Estimating effects from randomized trials with discontinuations: the need for intent-to-treat design and G-estimation

Greenland, Sander; Lanes, Stephan; Jara, Michele

doi:10.1177/1740774507087703

Cited by 50 publications

(39 citation statements)

References 29 publications

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“…The empirical support for psychodynamic psychotherapy comes down to these 25 comparisons (in boldface in online data supplement). Although intent-to-treat analyses also have certain limitations, they are the best starting point for addressing patient dropout in considering which treatments work best (38)(39)(40). RCT-PQRS that would allow for greater single-item reliability across raters.…”

Section: Discussionmentioning

confidence: 99%

A Quality-Based Review of Randomized Controlled Trials of Psychodynamic Psychotherapy

Gerber¹,

Kocsis²,

Milrod³

et al. 2011

AJP

141

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Existing randomized controlled trials of psychodynamic psychotherapy are promising but mostly show superiority of psychodynamic psychotherapy to an inactive comparator. This would be sufficient to make psychodynamic psychotherapy an "empirically validated" treatment (per American Psychological Association Division 12 standards) only if further randomized controlled trials of adequate quality and sample size replicated findings of existing positive trials for specific disorders. We do not yet know what will emerge when other psychotherapies are subjected to this form of quality-based review.

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Section: Discussionmentioning

confidence: 99%

A Quality-Based Review of Randomized Controlled Trials of Psychodynamic Psychotherapy

Gerber¹,

Kocsis²,

Milrod³

et al. 2011

AJP

141

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“…8 G-estimation (a general form of instrumental variable analysis) is another approach that can adjust for measured and unmeasured predictors 6–8 and that has been applied in several randomized trials. 4,26–31 We have previously compared the assumptions for the validity of adherence-adjusted estimates when using adjusted inverse probability weighting and g-estimation in randomized trials in which the outcome of interest is a continuous variable. 4 …”

Section: Discussionmentioning

confidence: 99%

Estimating Absolute Risks in the Presence of Nonadherence

Toh¹,

Hernández–Dı́az²,

Logan³

et al. 2010

Epidemiology

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The intention-to-treat (ITT) analysis provides a valid test of the null hypothesis and naturally results in both absolute and relative measures of risk. However, this analytic approach may miss the occurrence of serious adverse effects that would have been detected under full adherence to the assigned treatment. Inverse probability weighting of marginal structural models has been used to adjust for nonadherence, but most studies have provided only relative measures of risk. In this study, we used inverse probability weighting to estimate both absolute and relative measures of risk of invasive breast cancer under full adherence to the assigned treatment in the Women’s Health Initiative estrogen-plus-progestin trial. In contrast to an ITT hazard ratio (HR) of 1.25 (95% confidence interval [CI] = 1.01 to 1.54), the HR for 8-year continuous estrogen-plus-progestin use versus no use was 1.68 (1.24 to 2.28). The estimated risk difference (cases/100 women) at year 8 was 0.83 (−0.03 to 1.69) in the ITT analysis, compared with 1.44 (0.52 to 2.37) in the adherence-adjusted analysis. Results were robust across various dose-response models. We also compared the dynamic treatment regime “take hormone therapy until certain adverse events become apparent, then stop taking hormone therapy” with no use (HR= 1.64; 95% CI = 1.24 to 2.18). The methods described here are also applicable to observational studies with time-varying treatments.

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“…Such on-treatment analysis can encompass not only simple analysis confined to responses directly recorded during treatment but also more recent techniques such as randomizationbased efficacy estimation [14] and G-estimation [15].…”

Section: Itt and Per Protocol Analysismentioning

confidence: 99%

Intent‐to‐treat analysis in the presence of off‐treatment or missing data

Keene

2011

Pharmaceutical Statistics

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Intent-to-treat (ITT) analysis is viewed as the analysis of a clinical trial that provides the least bias, but difficult issues can arise. Common analysis methods such as mixed-effects and proportional hazards models are usually labeled as ITT analysis, but in practice they can often be inconsistent with a strict interpretation of the ITT principle. In trials where effective medications are available to patients withdrawing from treatment, ITT analysis can mask important therapeutic effects of the intervention studied in the trial. Analysis of on-treatment data may be subject to bias, but can address efficacy objectives when combined with careful review of the pattern of withdrawals across treatments particularly for those patients withdrawing due to lack of efficacy and adverse events.

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Estimating effects from randomized trials with discontinuations: the need for intent-to-treat design and G-estimation

Abstract: G-estimation should become a standard procedure for the analysis of trials with noncompliance. Software to do so is available in major packages, and the procedure is easily coded for other packages.

Cited by 50 publications

References 29 publications

A Quality-Based Review of Randomized Controlled Trials of Psychodynamic Psychotherapy

A Quality-Based Review of Randomized Controlled Trials of Psychodynamic Psychotherapy

Estimating Absolute Risks in the Presence of Nonadherence

Intent‐to‐treat analysis in the presence of off‐treatment or missing data

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