Intention-to-Treat Analysis and Accounting for Missing Data in Orthopaedic Randomized Clinical Trials

2009 ◽  
Vol 91 (9) ◽  
pp. 2137-2143 ◽  
Author(s):  
Amir Herman ◽  
Itamar Busheri Botser ◽  
Shay Tenenbaum ◽  
Ahron Chechick
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Randomized Clinical Trials ◽  
Intention To Treat ◽  
Treat Analysis

scholarly journals A novel complete-case analysis to determine statistical significance between treatments in an intention-to-treat population of randomized clinical trials involving missing data

2016 ◽  
Vol 27 (4) ◽  
pp. 1067-1075 ◽  
Author(s):  
Wei Liu ◽  
Jinhui Ding
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Randomized Clinical Trials ◽  
Statistical Significance ◽  
Outcome Data ◽  
Complete Case ◽  
Significant Treatment ◽  
Significance Level ◽  
Intention To Treat ◽  
The Impact

The application of the principle of the intention-to-treat (ITT) to the analysis of clinical trials is challenged in the presence of missing outcome data. The consequences of stopping an assigned treatment in a withdrawn subject are unknown. It is difficult to make a single assumption about missing mechanisms for all clinical trials because there are complicated reactions in the human body to drugs due to the presence of complex biological networks, leading to data missing randomly or non-randomly. Currently there is no statistical method that can tell whether a difference between two treatments in the ITT population of a randomized clinical trial with missing data is significant at a pre-specified level. Making no assumptions about the missing mechanisms, we propose a generalized complete-case (GCC) analysis based on the data of completers. An evaluation of the impact of missing data on the ITT analysis reveals that a statistically significant GCC result implies a significant treatment effect in the ITT population at a pre-specified significance level unless, relative to the comparator, the test drug is poisonous to the non-completers as documented in their medical records. Applications of the GCC analysis are illustrated using literature data, and its properties and limits are discussed.

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

scholarly journals Missing Data in Randomized Clinical Trials for Weight Loss: Scope of the Problem, State of the Field, and Performance of Statistical Methods

PLoS ONE ◽  
2009 ◽  
Vol 4 (8) ◽  
pp. e6624 ◽  
Author(s):  
Mai A. Elobeid ◽  
Miguel A. Padilla ◽  
Theresa McVie ◽  
Olivia Thomas ◽  
David W. Brock ◽  
...  
Keyword(s):  
Weight Loss ◽  
Clinical Trials ◽  
Missing Data ◽  
Statistical Methods ◽  
Randomized Clinical Trials ◽  
And Performance ◽  
State Of The Field

scholarly journals Maintaining Integrity of Worldwide FINGER Clinical Trials During the COVID-19 Pandemic

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 68-68
Author(s):  
Susanne Roehr
Keyword(s):  
Clinical Trials ◽  
Study Design ◽  
World Wide ◽  
Randomized Clinical Trials ◽  
Statistical Design ◽  
Sensitivity Analyses ◽  
Intervention Delivery ◽  
Intention To Treat ◽  
Approaches To Study ◽  
Intervention Trials

Abstract The COVID-19 pandemic presents challenges to the conduct of randomized clinical trials of lifestyle interventions. World-Wide FINGERS international network convened a forum for researchers to discuss statistical design and analysis issues they faced during the pandemic. We report experiences of three trials that, at various stages of conduct, altered designs and analysis plans to navigate these issues. We provide recommendations for future trials to consider as they develop and launch behavioral intervention trials. The pandemic led researchers to change recruitment plans, interrupt timelines for assessments and intervention delivery, and move to remote intervention and assessments protocols. The necessity of these changes add emphasis to the importance, in study design and analysis, of intention to treat approaches, flexibility, within site stratification, interim power projections, and sensitivity analyses. Robust approaches to study design and analysis are critical to negotiate issues related to the intervention.

Direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials

2005 ◽  
Vol 2 (5) ◽  
pp. 379-386 ◽  
Author(s):  
Caroline Beunckens ◽  
Geert Molenberghs ◽  
Michael G Kenward
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Randomized Clinical Trials ◽  
Likelihood Analysis
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