Stratified doubly robust estimators for the average causal effect

Biometrics ◽  
2014 ◽  
Vol 70 (2) ◽  
pp. 270-277 ◽  
Author(s):  
Satoshi Hattori ◽  
Masayuki Henmi
Keyword(s):  
Causal Effect ◽  
Robust Estimators ◽  
Average Causal Effect ◽  
Doubly Robust

Estimating the Magnitude of the Relation Between Bullying, E-Bullying, and Suicidal Behaviors Among United States Youth, 2015

Crisis ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 157-165 ◽  
Author(s):  
Kevin S. Kuehn ◽  
Annelise Wagner ◽  
Jennifer Velloza
Keyword(s):  
Adolescent Suicide ◽  
Suicide Attempts ◽  
Causal Effect ◽  
Strong Association ◽  
Cross Sectional ◽  
Average Causal Effect ◽  
Effects Of Bullying ◽  
Pc Algorithm ◽  
Nationally Representative ◽  
Significant Covariates

Abstract. Background: Suicide is the second leading cause of death among US adolescents aged 12–19 years. Researchers would benefit from a better understanding of the direct effects of bullying and e-bullying on adolescent suicide to inform intervention work. Aims: To explore the direct and indirect effects of bullying and e-bullying on adolescent suicide attempts (SAs) and to estimate the magnitude of these effects controlling for significant covariates. Method: This study uses data from the 2015 Youth Risk Behavior Surveillance Survey (YRBS), a nationally representative sample of US high school youth. We quantified the association between bullying and the likelihood of SA, after adjusting for covariates (i.e., sexual orientation, obesity, sleep, etc.) identified with the PC algorithm. Results: Bullying and e-bullying were significantly associated with SA in logistic regression analyses. Bullying had an estimated average causal effect (ACE) of 2.46%, while e-bullying had an ACE of 4.16%. Limitations: Data are cross-sectional and temporal precedence is not known. Conclusion: These findings highlight the strong association between bullying, e-bullying, and SA.

scholarly journals Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial

2020 ◽  
Vol 8 (1) ◽  
pp. 54-69
Author(s):  
Peter B. Gilbert ◽  
Bryan S. Blette ◽  
Bryan E. Shepherd ◽  
Michael G. Hudgens
Keyword(s):  
Causal Effect ◽  
Effect Modification ◽  
Hiv Vaccine ◽  
Strongly Correlated ◽  
Principal Stratification ◽  
Response Variable ◽  
Average Causal Effect ◽  
Intermediate Response ◽  
Structural Risk ◽  
Augmented Design

AbstractWhile the HVTN 505 trial showed no overall efficacy of the tested vaccine to prevent HIV infection over placebo, markers measuring immune response to vaccination were strongly correlated with infection. This finding generated the hypothesis that some marker-defined vaccinated subgroups were partially protected whereas others had their risk increased. This hypothesis can be assessed using the principal stratification framework (Frangakis and Rubin, 2002) for studying treatment effect modification by an intermediate response variable, using methods in the sub-field of principal surrogate (PS) analysis that studies multiple principal strata. Unfortunately, available methods for PS analysis require an augmented study design not available in HVTN 505, and make untestable structural risk assumptions, motivating a need for more robust PS methods. Fortunately, another sub-field of principal stratification, survivor average causal effect (SACE) analysis (Rubin, 2006) – which studies effects in a single principal stratum – provides many methods not requiring an augmented design and making fewer assumptions. We show how, for a binary intermediate response variable, methods developed for SACE analysis can be adapted to PS analysis, providing new and more robust PS methods. Application to HVTN 505 supports that the vaccine partially protected individuals with vaccine-induced T-cells expressing certain combinations of functions.

Paracetamol bei Rückenschmerzen nicht empfohlen

2020 ◽  
Vol 24 (02) ◽  
pp. 54-55
Author(s):  
Arne Vielitz
Keyword(s):  
Randomized Controlled Trial ◽  
Low Back Pain ◽  
Causal Effect ◽  
Controlled Trial ◽  
Low Back ◽  
Effect Analysis ◽  
Acute Low Back Pain ◽  
Randomized Controlled ◽  
Average Causal Effect ◽  
Complier Average Causal Effect

Schreijenberg M, Lin CC, McLachlan AJ et al. Paracetamol is Ineffective for Acute Low Back Pain even for Patients Who Comply with Treatment: Complier Average Causal Effect Analysis of a Randomized Controlled Trial. Pain 2019; 160: 2848–2854. doi: 10.1097/j.pain.0000000000001685

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.

Azithromycin distribution and childhood mortality in compliance-related subgroups in Niger: complier average causal effect and spillovers in a cluster-randomized, placebo-controlled trial

2021 ◽  
Author(s):  
Kieran S O’Brien ◽  
Ahmed M Arzika ◽  
Ramatou Maliki ◽  
Abdou Amza ◽  
Farouk Manzo ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Rate Ratio ◽  
Incidence Rate Ratio ◽  
Causal Effect ◽  
Controlled Trial ◽  
Effect Analysis ◽  
Intention To Treat ◽  
Average Causal Effect ◽  
Cluster Randomized ◽  
Complier Average Causal Effect

Abstract Background Biannual azithromycin distribution to children 1–59 months old reduced all-cause mortality by 18% [incidence rate ratio (IRR) 0.82, 95% confidence interval (CI): 0.74, 0.90] in an intention-to-treat analysis of a randomized controlled trial in Niger. Estimation of the effect in compliance-related subgroups can support decision making around implementation of this intervention in programmatic settings. Methods The cluster-randomized, placebo-controlled design of the original trial enabled unbiased estimation of the effect of azithromycin on mortality rates in two subgroups: (i) treated children (complier average causal effect analysis); and (ii) untreated children (spillover effect analysis), using negative binomial regression. Results In Niger, 594 eligible communities were randomized to biannual azithromycin or placebo distribution and were followed from December 2014 to August 2017, with a mean treatment coverage of 90% [standard deviation (SD) 10%] in both arms. Subgroup analyses included 2581 deaths among treated children and 245 deaths among untreated children. Among treated children, the incidence rate ratio comparing mortality in azithromycin communities to placebo communities was 0.80 (95% CI: 0.72, 0.88), with mortality rates (deaths per 1000 person-years at risk) of 16.6 in azithromycin communities and 20.9 in placebo communities. Among untreated children, the incidence rate ratio was 0.91 (95% CI: 0.69, 1.21), with rates of 33.6 in azithromycin communities and 34.4 in placebo communities. Conclusions As expected, this analysis suggested similar efficacy among treated children compared with the intention-to-treat analysis. Though the results were consistent with a small spillover benefit to untreated children, this trial was underpowered to detect spillovers.

scholarly journals Doubly Robust Estimation of Causal Effect

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiaochun Li ◽  
Changyu Shen
Keyword(s):  
Propensity Score ◽  
Causal Effect ◽  
Model Specification ◽  
Robust Estimator ◽  
Data Set ◽  
Multiple Regressions ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
Confounding Adjustment ◽  
The Relationship

Propensity score–based methods or multiple regressions of the outcome are often used for confounding adjustment in analysis of observational studies. In either approach, a model is needed: A model describing the relationship between the treatment assignment and covariates in the propensity score–based method or a model for the outcome and covariates in the multiple regressions. The 2 models are usually unknown to the investigators and must be estimated. The correct model specification, therefore, is essential for the validity of the final causal estimate. We describe in this article a doubly robust estimator which combines both models propitiously to offer analysts 2 chances for obtaining a valid causal estimate and demonstrate its use through a data set from the Lindner Center Study.

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