Development of a composite endpoint for randomized controlled trials in liver surgery (Br J Surg 2011; 98: 1138-1145)

2011 ◽  
Vol 98 (8) ◽  
pp. 1145-1146 ◽  
Author(s):  
V. E. de Meijer
Keyword(s):  
Randomized Controlled Trials ◽  
Liver Surgery ◽  
Composite Endpoint ◽  
Controlled Trials ◽  
Randomized Controlled

Development of a composite endpoint for randomized controlled trials in liver surgery

2011 ◽  
Vol 98 (8) ◽  
pp. 1138-1145 ◽  
Author(s):  
M. A. J. van den Broek ◽  
R. M. van Dam ◽  
G. J. P. van Breukelen ◽  
M. H. Bemelmans ◽  
E. Oussoultzoglou ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Liver Surgery ◽  
Composite Endpoint ◽  
Controlled Trials ◽  
Randomized Controlled

Development of a Composite Endpoint for Randomized Controlled Trials in Pancreaticoduodenectomy

2013 ◽  
Author(s):  
Marielle M. E. Coolsen ◽  
Stefan H. E. M. Clermonts ◽  
Ronald M. van Dam ◽  
Bjorn Winkens ◽  
Massimo Malagó ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Composite Endpoint ◽  
Controlled Trials ◽  
Randomized Controlled

Feasibility of randomized controlled trials in liver surgery using surgery-related mortality or morbidity as endpoint

2009 ◽  
Vol 96 (9) ◽  
pp. 1005-1014 ◽  
Author(s):  
M. A. J. van den Broek ◽  
R. M. van Dam ◽  
M. Malagó ◽  
C. H. C. Dejong ◽  
G. J. P. van Breukelen ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Liver Surgery ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Related Mortality

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.

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