Statistical Inference on the Estimators of the Adherer Average Causal Effect

Author(s):  
Ying Zhang ◽  
Haoda Fu ◽  
Stephen J. Ruberg ◽  
Yongming Qu
Crisis ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 157-165 ◽  
Author(s):  
Kevin S. Kuehn ◽  
Annelise Wagner ◽  
Jennifer Velloza

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.


2020 ◽  
Vol 8 (1) ◽  
pp. 54-69
Author(s):  
Peter B. Gilbert ◽  
Bryan S. Blette ◽  
Bryan E. Shepherd ◽  
Michael G. Hudgens

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.


2020 ◽  
Vol 24 (02) ◽  
pp. 54-55
Author(s):  
Arne Vielitz

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


2020 ◽  
Vol 102 (2) ◽  
pp. 355-367
Author(s):  
Gerard J. van den Berg ◽  
Petyo Bonev ◽  
Enno Mammen

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.


Author(s):  
Kieran S O’Brien ◽  
Ahmed M Arzika ◽  
Ramatou Maliki ◽  
Abdou Amza ◽  
Farouk Manzo ◽  
...  

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.


2020 ◽  
Vol 22 (1) ◽  
pp. 100-112 ◽  
Author(s):  
Nick Axford ◽  
Gretchen Bjornstad ◽  
Justin Matthews ◽  
Laura Whybra ◽  
Vashti Berry ◽  
...  

AbstractThe study, a two-arm, randomized controlled, parallel group, superiority trial, aimed to evaluate the implementation and effectiveness of a 12-month one-to-one volunteer mentoring program designed to improve behavioral and emotional outcomes in children aged 5 to 11 years who have teacher- and parent/carer-reported behavioral difficulties. Participants were 246 children (123 intervention, 123 control; mean age 8.4 years; 87% boys) in five sites in London, UK, scoring in the “abnormal” range on the teacher-rated Strengths and Difficulties Questionnaire (SDQ) Total Difficulties measure and in the “borderline” or abnormal range on the parent-rated SDQ Total Difficulties measure. Randomization on a 1:1 ratio took place using a computer-generated sequence and stratifying by site. Data collectors and statisticians were blind to participant allocation status. Outcome measures focused on parent- and teacher-rated child behavior and emotions, and child-rated self-perception and hope. Intention-to-treat analysis on all 246 randomized participants (using imputed data where necessary) showed that at post-intervention (16 months after randomization), there were no statistically significant effects on the primary outcome—parent-rated SDQ Total Difficulties (adjusted standardized mean difference = − 0.12; 95% CI: −0.38 to 0.13; p = 0.33)—or any secondary outcomes. Results from complier average causal effect (CACE) analysis using the primary outcome indicated the intervention was not effective for children who received the recommended duration of mentoring. Exploratory analyses found no sub-group effects on the primary outcome. The article concludes that the mentoring program had no effect on children’s behavior or emotional well-being, and that program content needs revising to satisfactorily address key risk and protective factors.


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