scholarly journals Instrumental variable estimation of early treatment effect in randomized screening trials

2021 ◽  
Author(s):  
Sudipta Saha ◽  
Zhihui Liu ◽  
Olli Saarela
Keyword(s):  
Treatment Effect ◽  
Early Treatment ◽  
Instrumental Variable ◽  
Instrumental Variable Estimation ◽  
Screening Trials

scholarly journals Statistical considerations in the design and analysis of non-inferiority trials with binary endpoints in the presence of non-adherence: a simulation study

2020 ◽  
Vol 4 ◽  
pp. 207 ◽  
Author(s):  
Yin Mo ◽  
Cherry Lim ◽  
Mavuto Mukaka ◽  
Ben S. Cooper
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Experimental Treatment ◽  
Inverse Probability Weighting ◽  
Alternative Methods ◽  
Sensitivity Analyses ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Instrumental Variable Estimation ◽  
The Impact

Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes, especially in non-inferiority trials. We performed simulations of a non-inferiority trial with a time-fixed treatment and a binary endpoint in order to: i) explore the impact of various patterns of non-adherence and analysis methods on treatment effect estimates; ii) quantify the probability of claiming non-inferiority when the experimental treatment effect is actually inferior; and iii) evaluate alternative methods such as inverse probability weighting and instrumental variable estimation. We found that the probability of concluding non-inferiority when the experimental treatment is actually inferior depends on whether non-adherence is due to confounding or non-confounding factors, and the actual treatments received by the non-adherent participants. With non-adherence, intention-to-treat analysis has a higher tendency to conclude non-inferiority when the experimental treatment is actually inferior under most patterns of non-adherence. This probability of concluding non-inferiority can be increased to as high as 0.1 from 0.025 when the adherence is relatively high at 90%. The direction of bias for the per-protocol analysis depends on the directions of influence the confounders have on adherence and probability of outcome. The inverse probability weighting approach can reduce bias but will only eliminate it if all confounders can be measured without error and are appropriately adjusted for. Instrumental variable estimation overcomes this limitation and gives unbiased estimates even when confounders are not known, but typically requires large sample sizes to achieve acceptable power. Investigators need to consider patterns of non-adherence and potential confounders in trial designs. Adjusted analysis of the per-protocol population with sensitivity analyses on confounders and other approaches, such as instrumental variable estimation, should be considered when non-compliance is anticipated. We provide an online power calculator allowing for various patterns of non-adherence using the above methods.

scholarly journals Statistical considerations in the design and analysis of non-inferiority trials with binary endpoints in the presence of non-adherence: a simulation study

2019 ◽  
Vol 4 ◽  
pp. 207
Author(s):  
Yin Mo ◽  
Cherry Lim ◽  
Mavuto Mukaka ◽  
Ben S. Cooper
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Experimental Treatment ◽  
Inverse Probability Weighting ◽  
Alternative Methods ◽  
Sensitivity Analyses ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Instrumental Variable Estimation ◽  
The Impact

Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes, especially in non-inferiority trials. We performed simulations of a non-inferiority trial with a time-fixed treatment and a binary endpoint in order to: i) explore the impact of various patterns of non-adherence and analysis methods on treatment effect estimates; ii) quantify the probability of claiming non-inferiority when the experimental treatment effect is actually inferior; and iii) evaluate alternative methods such as inverse probability weighting and instrumental variable estimation. We found that the probability of concluding non-inferiority when the experimental treatment is actually inferior depends on whether non-adherence is due to confounding or non-confounding factors, and the actual treatments received by the non-adherent participants. With non-adherence, intention-to-treat analysis has a higher tendency to conclude non-inferiority when the experimental treatment is actually inferior under most patterns of non-adherence. This probability of concluding non-inferiority can be increased to as high as 0.1 from 0.025 when the adherence is relatively high at 90%. The direction of bias for the per-protocol analysis depends on the directions of influence the confounders have on adherence and probability of outcome. The inverse probability weighting approach can reduce bias but will only eliminate it if all confounders can be measured without error and are appropriately adjusted for. Instrumental variable estimation overcomes this limitation and gives unbiased estimates even when confounders are not known, but typically requires large sample sizes to achieve acceptable power. Investigators need to consider patterns of non-adherence and potential confounders in trial designs. Adjusted analysis of the per-protocol population with sensitivity analyses on confounders and other approaches, such as instrumental variable estimation, should be considered when non-compliance is anticipated. We provide an online power calculator allowing for various patterns of non-adherence using the above methods.

Specification testing in nonparametric instrumental variable estimation

2012 ◽  
Vol 167 (2) ◽  
pp. 383-396 ◽  
Author(s):  
Joel L. Horowitz
Keyword(s):  
Instrumental Variable ◽  
Specification Testing ◽  
Instrumental Variable Estimation

scholarly journals Instrumental Variable Estimation of the Simple Errors-in-Variables Model

1980 ◽  
Vol 75 (371) ◽  
pp. 687-692 ◽  
Author(s):  
R. L. Carter ◽  
Wayne A. Fuller
Keyword(s):  
Instrumental Variable ◽  
Errors In Variables ◽  
Instrumental Variable Estimation

Bounding Treatment Effects with Contaminated and Censored Data: Assessing the Impact of Early Childbearing on Children

2005 ◽  
Vol 5 (1) ◽  
Author(s):  
Charles H Mullin
Keyword(s):  
Censored Data ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Child Outcomes ◽  
Well Being ◽  
Teenage Mothers ◽  
Age At First Birth ◽  
First Birth ◽  
The Impact

AbstractEmpirical researchers commonly invoke instrumental variable (IV) assumptions to identify treatment effects. This paper considers what can be learned under two specific violations of those assumptions: contaminated and corrupted data. Either of these violations prevents point identification, but sharp bounds of the treatment effect remain feasible. In an applied example, random miscarriages are an IV for women’s age at first birth. However, the inability to separate random miscarriages from behaviorally induced miscarriages (those caused by smoking and drinking) results in a contaminated sample. Furthermore, censored child outcomes produce a corrupted sample. Despite these limitations, the bounds demonstrate that delaying the age at first birth for the current population of non-black teenage mothers reduces their first-born child’s well-being.

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