scholarly journals Synthetic estimation for the complier average causal effect

2021 ◽  
Author(s):  
Denis Agniel ◽  
Matthew Cefalu ◽  
Bing Han
Keyword(s):  
Causal Effect ◽  
Synthetic Estimation ◽  
Average Causal Effect ◽  
Complier Average Causal Effect

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

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.

Estimation of Intervention Effects with Noncompliance: Alternative Model Specifications

2002 ◽  
Vol 27 (4) ◽  
pp. 385-409 ◽  
Author(s):  
Booil Jo
Keyword(s):  
Causal Effect ◽  
Critical Role ◽  
Estimation Method ◽  
Exclusion Restriction ◽  
Covariate Information ◽  
Average Causal Effect ◽  
Major Interest ◽  
Fundamental Difficulty ◽  
Study Participants ◽  
Complier Average Causal Effect

This study examines alternative ways of specifying models in the complier average causal effect (CACE) estimation method, where the major interest is in estimating causal effects of treatments for compliers. A fundamental difficulty involved in the CACE estimation method is in dealing with missing compliance information among study participants. Given that, the assumption of the exclusion restriction plays a critical role in separating the distributions of compliers and non-compliers. If no pretreatment covariates are available, assuming the exclusion restriction is unavoidable to obtain unique ML estimates in CACE models, although the assumption can be often unrealistic. One disadvantage of assuming the exclusion restriction is that the CACE estimate can be biased if the assumption is violated. Another disadvantage is that the assumption limits the flexibility of CACE modeling in practice. However, if pretreatment covariates are available, more modeling options other than strictly forcing the exclusion restriction can be considered to establish identifiability of CACE models. This study explores modeling possibilities of CACE estimation within an ML-EM framework in the presence of covariate information.

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