Inconsistent Definitions for Intention-To-Treat in Relation to Missing Outcome Data: Systematic Review of the Methods Literature

Abstract

BackgroundAuthors of randomized trial reports seem to hold a variety of views regarding the relationship between missing outcome data (MOD) and intention to treat (ITT). The objectives of this study were to systematically investigate how authors of methodology articles define ITT in the presence of MOD, how they recommend handling MOD under ITT, and to make a proposal for potential improvement in the definition and use of ITT in relation to MOD.Methods and FindingsWe systematically searched MEDLINE in February 2009 for methodological articles written in English that devoted at least one paragraph to ITT and two other paragraphs to either ITT or MOD. We excluded original trial reports, observational studies, and clinical systematic reviews. Working in teams of two, we independently extracted relevant information from each eligible article. Of 1007 titles and abstracts reviewed, 66 articles met eligibility criteria. Five (8%) did not provide a definition of ITT; 25 (38%) mentioned MOD but did not discuss its relationship to ITT; and 36 (55%) discussed the relationship of MOD with ITT. These 36 articles described one or more of three statements: complete follow-up is required for ITT (58%); ITT and MOD are separate issues (17%); and ITT requires a specific strategy for handling MOD (78%); 17 (47%) endorsed more than one relationship. The most frequently mentioned strategies for handling MOD within ITT were: using the last outcome carried forward (50%); sensitivity analysis (50%); and use of available data to impute missing data (46%).ConclusionWe found that there is no consensus on the definition of ITT in relation to MOD. For conceptual clarity, we suggest that both reports of randomized trials and systematic reviews separately consider and describe how they deal with participants with complete data and those with MOD.

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“…in many cases (60%-75%), the analysis can also be influenced by missing observations and the way the missing data are treated. 66 Despite the fact that most rCts on antihypertensive therapy are intended to test the efficacy of a given medication, the attempt to achieve the therapeutic goal is done almost invariably through various combinations of the initially studied medications with other drugs given afterward. in this context, the intention-to-treat analysis would not express the efficacy of the drug to which the patient has been initially randomized, but the efficacy of the variable combination and interaction of the initial with subsequent drugs.…”

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“…The primary outcome variable (average healthiness of ready meals and pizzas purchased in T-1, T1 and T2) included MOD if the participant did not purchase any own brand ready meals or pizzas using their loyalty card in any of the three study phases. A systematic review of methods used to cope with MOD in intention-to-treat analyses demonstrated that there is no consensus towards a preferred approach, with arguments for restricting to complete case analysis and for imputation of missing data [32]. For the FLICC study we dealt with MOD in the sales data by employing imputation techniques (multiple imputation for the primary outcome variable and single imputation for the secondary outcome variables).…”

Addressing missing participant outcome data in dental clinical trials

“…23 In ITT analysis all the participants in a trial are analyzed according to the intervention to which they were allocated, irrespectively of whether they received it or not. 19,21,23 ITT is desirable because it increases statistical power and precision, it preserves the aims of randomization and hence, it minimizes selection bias and confounding.…”

“…23 In ITT analysis all the participants in a trial are analyzed according to the intervention to which they were allocated, irrespectively of whether they received it or not. 19,21,23 ITT is desirable because it increases statistical power and precision, it preserves the aims of randomization and hence, it minimizes selection bias and confounding. 19,21,23 Moreover, ITT is favoured in the assessment of effectiveness (especially in pragmatic trials) as it mirrors the noncompliance and treatment changes that are likely to occur when the intervention is used in practice.…”

“…19,21,23 ITT is desirable because it increases statistical power and precision, it preserves the aims of randomization and hence, it minimizes selection bias and confounding. 19,21,23 Moreover, ITT is favoured in the assessment of effectiveness (especially in pragmatic trials) as it mirrors the noncompliance and treatment changes that are likely to occur when the intervention is used in practice. 24 However, in the presence of early dropouts, it is difficult to adequately apply an ITT analysis, because ITT requires all randomized participants to be analyzed irrespectively of whether they received the prescribed intervention or completed the study.…”