Diagnosing and responding to violations in the positivity assumption

The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-specific identifiability of causal effects. Positivity violations...

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Veröffentlicht in:	Statistical methods in medical research Jg. 21; H. 1; S. 31 - 54
Hauptverfasser:	Petersen, Maya L, Porter, Kristin E, Gruber, Susan, Wang, Yue, van der Laan, Mark J
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London, England SAGE Publications 01.02.2012 Sage Publications Ltd
Schlagworte:	Anti-HIV Agents - therapeutic use Bias Biomedical Research - statistics & numerical data Cohort Studies Computer Simulation - statistics & numerical data Confounding Factors, Epidemiologic Data Data Interpretation, Statistical Drug Resistance, Viral HIV Infections - drug therapy HIV Infections - genetics Humans Inference Lopinavir - therapeutic use Models, Statistical Modification Mutation Parameters Positive affect Proximity Trading Treatment methods Treatment Outcome Violations Working models realistic treatment rule positivity stabilised weights double robust parametric bootstrap trimming counterfactual experimental treatment assignment marginal structural model inverse probability weight causal inference truncation
ISSN:	0962-2802, 1477-0334, 1477-0334
Online-Zugang:	Volltext
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Abstract	The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-specific identifiability of causal effects. Positivity violations occur when certain subgroups in a sample rarely or never receive some treatments of interest. The resulting sparsity in the data may increase bias with or without an increase in variance and can threaten valid inference. The parametric bootstrap is presented as a tool to assess the severity of such threats and its utility as a diagnostic is explored using simulated and real data. Several approaches for improving the identifiability of parameters in the presence of positivity violations are reviewed. Potential responses to data sparsity include restriction of the covariate adjustment set, use of an alternative projection function to define the target parameter within a marginal structural working model, restriction of the sample, and modification of the target intervention. All of these approaches can be understood as trading off proximity to the initial target of inference for identifiability; we advocate approaching this tradeoff systematically.
AbstractList	The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-specific identifiability of causal effects. Positivity violations occur when certain subgroups in a sample rarely or never receive some treatments of interest. The resulting sparsity in the data may increase bias with or without an increase in variance and can threaten valid inference. The parametric bootstrap is presented as a tool to assess the severity of such threats and its utility as a diagnostic is explored using simulated and real data. Several approaches for improving the identifiability of parameters in the presence of positivity violations are reviewed. Potential responses to data sparsity include restriction of the covariate adjustment set, use of an alternative projection function to define the target parameter within a marginal structural working model, restriction of the sample, and modification of the target intervention. All of these approaches can be understood as trading off proximity to the initial target of inference for identifiability; we advocate approaching this tradeoff systematically. Adapted from the source document. The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-specific identifiability of causal effects. Positivity violations occur when certain subgroups in a sample rarely or never receive some treatments of interest. The resulting sparsity in the data may increase bias with or without an increase in variance and can threaten valid inference. The parametric bootstrap is presented as a tool to assess the severity of such threats and its utility as a diagnostic is explored using simulated and real data. Several approaches for improving the identifiability of parameters in the presence of positivity violations are reviewed. Potential responses to data sparsity include restriction of the covariate adjustment set, use of an alternative projection function to define the target parameter within a marginal structural working model, restriction of the sample, and modification of the target intervention. All of these approaches can be understood as trading off proximity to the initial target of inference for identifiability; we advocate approaching this tradeoff systematically. [PUBLICATION ABSTRACT] The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-specific identifiability of causal effects. Positivity violations occur when certain subgroups in a sample rarely or never receive some treatments of interest. The resulting sparsity in the data may increase bias with or without an increase in variance and can threaten valid inference. The parametric bootstrap is presented as a tool to assess the severity of such threats and its utility as a diagnostic is explored using simulated and real data. Several approaches for improving the identifiability of parameters in the presence of positivity violations are reviewed. Potential responses to data sparsity include restriction of the covariate adjustment set, use of an alternative projection function to define the target parameter within a marginal structural working model, restriction of the sample, and modification of the target intervention. All of these approaches can be understood as trading off proximity to the initial target of inference for identifiability; we advocate approaching this tradeoff systematically. The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-specific identifiability of causal effects. Positivity violations occur when certain subgroups in a sample rarely or never receive some treatments of interest. The resulting sparsity in the data may increase bias with or without an increase in variance and can threaten valid inference. The parametric bootstrap is presented as a tool to assess the severity of such threats and its utility as a diagnostic is explored using simulated and real data. Several approaches for improving the identifiability of parameters in the presence of positivity violations are reviewed. Potential responses to data sparsity include restriction of the covariate adjustment set, use of an alternative projection function to define the target parameter within a marginal structural working model, restriction of the sample, and modification of the target intervention. All of these approaches can be understood as trading off proximity to the initial target of inference for identifiability; we advocate approaching this tradeoff systematically.The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-specific identifiability of causal effects. Positivity violations occur when certain subgroups in a sample rarely or never receive some treatments of interest. The resulting sparsity in the data may increase bias with or without an increase in variance and can threaten valid inference. The parametric bootstrap is presented as a tool to assess the severity of such threats and its utility as a diagnostic is explored using simulated and real data. Several approaches for improving the identifiability of parameters in the presence of positivity violations are reviewed. Potential responses to data sparsity include restriction of the covariate adjustment set, use of an alternative projection function to define the target parameter within a marginal structural working model, restriction of the sample, and modification of the target intervention. All of these approaches can be understood as trading off proximity to the initial target of inference for identifiability; we advocate approaching this tradeoff systematically.
Author	Wang, Yue Gruber, Susan Petersen, Maya L van der Laan, Mark J Porter, Kristin E
AuthorAffiliation	1 Division of Biostatistics, University of California, Berkeley, CA 94110-7358, USA 2 Department of Clinical Information Services, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
AuthorAffiliation_xml	– name: 1 Division of Biostatistics, University of California, Berkeley, CA 94110-7358, USA – name: 2 Department of Clinical Information Services, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
Author_xml	– sequence: 1 givenname: Maya L surname: Petersen fullname: Petersen, Maya L organization: Department of Clinical Information Services, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA – sequence: 2 givenname: Kristin E surname: Porter fullname: Porter, Kristin E organization: Department of Clinical Information Services, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA – sequence: 3 givenname: Susan surname: Gruber fullname: Gruber, Susan organization: Department of Clinical Information Services, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA – sequence: 4 givenname: Yue surname: Wang fullname: Wang, Yue organization: Department of Clinical Information Services, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA – sequence: 5 givenname: Mark J surname: van der Laan fullname: van der Laan, Mark J organization: Department of Clinical Information Services, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/21030422$$D View this record in MEDLINE/PubMed
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Snippet	The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses...
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SubjectTerms	Anti-HIV Agents - therapeutic use Bias Biomedical Research - statistics & numerical data Cohort Studies Computer Simulation - statistics & numerical data Confounding Factors, Epidemiologic Data Data Interpretation, Statistical Drug Resistance, Viral HIV Infections - drug therapy HIV Infections - genetics Humans Inference Lopinavir - therapeutic use Models, Statistical Modification Mutation Parameters Positive affect Proximity Trading Treatment methods Treatment Outcome Violations Working models
Title	Diagnosing and responding to violations in the positivity assumption
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