scholarly journals Impact of a targeted monitoring on data‐quality and data‐management workload of randomized controlled trials: A prospective comparative study

2019 ◽  
Vol 85 (12) ◽  
pp. 2784-2792
Author(s):  
Claire Fougerou‐Leurent ◽  
Bruno Laviolle ◽  
Christelle Tual ◽  
Valérie Visseiche ◽  
Aurélie Veislinger ◽  
...  
Keyword(s):  
Comparative Study ◽  
Data Quality ◽  
Data Management ◽  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Prospective Comparative Study

scholarly journals Perspective: Laboratory Considerations and Clinical Data Management for Human Nutrition Randomized Controlled Trials: Guidance for Ensuring Quality and Integrity

2020 ◽  
Author(s):  
Kevin C Maki ◽  
Joshua W Miller ◽  
George P McCabe ◽  
Gowri Raman ◽  
Penny M Kris-Etherton
Keyword(s):  
Data Collection ◽  
Data Management ◽  
Randomized Controlled Trials ◽  
Clinical Data ◽  
Human Nutrition ◽  
Controlled Trials ◽  
Quality Of Data ◽  
Randomized Controlled ◽  
Nutrition Research ◽  
Human Nutrition Research

ABSTRACT In human nutrition randomized controlled trials (RCTs), planning, and careful execution of clinical data collection and management are vital for producing valid and reliable results. In this article, we provide an overview of best practices for biospecimen collection and analyses, and for the fundamentals of clinical data management, including preparation and study startup; data collection, entry, cleaning, and authentication; and database lock. The reader is also referred to additional resources for information to assist in the planning and conduct of human RCTs. The tools and strategies described are expected to improve the quality of data produced in human nutrition research that can, therefore, be used to support food and nutrition policies.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

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.

Interventions for suicide and self-injury: A meta-analysis of randomized controlled trials across nearly 50 years of research.

2020 ◽  
Vol 146 (12) ◽  
pp. 1117-1145
Author(s):  
Kathryn R. Fox ◽  
Xieyining Huang ◽  
Eleonora M. Guzmán ◽  
Kensie M. Funsch ◽  
Christine B. Cha ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Meta Analysis ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Self Injury
Sign in / Sign up

Export Citation Format

Share Document