Abstract P198: Racial Disparities in Cardiovascular Outcomes in REGARDS Persist After Using Inverse Probability Weighting to Account for Missing Data

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
D. Leann Long ◽  
George Howard ◽  
Suzanne Judd ◽  
Jennifer Manly ◽  
Leslie McClure ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Selection Bias ◽  
Ventricular Hypertrophy ◽  
Inverse Probability Weighting ◽  
Left Ventricular ◽  
Probability Weighting ◽  
Complete Case ◽  
Inverse Probability ◽  
Weighted Analysis ◽  
Inverse Probability Weighted

Introduction: When data are missing, analysts often choose to perform ‘complete case’ analysis, restricting statistical analysis to those participants with all necessary fields completed. However, this analysis is subject to selection bias if those participants excluded are somehow inherently different from those included. One approach to address the potential selection bias is inverse probability weighting, where participants with complete data are weighted to reflect the original population. We compare estimated racial disparities in hypertension and left ventricular hypertrophy using complete cases to those estimates from inverse probability weighted analysis. Methods: The REasons for Geographic and Racial Differences in Stroke (REGARDS) study enrolled 30,183 participants aged 45+ between 2003 and 2007 to study racial and geographic differences in stroke and cardiovascular health. When the second in-home visits were conducted, 18% (5542) of participants had died and 23% (7071) of participants had withdrawn. For the baseline population, the probabilities of being a complete case are estimated through logistic regression models using baseline characteristics. These predicted probabilities are inverted to create the weights used in statistical analysis, such that the complete participants are weighted to represent the enrolled population. Through logistic regression, we estimate the association between race and hypertension and left ventricular hypertrophy at the second in-home visit for complete data and compare these results to the results from inverse probability weighted analysis. All models are adjusted for sex, age and region. Results: The logistic models for dropout and death have low and moderate predictive abilities (c-statistics 0.602 and 0.811, respectively). For incident hypertension, the estimated odds ratio comparing blacks to whites differs little between the complete case (1.87 (1.66, 2.10)) and the weighted (1.83 (1.61, 2.09)) analysis. For left ventricular hypertrophy, the estimated odds ratio comparing blacks to whites changes little from the complete case analysis (1.54 (1.32, 1.79)) to the weighted analysis (1.45 (1.21, 1.74)). Discussion: Estimated racial inequalities in the odds of incident hypertension and left ventricular hypertrophy were similar in the complete case and inverse probability weighting analyses, indicating little evidence of selection bias in the estimation of racial inequalities for these outcomes.

scholarly journals Contextualizing selection bias in Mendelian randomization: how bad is it likely to be?

2018 ◽  
Vol 48 (3) ◽  
pp. 691-701 ◽  
Author(s):  
Apostolos Gkatzionis ◽  
Stephen Burgess
Keyword(s):  
Risk Factor ◽  
Selection Bias ◽  
Simulation Study ◽  
Cardiovascular Mortality ◽  
Mendelian Randomization ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Type 1 Error

Abstract Background Selection bias affects Mendelian randomization investigations when selection into the study sample depends on a collider between the genetic variant and confounders of the risk factor–outcome association. However, the relative importance of selection bias for Mendelian randomization compared with other potential biases is unclear. Methods We performed an extensive simulation study to assess the impact of selection bias on a typical Mendelian randomization investigation. We considered inverse probability weighting as a potential method for reducing selection bias. Finally, we investigated whether selection bias may explain a recently reported finding that lipoprotein(a) is not a causal risk factor for cardiovascular mortality in individuals with previous coronary heart disease. Results Selection bias had a severe impact on bias and Type 1 error rates in our simulation study, but only when selection effects were large. For moderate effects of the risk factor on selection, bias was generally small and Type 1 error rate inflation was not considerable. Inverse probability weighting ameliorated bias when the selection model was correctly specified, but increased bias when selection bias was moderate and the model was misspecified. In the example of lipoprotein(a), strong genetic associations and strong confounder effects on selection mean the reported null effect on cardiovascular mortality could plausibly be explained by selection bias. Conclusions Selection bias can adversely affect Mendelian randomization investigations, but its impact is likely to be less than other biases. Selection bias is substantial when the effects of the risk factor and confounders on selection are particularly large.

scholarly journals Likelihood Approach for Bayesian Logistic Weighted Model

2020 ◽  
Vol 4 (2) ◽  
pp. 9-12
Author(s):  
Dler H. Kadir
Keyword(s):  
Cognitive Development ◽  
Case Analysis ◽  
Inverse Probability Weighting ◽  
Complete Case Analysis ◽  
Parameter Estimates ◽  
Probability Weighting ◽  
Complete Case ◽  
Inverse Probability ◽  
Very Preterm ◽  
Weighted Model

Increasing the response rate and minimizing non-response rates represent the primary challenges to researchers in performing longitudinal and cohort research. This is most obvious in the area of paediatric medicine. When there are missing data, complete case analysis makes findings biased. Inverse Probability Weighting (IPW) is one of many available approaches for reducing the bias using a complete case analysis. Here, a complete case is weighted by probability inverse of complete cases. The data of this work is collected from the neonatal intensive care unit at Erbil maternity hospital for the years 2012 to 2017. In total, 570 babies (288 male and 282 females) were born very preterm. The aim of this paper is to use inverse probability weighting on the Bayesian logistic model developmental outcome. The Mental Development Index (MDI) approach is used for assessing the cognitive development of those born very preterm. Almost half of the information for the babies was missing, meaning that we do not know whether they have cognitive development issues or they have not. We obtained greater precision in results and standard deviation of parameter estimates which are less in the posterior weighted model in comparison with frequent analysis.

Adjusting for selection bias due to missing data in electronic health records-based research

2021 ◽  
Vol 30 (10) ◽  
pp. 2221-2238
Author(s):  
Sarah B Peskoe ◽  
David Arterburn ◽  
Karen J Coleman ◽  
Lisa J Herrinton ◽  
Michael J Daniels ◽  
...  
Keyword(s):  
Missing Data ◽  
Electronic Health Records ◽  
Selection Bias ◽  
Inverse Probability Weighting ◽  
Small Sample ◽  
Data Provenance ◽  
Probability Weighting ◽  
Health Records ◽  
Inverse Probability ◽  
Electronic Health

While electronic health records data provide unique opportunities for research, numerous methodological issues must be considered. Among these, selection bias due to incomplete/missing data has received far less attention than other issues. Unfortunately, standard missing data approaches (e.g. inverse-probability weighting and multiple imputation) generally fail to acknowledge the complex interplay of heterogeneous decisions made by patients, providers, and health systems that govern whether specific data elements in the electronic health records are observed. This, in turn, renders the missing-at-random assumption difficult to believe in standard approaches. In the clinical literature, the collection of decisions that gives rise to the observed data is referred to as the data provenance. Building on a recently-proposed framework for modularizing the data provenance, we develop a general and scalable framework for estimation and inference with respect to regression models based on inverse-probability weighting that allows for a hierarchy of missingness mechanisms to better align with the complex nature of electronic health records data. We show that the proposed estimator is consistent and asymptotically Normal, derive the form of the asymptotic variance, and propose two consistent estimators. Simulations show that naïve application of standard methods may yield biased point estimates, that the proposed estimators have good small-sample properties, and that researchers may have to contend with a bias-variance trade-off as they consider how to handle missing data. The proposed methods are motivated by an on-going, electronic health records-based study of bariatric surgery.

Predictors of follow-up and assessment of selection bias from dropouts using inverse probability weighting in a cohort of university graduates

2006 ◽  
Vol 21 (5) ◽  
pp. 351-358 ◽  
Author(s):  
Alvaro Alonso ◽  
María Seguí-Gómez ◽  
Jokin de Irala ◽  
Almudena Sánchez-Villegas ◽  
Juan José Beunza ◽  
...  
Keyword(s):  
Selection Bias ◽  
Inverse Probability Weighting ◽  
University Graduates ◽  
Probability Weighting ◽  
Inverse Probability

scholarly journals On weighting approaches for missing data

2011 ◽  
Vol 22 (1) ◽  
pp. 14-30 ◽  
Author(s):  
Lingling Li ◽  
Changyu Shen ◽  
Xiaochun Li ◽  
James M Robins
Keyword(s):  
Missing Data ◽  
Selection Bias ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Full Data ◽  
Inverse Probability ◽  
Intuitive Idea ◽  
Complex Settings ◽  
Conceptual Overview

We review the class of inverse probability weighting (IPW) approaches for the analysis of missing data under various missing data patterns and mechanisms. The IPW methods rely on the intuitive idea of creating a pseudo-population of weighted copies of the complete cases to remove selection bias introduced by the missing data. However, different weighting approaches are required depending on the missing data pattern and mechanism. We begin with a uniform missing data pattern (i.e. a scalar missing indicator indicating whether or not the full data is observed) to motivate the approach. We then generalise to more complex settings. Our goal is to provide a conceptual overview of existing IPW approaches and illustrate the connections and differences among these approaches.

scholarly journals Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Prabin Dahal ◽  
Kasia Stepniewska ◽  
Philippe J. Guerin ◽  
Umberto D’Alessandro ◽  
Ric N. Price ◽  
...  
Keyword(s):  
Analytical Solution ◽  
Multiple Imputation ◽  
Drug Efficacy ◽  
Analysis Data ◽  
Inverse Probability Weighting ◽  
Accurate Estimate ◽  
Probability Weighting ◽  
Complete Case ◽  
Inverse Probability ◽  
Dependent Mechanism

Abstract Background Antimalarial clinical efficacy studies for uncomplicated Plasmodium falciparum malaria frequently encounter situations in which molecular genotyping is unable to discriminate between parasitic recurrence, either new infection or recrudescence. The current WHO guideline recommends excluding these individuals with indeterminate outcomes in a complete case (CC) analysis. Data from the four artemisinin-based combination (4ABC) trial was used to compare the performance of multiple imputation (MI) and inverse probability weighting (IPW) against the standard CC analysis for dealing with indeterminate recurrences. Methods 3369 study participants from the multicentre study (4ABC trial) with molecularly defined parasitic recurrence treated with three artemisinin-based combination therapies were used to represent a complete dataset. A set proportion of recurrent infections (10, 30 and 45%) were reclassified as missing using two mechanisms: a completely random selection (mechanism 1); missingness weakly dependent (mechanism 2a) and strongly dependent (mechanism 2b) on treatment and transmission intensity. The performance of MI, IPW and CC approaches in estimating the Kaplan-Meier (K-M) probability of parasitic recrudescence at day 28 was then compared. In addition, the maximum likelihood estimate of the cured proportion was presented for further comparison (analytical solution). Performance measures (bias, relative bias, standard error and coverage) were reported as an average from 1000 simulation runs. Results The CC analyses resulted in absolute underestimation of K-M probability of day 28 recrudescence by up to 1.7% and were associated with reduced precision and poor coverage across all the scenarios studied. Both MI and IPW method performed better (greater consistency and greater efficiency) compared to CC analysis. In the absence of censoring, the analytical solution provided the most consistent and accurate estimate of cured proportion compared to the CC analyses. Conclusions The widely used CC approach underestimates antimalarial failure; IPW and MI procedures provided efficient and consistent estimates and should be considered when reporting the results of antimalarial clinical trials, especially in areas of high transmission, where the proportion of indeterminate outcomes could be large. The analytical solution estimating the cured proportion could provide an alternative approach, in scenarios with minimal censoring due to loss to follow-up or new infections.

Sign in / Sign up

Export Citation Format

Share Document