Does intention-to-treat analysis answer all questions in long-term mortality trials? Considerations on the basis of the ANZ trial

Hc, Tillmann; Sharpe, Norman; Sponer, G.; Wehling, M.

doi:10.5414/cpp39205

Cited by 4 publications

(4 citation statements)

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“…They found that incorporating compliance data into the conventional ITT analysis resulted in a more accurate effect size estimation. 23 According to these authors, this approach may strengthen the validity of the conventional ITT analysis and may be applicable to other similar situations.…”

Section: Compliance and Intention To Treat Analysismentioning

confidence: 96%

“…As proposed by Tillmann et al, 23 in conventional ITT analysis, the results are independent of the actual time a subject complies with the treatment since every participant will contribute data at the moment of the evaluation. Tillmann et al 23 analysed long-term mortality in a large-scale clinical trial performed in Australia. They added data regarding compliance and actual time on trial medication to the traditional ITT analysis using an ITT-based Cox proportional hazard model (modified ITT).…”

Section: Compliance and Intention To Treat Analysismentioning

confidence: 99%

“…This indicates that both approaches are necessary: to determine whether the treatment is efficacious in a predetermined population, and if the treatment is effective in a real life situation. According to Tillman et al, 23 if withdrawal is due to adverse effects and these patients are excluded Comparisons among intention to treat, as treated and per protocol to analyse randomised clinical trials: advantages and limitations…”

Section: Per Protocol Analysismentioning

confidence: 99%

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Intention to treat analysis, compliance, drop-outs and how to deal with missing data in clinical research: a review

Armijo‐Olivo¹,

Warren²,

Magee³

2009

Physical Therapy Reviews

311

213

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Background: Pragmatically, intention to treat (ITT) analysis has become the 'gold standard' for analysing the results of clinical trials. Despite its popularity and wide use, ITT is not without its critics, controversies and misunderstandings. To perform an ideal ITT requires a full set of data, where all patients providing data are followed, independent of any protocol deviation. However, most of the time, clinicians and researchers are faced with non-compliance and drop-outs. Thus, researchers should be familiar with the concepts associated with ITT and strategies to deal with missing data. Objectives: The objective of this review is to clarify and summarise the important aspects of ITT limitations, and contributions to clinical research. In addition, the concepts of effectiveness and efficacy will be discussed in the context of randomised controlled trial (RCT) analysis. This will help clinicians and researchers to have a greater understanding of ITT and apply this knowledge when designing, evaluating, and reporting RCTs. Conclusions: Depending on the objective of the trial, different approaches to data analysis could be used. If the trial's objective is purely explanatory, an 'as treated' or 'per protocol' analysis could be a reasonable option. Nevertheless, it is advised that these approaches are not reported on their own because of their susceptibility to bias when estimating treatment effects. If the objective of the trial is pragmatic, that is, it addresses the effectiveness of a specific treatment in real life (clinical setting), an ITT should be the primary analysis.

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Section: Compliance and Intention To Treat Analysismentioning

confidence: 96%

Section: Compliance and Intention To Treat Analysismentioning

confidence: 99%

Section: Per Protocol Analysismentioning

confidence: 99%

See 1 more Smart Citation

Intention to treat analysis, compliance, drop-outs and how to deal with missing data in clinical research: a review

Armijo‐Olivo¹,

Warren²,

Magee³

2009

Physical Therapy Reviews

311

213

View full text Add to dashboard Cite

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“…The evaluation of clinical trials with present non‐compliance is an often and controversially discussed problem. Among others, Pocock and Abdalla , Tillman et al , Little and Yau , Cuzick et al , Sommer and Zeger and Elfron and Feldman explored different aspects and analysis strategies for compliance data. An overview paper of Armijo‐Olivio et al deals with the problems of missing data and non‐compliance.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the treatment effect in the presence of non‐compliance and missing data

Leuchs

Zinserling

Schlosser-Weber

et al. 2013

Statistics in Medicine

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Treatment non-compliance and missing data are common problems in clinical trials. Non-compliance is a broad term including any kind of deviation from the assigned treatment protocol, such as dose modification, treatment discontinuation or switch, often resulting in missing values. Missing values and treatment non-compliance may bias study results. Follow-up of all patients until the planned end of treatment period irrespective of their protocol adherence may provide useful information on the effectiveness of a study drug, taking the actual compliance into account. In this paper, we consider non-compliance as discontinuation of treatment and assume that the endpoint of interest is recorded for some non-complying patients after treatment discontinuation. As a result, the patient's longitudinal profile is dividable into on- and off-treatment observations. Within the framework of depression trials, which usually show a considerably high amount of dropouts, we compare different analysis strategies including both on- and off-treatment observations to gain insight into how the additional use of off-treatment data may affect the trial's outcome. We compare naïve strategies, which simply ignore off-treatment data or treat on- and off-treatment data in the same way, with more complex strategies based on piecewise linear mixed models, which assume different treatment effects for on- and off-treatment data. We show that naïve strategies may considerably overestimate treatment effects. Therefore, it is worthwhile to follow up as many patients as possible until the end of their planned treatment period irrespective of compliance, including all available data in an analysis that accounts for the different treatment conditions.

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Session 8: Providing Scientific Evidence: RCTs and Beyond

Sørensen

Hadengue

Salerno

et al. 2006

Portal Hypertension IV

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Does intention-to-treat analysis answer all questions in long-term mortality trials? Considerations on the basis of the ANZ trial

Cited by 4 publications

References 0 publications

Intention to treat analysis, compliance, drop-outs and how to deal with missing data in clinical research: a review

Intention to treat analysis, compliance, drop-outs and how to deal with missing data in clinical research: a review

Estimation of the treatment effect in the presence of non‐compliance and missing data

Session 8: Providing Scientific Evidence: RCTs and Beyond

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