Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide

Cro, Suzie; Morris, Tim P.; Kenward, Michael G.; Carpenter, James R.

doi:10.1002/sim.8569

Cited by 203Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide

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Cro, Suzie, Morris, Tim P., Kenward, Michael G., Carpenter, James R.·2020·Statistics in Medicine

DOI 10.1002/sim.8569

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Abstract

Missing data due to loss to follow-up or intercurrent events are unintended, but unfortunately inevitable in clinical trials. Since the true values of missing data are never known, it is necessary to assess the impact of untestable and unavoidable assumptions about any unobserved data in sensitivity analysis. This tutorial provides an overview of controlled multiple imputation (MI) techniques and a practical guide to their use for sensitivity analysis of trials with missing continuous outcome data. These include δ- and reference-based MI procedures. In δ-based imputation, an offset term, δ, is typically added to the expected value of the missing data to assess the impact of unobserved participants having a worse or better response than those observed. Reference-based imputation draws imputed values with some reference to observed data in other groups of the trial, typically in other treatment arms. We illustrate the accessibility of these methods using data from a pediatric eczema trial and a chronic headache trial and provide Stata code to facilitate adoption. We discuss issues surrounding the choice of δ in δ-based sensitivity analysis. We also review the debate on variance estimation within reference-based analysis and justify the use of Rubin's variance estimator in this setting, since as we further elaborate on within, it provides information anchored inference.

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Sensitivity analyses using delta adjustments assessed robustness to deviations from the missing at random assumption.
“To assess the robustness to deviations of the missing at random (MAR) assumption, we applied sensitivity analyses through delta adjustments [52].”
From Effects of a self-guided digital mental health self-help intervention for Syrian refugees in Egypt: A pragmatic randomized controlled trial (2024) · cites this paper as [52]

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Missing Outcome Data in Recent Perinatal and Neonatal Clinical Trials
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Effect of Lifestyle Modification Through Web-Based Telerehabilitation Monitoring Combined With Supervised Sensorimotor Training After Total Knee Arthroplasty: Randomized Controlled Trial
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The Association of Blood Biomarkers and Body Mass Index in Knee Osteoarthritis: A Cross-Sectional Study
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Beyond binary endpoints: modernizing aesthetic trial analytics: Simulation-Based reappraisal of phase 2/3 BoNT-A studies using advanced statistical models
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Missing data in randomised controlled trials of rheumatoid arthritis drug therapy are substantial and handled inappropriately
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Now You See It, Now You Don’t: A Simulation and Illustration of the Importance of Treating Incomplete Data in Estimating Race Effects in Sentencing
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Clinical and cost-effectiveness of DREAMS START (Dementia RElAted Manual for Sleep; STrAtegies for RelaTives) for people living with dementia and their carers: a study protocol for a parallel multicentre randomised controlled trial
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Preference of mode of delivery and associated factors among mothers in East Africa: systematic review and meta-analysis
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Continuous care intervention with carbohydrate restriction improves physical function of the knees among patients with type 2 diabetes: a non-randomized study
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Imputation methods for informative censoring in survival analysis with time dependent covariates
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A triple-masked, two-center, randomized parallel clinical trial to assess the superiority of eight weeks of grape seed flour supplementation against placebo for weight loss attenuation during perioperative period in patients with cachexia associated with colorectal cancer: a study protocol
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Efficacy and Safety of Ultrasound-Guided Acupotomy Versus Celecoxib in Patients with Thoracodorsal Myofascial Pain Syndrome: A Randomized Controlled Trial
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Sensitivity to missing not at random dropout in clinical trials: Use and interpretation of the trimmed means estimator
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Suubi + Adherence4Youth: a study protocol to optimize the Suubi Intervention for Adherence to HIV treatment for youth living with HIV in Uganda
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Vaporized cannabis versus placebo for acute migraine: A randomized, double‐blind, placebo‐controlled crossover trial
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Estimands for atopic dermatitis clinical trials: Expert opinion on the importance of intercurrent events
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Digitally managing depression: A fully remote randomised attention-placebo controlled trial
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Preventing recurrence of endometriosis-related pain by means of long-acting progestogen therapy: the PRE-EMPT RCT
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Efficacy of a mobile-based self-directed parent management training for parents of children with attention-deficit/hyperactivity disorder with or without oppositional defiant disorder– a randomized controlled trial
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AZTEC—azithromycin therapy for prevention of chronic lung disease of prematurity: a statistical analysis plan for clinical outcomes
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Improving Humanoid Grasp Success Rate based on Uncertainty-aware Metrics and Sensitivity Optimization
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Opening Doors to Recovery
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Unraveling large-scale spatial patterns and key drivers of heavy metals uptake by plants
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Reference-based multiple imputation for sensitivity analysis of clinical trials with missing dataPreprint
Cro, S. (2016) · RePEc: Research Papers in Economics
Self-citation
Relevant Accessible Sensitivity Analysis for Clinical Trials with Missing Data
Cro, S. (2017) · LSHTM Research Online (London School of Hygiene and Tropical Medicine) · doi:10.17037/pubs.03817571 · Cited by 1 · see citing works
Self-citation
Reference-based Sensitivity Analysis via Multiple Imputation for Longitudinal Trials with Protocol Deviation
Cro, S., Morris, T. P., Kenward, M. G., & Carpenter, J. R. (2016) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x1601600211 · Cited by 67 · see citing works
Self-citation
Control-Based Imputation and Delta-Adjustment Stress Test for Missing Data Analysis in Longitudinal Clinical Trials
Liu, G. F., & Pang, L. (2016) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2016.1256830 · Cited by 17 · see citing works
Reference based sensitivity analysis for longitudinal trials with protocol deviation via multiple imputation
Cro, S., Morris, T. P., Kenward, M., & Carpenter, M. P. (2016) · UCL Discovery (University College London)
Self-citation

Authors: Cro, S., Morris, T. P., Kenward, M. G., & Carpenter, J. R.
Affiliations: Imperial College London (GB)
London School of Hygiene & Tropical Medicine (GB)
Ashkirk Scotland UK
Corresponding author: Cro, Suzie
ORCID: Cro, Suzie
Morris, Tim P.
Kenward, Michael G.
Carpenter, James R.
Venue: Statistics in Medicine
Publisher: Wiley
Published: 2020-05-17
Volume: 39
Issue: 21
Pages: 2815-2842
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.8569
OpenAlex: W3026799785
Cited by: 203
References: 58
Citations by year: 2026: 29 · 2025: 45 · 2024: 36 · 2023: 30 · 2022: 39 · 2021: 21 · 2020: 3
FWCI: 16.40
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Statistics in Medicine (publishedVersion, cc-by)
PubMed (publishedVersion)
LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
UCL Discovery (University College London) (submittedVersion)
Spiral (Imperial College London) (submittedVersion, other-oa)
Funders: Medical Research Council (MC_UU_12023/21, MC_UU_12023/29, grant numbers MC_UU_12023/21 and MC_UU_12023/29)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: missing data, sensitivity analysis, loss to follow-up, intercurrent events, Stata code
MeSH terms: Child, Data Interpretation, Statistical, Humans

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Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide

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