12. Sensitivity Analysis for Missing Data and Term Dominance Testing for Chemical Hazard Scoring Algorithm for Workers and Environment

Abstract Background Missing data is common in IBD trials. Depending on the volume and nature of missing data, it can reduce statistical power for detecting treatment difference, introduce potential bias and invalidate conclusions. Non-responder imputation (NRI), where patients (patients) with missing data are considered treatment failures, is widely used to handle missing data for dichotomous efficacy endpoints in IBD trials. However, it does not consider the mechanisms leading to missing data and can potentially underestimate the treatment effect. We proposed a hybrid (HI) approach combining NRI and multiple imputation (MI) as an alternative to NRI in the analyses of two phase 3 trials of vedolizumab (VDZ) in patients with moderate-to-severe UC – VISIBLE 11 and VARSITY2. Methods VISIBLE 1 and VARSITY assessed efficacy using dichotomous endpoints based on complete Mayo score. Full methodologies reported previously.1,2 Our proposed HI approach is aimed at imputing missing Mayo scores, instead of imputing the missing dichotomous efficacy endpoint. To assess the impact of dropouts for different missing data mechanisms (categorised as ‘missing not at random [MNAR]’ and ‘missing at random [MAR]’, HI was implemented as a potential sensitivity analysis, where dropouts owing to safety or lack of efficacy were imputed using NRI (assuming MNAR) and other missing data were imputed using MI (assuming MAR). For MI, each component of the Mayo score was imputed via a multivariate stepwise approach using a fully conditional specification ordinal logistic method. Missing baseline scores were imputed using baseline characteristics data. Missing scores from each subsequent visit were imputed using all previous visits in a stepwise fashion. Fifty imputation datasets were computed for each component of Mayo score. The complete Mayo score and relevant efficacy endpoints were derived subsequently. The analysis was performed within each imputed dataset to determine treatment difference, 95% CI and p-value, which were then combined via Rubin’s rules3. Results Tables 1 and 2 show a comparison of efficacy in the two studies using the primary NRI analysis vs. the alternative HI approach for handling missing data. Conclusion HI and NRI approaches can provide consistent efficacy analyses in IBD trials. The HI approach can serve as a useful sensitivity analysis to assess the impact of dropouts under different missing data mechanisms and evaluate the robustness of efficacy conclusions. Reference

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Bayesian Sensitivity Analysis for Non-ignorable Missing Data in Longitudinal Studies

Statistics in Biosciences ◽

10.1007/s12561-019-09234-6 ◽

2019 ◽

Vol 11 (1) ◽

pp. 184-205

Author(s):

Tian Li ◽

Julian M. Somers ◽

Xiaoqiong J. Hu ◽

Lawrence C. McCandless

Keyword(s):

Sensitivity Analysis ◽

Missing Data ◽

Longitudinal Studies

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Erratum to: What impact do assumptions about missing data have on conclusions? a practical sensitivity analysis for a cancer survival registry

BMC Medical Research Methodology ◽

10.1186/s12874-017-0323-7 ◽

2017 ◽

Vol 17 (1) ◽

Cited By ~ 2

Author(s):

M. Smuk ◽

J. R. Carpenter ◽

T. P. Morris

Keyword(s):

Sensitivity Analysis ◽

Missing Data ◽

Cancer Survival

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To what degree does the missing-data technique influence the estimated growth in learning strategies over time? A tutorial example of sensitivity analysis for longitudinal data

PLoS ONE ◽

10.1371/journal.pone.0182615 ◽

2017 ◽

Vol 12 (9) ◽

pp. e0182615 ◽

Cited By ~ 5

Author(s):

Liesje Coertjens ◽

Vincent Donche ◽

Sven De Maeyer ◽

Gert Vanthournout ◽

Peter Van Petegem

Keyword(s):

Sensitivity Analysis ◽

Missing Data ◽

Longitudinal Data ◽

Learning Strategies ◽

Over Time

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Missing data in randomised controlled trials of rheumatoid arthritis drug therapy are substantial and handled inappropriately

RMD Open ◽

10.1136/rmdopen-2021-001708 ◽

2021 ◽

Vol 7 (2) ◽

pp. e001708

Author(s):

Nasim A Khan ◽

Karina D Torralba ◽

Fawad Aslam

Keyword(s):

Rheumatoid Arthritis ◽

Sensitivity Analysis ◽

Drug Therapy ◽

Missing Data ◽

Primary Outcome ◽

Outcome Data ◽

Data Handling ◽

Significant Discrepancy ◽

Imputation Methods ◽

Simple Imputation

ObjectivesTo analyse the amount, reporting and handling of missing data, approach to intention-to-treat (ITT) principle application and sensitivity analysis utilisation in randomised clinical trials (RCTs) of rheumatoid arthritis (RA). To assess the trend in such reporting 10 years apart (2006 and 2016).MethodsParallel group drug therapy RA RCTs with a clinical primary endpoint.Results176 studies enrolling a median of 160 (IQR 62–339) patients were eligible. In terms of actual analysis: 81 (46%) RCTs conducted ITT, 42 (23.9%) conducted modified ITT while 53 (30.1%) conducted non-ITT analysis. Only 58 of 97 (59.8%) RCTs reporting an ITT analysis actually performed it. The median (IQR) numbers of participants completing the trial and included in analysis for primary outcome were 86% (74%–91%) and 100% (97.1%–100%), respectively. 53 (32.7%) and 65 (40.1%) RCTs had >20% and 10%–20% missing primary outcome data, respectively. Missing data handling was unreported by 58 of 171 (33.9%) RCTs. When reported, vast majority used simple imputation methods. No significant trend towards improved reporting was seen between 2006 and 2016. Sensitivity analysis numerically improved from 2006 to 2016 (14.7% vs 21.4%).ConclusionsThere is significant discrepancy in the reported and the actual performed analysis in RA drug therapy RCTs. Nearly one-third of RCTs had >20% missing data. The reporting and methods of missing data handling remain inadequate with high usage of non-preferred simple imputation methods. Sensitivity analysis utilisation was low. No trend towards better missing data reporting and handling was seen.

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Data Missing Not at Random in Mobile Health Research: Assessment of the Problem and a Case for Sensitivity Analyses

Journal of Medical Internet Research ◽

10.2196/26749 ◽

2021 ◽

Vol 23 (6) ◽

pp. e26749

Author(s):

Simon B Goldberg ◽

Daniel M Bolt ◽

Richard J Davidson

Keyword(s):

Sensitivity Analysis ◽

Missing Data ◽

Maximum Likelihood ◽

Multiple Imputation ◽

Mixture Model ◽

Mobile Health ◽

Sensitivity Analyses ◽

Research Assessment ◽

Missing Not At Random ◽

Pattern Mixture Model

Background Missing data are common in mobile health (mHealth) research. There has been little systematic investigation of how missingness is handled statistically in mHealth randomized controlled trials (RCTs). Although some missing data patterns (ie, missing at random [MAR]) may be adequately addressed using modern missing data methods such as multiple imputation and maximum likelihood techniques, these methods do not address bias when data are missing not at random (MNAR). It is typically not possible to determine whether the missing data are MAR. However, higher attrition in active (ie, intervention) versus passive (ie, waitlist or no treatment) conditions in mHealth RCTs raise a strong likelihood of MNAR, such as if active participants who benefit less from the intervention are more likely to drop out. Objective This study aims to systematically evaluate differential attrition and methods used for handling missingness in a sample of mHealth RCTs comparing active and passive control conditions. We also aim to illustrate a modern model-based sensitivity analysis and a simpler fixed-value replacement approach that can be used to evaluate the influence of MNAR. Methods We reanalyzed attrition rates and predictors of differential attrition in a sample of 36 mHealth RCTs drawn from a recent meta-analysis of smartphone-based mental health interventions. We systematically evaluated the design features related to missingness and its handling. Data from a recent mHealth RCT were used to illustrate 2 sensitivity analysis approaches (pattern-mixture model and fixed-value replacement approach). Results Attrition in active conditions was, on average, roughly twice that of passive controls. Differential attrition was higher in larger studies and was associated with the use of MAR-based multiple imputation or maximum likelihood methods. Half of the studies (18/36, 50%) used these modern missing data techniques. None of the 36 mHealth RCTs reviewed conducted a sensitivity analysis to evaluate the possible consequences of data MNAR. A pattern-mixture model and fixed-value replacement sensitivity analysis approaches were introduced. Results from a recent mHealth RCT were shown to be robust to missing data, reflecting worse outcomes in missing versus nonmissing scores in some but not all scenarios. A review of such scenarios helps to qualify the observations of significant treatment effects. Conclusions MNAR data because of differential attrition are likely in mHealth RCTs using passive controls. Sensitivity analyses are recommended to allow researchers to assess the potential impact of MNAR on trial results.

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