Use of G-Methods for Handling Time-Varying Confounding in Observational Research

Tehran University of Medical Sciences

Science Communicator Platform

Share By

Use of G-Methods for Handling Time-Varying Confounding in Observational Research Publisher Pubmed

Doostiirani A¹ ; Mansournia MA² ; Collins G³

Source: The Lancet Global Health Published:2019

Abstract

[No abstract available]

Related Docs

View other Related Docs

1. Longitudinal Effects of Lipid Indices on Incident Cardiovascular Diseases Adjusting for Time-Varying Confounding Using Marginal Structural Models: 25 Years Follow-Up of Two Us Cohort Studies, Global Epidemiology (2022)

2. Longitudinal Causal Effect of Modified Creatinine Index on All-Cause Mortality in Patients With End-Stage Renal Disease: Accounting for Time-Varying Confounders Using G-Estimation, PLoS ONE (2022)

3. Protective Effect of Intensive Glucose Lowering Therapy on All-Cause Mortality, Adjusted for Treatment Switching Using G-Estimation Method, the Accord Trial, Scientific Reports (2023)

Experts (# of related papers)

View all Related Experts

Mohammad Ali Mansournia (18)

Saharnaz Nedjat (4)

Other Related Docs

4. The Implications of Using Lagged and Baseline Exposure Terms in Longitudinal Causal and Regression Models, American Journal of Epidemiology (2019)

5. Risk Ratio Estimation in Longitudinal Studies, Archives of Iranian Medicine (2019)

6. Accounting for Time-Varying Confounding in the Relationship Between Obesity and Coronary Heart Disease: Analysis With G-Estimation, American Journal of Epidemiology (2018)

7. Unsafe Injection Is Associated With Higher Hiv Testing After Bayesian Adjustment for Unmeasured Confounding, Archives of Iranian Medicine (2020)

8. Estimating Effect of Obesity on Stroke Using G-Estimation: The Aric Study, Obesity (2019)

9. To Adjust or Not to Adjust: The Role of Different Covariates in Cardiovascular Observational Studies, American Heart Journal (2021)

10. Comparative Effectiveness Study in Multiple Sclerosis Patients Using Instrumental Variable Analysis, Archives of Iranian Medicine (2018)

11. Longitudinal Causal Effects of Normalized Protein Catabolic Rate on All-Cause Mortality in Patients With End-Stage Renal Disease: Adjusting for Time-Varying Confounders Using the G-Estimation Method, American Journal of Epidemiology (2021)

12. Contribution of Obesity and Cardiometabolic Risk Factors in Developing Cardiovascular Disease: A Population-Based Cohort Study, Scientific Reports (2022)

13. Does Weight Mediate the Effect of Smoking on Coronary Heart Disease? Parametric Mediational G-Formula Analysis, PLoS ONE (2022)

14. The Causal Effect and Impact of Reproductive Factors on Breast Cancer Using Super Learner and Targeted Maximum Likelihood Estimation: A Case-Control Study in Fars Province, Iran, BMC Public Health (2021)

15. Estimating the Marginal Causal Effect and Potential Impact of Waterpipe Smoking on Risk of Multiple Sclerosis Using the Targeted Maximum Likelihood Estimation Method: A Large, Population-Based Incident Case-Control Study, American Journal of Epidemiology (2021)

16. The Marginal Causal Effect of Opium Consumption on the Upper Gastrointestinal Cancer Death Using Parametric G-Formula: An Analysis of 49,946 Cases in the Golestan Cohort Study, Iran, PLoS ONE (2021)

17. Prevalence and Correlates of Low Physical Activity in the Iranian Population: National Survey on Non-Communicable Diseases in 2011, Scandinavian Journal of Medicine and Science in Sports (2018)

18. Estimating the Marginal Causal Effect of Fish Consumption During Adolescence on Multiple Sclerosis: A Population-Based Incident Case-Control Study, Neuroepidemiology (2018)

19. Impact of Alcohol Consumption on Multiple Sclerosis Using Model-Based Standardization and Misclassification Adjustment Via Probabilistic Bias Analysis, Archives of Iranian Medicine (2023)

20. Some Methodological Issues in the Design and Analysis of Cluster Randomised Trials, British Journal of Sports Medicine (2019)

Style	Citing Format
MLA	Doostiirani A, Mansournia MA, Collins G. "Use of G-Methods for Handling Time-Varying Confounding in Observational Research." The Lancet Global Health, vol. 7, no. 1, 2019, pp. e35-.
APA	Doostiirani A, Mansournia MA, Collins G (2019). Use of G-Methods for Handling Time-Varying Confounding in Observational Research. The Lancet Global Health, 7(1), e35-.
Chicago	Doostiirani A, Mansournia MA, Collins G. "Use of G-Methods for Handling Time-Varying Confounding in Observational Research." The Lancet Global Health 7, no. 1 (2019): e35-.
Harvard	Doostiirani A, Mansournia MA, Collins G (2019) 'Use of G-Methods for Handling Time-Varying Confounding in Observational Research', The Lancet Global Health, 7(1), pp. e35-.
Vancouver	Doostiirani A, Mansournia MA, Collins G. Use of G-Methods for Handling Time-Varying Confounding in Observational Research. The Lancet Global Health. 2019;7(1):e35-.
BibTex	@article{ author = {Doostiirani A and Mansournia MA and Collins G}, title = {Use of G-Methods for Handling Time-Varying Confounding in Observational Research}, journal = {The Lancet Global Health}, volume = {7}, number = {1}, pages = {e35-}, year = {2019} }
RIS	TY - JOUR AU - Doostiirani A AU - Mansournia MA AU - Collins G TI - Use of G-Methods for Handling Time-Varying Confounding in Observational Research JO - The Lancet Global Health VL - 7 IS - 1 SP - e35 EP - PY - 2019 ER -