Intention-to-treat concept: A review

By S. Gupta. Perspectives in Clinical Research, 2011

1,857 citations recorded by the source corpus.

Randomized controlled trials often suffer from two major complications, i.e., noncompliance and missing outcomes. One potential solution to this problem is a statistical concept called intention-to-treat (ITT) analysis. ITT analysis includes every subject who is randomized according to randomized treatment assignment. It ignores noncompliance, protocol deviations, withdrawal, and anything that happens after randomization. ITT analysis maintains prognostic balance generated from the original random treatment allocation. In ITT analysis, estimate of treatment effect is generally conservative. A better application of the ITT approach is possible if complete outcome data are available for all randomized subjects. Per-protocol population is defined as a subset of the ITT population who completed the study without any major protocol violations.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cb2f290883dd2edba07221f639072b4803ba2cf3

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Citations in the corpus

Meta-Analysis of the Efficacy of Virtual Reality Exposure Therapy for Social Anxiety (2018, positive): “If available, we used carry-forward intention-to-treat (ITT) data because it offers a more conservative estimate of treatment outcomes (Gupta, 2011).”
On doing relevant and rigorous experiments: Review and recommendations (2018, background): “All these issues are known as “non-compliance” problems and pose a challenge to randomized experiments when the non-compliance status is correlated with the treatment status (Glennerster & Takavarasha, 2013; Gupta, 2011).”
Randomized Control Trial of Teaching Recovery Techniques in Rural Occupied Palestine: Effect on Adolescent Dissociation (2016, methodology): “A conservative estimate of treatment was used where participant pretest scores were also used as posttest scores (Gupta, 2011).”
An Experimental Evaluation of the Impact of Intensive Supervision on the Recidivism of High-Risk Probationers (2017, methodology): “Although this method is relatively conservative, and may understate the magnitude of the observed effects (Aos, Miller & Drake, 2007; Gupta, 2011; Hollis & Campbell, 1999), it remains the best measure of the impact that implementing ISP for high risk probationers would have in a “real world” policy ”
Sample Size Justification (2021, neutral): “…do not comply with the protocol are maintained in the analysis such that the effect size from the study accurately represents the effect of implementing the intervention in the population, and not the effect of the intervention only on those people who perfectly follow the protocol (Gupta, 2011).”
A Growth Mindset Intervention: Enhancing Students’ Entrepreneurial Self-Efficacy and Career Development (2020, neutral): “Gupta, S. K. (2011).”
The Influence of Attrition in Evaluating the Efficacy and Effectiveness of Mindfulness-Based Interventions (2016, methodology): “The modified ITT technique, on the other hand, allows investigators to exclude some randomized participants such as participants who did not begin the intervention or participants who are found to be ineligible to participate postrandomization (Gupta 2011).”
Exercise as a treatment for depression: A meta-analysis. (2016, background): “However, they ignore other factors that can introduce bias, such as noncompliance, protocol deviations, and withdrawal (Gupta, 2011).”
The impact of trial stage, developer involvement and international transferability on universal social and emotional learning programme outcomes: a meta-analysis (2016, methodology): “…adopted methods to capture the complexities and nuances in relation to the unique ecology of the implementation context, most notably due to an emphasis on randomised control trial (RCT) methodology (and its variants), specifically incorporating ‘intent to treat’ (ITT) assessment (Gupta, 2011).”