Intention-to-Treat Analyses in Behavioral Medicine Randomized Clinical Trials

2009 ◽  
Vol 16 (4) ◽  
pp. 316-322 ◽  
Author(s):  
Sherry L. Pagoto ◽  
Andrea T. Kozak ◽  
Priya John ◽  
Jamie S. Bodenlos ◽  
Donald Hedeker ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Behavioral Medicine ◽  
Randomized Clinical Trials ◽  
Intention To Treat

Randomized Clinical Trials in Behavioral Medicine

2018 ◽  
pp. 111-136
Author(s):  
Kenneth E. Freedland ◽  
Sara J. Becker ◽  
James A. Blumenthal
Keyword(s):  
Clinical Trials ◽  
Behavioral Medicine ◽  
Randomized Clinical Trials

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.

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

Beware of on-treatment safety analyses

2018 ◽  
Vol 16 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Fred Yang ◽  
Janet Wittes ◽  
Bertram Pitt
Keyword(s):  
Clinical Trials ◽  
Adverse Events ◽  
Randomized Clinical Trials ◽  
Safety Data ◽  
Intention To Treat ◽  
Treatment Analysis ◽  
Treatment Safety ◽  
Intent To Treat ◽  
Biased Estimates

Introduction: Assessing safety is important to evaluating new medications. In many randomized clinical trials, assessment of safety relies on so-called on-treatment analysis, where data on adverse events are collected only while the participant is taking study medication and perhaps for a few (7, 14, or 30) days after stopping. This article discusses the consequence of such failure to use intent-to-treat analyses in assessing safety. Methods: This article discusses two approaches to analysis of safety data: intention-to-treat and on-treatment analysis with reference to principles of the design of randomized clinical trial. Results: On-treatment analysis violates randomization and is often not well defined. Moreover, because the typical on-treatment analysis ignores the reason participants in clinical trials stop treatment, on-treatment analyses can lead to biased estimates of risk. Examples show biases that can result from failure to count all adverse events. An example from a study of rofecoxib shows an on-treatment analysis that led to likely underestimation of harm; an example from a study of saxagliptin shows an on-treatment analysis that led to a likely overestimate of harms. Conclusion: For major safety outcomes in long-term clinical trials, intention-to-treat analysis should be performed in the framework of benefit–risk evaluation. More generally, analyses of safety should be tailored to the specific question being asked with the specific study design under consideration. On-treatment analyses are subject to bias; however, the direction of that bias is not necessarily clear.

scholarly journals A Method to Estimate the Efficacy vs Effectiveness in Meta-Analysis of Clinical Trials with Different Adherence Scenarios: A Monte Carlo Simulation Study in Nutrition

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2352
Author(s):  
Miguel Ángel López-Espinoza ◽  
José Antonio Lozano-Lozano ◽  
David Prieto-Merino
Keyword(s):  
Clinical Trials ◽  
Monte Carlo ◽  
Randomized Clinical Trials ◽  
Expert Knowledge ◽  
Meta Analysis ◽  
Specific Effect ◽  
Cochrane Systematic Review ◽  
Monte Carlo Simulation Study ◽  
Intention To Treat ◽  
Meta Analyses

Randomized clinical trials (RCTs) evaluating the effectiveness of interventions to promote fruit and vegetable (FV) consumption usually report intention-to-treat (ITT) analysis as the main outcome. These analyses compare the randomly assigned groups and accept that some individuals may not follow the recommendations received in their group. The ITT analysis is useful to quantify the global effect of promoting the consumption of FV in a population (effectiveness) but, if non-adherence is significant in the RCT, they cannot estimate the specific effect in the individuals that increased their FV consumption (efficacy). To calculate the efficacy of FV consumption, a per protocol analysis (PP) would have to be carried out, in which groups of individuals are compared according to their actual adherence to FV consumption, regardless of the group to which they were assigned; unfortunately, many RCTs do not report the PP analysis. The objective of this article is to apply a new method to estimate the efficacy of Meta-analysis (MA) PP which include RCTs of effectiveness by ITT, without estimates of adherence. The method is based on generating Monte Carlo simulations of percentages of adherence in each allocation group from prior distributions informed by expert knowledge. We illustrate the method reanalyzing a Cochrane Systematic Review (SR) of RCTs on increased FV consumption reported with ITT, simulating 1000 times the estimation of a PP meta-analyses, and obtaining means and ranges of the potential PP effects. In some cases, the range of estimated PP effects was clearly more favourable than the effect calculated with the original ITT assumption, and therefore this corrected analysis must be considered when estimating the true effect of the consumption of a certain food.

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.

A23 analysis by intention to treat in randomized clinical trials: A survey of the literature

1996 ◽  
Vol 17 (2) ◽  
pp. S51
Author(s):  
Linda Jansen-McWilliams ◽  
Fern Schwartz ◽  
Linda S. Kalcevic ◽  
Charnita M. Zeigler
Keyword(s):  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Intention To Treat
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