Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
The Implications of Using Lagged and Baseline Exposure Terms in Longitudinal Causal and Regression Models Publisher Pubmed



Mansournia MA1 ; Naimi AI2 ; Greenland S3, 4
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran
  2. 2. Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States
  3. 3. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
  4. 4. Department of Statistics, College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, United States

Source: American Journal of Epidemiology Published:2019


Abstract

There are now many published applications of causal (structural) models for estimating effects of time-varying exposures in the presence of confounding by earlier exposures and confounders affected by earlier exposures. Results from these models can be highly sensitive to inclusion of lagged and baseline exposure terms for different visits. This sensitivity is often overlooked in practice; moreover, results from these models are not directly comparable to results from conventional time-dependent regression models, because the latter do not estimate the same causal parameter even when no bias is present. We thus explore the implications of including lagged and baseline exposure terms in causal and regression models, using a public data set (Caerphilly Heart Disease Study in the United Kingdom, 1979-1998) relating smoking to cardiovascular outcomes. © 2018 The Author(s).
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. Unsafe Injection Is Associated With Higher Hiv Testing After Bayesian Adjustment for Unmeasured Confounding, Archives of Iranian Medicine (2020)
3. Handling Time Varying Confounding in Observational Research, BMJ (Online) (2017)
4. 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)
5. Risk Ratio Estimation in Longitudinal Studies, Archives of Iranian Medicine (2019)
6. Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models, Iranian Journal of Public Health (2018)
7. Standardization As a Tool for Causal Inference in Medical Research, Archives of Iranian Medicine (2016)
8. 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)
Experts (# of related papers)
View all Related Experts
Mohammad Ali Mansournia (58)
Saharnaz Nedjat (12)
Other Related Docs
9. 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)
10. A Checklist for Statistical Assessment of Medical Papers (The Champ Statement): Explanation and Elaboration, British Journal of Sports Medicine (2021)
11. Impact of Alcohol Consumption on Multiple Sclerosis Using Model-Based Standardization and Misclassification Adjustment Via Probabilistic Bias Analysis, Archives of Iranian Medicine (2023)
12. Comparison of Outcomes Between Off-Pump and On-Pump Coronary Artery Bypass Graft Surgery Using Collaborative Targeted Maximum Likelihood Estimation, Scientific Reports (2024)
13. 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)
14. 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)
15. The Relation of Collapsibility and Confounding to Faithfulness and Stability, Epidemiology (2015)
16. Comparative Effectiveness Study in Multiple Sclerosis Patients Using Instrumental Variable Analysis, Archives of Iranian Medicine (2018)
17. On Hazard Ratio Estimators by Proportional Hazards Models in Matched-Pair Cohort Studies, Emerging Themes in Epidemiology (2017)
18. The Effect of Smoking on Latent Hazard Classes of Metabolic Syndrome Using Latent Class Causal Analysis Method in the Iranian Population, BMC Public Health (2023)
19. Case–Control Matching: Effects, Misconceptions, and Recommendations, European Journal of Epidemiology (2018)
20. Causal Methods for Observational Research: A Primer, Archives of Iranian Medicine (2018)
21. 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)
22. Estimation of the Marginal Causal Risk Ratio in Survival Data Using Inverse Probability Treatment Weighting (Iptw), Iranian Journal of Epidemiology (2018)
23. Estimating the Marginal Causal Effect of Fish Consumption During Adolescence on Multiple Sclerosis: A Population-Based Incident Case-Control Study, Neuroepidemiology (2018)
24. To Adjust or Not to Adjust: The Role of Different Covariates in Cardiovascular Observational Studies, American Heart Journal (2021)
25. Accounting for Time-Varying Confounding in the Relationship Between Obesity and Coronary Heart Disease: Analysis With G-Estimation, American Journal of Epidemiology (2018)
26. Theory and Methodology: Essential Tools That Can Become Dangerous Belief Systems, European Journal of Epidemiology (2018)
27. Using Causal Diagrams for Biomedical Research, Annals of Emergency Medicine (2023)
28. Interaction Contrasts and Collider Bias, American Journal of Epidemiology (2022)
29. Using Causal Diagrams to Improve the Design and Interpretation of Medical Research, Chest (2020)
30. The Effects of Smoking on Metabolic Syndrome and Its Components Using Causal Methods in the Iranian Population, International Journal of Preventive Medicine (2021)
31. Hazard Ratio Estimators After Terminating Observation Within Matched Pairs in Sibling and Propensity Score Matched Designs, International Journal of Biostatistics (2019)
32. Case–Control Matching on Confounders Revisited, European Journal of Epidemiology (2023)
33. Use of G-Methods for Handling Time-Varying Confounding in Observational Research, The Lancet Global Health (2019)
34. Causal Effects of Intensive Lifestyle and Metformin Interventions on Cardiovascular Disease Risk Factors in Pre-Diabetic People: An Application of G-Estimation, Archives of Iranian Medicine (2017)
35. Errors in Causal Inference: An Organizational Schema for Systematic Error and Random Error, Annals of Epidemiology (2016)
36. Limitations of Individual Causal Models, Causal Graphs, and Ignorability Assumptions, As Illustrated by Random Confounding and Design Unfaithfulness, European Journal of Epidemiology (2015)
37. Interaction Between Opium Use and Cigarette Smoking on Bladder Cancer: An Inverse Probability Weighting Approach Based on a Multicenter Case-Control Study in Iran, Global Epidemiology (2025)
38. Reflection on Modern Methods: Demystifying Robust Standard Errors for Epidemiologists, International Journal of Epidemiology (2021)
39. Estimating Effect of Obesity on Stroke Using G-Estimation: The Aric Study, Obesity (2019)
40. Effects of Hypothetical Interventions on Ischemic Stroke Using Parametric G-Formula, Stroke (2019)
41. A Practical Guide to Handling Competing Events in Etiologic Time-To-Event Studies, Global Epidemiology (2022)
42. Stress-Full Life Events and Multiple Sclerosis: A Population-Based Incident Case-Control Study, Multiple Sclerosis and Related Disorders (2018)
43. The Targeted Maximum Likelihood Estimation to Estimate the Causal Effects of the Previous Tuberculosis Treatment in Multidrug-Resistant Tuberculosis in Sudan, PLoS ONE (2023)
44. Comparing Different Propensity Score Estimation Methods for Estimating the Marginal Causal Effect Through Standardization to Propensity Scores, Communications in Statistics: Simulation and Computation (2018)
45. Medical History and Multiple Sclerosis: A Population-Based Incident Case-Control Study, Neuroepidemiology (2019)
46. Direct and Indirect Effects of Central and General Adiposity on Cardiovascular Diseases: The Tehran Lipid and Glucose Study, European Journal of Preventive Cardiology (2018)
47. The Role of Nutritional Factors During Adolescence in Multiple Sclerosis Onset: A Population-Based Incident Case–Control Study, Nutritional Neuroscience (2021)
48. Population Attributable Fraction in Textbooks: Time to Revise, Global Epidemiology (2021)
49. Course and Predictors of Posttraumatic Stress Disorder in a Cohort of Psychologically Distressed Patients With Cancer: A 4-Year Follow-Up Study—Methodological and Statistical Issues, Cancer (2018)
50. No Association Between Socioeconomic Status and Risk of Multiple Sclerosis: A Population-Based Incident Case-Control Study in a Developing Country, Multiple Sclerosis and Related Disorders (2018)