Maya Bode Mathur - Epidemiology

Author Information

Maya B. Mathur is affiliated with the Quantitative Sciences Unit at Stanford University, located in Palo Alto, CA, USA. Her work primarily focuses on epidemiological research, with a particular interest in meta-analysis, causal mediation analysis, and evidence synthesis. Her role in the Quantitative Sciences Unit suggests a strong emphasis on rigorous quantitative methodologies in her research.

Research Contributions

Maya B. Mathur has made significant contributions to the field of epidemiology, particularly in the area of meta-analysis and causal inference. Her recent works include addressing complex methodological issues such as reporting E-values for meta-analyses, examining the relationship between multi-laboratory replications and meta-analyses, and developing methods to address confounding biases in meta-analyses. She has also explored interventions aimed at reducing meat consumption and understanding the health implications of menopausal hormone therapy through systematic reviews and meta-analyses.

Aliases

Maya B. Mathur is also known by the aliases Maya B Mathur, Maya Bode Mathur, and Maya Mathur. These variations of her name are used across different publications and platforms.

Publication and Citation Metrics

Metric	Value
Total Citation Count	8717
H-index	27
Total Paper Count	113

Publications:

The puzzling relationship between multi-laboratory replications and meta-analyses of the published literature

DOI: 10.1098/rsos.211499

Year: 2022

How to report E-values for meta-analyses: Recommended improvements and additions to the new GRADE approach

DOI: 10.1016/j.envint.2021.107032

Year: 2022

R package regmedint: extension of regression-based causal mediation analysis with effect measure modification by covariates

DOI: 10.31219/osf.io/d4brv

Year: 2022

E-values for effect heterogeneity and approximations for causal interaction.

DOI: 10.1093/ije/dyac073

Year: 2022

Menopausal hormone therapy and womens health: an umbrella review of systematic reviews and meta-analyses of randomized controlled trials and observational epidemiological studies

DOI: 10.1530/ENDOABS.73.AEP414

Year: 2021

Meta‐regression methods to characterize evidence strength using meaningful‐effect percentages conditional on study characteristics

DOI: 10.1002/jrsm.1504

Year: 2021

Methods to address confounding and other biases in meta-analyses: Review and recommendations

DOI: 10.31219/osf.io/v7dtq

Year: 2021

Interventions to reduce meat consumption by appealing to animal welfare: Meta-analysis and evidence-based recommendations

DOI: 10.31219/osf.io/bc2wy

Year: 2021

E-values for effect heterogeneity and approximations for causal interaction

DOI: 10.31219/osf.io/h6pru

Year: 2021

Interventions that Influence Animal-Product Consumption: A Meta-Review

DOI: 10.1016/j.fufo.2021.100111

Year: 2021

What is Microbial Evolution?

What is NHSN?

What are the Barriers to Knowledge Translation?

Why are REMS Important in Epidemiology?

What are the Challenges in Implementing DBT?

How are Mild Adverse Events Monitored?

When are Isolation Measures Implemented?

How to Conduct a DPIA in Epidemiology?

What are Some Innovative Funding Mechanisms?

What Are the Challenges Faced by PHAC?

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