The positivity assumption and marginal structural models: the example of warfarin use and risk of bleeding

Estimates of the average causal effect (ACE) of warfarin on the risk of bleeding may be confounded by indication as patients at high risk of bleeding are unlikely to be prescribed warfarin. One approach to estimating the ACE is inverse probability of treatment weighting (IPTW).This study was designe...

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Published inEuropean journal of epidemiology Vol. 27; no. 2; pp. 77 - 83
Main Authors Platt, Robert William, Delaney, Joseph Austin Christopher, Suissa, Samy
Format Journal Article
LanguageEnglish
Published Dordrecht Springer 01.02.2012
Springer Netherlands
Springer Nature B.V
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ISSN0393-2990
1573-7284
1573-7284
DOI10.1007/s10654-011-9637-7

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Abstract Estimates of the average causal effect (ACE) of warfarin on the risk of bleeding may be confounded by indication as patients at high risk of bleeding are unlikely to be prescribed warfarin. One approach to estimating the ACE is inverse probability of treatment weighting (IPTW).This study was designed to examine the use of IPTW in this setting, and to demonstrate problems with the violation of the positivity assumption. We analyzed a case-control study on 4,028 cases of gastro-intestinal bleeding and 79,239 controls set in the United Kingdom's General Practice Research Database. Warfarin exposure was defined as a prescription issued in the 90 days before the index date. Secondary analyses were conducted restricted to patients more likely to receive warfarin and with a truncated weight distribution, to exclude subjects highly unlikely to be treated. The estimated association between warfarin use and bleeding was stronger with IPTW [odds ratio (OR): 17.2; 95% confidence interval (CI): 6.5-37.7] than with a standard logistic regression model (OR: 2.1; 95% CI: 1.7-2.5). The presence of large weights (five subjects with stabilized weight > 500) indicated a potential violation of the positivity assumption. In the restricted analysis, both IPTW (OR: 2.0; 95% CI: 0.4-9.6) and standard regression (OR: 1.6; 95% CI: 1.3-2.0) were compatible with a meta-analysis of randomized trials inverse probability of treatment weighting is sensitive to the positivity assumption; however, such sensitivity may assist in diagnosing off-support inference.
AbstractList Estimates of the average causal effect (ACE) of warfarin on the risk of bleeding may be confounded by indication as patients at high risk of bleeding are unlikely to be prescribed warfarin. One approach to estimating the ACE is inverse probability of treatment weighting (IPTW). This study was designed to examine the use of IPTW in this setting, and to demonstrate problems with the violation of the positivity assumption. We analyzed a case-control study on 4,028 cases of gastro-intestinal bleeding and 79,239 controls set in the United Kingdom's General Practice Research Database. Warfarin exposure was defined as a prescription issued in the 90 days before the index date. Secondary analyses were conducted restricted to patients more likely to receive warfarin and with a truncated weight distribution, to exclude subjects highly unlikely to be treated. The estimated association between warfarin use and bleeding was stronger with IPTW [odds ratio (OR): 17.2; 95% confidence interval (CI): 6.5-37.7] than with a standard logistic regression model (OR: 2.1; 95% CI: 1.7-2.5). The presence of large weights (five subjects with stabilized weight >500) indicated a potential violation of the positivity assumption. In the restricted analysis, both IPTW (OR: 2.0; 95% CI: 0.4-9.6) and standard regression (OR: 1.6; 95% CI: 1.3-2.0) were compatible with a meta-analysis of randomized trials inverse probability of treatment weighting is sensitive to the positivity assumption; however, such sensitivity may assist in diagnosing off-support inference.
Estimates of the average causal effect (ACE) of warfarin on the risk of bleeding may be confounded by indication as patients at high risk of bleeding are unlikely to be prescribed warfarin. One approach to estimating the ACE is inverse probability of treatment weighting (IPTW). This study was designed to examine the use of IPTW in this setting, and to demonstrate problems with the violation of the positivity assumption. We analyzed a case–control study on 4,028 cases of gastro-intestinal bleeding and 79,239 controls set in the United Kingdom’s General Practice Research Database. Warfarin exposure was defined as a prescription issued in the 90 days before the index date. Secondary analyses were conducted restricted to patients more likely to receive warfarin and with a truncated weight distribution, to exclude subjects highly unlikely to be treated. The estimated association between warfarin use and bleeding was stronger with IPTW [odds ratio (OR): 17.2; 95% confidence interval (CI): 6.5–37.7] than with a standard logistic regression model (OR: 2.1; 95% CI: 1.7–2.5). The presence of large weights (five subjects with stabilized weight >500) indicated a potential violation of the positivity assumption. In the restricted analysis, both IPTW (OR: 2.0; 95% CI: 0.4–9.6) and standard regression (OR: 1.6; 95% CI: 1.3–2.0) were compatible with a meta-analysis of randomized trials inverse probability of treatment weighting is sensitive to the positivity assumption; however, such sensitivity may assist in diagnosing off-support inference.
Estimates of the average causal effect (ACE) of warfarin on the risk of bleeding may be confounded by indication as patients at high risk of bleeding are unlikely to be prescribed warfarin. One approach to estimating the ACE is inverse probability of treatment weighting (IPTW). This study was designed to examine the use of IPTW in this setting, and to demonstrate problems with the violation of the positivity assumption. We analyzed a case-control study on 4,028 cases of gastrointestinal bleeding and 79,239 controls set in the United Kingdom's General Practice Research Database. Warfarin exposure was defined as a prescription issued in the 90 days before the index date. Secondary analyses were conducted restricted to patients more likely to receive warfarin and with a truncated weight distribution, to exclude subjects highly unlikely to be treated. The estimated association between warfarin use and bleeding was stronger with IPTW [odds ratio (OR): 17.2; 95% confidence interval (CI): 6.5-37.7] than with a standard logistic regression model (OR: 2.1; 95% CI: 1.7-2.5). The presence of large weights (five subjects with stabilized weight >500) indicated a potential violation of the positivity assumption. In the restricted analysis, both IPTW (OR: 2.0; 95% CI: 0.4-9.6) and standard regression (OR: 1.6; 95% CI: 1.3-2.0) were compatible with a meta-analysis of randomized trials inverse probability of treatment weighting is sensitive to the positivity assumption; however, such sensitivity may assist in diagnosing off-support inference.[PUBLICATION ABSTRACT]
Estimates of the average causal effect (ACE) of warfarin on the risk of bleeding may be confounded by indication as patients at high risk of bleeding are unlikely to be prescribed warfarin. One approach to estimating the ACE is inverse probability of treatment weighting (IPTW). This study was designed to examine the use of IPTW in this setting, and to demonstrate problems with the violation of the positivity assumption. We analyzed a case-control study on 4,028 cases of gastro-intestinal bleeding and 79,239 controls set in the United Kingdom's General Practice Research Database. Warfarin exposure was defined as a prescription issued in the 90 days before the index date. Secondary analyses were conducted restricted to patients more likely to receive warfarin and with a truncated weight distribution, to exclude subjects highly unlikely to be treated. The estimated association between warfarin use and bleeding was stronger with IPTW [odds ratio (OR): 17.2; 95% confidence interval (CI): 6.5-37.7] than with a standard logistic regression model (OR: 2.1; 95% CI: 1.7-2.5). The presence of large weights (five subjects with stabilized weight >500) indicated a potential violation of the positivity assumption. In the restricted analysis, both IPTW (OR: 2.0; 95% CI: 0.4-9.6) and standard regression (OR: 1.6; 95% CI: 1.3-2.0) were compatible with a meta-analysis of randomized trials inverse probability of treatment weighting is sensitive to the positivity assumption; however, such sensitivity may assist in diagnosing off-support inference.Estimates of the average causal effect (ACE) of warfarin on the risk of bleeding may be confounded by indication as patients at high risk of bleeding are unlikely to be prescribed warfarin. One approach to estimating the ACE is inverse probability of treatment weighting (IPTW). This study was designed to examine the use of IPTW in this setting, and to demonstrate problems with the violation of the positivity assumption. We analyzed a case-control study on 4,028 cases of gastro-intestinal bleeding and 79,239 controls set in the United Kingdom's General Practice Research Database. Warfarin exposure was defined as a prescription issued in the 90 days before the index date. Secondary analyses were conducted restricted to patients more likely to receive warfarin and with a truncated weight distribution, to exclude subjects highly unlikely to be treated. The estimated association between warfarin use and bleeding was stronger with IPTW [odds ratio (OR): 17.2; 95% confidence interval (CI): 6.5-37.7] than with a standard logistic regression model (OR: 2.1; 95% CI: 1.7-2.5). The presence of large weights (five subjects with stabilized weight >500) indicated a potential violation of the positivity assumption. In the restricted analysis, both IPTW (OR: 2.0; 95% CI: 0.4-9.6) and standard regression (OR: 1.6; 95% CI: 1.3-2.0) were compatible with a meta-analysis of randomized trials inverse probability of treatment weighting is sensitive to the positivity assumption; however, such sensitivity may assist in diagnosing off-support inference.
Author Platt, Robert William
Delaney, Joseph Austin Christopher
Suissa, Samy
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Issue 2
Keywords Inverse probability weighting
Warfarin
Causal modeling
Bias
Positivity assumption
Human
Coumarine derivatives
Use
Probability
Risk
Anticoagulant
Epidemiology
Hemorrhage
Modeling
Antivitamin K
Cause
Risk factor
Structural model
Public health
Language English
License http://www.springer.com/tdm
CC BY 4.0
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16198396 - Thromb Res. 2006;118(3):321-33
16371515 - Am J Epidemiol. 2006 Feb 1;163(3):262-70
19418232 - Eur J Epidemiol. 2009;24(7):343-9
14507605 - Am J Epidemiol. 2003 Oct 1;158(7):687-94
17504780 - Am J Epidemiol. 2007 Aug 1;166(3):332-9
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18516239 - Stat Sci. 2007;22(4):569-573
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20306452 - Pharmacoepidemiol Drug Saf. 2010 Jun;19(6):618-26
16014771 - Am J Epidemiol. 2005 Aug 15;162(4):382-8
15308962 - Epidemiology. 2004 Sep;15(5):615-25
16441879 - Epidemiol Perspect Innov. 2006 Jan 27;3:2
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SubjectTerms Adult
Aged
Anticoagulants
Anticoagulants - adverse effects
Biological and medical sciences
Bleeding
Cardiology
Case-Control Studies
Databases, Factual
Epidemiology
Female
General aspects
General Practice
Hemorrhage
Hemorrhage - chemically induced
Hemorrhage - epidemiology
Humans
Infectious Diseases
Inference
Logistic regression
Male
Medical sciences
Medicine
Medicine & Public Health
METHODS
Middle Aged
Miscellaneous
Modeling
Models, Theoretical
Oncology
Population estimates
Population inversion
Population mean
Public Health
Public health. Hygiene
Public health. Hygiene-occupational medicine
Risk Assessment
Risk factors
Structural models
Term weighting
United Kingdom - epidemiology
Warfarin - adverse effects
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Title The positivity assumption and marginal structural models: the example of warfarin use and risk of bleeding
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