Information-Anchored Sensitivity Analysis: Theory and Application

Cro, Suzie; Carpenter, James; Kenward, Michael G.

doi:10.1111/rssa.12423

Cited by 37 publications

(63 citation statements)

References 30 publications

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“…These results are in line with the theory for continuous data (Cro et al), which shows that the approximation of Rubin's rules to information anchoring improves as the treatment effect decreases. To explore this further, we now consider additional scenarios.…”

Section: Simulation Studysupporting

confidence: 90%

“…In the context of longitudinal data, Carpenter et al sketch that, because distributional information is borrowed under reference‐based methods, the standard likelihood calculation results in an artificial gain in statistical information about the treatment effect, relative to what we would expect to see if the missing data were able to be actually observed, and their distribution corresponded to that under the reference‐based assumption. By contrast, they propose, and Cro et al prove, that for continuous longitudinal data using Rubin's rules is—to a good approximation— information anchored . This means that reference‐based imputation using Rubin's rules in the conventional way approximately preserves the fraction of information lost due to missing data across each of the assumptions.…”

Section: Simulation Studymentioning

confidence: 97%

“…There has been some discussion of the use of Rubin's rules to estimate the variance for reference‐based multiple imputation—with the alternative being the empirical standard error from fitting the model to bootstrapped data (or theoretical approximations to this). In the context of longitudinal data, Carpenter et al sketch that, because distributional information is borrowed under reference‐based methods, the standard likelihood calculation results in an artificial gain in statistical information about the treatment effect, relative to what we would expect to see if the missing data were able to be actually observed, and their distribution corresponded to that under the reference‐based assumption.…”

Section: Simulation Studymentioning

confidence: 99%

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Reference‐based sensitivity analysis for time‐to‐event data

Atkinson

Kenward²,

Clayton

et al. 2019

Pharmaceutical Statistics

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The analysis of time‐to‐event data typically makes the censoring at random assumption, ie, that—conditional on covariates in the model—the distribution of event times is the same, whether they are observed or unobserved (ie, right censored). When patients who remain in follow‐up stay on their assigned treatment, then analysis under this assumption broadly addresses the de jure, or “while on treatment strategy” estimand. In such cases, we may well wish to explore the robustness of our inference to more pragmatic, de facto or “treatment policy strategy,” assumptions about the behaviour of patients post‐censoring.This is particularly the case when censoring occurs because patients change, or revert, to the usual (ie, reference) standard of care. Recent work has shown how such questions can be addressed for trials with continuous outcome data and longitudinal follow‐up, using reference‐based multiple imputation. For example, patients in the active arm may have their missing data imputed assuming they reverted to the control (ie, reference) intervention on withdrawal. Reference‐based imputation has two advantages: (a) it avoids the user specifying numerous parameters describing the distribution of patients' postwithdrawal data and (b) it is, to a good approximation, information anchored, so that the proportion of information lost due to missing data under the primary analysis is held constant across the sensitivity analyses. In this article, we build on recent work in the survival context, proposing a class of reference‐based assumptions appropriate for time‐to‐event data. We report a simulation study exploring the extent to which the multiple imputation estimator (using Rubin's variance formula) is information anchored in this setting and then illustrate the approach by reanalysing data from a randomized trial, which compared medical therapy with angioplasty for patients presenting with angina.

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Section: Simulation Studysupporting

confidence: 90%

Section: Simulation Studymentioning

confidence: 97%

Section: Simulation Studymentioning

confidence: 99%

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Reference‐based sensitivity analysis for time‐to‐event data

Atkinson

Kenward²,

Clayton

et al. 2019

Pharmaceutical Statistics

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“…2, panel A, region b). The ranges associated with each category of symptoms were drawn from published literature: 'mild' (1-10), 'moderate' (11)(12)(13)(14)(15)(16)(17)(18)(19)(20), 'moderate to severe' (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) and 'severe' (36-51). Figure 2, panel A, region a, shows illustrative results from an elicitation.…”

Section: Symptom Severity Scalementioning

confidence: 99%

A framework for extending trial design to facilitate missing data sensitivity analyses

Mason

Grieve

Richards-Belle

et al. 2020

BMC Med Res Methodol

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Background: Missing data are an inevitable challenge in Randomised Controlled Trials (RCTs), particularly those with Patient Reported Outcome Measures. Methodological guidance suggests that to avoid incorrect conclusions, studies should undertake sensitivity analyses which recognise that data may be 'missing not at random' (MNAR). A recommended approach is to elicit expert opinion about the likely outcome differences for those with missing versus observed data. However, few published trials plan and undertake these elicitation exercises, and so lack the external information required for these sensitivity analyses. The aim of this paper is to provide a framework that anticipates and allows for MNAR data in the design and analysis of clinical trials. Methods: We developed a framework for performing and using expert elicitation to frame sensitivity analysis in RCTs with missing outcome data. The framework includes the following steps: first defining the scope of the elicitation exercise, second developing the elicitation tool, third eliciting expert opinion about the missing outcomes, fourth evaluating the elicitation results, and fifth analysing the trial data. We provide guidance on key practical challenges that arise when adopting this approach in trials: the criteria for identifying relevant experts, the outcome scale for presenting data to experts, the appropriate representation of expert opinion, and the evaluation of the elicitation results. The framework was developed within the POPPI trial, which investigated whether a preventive, complex psychological intervention, commenced early in ICU, would reduce the development of patient-reported post-traumatic stress disorder symptom severity, and improve health-related quality of life. We illustrate the key aspects of the proposed framework using the POPPI trial. Results: For the POPPI trial, 113 experts were identified with potentially suitable knowledge and asked to participate in the elicitation exercise. The 113 experts provided 59 usable elicitation questionnaires. The sensitivity analysis found that the results from the primary analysis were robust to alternative MNAR mechanisms. Conclusions: Future studies can adopt this framework to embed expert elicitation within the design of clinical trials. This will provide the information required for MNAR sensitivity analyses that examine the robustness of the trial conclusions to alternative, but realistic assumptions about the missing data.

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“…Controlled multiple imputation also provides an accessible, exible tool for contextually relevant sensitivity analysis which is the next and nal key step when handling missing outcome data during a pandemic. We have previously shown that the aforementioned controlled procedures (delta-and reference-based multiple imputation) provide valid inference, as the proportion of information lost due to missing data under MAR is approximately preserved in the analysis (21); That is information anchored inference will be obtained.…”

Section: Participants Lost To Follow-up During Pandemic Times (Iii)mentioning

confidence: 99%

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

Cro

Morris

Kahan

et al. 2020

Preprint

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Background: The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking.Methods: We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a ‘pandemic-free world’ and ‘world including a pandemic’ are of interest. Results: In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a ‘pandemic-free world’, participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the ‘world including a pandemic’, all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumption – potentially incorporating a pandemic time-period indicator and participant infection status – or a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses.Conclusions: Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.

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Information-Anchored Sensitivity Analysis: Theory and Application

Cited by 37 publications

References 30 publications

Reference‐based sensitivity analysis for time‐to‐event data

Reference‐based sensitivity analysis for time‐to‐event data

A framework for extending trial design to facilitate missing data sensitivity analyses

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

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