Estimands: improving inference in randomized controlled trials in clinical nutrition in the presence of missing values

2018 ◽  
Vol 72 (9) ◽  
pp. 1291-1295 ◽  
Author(s):  
Christian Ritz ◽  
Birgitte Rønn
2019 ◽  
Vol 38 ◽  
pp. S180
Author(s):  
M. Mizera ◽  
M. Wysocki ◽  
P. Małczak ◽  
T. Stefura ◽  
N. Gajewska ◽  
...  

Author(s):  
Connie M Weaver ◽  
Naomi K Fukagawa ◽  
DeAnn Liska ◽  
Richard D Mattes ◽  
Gregory Matuszek ◽  
...  

ABSTRACT Training to ensure good documentation practices and adherence to regulatory requirements in human nutrition randomized controlled trials has not been given sufficient attention. Furthermore, it is difficult to find this information conveniently organized or in a form relevant to nutrition protocols. Current gaps in training and research surveillance exist in clinical nutrition research because training modules emphasize drugs and devices, promote reliance on monitoring boards, and lack nutrition expertise on human nutrition research teams. Additionally, because eating is essential, ongoing, and highly individualized, it is difficult to distinguish risks associated with interventions from eating under free-living conditions. Controlled-feeding trials provide an option to gain more experimental control over food consumed, but at a price of less external validity, and may pose human behavior issues that are unrelated to the intervention. This paper covers many of the expected practices for documentation and regulation that may be encountered in planning and conducting nutrition intervention trials with examples and references that should be useful to clinical nutrition researchers, funders of research, and research institutions. Included are definitions and guidance on clinical nutrition research oversight (institutional review boards, data safety and monitoring boards, US FDA); participant safety; standard operating procedures; training of investigators, staff, and students; and local culture and reporting requirements relevant to diet-related clinical research conduct and documentation.


2020 ◽  
Vol 39 (4) ◽  
pp. 1284-1291
Author(s):  
Michał Pędziwiatr ◽  
Magdalena Mizera ◽  
Michał Wysocki ◽  
Piotr Małczak ◽  
Tomasz Stefura ◽  
...  

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.


2020 ◽  
Vol 146 (12) ◽  
pp. 1117-1145
Author(s):  
Kathryn R. Fox ◽  
Xieyining Huang ◽  
Eleonora M. Guzmán ◽  
Kensie M. Funsch ◽  
Christine B. Cha ◽  
...  

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