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Adineh HA, Hoseini K, Zareban I, Jalali A, Nazemipour M, Mansournia MA. Comparison of outcomes between off-pump and on-pump coronary artery bypass graft surgery using collaborative targeted maximum likelihood estimation. Sci Rep 2024; 14:11373. [PMID: 38762564 DOI: 10.1038/s41598-024-61846-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 05/10/2024] [Indexed: 05/20/2024] Open
Abstract
There are some discrepancies about the superiority of the off-pump coronary artery bypass grafting (CABG) surgery over the conventional cardiopulmonary bypass (on-pump). The aim of this study was estimating risk ratio of mortality in the off-pump coronary bypass compared with the on-pump using a causal model known as collaborative targeted maximum likelihood estimation (C-TMLE). The data of the Tehran Heart Cohort study from 2007 to 2020 was used. A collaborative targeted maximum likelihood estimation and targeted maximum likelihood estimation, and propensity score (PS) adjustment methods were used to estimate causal risk ratio adjusting for the minimum sufficient set of confounders, and the results were compared. Among 24,883 participants (73.6% male), 5566 patients died during an average of 8.2 years of follow-up. The risk ratio estimates (95% confidence intervals) by unadjusted log-binomial regression model, PS adjustment, TMLE, and C-TMLE methods were 0.86 (0.78-0.95), 0.88 (0.80-0.97), 0.88 (0.80-0.97), and 0.87(0.85-0.89), respectively. This study provides evidence for a protective effect of off-pump surgery on mortality risk for up to 8 years in diabetic and non-diabetic patients.
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Affiliation(s)
- Hossein Ali Adineh
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Kaveh Hoseini
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Iraj Zareban
- Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Arash Jalali
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
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Taheri Soodejani M, Tabatabaei SM, Lotfi MH, Nazemipour M, Mansournia MA. Adjustment for collider bias in the hospitalized Covid-19 setting. Glob Epidemiol 2023; 6:100120. [PMID: 38111522 PMCID: PMC10726228 DOI: 10.1016/j.gloepi.2023.100120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/14/2023] [Accepted: 08/22/2023] [Indexed: 12/20/2023] Open
Abstract
Background Causal directed acyclic graphs (cDAGs) are frequently used to identify confounding and collider bias. We demonstrate how to use causal directed acyclic graphs to adjust for collider bias in the hospitalized Covid-19 setting. Materials and methods According to the cDAGs, three types of modeling have been performed. In model 1, only vaccination is entered as an independent variable. In model 2, in addition to vaccination, age is entered the model to adjust for collider bias due to the conditioning of hospitalization. In model 3, comorbidities are also included for adjustment of collider bias due to the conditioning of hospitalization in different biasing paths intercepting age and comorbidities. Results There was no evidence of the effect of vaccination on preventing death due to Covid-19 in model 1. In the second model, where age was included as a covariate, a protective role for vaccination became evident. In model 3, after including chronic diseases as other covariates, the protective effect was slightly strengthened. Conclusion Studying hospitalized patients is subject to collider-stratification bias. Like confounding, this type of selection bias can be adjusted for by inclusion of the risk factors of the outcome which also affect hospitalization in the regression model.
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Affiliation(s)
- Moslem Taheri Soodejani
- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Seyyed Mohammad Tabatabaei
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Hassan Lotfi
- Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Khodamoradi F, Nazemipour M, Mansournia N, Yazdani K, Khalili D, Arshadi M, Etminan M, Mansournia MA. 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; 23:2058. [PMID: 37864179 PMCID: PMC10588163 DOI: 10.1186/s12889-023-16863-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/29/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis. METHODS In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005-2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014-2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set. RESULTS Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome. CONCLUSIONS Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.
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Affiliation(s)
- Farzad Khodamoradi
- Department of Social Medicine, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran
| | - Nasrin Mansournia
- Department of Endocrinology, AJA University of Medical Sciences, Tehran, Iran
| | - Kamran Yazdani
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maedeh Arshadi
- Department of Epidemiology and Biostatistics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Mahyar Etminan
- Departments of Ophthalmology and Visual Sciences, Medicine and Pharmacology, University of British Columbia, Vancouver, Canada
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran.
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Malekifar P, Nedjat S, Abdollahpour I, Nazemipour M, Malekifar S, Mansournia MA. Impact of Alcohol Consumption on Multiple Sclerosis Using Model-based Standardization and Misclassification Adjustment Via Probabilistic Bias Analysis. Arch Iran Med 2023; 26:567-574. [PMID: 38310413 PMCID: PMC10862089 DOI: 10.34172/aim.2023.83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 08/06/2023] [Indexed: 02/05/2024]
Abstract
BACKGROUND The etiology of multiple sclerosis (MS) is still not well-demonstrated, and assessment of some risk factors like alcohol consumption has problems like confounding and measurement bias. To determine the causal effect of alcohol consumption on MS after adjusting for alcohol consumption misclassification bias and confounders. METHODS In a population-based incident case-control study, 547 patients with MS and 1057 healthy people were recruited. A minimally sufficient adjustment set of confounders was derived using the causal directed acyclic graph. The probabilistic bias analysis method (PBAM) using beta, logit-logistic, and triangular probability distributions for sensitivity/specificity to adjust for misclassification bias in self-reporting alcohol consumption and model-based standardization (MBS) to estimate the causal effect of alcohol consumption were used. Population attributable fraction (PAF) estimates with 95% Monte Carlo sensitivity analysis (MCSA) intervals were calculated using PBAM and MBS analysis. Bootstrap was used to deal with random errors. RESULTS The adjusted risk ratio (95% MCSA interval) from the probabilistic bias analysis and MBS between alcohol consumption and MS using the three distribution was in the range of 1.93 (1.07 to 4.07) to 2.02 (1.15 to 4.69). The risk difference (RD) in all three scenarios was 0.0001 (0.0000 to 0.0005) and PAF was in the range of 0.15 (0.010 to 0.50) to 0.17 (0.001 to 0.47). CONCLUSION After adjusting for measurement bias, confounding, and random error alcohol consumption had a positive causal effect on the incidence of MS.
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Affiliation(s)
- Pooneh Malekifar
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Saharnaz Nedjat
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Ibrahim Abdollahpour
- Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Science, Isfahan, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Saeed Malekifar
- Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Pakzad R, Nedjat S, Salehiniya H, Mansournia N, Etminan M, Nazemipour M, Pakzad I, Mansournia MA. Effect of alcohol consumption on breast cancer: probabilistic bias analysis for adjustment of exposure misclassification bias and confounders. BMC Med Res Methodol 2023; 23:157. [PMID: 37403100 DOI: 10.1186/s12874-023-01978-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/15/2023] [Indexed: 07/06/2023] Open
Abstract
PURPOSE This study was conducted to evaluate the effect of alcohol consumption on breast cancer, adjusting for alcohol consumption misclassification bias and confounders. METHODS This was a case-control study of 932 women with breast cancer and 1000 healthy control. Using probabilistic bias analysis method, the association between alcohol consumption and breast cancer was adjusted for the misclassification bias of alcohol consumption as well as a minimally sufficient set of adjustment of confounders derived from a causal directed acyclic graph. Population attributable fraction was estimated using the Miettinen's Formula. RESULTS Based on the conventional logistic regression model, the odds ratio estimate between alcohol consumption and breast cancer was 1.05 (95% CI: 0.57, 1.91). However, the adjusted estimates of odds ratio based on the probabilistic bias analysis ranged from 1.82 to 2.29 for non-differential and from 1.93 to 5.67 for differential misclassification. Population attributable fraction ranged from 1.51 to 2.57% using non-differential bias analysis and 1.54-3.56% based on differential bias analysis. CONCLUSION A marked measurement error was in self-reported alcohol consumption so after correcting misclassification bias, no evidence against independence between alcohol consumption and breast cancer changed to a substantial positive association.
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Affiliation(s)
- Reza Pakzad
- Department of Epidemiology, Faculty of Health, Ilam University of Medical Sciences, Ilam, Iran
- Student Research Committee, Ilam University of Medical Sciences, Ilam, Iran
| | - Saharnaz Nedjat
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran
| | - Hamid Salehiniya
- Department of Epidemiology and Biostatistics, School of Health, Birjand University of Medical Sciences, South Khorasan, Iran
| | - Nasrin Mansournia
- Department of Endocrinology, School of Medicine, AJA University of Medical Sciences, Tehran, Iran
| | - Mahyar Etminan
- Departments of Ophthalmology and Visual Sciences, Medicine and Pharmacology, University of British Columbia, Vancouver, Canada
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran
| | - Iraj Pakzad
- Department of Microbiology, School of Medicine, Ilam University of Medical Sciences, Ilam, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran.
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Shakiba M, Nazemipour M, Mansournia N, Mansournia MA. Protective effect of intensive glucose lowering therapy on all-cause mortality, adjusted for treatment switching using G-estimation method, the ACCORD trial. Sci Rep 2023; 13:5833. [PMID: 37037931 PMCID: PMC10086045 DOI: 10.1038/s41598-023-32855-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/03/2023] [Indexed: 04/12/2023] Open
Abstract
Previous analysis of the action to control cardiovascular risk in diabetes showed an increased risk of mortality among patients receiving intensive glucose lowering therapy using conventional regression method with intention to treat approach. This method is biased when time-varying confounder is affected by the previous treatment. We used 15 follow-up visits of ACCORD trial to compare the effect of time-varying intensive vs. standard treatment of glucose lowering drugs on cardiovascular and mortality outcomes in diabetic patients. The treatment effect was estimated using G-estimation and compared with accelerated failure time model using two modeling strategies. The first model adjusted for baseline confounders and the second adjusted for both baseline and time-varying confounders. While the hazard ratio of all-cause mortality for intensive compared to standard therapy in AFT model adjusted for baseline confounders was 1.17 (95% CI 1.01-1.36), the result of time-dependent AFT model was compatible with both protective and risk effects. However, the hazard ratio estimated by G-estimation was 0.64 (95% CI 0.39-0.92). The results of this study revealed a protective effect of intensive therapy on all-cause mortality compared with standard therapy in ACCORD trial.
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Affiliation(s)
- Maryam Shakiba
- Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
- Department of Biostatistics and Epidemiology, School of Health, Guilan University of Medical Sciences, Rasht, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran
| | - Nasrin Mansournia
- Department of Endocrinology, AJA University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box: 14155-6446, Tehran, Iran.
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Mansournia MA, Nazemipour M, Etminan M. A practical guide to handling competing events in etiologic time-to-event studies. Glob Epidemiol 2022; 4:100080. [PMID: 37637022 PMCID: PMC10446108 DOI: 10.1016/j.gloepi.2022.100080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/09/2022] [Accepted: 07/09/2022] [Indexed: 11/29/2022] Open
Abstract
Competing events are events that preclude the occurrence of the primary outcome. Much has been written on mainly the statistics behind competing events analyses. However, many of these publications and tutorials have a strong statistical tone and might fall short in providing a practical guide to clinician researchers as to when to use a competing event analysis and more importantly which method to use and why. Here we discuss the different target effects in the Fine-Gray and cause-specific methods using simple causal diagrams and provide strengths and limitations of both approaches for addressing etiologic questions. We argue why the Fine-Gray method might not be the best approach for handling competing events in etiological time-to-event studies.
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Affiliation(s)
- Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Nazemipour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahyar Etminan
- Department of Ophthalmology, Medicine and Pharmacology, University of British Columbia, Vancouver, Canada
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Koohi F, Khalili D, Soori H, Nazemipour M, Mansournia MA. 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. Glob Epidemiol 2022; 4:100075. [PMID: 37637024 PMCID: PMC10445971 DOI: 10.1016/j.gloepi.2022.100075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 05/14/2022] [Accepted: 05/16/2022] [Indexed: 10/18/2022] Open
Abstract
Background This study assesses the effect of blood lipid indices and lipid ratios on cardiovascular diseases (CVDs) using inverse probability-of-exposure weighted estimation of marginal structural models (MSMs). Methods A pooled dataset of two US representative cohort studies, including 16736 participants aged 42-84 years with complete information at baseline, was used. The effect of each lipid index, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), ratios of TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C on coronary heart disease (CHD) and stroke were estimated using weighted Cox regression. Results There were 1638 cases of CHD and 1017 cases of stroke during a median follow-up of 17.1 years (interquartile range: 8.5 to 25.7). Compared to optimal levels, the risk of CVD outcomes increased substantially in high levels of TC, LDL-C, TC/HDL-C, and LDL-C/HDL-C. If everyone had always had high levels of TC (≥240 mg/dL), risk of CHD would have been 2.15 times higher, and risk of stroke 1.35 times higher than if they had always had optimal levels (<200 mg/dL). Moreover, if all participants had been kept at very high (≥190 mg/dL) levels of LDL-C, risk of CHD would have been 2.62 times higher and risk of stroke would have been 1.92 times higher than if all participants had been kept at optimal levels, respectively. Our results suggest that high levels of HDL-C may be protective for CHD, but not for stroke. There was also no evidence of an adverse effect of high triglyceride levels on stroke. Conclusions Using MSM, this study highlights the effect of TC and LDL-C on CVD, with a stronger effect on CHD than on stroke. There was no evidence for a protective effect of high levels of HDL-C on stroke. Besides, triglyceride was not found to affect stroke.
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Affiliation(s)
- Fatemeh Koohi
- Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Soori
- Safety Promotion and Injury Prevention Research center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Nazemipour
- Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Mansournia MA, Nazemipour M, Etminan M. Interaction Contrasts and Collider Bias. Am J Epidemiol 2022; 191:1813-1819. [PMID: 35689644 DOI: 10.1093/aje/kwac103] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 04/13/2022] [Accepted: 06/08/2022] [Indexed: 01/29/2023] Open
Abstract
Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove this statement and, along with intuitions, formally examine the direction and magnitude of the associations between 2 risk factors of a binary collider using interaction contrasts. Among level one of the collider, 2 variables are independent, positively associated, and negatively associated if multiplicative risk interaction contrast is equal to, more than, and less than 0, respectively; the same results hold for the other level of the collider if the multiplicative survival interaction contrast, equal to multiplicative risk interaction contrast minus the additive risk interaction contrast, is compared with 0. The strength of the association depends on the magnitude of the interaction contrast: The stronger the interaction is, the larger the magnitude of the association will be. However, the common conditional odds ratio under the homogeneity assumption will be bounded. A figure is presented that succinctly illustrates our results and helps researchers to better visualize the associations introduced upon conditioning on a collider.
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Aryaie M, Sharifi H, Saber A, Salehi F, Etminan M, Nazemipour M, Mansournia MA. 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; 17:e0272212. [PMID: 35984783 PMCID: PMC9390931 DOI: 10.1371/journal.pone.0272212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 07/14/2022] [Indexed: 11/19/2022] Open
Abstract
Background Standard regression modeling may cause biased effect estimates in the presence of time-varying confounders affected by prior exposure. This study aimed to quantify the relationship between declining in modified creatinine index (MCI), as a surrogate marker of lean body mass, and mortality among end stage renal disease (ESRD) patients using G-estimation accounting appropriately for time-varying confounders. Methods A retrospective cohort of all registered ESRD patients (n = 553) was constructed over 8 years from 2011 to 2019, from 3 hemodialysis centers at Kerman, southeast of Iran. According to changes in MCI, patients were dichotomized to either the decline group or no-decline group. Subsequently the effect of interest was estimated using G-estimation and compared with accelerated failure time (AFT) Weibull models using two modelling strategies. Results Standard models demonstrated survival time ratios of 0.91 (95% confidence interval [95% CI]: 0.64 to 1.28) and 0.84 (95% CI: 0.58 to 1.23) in patients in the decline MCI group compared to those in no-decline MCI group. This effect was demonstrated to be 0.57 (-95% CI: 0.21 to 0.81) using G-estimation. Conclusion Declining in MCI increases mortality in patients with ESRD using G-estimation, while the AFT standard models yield biased effect estimate toward the null.
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