Missing data in clinical trials: control‐based mean imputation and sensitivity analysis

Mehrotra, Devan V.; Liu, Fang; Permutt, Thomas

doi:10.1002/pst.1817

Cited by 59 publications

(61 citation statements)

References 24 publications

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“…Permutt and Li provided technical details for the trimmed mean approach, with additional description and software code to implement the method in Mehrotra et al Permutt and Li summarized the method as follows: Stigler gave distribution theory for the trimmed mean, including the asymmetric case. The trimmed mean is asymptotically normal under regularity conditions, and its expectation is the population trimmed mean .…”

Section: Description Of the Trimmed Mean Approachmentioning

confidence: 99%

“…The percent of data to be trimmed (1-X) can be based on an a priori chosen fixed percentage that, based on previous trials, ensures all patients with relevant inter-current are trimmed; or, trimming can be adaptive based on the actual results of the trial. 6 Permutt and Li 6 provided technical details for the trimmed mean approach, with additional description and software code to implement the method in Mehrotra et al 8 Permutt and Li 6 summarized the method as follows: Stigler 9 gave distribution theory for the trimmed mean, including the asymmetric case. The trimmed mean is asymptotically normal under regularity conditions, and its expectation is the population trimmed mean.…”

Section: Description Of the Trimmed Mean Approachmentioning

confidence: 99%

“…Analyses for mBOCF and mLOCF used ANCOVA with a model that included treatment and baseline values. The MMRM analysis included all postbaseline observations (weeks 1,2,4,8,12,14,16,20,24). Data were analyzed using likelihood-based estimation with a fixed effects model that included treatment, baseline values, visit and treatment by visit interaction.…”

Section: Clinical Trial Examplementioning

confidence: 99%

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An evaluation of the trimmed mean approach in clinical trials with dropout

Wang

Liu

Molenberghs

et al. 2018

Pharmaceutical Statistics

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The trimmed mean is a method of dealing with patient dropout in clinical trials that considers early discontinuation of treatment a bad outcome rather than leading to missing data. The present investigation is the first comprehensive assessment of the approach across a broad set of simulated clinical trial scenarios. In the trimmed mean approach, all patients who discontinue treatment prior to the primary endpoint are excluded from analysis by trimming an equal percentage of bad outcomes from each treatment arm. The untrimmed values are used to calculated means or mean changes. An explicit intent of trimming is to favor the group with lower dropout because having more completers is a beneficial effect of the drug, or conversely, higher dropout is a bad effect. In the simulation study, difference between treatments estimated from trimmed means was greater than the corresponding effects estimated from untrimmed means when dropout favored the experimental group, and vice versa. The trimmed mean estimates a unique estimand. Therefore, comparisons with other methods are difficult to interpret and the utility of the trimmed mean hinges on the reasonableness of its assumptions: dropout is an equally bad outcome in all patients, and adherence decisions in the trial are sufficiently similar to clinical practice in order to generalize the results. Trimming might be applicable to other inter-current events such as switching to or adding rescue medicine. Given the well-known biases in some methods that estimate effectiveness, such as baseline observation carried forward and non-responder imputation, the trimmed mean may be a useful alternative when its assumptions are justifiable.

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Section: Description Of the Trimmed Mean Approachmentioning

confidence: 99%

Section: Description Of the Trimmed Mean Approachmentioning

confidence: 99%

Section: Clinical Trial Examplementioning

confidence: 99%

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An evaluation of the trimmed mean approach in clinical trials with dropout

Wang

Liu

Molenberghs

et al. 2018

Pharmaceutical Statistics

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“…When using difference in medians, a preferable approach is to derive CIs for treatment effects using a bootstrap approach. 11,12 This avoids the need to impute values for the surgery outcome and adjustment for covariates can be made via quantile regression. 12 An alternative summary variable is the estimated difference in trimmed means.…”

Section: Options For Composite Estimands: Example Trial In Nasal Pomentioning

confidence: 99%

Strategies for composite estimands in confirmatory clinical trials: Examples from trials in nasal polyps and steroid reduction

Keene

2018

Pharmaceutical Statistics

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Summary The draft addendum to the ICH E9 regulatory guideline asks for explicit definition of the treatment effect to be estimated in clinical trials. The draft guideline also introduces the concept of intercurrent events to describe events that occur post‐randomisation that may affect efficacy assessment. Composite estimands allow incorporation of intercurrent events in the definition of the endpoint. A common example of an intercurrent event is discontinuation of randomised treatment and use of a composite strategy would assess treatment effect based on a variable that combines the outcome variable of interest with discontinuation of randomised treatment. Use of a composite estimand may avoid the need for imputation which would be required by a treatment policy estimand. The draft guideline gives the example of a binary approach for specifying a composite estimand. When the variable is measured on a non‐binary scale, other options are available where the intercurrent event is given an extreme unfavourable value, for example comparison of median values or analysis based on categories of response. This paper reviews approaches to deriving a composite estimand and contrasts the use of this estimand to the treatment policy estimand. The benefits of using each strategy are discussed and examples of the use of the different approaches are given for a clinical trial in nasal polyposis and a steroid reduction trial in severe asthma.

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“…As this assumption is debatable in many settings, and because the LOCF approach may result in bias in favour of the tested therapy, a 2010 National Research Council (NRC) report, commissioned by the FDA, subsequently recommended against use of LOCF as a primary approach to handle missing data unless scientifically justified . Other methods to handle missing data have since been adopted, including multiple imputation approaches and the mixed model for repeated measures (MMRM), which are employed according to the type of treatment effect to be estimated …”

Section: Introductionmentioning

confidence: 99%

Incorporating and interpreting regulatory guidance on estimands in diabetes clinical trials: The PIONEER 1 randomized clinical trial as an example

Aroda

Saugstrup

Buse

et al. 2019

Diabetes Obesity Metabolism

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Regulatory guidelines describe the use of estimands in designing and conducting clinical trials. Estimands ensure alignment of the objectives with the design, conduct and analysis of a trial. An estimand is defined by four inter‐related attributes: the population of interest, the variable (endpoint) of interest, the way intercurrent events are handled and the population level summary. A trial may employ multiple estimands to evaluate treatment effects from different perspectives in order to address different scientific questions. As estimands may be an unfamiliar concept for many clinicians treating diabetes, this paper reviews the estimand concept and uses the PIONEER 1 phase 3a clinical trial, which investigated the efficacy and safety of oral semaglutide vs placebo, as an example of the way in which estimands can be implemented and interpreted. In the PIONEER 1 trial, two estimands were employed for each efficacy endpoint and were labelled as: (a) the treatment policy estimand, used to assess the treatment effect regardless of use of rescue medication or discontinuation of trial product, and provides a broad perspective of the treatment effect in the population of patients with type 2 diabetes in clinical practice; and (b) the trial product estimand, used to assess the treatment effect if all patients had continued to use trial product for the planned duration of the trial without rescue medication, thereby providing information on the anticipated treatment effect of the medication. Both approaches are complementary to understanding the effect of the studied treatments.

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Missing data in clinical trials: control‐based mean imputation and sensitivity analysis

Cited by 59 publications

References 24 publications

An evaluation of the trimmed mean approach in clinical trials with dropout

An evaluation of the trimmed mean approach in clinical trials with dropout

Strategies for composite estimands in confirmatory clinical trials: Examples from trials in nasal polyps and steroid reduction

Incorporating and interpreting regulatory guidance on estimands in diabetes clinical trials: The PIONEER 1 randomized clinical trial as an example

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