confidence interval: 0.89,1.03). However, the RR estimate decreased to 0.82 (95% credible interval: 0.64, 0.99) after adjusting for insufficient knowledge as an unmeasured confounder. Conclusion: Our Bayesian approach that uses expert opinion to adjust for unmeasured confounders revealed that PWID who practice unsafe injection are more likely to be tested for HIV – an association that was not seen by conventional analysis.

scholarly journals Revisiting Statistics and Evidence-Based Medicine: On the Fallacy of the Effect Size Based on Correlation and Misconception of Contingency Tables

2021 ◽  
Author(s):  
Sergey Roussakow
Keyword(s):  
Effect Size ◽  
Evidence Based Medicine ◽  
Binary Data ◽  
Contingency Tables ◽  
Evidence Based ◽  
Mean Square ◽  
Bias Analysis ◽  
Contingency Coefficient ◽  
Meta Analyses ◽  
Based Medicine

Abstract BACKGROUND: Evidence-based medicine (EBM) is in crisis, in part due to bad methods, which are understood as misuse of statistics that is considered correct in itself. The correctness of the basic statistics related to the effect size (ES) based on correlation (CBES) was questioned. METHODS: Monte Carlo simulation of two paired binary samples, mathematical analysis, conceptual analysis, bias analysis. RESULTS: Actual effect size and CBES are not related. CBES is a fallacy based on misunderstanding of correlation and ES and confusion with 2 × 2 tables that makes no distinction between gross crosstabs (GCTs) and contingency tables (CTs). This leads to misapplication of Pearson’s Phi, designed for CTs, to GCTs and confusion of the resulting gross Pearson Phi, or mean-square effect half-size, with the implied Pearson mean square contingency coefficient. Generalizing this binary fallacy to continuous data and the correlation in general (Pearson’s r) resulted in flawed equations directly expressing ES in terms of the correlation coefficient, which is impossible without including covariance, so these equations and the whole CBES concept are fundamentally wrong. misconception of contingency tables (MCT) is a series of related misconceptions due to confusion with 2 × 2 tables and misapplication of related statistics. Problems arising from these fallacies are discussed and the necessary changes to the corpus of statistics are proposed resolving the problem of correlation and ES in paired binary data. CONCLUSIONS: Two related common misconceptions in statistics have been exposed, CBES and MCT. The misconceptions are threatening because most of the findings from contingency tables, including meta-analyses, can be misleading. Since exposing these fallacies casts doubt on the reliability of the statistical foundations of EBM in general, we urgently need to revise them.

scholarly journals Does Fingolimod have anticancer effects? A meta-analysis and systematic review based on experimental animal models of various cancers

2020 ◽  
Author(s):  
Yuze Ge ◽  
Ping Zhou ◽  
Lu Kong
Keyword(s):  
Renal Cell Carcinoma ◽  
Animal Models ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Meta Analysis ◽  
Signal Pathways ◽  
Bias Analysis ◽  
Immunodeficient Mice ◽  
Anticancer Effects

Abstract Background: Numerous studies have explored the anticancer effect of FTY720 (Fingolimod) in animal models, a sphingosine-1-phosphate (S1P) receptor antagonist and an immunosuppressant, but little clinical evidence guides the use of FTY720 in cancer patients.Methods: Strictly, only related published articles about the treatment with FTY720 for various cancers in vivo from January 1998 to January 2020 were selected from PubMed, Web of Science, Ovid, Embase, CNKI and Cochrane databases, and which were qualified. We acquired agreement through discussion. Then, we conducted meta-analysis, subgroups analysis, publication bias analysis and sensitivity analysis based on selected studies. In the last two sections, we summaried and compared side effects, drug combination effects and molecular pathways from selected studies.Results: In the 31 articles included from 2002 to 2019, FTY720 was found to reduce tumor volume (SMD =-2.58, 95% CI: -3.42, -1.75, Z = 6.09, P = 0.000), tumor weight (SMD = -3.69, 95% CI: -5.17, -2.21, Z = 4.88, P = 0.000) and body weight (SMD = -0.86, 95% CI: -1.61, -0.11, Z = 2.23, P = 0.025) in 14 types of cancer. Relevant frequent signal pathways include the Akt pathway, S1PRs-Caspase pathway and the STAT3-PP2A pathway. FTY720 has significant independent or in combination anticancer effects and a lower toxicity in renal cell carcinoma and neuroblastoma mice models. However, it should be noted that FTY720 achieved a significant therapeutic effect in immunodeficient mice, not in immunecompetent mice. Also, the dosage-safety of FTY720 alone in clinical use is a noteworthy issue. In mouse models, the mechanism of the FTY720 treatment of tumors lies in inducing the tumor cells apoptosis through important signaling moleculars.Conclusions: FTY720 alone or in combination exerted significant anti-tumor effects for neuroblastoma and renal cell carcinoma, however not for melanoma. Due to insufficient evidence, more specific studies of FTY720 only and in combination included in immunity, inflammation and melanoma should be carried out in the future preclinical and clinical studies.

Sign in / Sign up

Export Citation Format

Share Document