Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness

Seaman, Shaun; White, Ian R.

doi:10.1080/03610926.2012.700371

Cited by 56 publications

(76 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach is comparable to the 'Within approach' from Mitra and Reiter (2012), who applied PS matching after MI[33]. In contrast, other approaches to overcome the problem of missing values in PS estimation have been studied[9;11;12]. For example, Qu and Lipkovich (2009) proposed an adaptation including indicators of missing data patterns in the PS model.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Propensity Scoring after Multiple Imputation in a Retrospective Study on Adjuvant Radiation Therapy in Lymph-Node Positive Vulvar Cancer

et al. 2016

View full text Add to dashboard Cite

Propensity scoring (PS) is an established tool to account for measured confounding in non-randomized studies. These methods are sensitive to missing values, which are a common problem in observational data. The combination of multiple imputation of missing values and different propensity scoring techniques is addressed in this work. For a sample of lymph node-positive vulvar cancer patients, we re-analyze associations between the application of radiotherapy and disease-related and non-related survival. Inverse-probability-of-treatment-weighting (IPTW) and PS stratification are applied after multiple imputation by chained equation (MICE). Methodological issues are described in detail. Interpretation of the results and methodological limitations are discussed.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Different methods like MI, as applied here, or the use of an missing values pattern indicator[9;11;12] are available. In our example, results from a complete case analysis did not differ much from PS after MI.…”

Section: Discussionmentioning

confidence: 99%

Propensity Scoring after Multiple Imputation in a Retrospective Study on Adjuvant Radiation Therapy in Lymph-Node Positive Vulvar Cancer

et al. 2016

View full text Add to dashboard Cite

show abstract

“…In other words, the source separation program might be implemented according to the characteristics of the city, who in turn self-select the given program. We therefore used inverse probability weighting (IPW) method to address this problem, which can effectively adjust for these confounding and selection bias in non-randomized studies [44]. Its application procedure starts with an estimation of the propensity score (i.e., the predicted probability of an individual receives the active treatment) on related covariates through a logit or probit model.…”

Section: Methodology Panel Data Modelmentioning

confidence: 99%

The impact of the policy of municipal solid waste source-separated collection on waste reduction: a case study of China

Han

Zhang

2015

J Mater Cycles Waste Manag

View full text Add to dashboard Cite

To fight against environmental pressure arising from growing municipal solid waste (MSW), the program of MSW source-separated collection has been launched in eight pilot cities of China since 2000. What is the impact of this pilot program on waste generation? Based on the citylevel data for 1998-2012, this study examines its impact on per capita amount of MSW by employing panel data model and inverse probability weighting method. It is shown that the policy of MSW source-separated collection has insignificant influence on reduction of per capita quantity of MSW while consumption expenditure per capita, average household size and average education level significantly accelerate per capita waste generation. Although the program of MSW source separation has not led to significant waste reduction in our analysis, source separation has provided the basis for reuse, recycling and recovery of MSW by classifying waste into categories of recyclable and recycling materials. In response to the pressure of growing MSW as a result of industrialization, urbanization and economic growth, source separation is the best approach to waste management to reduce the burden of waste disposals. Therefore, it is necessary to facilitate public participation in source separation by adoption of mixed policies combining voluntary with mandatory as well as education approaches.

show abstract

“…Although in MCAR and MAR scenarios, use of missing indicators can introduce bias [4,5], use of missing indicators may reduce bias where the missingness is informative, and particularly when the missing indicator is used in conjunction with multiple imputation (MIMI) [3,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Use of Missing Indicators as Proxies for Unmeasured Variables: Simulation Study

Sperrin¹,

Martin²

2020

Preprint

View full text Add to dashboard Cite

Background Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative. While the naive use of missing indicators to try to exploit such information can introduce bias when used inappropriately, its use in conjunction with other imputation approaches may unlock the potential value of missingness to reduce bias and improve prediction.Methods We conducted a simulation study to determine when the use of a missing indicator, combined with an imputation approach, such as multiple imputation, would lead to improved model performance, in terms of minimising bias for causal effect estimation, and improving predictive accuracy, under a range of scenarios with unmeasured variables. We use directed acyclic graphs and structural models to elucidate causal structures of interest. We consider a variety of missingness mechanisms, then handle these using complete case analysis, unconditional mean imputation, regression imputation and multiple imputation. In each case we evaluate supplementing these approaches with missing indicator terms. Results For estimating causal effects, we find that multiple imputation combined with a missing indicator gives minimal bias in most scenarios. For prediction, we find that regression imputation combined with a missing indicator minimises mean squared error.Conclusion In the presence of missing data, careful use of missing indicators, combined with appropriate imputation, can improve both causal estimation and prediction accuracy.

show abstract

Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness

Abstract: Inverse probability weighting (IPW) can deal with confounding in non randomized

Cited by 56 publications

References 21 publications

Propensity Scoring after Multiple Imputation in a Retrospective Study on Adjuvant Radiation Therapy in Lymph-Node Positive Vulvar Cancer

Propensity Scoring after Multiple Imputation in a Retrospective Study on Adjuvant Radiation Therapy in Lymph-Node Positive Vulvar Cancer

The impact of the policy of municipal solid waste source-separated collection on waste reduction: a case study of China

Use of Missing Indicators as Proxies for Unmeasured Variables: Simulation Study

Contact Info

Product

Resources

About