scholarly journals Handling Missing Data in Instrumental Variable Methods for Causal Inference

2019 ◽  
Vol 6 (1) ◽  
pp. 125-148
Author(s):  
Edward H. Kennedy ◽  
Jacqueline A. Mauro ◽  
Michael J. Daniels ◽  
Natalie Burns ◽  
Dylan S. Small
Keyword(s):  
Longitudinal Study ◽  
Instrumental Variable ◽  
Propensity Scores ◽  
Asymptotic Properties ◽  
Missing At Random ◽  
Robust Estimators ◽  
Genotyping Platform ◽  
Wisconsin Longitudinal Study ◽  
Doubly Robust ◽  
Nonparametric Efficiency

In instrumental variable studies, missing instrument data are very common. For example, in the Wisconsin Longitudinal Study, one can use genotype data as a Mendelian randomization–style instrument, but this information is often missing when subjects do not contribute saliva samples or when the genotyping platform output is ambiguous. Here we review missing at random assumptions one can use to identify instrumental variable causal effects, and discuss various approaches for estimation and inference. We consider likelihood-based methods, regression and weighting estimators, and doubly robust estimators. The likelihood-based methods yield the most precise inference and are optimal under the model assumptions, while the doubly robust estimators can attain the nonparametric efficiency bound while allowing flexible nonparametric estimation of nuisance functions (e.g., instrument propensity scores). The regression and weighting estimators can sometimes be easiest to describe and implement. Our main contribution is an extensive review of this wide array of estimators under varied missing-at-random assumptions, along with discussion of asymptotic properties and inferential tools. We also implement many of the estimators in an analysis of the Wisconsin Longitudinal Study, to study effects of impaired cognitive functioning on depression.

scholarly journals A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS

2017 ◽  
Vol 34 (1) ◽  
pp. 112-133 ◽  
Author(s):  
Tymon Słoczyński ◽  
Jeffrey M. Wooldridge
Keyword(s):  
Instrumental Variable ◽  
Exponential Family ◽  
Treatment Effects ◽  
Robust Estimators ◽  
Conditional Mean ◽  
Double Robustness ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Inverse Probability Weighted ◽  
Doubly Robust

In this paper we study doubly robust estimators of various average and quantile treatment effects under unconfoundedness; we also consider an application to a setting with an instrumental variable. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for subpopulation-specific average treatment effects where subpopulations can be based on covariate values in an arbitrary way. Similar to Wooldridge (2007), we then discuss estimation of the conditional mean using quasi-log likelihoods (QLL) from the linear exponential family.

scholarly journals Doubly robust pseudo-likelihood for incomplete hierarchical binary data

2018 ◽  
Vol 20 (1) ◽  
pp. 42-57
Author(s):  
Lisa Hermans ◽  
Anna Ivanova ◽  
Cristina Sotto ◽  
Geert Molenberghs ◽  
Geert Verbeke ◽  
...  
Keyword(s):  
Missing Data ◽  
Binary Data ◽  
Standard Form ◽  
Missing At Random ◽  
Correlated Data ◽  
Marginal Model ◽  
Robust Estimator ◽  
Robust Estimators ◽  
Pseudo Likelihood ◽  
Doubly Robust

Missing data is almost inevitable in correlated-data studies. For non-Gaussian outcomes with moderate to large sequences, direct-likelihood methods can involve complex, hard-to-manipulate likelihoods. Popular alternative approaches, like generalized estimating equations, that are frequently used to circumvent the computational complexity of full likelihood, are less suitable when scientific interest, at least in part, is placed on the association structure; pseudo-likelihood (PL) methods are then a viable alternative. When the missing data are missing at random, Molenberghs et al. (2011, Statistica Sinica, 21,187–206) proposed a suite of corrections to the standard form of PL, taking the form of singly and doubly robust estimators. They provided the basis and exemplified it in insightful yet primarily illustrative examples. We here consider the important case of marginal models for hierarchical binary data, provide an effective implementation and illustrate it using data from an analgesic trial. Our doubly robust estimator is more convenient than the classical doubly robust estimators. The ideas are illustrated using a marginal model for a binary response, more specifically a Bahadur model.

scholarly journals Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data

Biometrika ◽  
2009 ◽  
Vol 96 (3) ◽  
pp. 723-734 ◽  
Author(s):  
Weihua Cao ◽  
Anastasios A. Tsiatis ◽  
Marie Davidian
Keyword(s):  
Propensity Score ◽  
Incomplete Data ◽  
Propensity Scores ◽  
Robust Estimator ◽  
Robust Estimators ◽  
Population Mean ◽  
Recent Interest ◽  
Improved Performance ◽  
Doubly Robust

Abstract Considerable recent interest has focused on doubly robust estimators for a population mean response in the presence of incomplete data, which involve models for both the propensity score and the regression of outcome on covariates. The usual doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for some observations. We propose alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods, even with some estimated propensity scores close to zero.

scholarly journals Multigene interactions and the prediction of depression in the Wisconsin Longitudinal Study

BMJ Open ◽  
2012 ◽  
Vol 2 (4) ◽  
pp. e000944 ◽  
Author(s):  
Nicholas S Roetker ◽  
James A Yonker ◽  
Chee Lee ◽  
Vicky Chang ◽  
Jacob J Basson ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Wisconsin Longitudinal Study

scholarly journals Nonparametric Instrumental Variable Methods for Dynamic Treatment Evaluation

2020 ◽  
Vol 102 (2) ◽  
pp. 355-367
Author(s):  
Gerard J. van den Berg ◽  
Petyo Bonev ◽  
Enno Mammen
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Asymptotic Properties ◽  
Right Censoring ◽  
Unemployment Duration ◽  
Dynamic Selection ◽  
Average Causal Effect ◽  
Variable Approach ◽  
Dynamic Treatment ◽  
Job Finding

We develop an instrumental variable approach for identification of dynamic treatment effects on survival outcomes in the presence of dynamic selection, noncompliance, and right-censoring. The approach is nonparametric and does not require independence of observed and unobserved characteristics or separability assumptions. We propose estimation procedures and derive asymptotic properties. We apply our approach to evaluate a policy reform in which the pathway of unemployment benefits as a function of the unemployment duration is modified. Those who were unemployed at the reform date could choose between the old and the new regime. We find that the new regime has a positive average causal effect on the job finding rate.

scholarly journals Childhood Socioeconomic Status and Later Life Cognition: Evidence From the Wisconsin Longitudinal Study

2018 ◽  
Vol 31 (9) ◽  
pp. 1589-1615 ◽  
Author(s):  
Emily A. Greenfield ◽  
Sara M. Moorman
Keyword(s):  
Socioeconomic Status ◽  
Executive Function ◽  
Longitudinal Study ◽  
Household Income ◽  
Statistical Significance ◽  
Occupational Status ◽  
Later Life ◽  
Socioeconomic Differences ◽  
Wisconsin Longitudinal Study ◽  
Childhood Socioeconomic Status

Objectives:This study examined childhood socioeconomic status (SES) as a predictor of later life cognition and the extent to which midlife SES accounts for associations. Methods: Data came from 5,074 participants in the Wisconsin Longitudinal Study. Measures from adolescence included parents’ educational attainment, father’s occupational status, and household income. Memory and language/executive function were assessed at ages 65 and 72 years. Results: Global childhood SES was a stronger predictor of baseline levels of language/executive function than baseline memory. Associations involving parents’ education were reduced in size and by statistical significance when accounting for participants’ midlife SES, whereas associations involving parental income and occupational status became statistically nonsignificant. We found no associations between childhood SES and change in cognition. Discussion: Findings contribute to growing evidence that socioeconomic differences in childhood have potential consequences for later life cognition, particularly in terms of the disparate levels of cognition with which people enter later life.

College Selectivity and Later-Life Memory Function: Evidence From the Wisconsin Longitudinal Study

2020 ◽  
Vol 43 (1) ◽  
pp. 14-24
Author(s):  
Sarah Garcia ◽  
Sara M. Moorman
Keyword(s):  
Socioeconomic Status ◽  
Longitudinal Study ◽  
Memory Function ◽  
Later Life ◽  
College Attendance ◽  
Achievement Level ◽  
Selective College ◽  
College Selectivity ◽  
Memory Functioning ◽  
Wisconsin Longitudinal Study

Research has shown a consistent association between college completion and laterlife cognition. We extend this work by examining whether college selectivity—the achievement level required to gain admission to a college—is associated with memory functioning more than 50 years later. We analyze data from 10,317 participants in the 1957–2011 Wisconsin Longitudinal Study to examine the relationship between college selectivity and later-life memory. Models control for childhood, midlife socioeconomic status, and later-life health and adjust for selection bias. Selective college attendance was associated with small benefits in memory at age of 72 even after accounting for socioeconomic status in both childhood and midlife and later-life health. The results of this study suggest that college selectivity may be an important component of the education–cognitive functioning relationship that has modest implications for intracohort differences in later-life cognition.

scholarly journals Causal inference for recurrent event data using pseudo-observations

Biostatistics ◽  
2020 ◽  
Author(s):  
Chien-Lin Su ◽  
Robert W Platt ◽  
Jean-François Plante
Keyword(s):  
Goodness Of Fit ◽  
Variance Estimation ◽  
Recurrent Event ◽  
Event Data ◽  
Robust Estimators ◽  
Recurrent Event Data ◽  
Finite Sample ◽  
Data Set ◽  
Asymptotically Normal ◽  
Doubly Robust

Summary Recurrent event data are commonly encountered in observational studies where each subject may experience a particular event repeatedly over time. In this article, we aim to compare cumulative rate functions (CRFs) of two groups when treatment assignment may depend on the unbalanced distribution of confounders. Several estimators based on pseudo-observations are proposed to adjust for the confounding effects, namely inverse probability of treatment weighting estimator, regression model-based estimators, and doubly robust estimators. The proposed marginal regression estimator and doubly robust estimators based on pseudo-observations are shown to be consistent and asymptotically normal. A bootstrap approach is proposed for the variance estimation of the proposed estimators. Model diagnostic plots of residuals are presented to assess the goodness-of-fit for the proposed regression models. A family of adjusted two-sample pseudo-score tests is proposed to compare two CRFs. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to a hospital readmission data set.

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