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Intention-to-treat analysis

Intention-to-treat (ITT) analysis is a fundamental statistical principle employed in randomized controlled trials (RCTs) to evaluate treatment effects by including all participants in the analysis according to their originally assigned groups, regardless of whether they adhered to the protocol, received the intended intervention, or completed the study.^[1]^[2] This approach, often summarized as "once randomized, always analyzed," ensures that deviations such as noncompliance, protocol violations, or withdrawals do not alter the initial group assignments.^[1] The primary rationale for ITT analysis lies in preserving the integrity of randomization, which is the cornerstone of RCTs for minimizing selection bias and maintaining comparability between treatment groups.^[2] By analyzing participants as originally allocated, ITT avoids the introduction of bias that could arise from excluding non-adherent subjects, thereby providing a more realistic estimate of treatment efficacy in clinical practice where perfect adherence is rare.^[1]^[2] It contrasts with per-protocol analysis, which only includes data from participants who fully comply with the study protocol, potentially overestimating treatment effects by ignoring real-world deviations.^[1] ITT analysis offers several advantages, including the maintenance of statistical power through larger sample sizes and the reflection of pragmatic outcomes that mirror routine healthcare delivery.^[1] However, it can dilute estimated treatment effects due to noncompliance and may complicate interpretation when significant heterogeneity is introduced by protocol deviations or missing data.^[1] Despite these challenges, regulatory agencies and clinical guidelines, such as those from the CONSORT statement, recommend ITT as the preferred primary analysis in RCTs to ensure unbiased and generalizable results.^[2]

Definition and Principles

Core Definition

Intention-to-treat (ITT) analysis is a fundamental principle in the evaluation of randomized controlled trials (RCTs), requiring that all participants who are randomized to treatment groups be included in the primary analysis according to their original assignment, regardless of subsequent adherence to the protocol, receipt of the intended intervention, withdrawal from the study, or any deviations from the planned treatment.^[3] This method, often summarized by the phrase "once randomized, always analyzed," aims to reflect the full effects of the treatment policy as assigned, thereby avoiding biases that could arise from selective exclusion of participants.^[4] The concept of ITT emerged in the 1950s amid growing recognition of biases introduced by non-compliance and participant losses in clinical trials, with early articulations appearing in methodological discussions during the 1959 Vienna Conference on Controlled Clinical Trials.^[5] The term "intention-to-treat" was coined by statistician Austin Bradford Hill in the 1961 seventh edition of his influential textbook Principles of Medical Statistics, building on prior ideas like "analysis by intended treatment" proposed by Peter Armitage in 1960.^[6] Its standardization for trial reporting occurred in the 1990s through the Consolidated Standards of Reporting Trials (CONSORT) guidelines, first published in 1996, which recommended ITT as the primary analysis to promote transparency and reduce reporting biases in RCTs.^[7] ITT analysis finds its primary scope in superiority trials conducted in clinical research, where the objective is to establish that an experimental treatment outperforms a control or standard therapy.^[8] By analyzing the complete randomized sample, ITT emphasizes the effects attributable to the assignment of treatments rather than their actual receipt, providing a robust framework for assessing intervention efficacy across the full study population in such designs.^[8]

Fundamental Principles

The intention-to-treat (ITT) principle fundamentally requires that all participants who are randomized into a clinical trial be included in the primary analysis according to their originally assigned groups, irrespective of whether they receive the intended intervention, experience deviations from the protocol, or withdraw from the study. This "once randomized, always analyzed" approach ensures that the analysis reflects the full scope of the randomized population and avoids selective exclusions that could distort the trial's findings. By adhering to this inclusion rule, ITT maintains the integrity of the trial's design from randomization through to evaluation, providing a pragmatic assessment of the intervention's effects in a diverse participant pool.^[3]^[9] A core assumption underlying ITT is exchangeability, which posits that randomization creates comparable groups at baseline with respect to both known and unknown prognostic factors. ITT analysis preserves this exchangeability by analyzing participants in their assigned groups, thereby minimizing selection bias and ensuring that any observed differences in outcomes can be attributed to the intervention rather than post-randomization imbalances. This principle leverages the randomization process to establish baseline equivalence, allowing for unbiased estimates of treatment effects even when compliance varies across groups. ITT corresponds to the 'treatment policy' estimand in the ICH E9(R1) framework (2019), which clarifies the target of estimation while preserving randomization.^[3]^[10]^[11] In contrast to strict ITT, modified ITT (mITT) represents a pragmatic adaptation where certain post-randomization exclusions are permitted, such as participants who never receive any treatment or those deemed ineligible after randomization, while still retaining most deviations to approximate the full ITT benefits. This distinction arises because full ITT can be challenging in practice due to missing data or ineligibility, but mITT must be clearly defined and justified to avoid undermining randomization's protections. Regulatory guidelines emphasize that mITT should only deviate minimally from the full set to prevent overestimation of efficacy.^[12]^[9]

Rationale and Advantages

Preservation of Randomization

Randomization in clinical trials is a foundational method designed to create comparable groups at baseline by distributing known and unknown prognostic factors evenly across treatment arms, thereby enabling unbiased estimation of treatment effects.^[3] The intention-to-treat (ITT) analysis upholds this balance by including all randomized participants in the analysis according to their original assignment, regardless of subsequent deviations such as non-compliance or dropout. This approach prevents post-randomization exclusions that could disrupt the initial equivalence of groups, ensuring that the trial's internal validity remains intact.^[13] Excluding non-compliers from analysis, as in alternative approaches, introduces selection bias by altering group comparability; for instance, if treatment group dropouts are disproportionately those with poorer prognoses, the remaining adherers may appear healthier, inflating the perceived treatment benefit.^[14] ITT mitigates this by retaining all participants, thus avoiding the introduction of such imbalances that could favor one group over another based on post-randomization events.^[15] Empirical evidence supports ITT's role in bias reduction, with studies demonstrating that it minimizes type I errors compared to selective analyses by preserving the full randomized sample and adopting a more conservative estimation strategy. For example, a review of ITT principles found that this method reduces the inflation of type I error rates that occurs when exclusions are made based on protocol adherence.^[4]

Reflection of Real-World Outcomes

Intention-to-treat (ITT) analysis is particularly well-suited to pragmatic clinical trials, which aim to evaluate interventions under real-world conditions rather than controlled, idealized settings. In pragmatic trials, ITT mirrors actual clinical practice by including all randomized participants in the analysis according to their original group assignment, regardless of adherence, protocol deviations, or withdrawals. This approach accounts for non-adherence rates observed in everyday healthcare, such as patients discontinuing treatment due to logistical challenges or personal preferences, thereby providing a more generalizable estimate of how a treatment performs when implemented broadly.^[16]^[17] By contrast, explanatory trials focus on efficacy under optimal conditions, often excluding non-compliant participants to isolate the treatment's biological effect, whereas ITT in pragmatic trials yields estimates of effectiveness—how well the treatment works in routine practice amid varying patient behaviors and real-life factors. This distinction ensures that ITT captures the pragmatic impact, including the dilution of effects from imperfect adherence, which is common in community settings where close monitoring is absent. Regulatory bodies and researchers emphasize ITT for such trials to inform policy and clinical decision-making, as it reflects the full spectrum of outcomes patients might experience post-approval.^[17]^[18] For instance, in a randomized drug trial for a new antihypertensive medication, ITT analysis would include patients who drop out due to intolerable side effects, such as dizziness or gastrointestinal discomfort, assigning them outcomes based on their randomized group rather than excluding them. This inclusion reflects the true patient experience in clinical practice, where side effects lead to discontinuation rates of 30–50% or higher, providing a realistic assessment of the drug's benefits and risks rather than an overly optimistic view from only adherent participants. Such an approach highlights potential tolerability issues that could affect long-term use and population-level health outcomes.^[19]

Comparison to Alternative Approaches

Per-Protocol Analysis

Per-protocol (PP) analysis is a statistical approach in clinical trials that evaluates outcomes solely among participants who fully adhere to the study protocol, excluding those who deviate from the assigned treatment, such as through non-compliance, protocol violations, or dropouts.^[15] This method aims to assess the treatment effect under ideal conditions where the intervention is delivered and received as intended.^[3] PP analysis is commonly used as a secondary or complementary evaluation alongside the primary intention-to-treat (ITT) method, particularly to explore efficacy in a compliant subgroup and provide insights into the potential benefits when adherence is maximized.^[9] For instance, in non-inferiority trials, it helps examine whether the test treatment performs comparably to the control under perfect protocol conditions.^[9] Despite its utility, PP analysis has significant limitations. The exclusion of non-adherent participants often results in a smaller sample size, which reduces the study's statistical power and increases the risk of type II errors.^[15] Additionally, the selective inclusion can introduce bias, as deviators may differ systematically from adherers in ways that affect outcomes, potentially overestimating treatment effects.^[15]

As-Treated Analysis

As-treated analysis, sometimes referred to as on-treatment analysis, reassigns participants in a clinical trial to analysis groups based on the interventions they actually received, rather than adhering to their original randomized assignments.^[3] This approach disregards the initial randomization process and instead categorizes individuals according to the treatment delivered, such as grouping those who received the experimental intervention together regardless of their assigned arm.^[20] It differs from intention-to-treat by focusing solely on exposure to the treatment, often excluding or reclassifying those who did not receive any intervention or who switched treatments unintentionally.^[21] This method finds application in exploratory analyses, where investigators seek to evaluate the pragmatic effects of treatments as administered in practice, particularly in trials with intentional crossovers between arms, such as in oncology studies allowing switches due to progression.^[22] For example, it can illuminate outcomes associated with real-world deviations from protocol, providing supplementary insights when primary intention-to-treat results are diluted by non-adherence.^[23] Such uses are common in non-inferiority designs or when assessing treatment switching patterns to inform subsequent trial interpretations.^[24] A major drawback of as-treated analysis is that it violates the core principle of randomization, which ensures baseline comparability between groups and minimizes selection bias.^[25] By basing groupings on actual treatment receipt, it introduces confounding, as factors influencing adherence—such as patient prognosis, disease severity, or side effects—may correlate with outcomes, thereby distorting causal inferences about the intervention's efficacy.^[20] This potential for bias makes it unsuitable as a primary analysis in most randomized controlled trials, where preserving randomization is prioritized to maintain validity.^[26]

Implementation Strategies

Data Handling Techniques

In intention-to-treat (ITT) analyses, inclusion strategies are essential to maintain the original sample size and randomization integrity when participants have incomplete data. While historically common, last observation carried forward (LOCF) is no longer recommended as the primary imputation method in ITT analyses per regulatory guidelines such as the FDA's 2014 report and CONSORT 2025, due to potential bias under non-stability assumptions; it involves using the most recent available measurement for a participant to impute subsequent missing values, particularly for interim data points in longitudinal studies, and is suitable only for exploratory analyses.^[27]^[28] This method assumes stability in outcomes after the last observation. A preferred approach for primary analyses is multiple imputation (MI), which creates multiple plausible imputed datasets based on observed data patterns under the missing at random (MAR) assumption, analyzes each dataset separately, and pools the results using Rubin's rules to account for imputation uncertainty; MI preserves the benefits of ITT while providing unbiased estimates when MAR holds.^[27] For dropouts, worst-case scenario imputation assigns the least favorable outcome to missing data, such as assuming treatment failure or maximum adverse effect, to conservatively estimate effects and test robustness against attrition.^[29] This technique helps evaluate the impact of non-compliance without overly optimistic assumptions.^[30] Censoring methods address missing outcomes by categorizing them in ways that align with ITT principles, especially for binary endpoints like success/failure or event occurrence. A standard practice is to treat missing outcomes as failures, effectively assuming that dropouts or non-responders did not achieve the positive endpoint, which prevents inflation of treatment benefits and reflects pragmatic trial conditions.^[31] This approach is particularly useful in superiority trials where conservative handling underscores real-world applicability.^[32] Practical implementation of these techniques often relies on statistical software tailored for clinical data management. In SAS, LOCF is commonly implemented using data steps, PROC SQL, or custom macros to impute missing values prior to analysis; procedures like PROC MI for multiple imputation or PROC MIXED for mixed models can then be used to prepare ITT datasets, with custom macros for worst-case assignments, ensuring all randomized participants are analyzed.^[33] Similarly, in R, packages such as mice for imputation strategies or survival for censoring binary outcomes enable ITT handling through functions like complete.cases() for initial dataset assembly and ampute() for simulating dropouts in sensitivity checks.^[34] These tools facilitate reproducible data preparation while adhering to regulatory standards for transparency.^[35]

Statistical Methods

Intention-to-treat (ITT) analyses utilize standard parametric and non-parametric statistical tests applied to the complete randomized sample, including all participants regardless of compliance or protocol adherence, to maintain the integrity of randomization and minimize bias. For continuous outcomes in two-arm trials, the independent samples t-test compares group means, while analysis of variance (ANOVA) extends this to multiple treatment arms by assessing overall differences across groups. These methods leverage the full sample size to compute test statistics and p-values, providing unbiased estimates of treatment effects under the null hypothesis of no difference between randomized groups. For categorical outcomes, the chi-square test evaluates associations between treatment assignment and event frequencies or proportions in contingency tables constructed from the entire randomized cohort, with Fisher's exact test used when expected cell counts are small. The basic ITT comparison for continuous outcomes involves estimating the difference in population means, \Delta = \mu_{\text{treatment}} - \mu_{\text{control}}, where the standard error is derived from the pooled variance across the full sample size N, ensuring the analysis reflects the initial randomization rather than post-randomization events:

SE(\Delta) = \sqrt{\frac{s^2_p}{n_{\text{treatment}}} + \frac{s^2_p}{n_{\text{control}}}}

with s^2_p as the pooled variance and n denoting group sizes from randomization. This approach yields a t-statistic for hypothesis testing, t = \Delta / SE(\Delta), distributed under the t-distribution with degrees of freedom approximating N - 2.^[36] Advanced ITT methods address complexities such as repeated measures or missing data while adhering to the full randomized sample. Mixed-effects models are particularly suited for longitudinal data, incorporating fixed effects for treatment and time, alongside random effects for subject-specific variability, to estimate marginal treatment effects under missing at random assumptions; these models use maximum likelihood estimation to handle unbalanced data without explicit imputation. For instance, a linear mixed model might be specified as Y_{ij} = \beta_0 + \beta_1 \text{Treatment}_i + \beta_2 \text{Time}_j + b_i + \epsilon_{ij}, where b_i captures random intercepts and the treatment coefficient \beta_1 represents the ITT effect averaged over time.^[37] To mitigate bias from missing outcome data in ITT analyses, inverse probability weighting (IPW) assigns weights to observed cases equal to the inverse of the estimated probability of observation, conditional on observed covariates and treatment; this creates a pseudo-population where missingness is balanced, allowing standard estimators like weighted means or regression coefficients to approximate the ITT estimand. IPW is often combined with logistic regression to model the probability of non-missingness, with stabilized weights preferred to avoid extreme values and improve efficiency. These techniques ensure ITT principles are upheld even with incomplete data, though sensitivity analyses are recommended to assess robustness to missing data mechanisms.^[38]

Challenges and Limitations

Bias from Non-Compliance

Non-compliance in intention-to-treat (ITT) analysis occurs when participants do not adhere to their assigned treatment protocol, such as by discontinuing therapy or not following dosing instructions, which can introduce bias by altering the observed treatment effects.^[4] This bias arises because ITT includes all randomized participants in the analysis according to their original assignment, regardless of actual treatment received, potentially leading to estimates that do not fully reflect the intervention's potential under ideal conditions.^[39] Dilution bias is a primary concern, where non-adherence dilutes the apparent treatment effect by including outcomes from participants who do not respond due to non-compliance, resulting in conservative estimates that underestimate the true efficacy. For instance, if a substantial proportion of the treatment group fails to take the medication, the overall group difference in outcomes shrinks, masking the benefit observed among adherers.^[4] This effect is particularly pronounced in trials with high non-compliance rates, as the inclusion of non-responders effectively averages the results toward the null hypothesis.^[40] Crossover effects exacerbate this issue when participants switch from their assigned treatment to the alternative, such as control patients receiving the experimental therapy due to disease progression, further masking the true efficacy in ITT analyses. In such cases, the proportion of participants receiving the opposite treatment dilutes the group differences, reducing the observed effect size and potentially leading to incorrect conclusions about treatment superiority.^[39] High crossover rates can diminish statistical power and bias estimates toward no difference between arms.^[41] To mitigate these biases, sensitivity analyses are recommended to evaluate the robustness of ITT results by exploring various assumptions about non-compliance patterns, such as estimating effects under different compliance scenarios or comparing ITT to adjusted models. These analyses help quantify the potential impact of non-compliance on conclusions without relying solely on the primary ITT estimate.^[42] For example, bounding approaches or instrumental variable methods in sensitivity testing can provide a range of plausible effect sizes, informing the reliability of findings.^[43] While per-protocol analysis excludes non-compliers to avoid dilution, it risks introducing selection bias and is typically used only supplementally.^[44]

Missing Data Complications

Missing data pose significant challenges in intention-to-treat (ITT) analyses of clinical trials, as ITT principles require including all randomized participants regardless of compliance or data availability to preserve randomization and provide pragmatic estimates of treatment effects.^[38] Missingness can arise from various sources, such as participant withdrawal, loss to follow-up, or incomplete measurements, and its handling directly affects the validity of ITT inferences. The classification of missing data mechanisms, originally formalized by Donald Rubin, is crucial for understanding these complications and includes three primary types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).^[27] Under MCAR, the probability of data being missing is independent of both observed and unobserved data, implying that missing values are essentially random and do not introduce systematic bias if complete-case analysis is used, though it reduces statistical power.^[45] In contrast, MAR occurs when missingness depends only on observed data (e.g., baseline covariates like age or initial severity), allowing for valid inferences using methods that adjust for these observables, but assuming MAR when it does not hold can still lead to bias.^[46] MNAR represents the most complex scenario, where missingness depends on the unobserved missing values themselves (e.g., participants with worse outcomes are more likely to drop out), making unbiased estimation particularly difficult without strong assumptions or sensitivity analyses, as standard ITT approaches may fail to account for this dependency.^[47] The presence of missing data, especially under MAR or MNAR mechanisms, can violate core ITT assumptions by introducing bias in treatment effect estimates, as naive analyses excluding missing cases may overestimate or underestimate effects depending on the dropout pattern.^[41] For instance, if missingness is informative (MAR or MNAR), the analyzed sample becomes a non-random subset, potentially distorting the overall ITT effect and leading to invalid conclusions about real-world applicability.^[34] This bias is exacerbated in trials with substantial missingness, where even small deviations from MCAR can inflate Type I or Type II errors, undermining the trial's reliability.^[48] A common pattern in clinical trials is differential missingness, with higher dropout rates often observed in the active treatment arm due to adverse events, compared to the control arm, which can systematically skew ITT results toward the null or exaggerate harms.^[49] For example, in trials of pharmacological interventions, adverse reactions may prompt more withdrawals from the treatment group, creating MNAR patterns where missing data reflect poorer tolerability, thus biasing estimates of efficacy and safety.^[50] Such imbalances highlight the need for careful assessment of missing data patterns to ensure ITT analyses remain robust. Various data handling techniques can address these complications, though their selection depends on the assumed missingness mechanism. For MAR, multiple imputation (e.g., using 20 or more imputations) or model-based approaches like linear mixed models are recommended; for MNAR, sensitivity analyses are essential to explore different assumptions. Last observation carried forward is generally discouraged due to potential bias. These methods align with updated CONSORT 2025 guidelines for transparent reporting and bias minimization in RCTs as of April 2025.^[45]^[28]

Regulatory and Practical Applications

Guidelines from Authorities

The Consolidated Standards of Reporting Trials (CONSORT) statement, first published in 1996 and revised in 2001, 2010, 2017, and later, emphasizes the importance of intention-to-treat (ITT) analysis in reporting randomized controlled trials (RCTs). The original 1996 checklist recommends that trial reports describe whether the primary analysis was conducted on an intention-to-treat basis to maintain the benefits of randomization and provide pragmatic estimates of treatment effects. Subsequent updates, such as the 2001 revision, explicitly require authors in item 16 to state whether the reported analysis follows the ITT approach, including details on handling deviations from protocol and losses to follow-up. The 2010 revision refines this by mandating reporting of the number of participants analyzed in each group according to their original randomization assignment, effectively promoting ITT as the standard for primary outcomes to enhance transparency and reduce bias in trial reporting. The 2017 update maintains these requirements while providing additional guidance on statistical methods and handling missing data in ITT analyses.^[51]^[10]^[52] The International Council for Harmonisation (ICH) E9 guideline, titled "Statistical Principles for Clinical Trials" and issued in 1998, provides foundational endorsement of ITT for confirmatory trials. It defines the full analysis set (FAS) as the subset of the ITT population that is as close as possible to the ideal ITT principle, comprising all randomized subjects analyzed together regardless of compliance or protocol adherence, to preserve randomization integrity and yield unbiased effectiveness estimates. ICH E9 specifies that for primary confirmatory analyses in superiority trials, the FAS—aligned with ITT—should be used unless justified otherwise, as it best reflects real-world treatment effects while minimizing bias from selective exclusions. This principle applies to the evaluation of safety and efficacy endpoints, with sensitivity analyses recommended to explore deviations. The 2019 ICH E9(R1) addendum extends these principles by introducing the estimand framework, which clarifies how intercurrent events (such as non-compliance) are handled in ITT-based analyses to better align trial objectives with regulatory decision-making.^[53]^[54] Regulatory authorities such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) incorporate ICH E9 into their policies, mandating ITT as the primary analysis for pivotal clinical trials supporting drug approval applications. The FDA requires ITT-based analyses in confirmatory studies to ensure estimates of treatment benefits are not inflated by excluding non-compliant participants, thereby supporting claims of clinical effectiveness under conditions of use. Similarly, the EMA guidelines stipulate that primary efficacy analyses in marketing authorization submissions adhere to the ITT principle via the FAS, to provide robust, unbiased evidence of therapeutic value while accounting for real-world variability in patient adherence. These mandates apply across therapeutic areas, with both agencies emphasizing ITT's role in maintaining trial validity during regulatory review.^[55]^[56]

Use in Trial Reporting

In clinical trial reporting, intention-to-treat (ITT) analysis is presented through standardized flow diagrams that illustrate the progression of participants from randomization to analysis, including the numbers randomized, those who received interventions, experienced events, and were included in the primary analysis for each group.^[10] This approach, as outlined in the CONSORT guidelines, ensures transparency by highlighting deviations such as dropouts or protocol violations, allowing readers to assess the extent of adherence to randomization.^[10] Additionally, reports typically include sensitivity analyses to evaluate the robustness of ITT results against variations in handling missing data or per-protocol subsets, demonstrating how alternative assumptions might alter conclusions.^[10] Major medical journals, including The New England Journal of Medicine (NEJM) and The Lancet, strongly prefer ITT as the primary analysis strategy in randomized controlled trials, aligning with their endorsement of CONSORT reporting standards.^[10] Compliance with these guidelines has been shown to increase ITT reporting rates to approximately 70% in adhering journals, compared to 48% in non-adhering ones, underscoring the influence on publication quality.^[10] This preference reflects ITT's role in preserving trial validity and generalizability to real-world settings. A notable example is the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), a large-scale study published in JAMA that compared chlorthalidone, amlodipine, and lisinopril in 33,357 hypertensive patients.^[57] The trial employed ITT analysis by including all randomized participants in their assigned groups regardless of adherence or crossover, which revealed chlorthalidone's superior or equivalent efficacy in reducing cardiovascular events compared to newer agents.^[57] These ITT findings directly influenced U.S. hypertension guidelines, such as JNC 7, by reinforcing thiazide diuretics as first-line therapy and supporting regulatory approvals for their expanded use in initial treatment regimens.

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