Understanding the Intention-to-treat Principle in Randomized Controlled Trials

Abstract

Clinicians, institutions, and policy makers use results from randomized controlled trials to make decisions regarding therapeutic interventions for their patients and populations. Knowing the effect the intervention has on patients in clinical trials is critical for making both individual patient as well as population-based decisions. However, patients in clinical trials do not always adhere to the protocol. Excluding patients from the analysis who violated the research protocol (did not get their intended treatment) can have significant implications that impact the results and analysis of a study.Intention-to-treat analysis is a method for analyzing results in a prospective randomized study where all participants who are randomized are included in the statistical analysis and analyzed according to the group they were originally assigned, regardless of what treatment (if any) they received. This method allows the investigator (or consumer of the medical literature) to draw accurate (unbiased) conclusions regarding the effectiveness of an intervention. This method preserves the benefits of randomization, which cannot be assumed when using other methods of analysis.The risk of bias is increased whenever treatment groups are not analyzed according to the group to which they were originally assigned. If an intervention is truly effective (truth), an intention-to-treat analysis will provide an unbiased estimate of the efficacy of the intervention at the level of adherence in the study. This article will review the “intention-to-treat” principle and its converse, “per-protocol” analysis, and illustrate how using the wrong method of analysis can lead to a significantly biased assessment of the effectiveness of an intervention.

Cluster randomized controlled trial testing the effectiveness of a self‐management intervention using the teach‐back method for people with heart failure

“…Study variables were inputted into a SPSS file version 20 (IBM Corp, 2011) and analyzed following intention‐to‐treat principles (McCoy, ). HF knowledge score was calculated using a sum of correct answers (Van Der Wal et al, ).…”

Evaluation of mobile applications for fitness training and physical activity in healthy low-trained people - A modular interdisciplinary framework

“…In longitudinal studies with fitness apps, dropouts and withdrawals as well as technical issues may cause missing data issues. However, to amend or at least mitigate this problem, appropriate statistical procedures have been proposed, e.g., multiple imputations or full information maximum likelihood (e.g., Lang & Little, 2018) or intention-to-treat analysis (e.g., McCoy, 2017).…”

ALSUntangled no. 49: resveratrol

“…After four months of treatment, the PALS taking EH301 had statistically significant improvements in ALS functional rating scalerevised (ALSFRS-R) scores, forced vital capacity (FVC) respiratory testing, and manual muscle testing relative to PALS taking a placebo (55). Interpretation of these results is limited by the small sample size, short duration, imbalances between treated and control groups after randomization, lack of intention-to-treat analyses, and authorship by members of the company that owns the product (56,57).…”