scholarly journals Exact confidence intervals for the average causal effect on a binary outcome

2016 ◽  
Vol 35 (13) ◽  
pp. 2296-2296
Author(s):  
Xinran Li ◽  
Peng Ding
Keyword(s):  
Confidence Intervals ◽  
Causal Effect ◽  
Binary Outcome ◽  
Average Causal Effect ◽  
Exact Confidence Intervals

scholarly journals Exact confidence intervals for the average causal effect on a binary outcome

2016 ◽  
Vol 35 (6) ◽  
pp. 957-960 ◽  
Author(s):  
Xinran Li ◽  
Peng Ding
Keyword(s):  
Confidence Intervals ◽  
Causal Effect ◽  
Binary Outcome ◽  
Average Causal Effect ◽  
Exact Confidence Intervals

scholarly journals Should We or Should We Not Include Confidence Intervals in COVID-19 Death Forecasting? Evidence from a Survey Experiment

2021 ◽  
pp. 147892992098568
Author(s):  
Jean-François Daoust ◽  
Frédérick Bastien
Keyword(s):  
Public Health ◽  
Confidence Intervals ◽  
Causal Effect ◽  
Negative Bias ◽  
Survey Experiment ◽  
Health Measures ◽  
Average Causal Effect ◽  
Preventive Public Health ◽  
Citizens Attitudes

Forecasting during the COVID-19 pandemic entails a great deal of uncertainty. The same way that we would like electoral forecasters to systematically include their confidence intervals to account for such uncertainty, we assume that COVID-19-related forecasts should follow that norm. Based on literature on negative bias, we may expect the presence of uncertainty to affect citizens’ attitudes and behaviours, which would in turn have major implications on how we should present these sensitive forecasts. In this research we present the main findings of a survey experiment where citizens were exposed to a projection of the total number of deaths. We manipulated the exclusion (and inclusion) of graphically depicted confidence intervals in order to isolate the average causal effect of uncertainty. Our results show that accounting for uncertainty does not change (1) citizens’ perceptions of projections’ reliability, nor does it affect (2) their support for preventive public health measures. We conclude by discussing the implications of our findings.

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.

SHORT EXACT CONFIDENCE INTERVALS FOR THE POISSON MEAN

2001 ◽  
Vol 30 (2) ◽  
pp. 257-261 ◽  
Author(s):  
John Byrne ◽  
Paul Kabaila
Keyword(s):  
Confidence Intervals ◽  
Exact Confidence Intervals

Statistical Inference on the Estimators of the Adherer Average Causal Effect

2021 ◽  
pp. 1-4
Author(s):  
Ying Zhang ◽  
Haoda Fu ◽  
Stephen J. Ruberg ◽  
Yongming Qu
Keyword(s):  
Statistical Inference ◽  
Causal Effect ◽  
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

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