Sensitivity Analysis with Multiple Imputation

2014 ◽  
pp. 459-494
Keyword(s):  
Sensitivity Analysis ◽  
Multiple Imputation

scholarly journals The use of multiple imputation method for the validation of 24-h food recalls by part-time observation of dietary intake in school

2016 ◽  
Vol 116 (5) ◽  
pp. 904-912 ◽  
Author(s):  
Emil Kupek ◽  
Maria Alice A. de Assis
Keyword(s):  
Sensitivity Analysis ◽  
Dietary Intake ◽  
Multiple Imputation ◽  
Parental Education ◽  
Family Income ◽  
Statistical Significance ◽  
External Validation ◽  
Part Time ◽  
Food Recall ◽  
The Impact

AbstractExternal validation of food recall over 24 h in schoolchildren is often restricted to eating events in schools and is based on direct observation as the reference method. The aim of this study was to estimate the dietary intake out of school, and consequently the bias in such research design based on only part-time validated food recall, using multiple imputation (MI) conditioned on the information on child age, sex, BMI, family income, parental education and the school attended. The previous-day, web-based questionnaire WebCAAFE, structured as six meals/snacks and thirty-two foods/beverage, was answered by a sample of 7–11-year-old Brazilian schoolchildren (n 602) from five public schools. Food/beverage intake recalled by children was compared with the records provided by trained observers during school meals. Sensitivity analysis was performed with artificial data emulating those recalled by children on WebCAAFE in order to evaluate the impact of both differential and non-differential bias. Estimated bias was within ±30 % interval for 84·4 % of the thirty-two foods/beverages evaluated in WebCAAFE, and half of the latter reached statistical significance (P<0·05). Rarely (<3 %) consumed dietary items were often under-reported (fish/seafood, vegetable soup, cheese bread, French fries), whereas some of those most frequently reported (meat, bread/biscuits, fruits) showed large overestimation. Compared with the analysis restricted to fully validated data, MI reduced differential bias in sensitivity analysis but the bias still remained large in most cases. MI provided a suitable statistical framework for part-time validation design of dietary intake over six daily eating events.

scholarly journals Evaluation of a weighting approach for performing sensitivity analysis after multiple imputation

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Panteha Hayati Rezvan ◽  
Ian R. White ◽  
Katherine J. Lee ◽  
John B. Carlin ◽  
Julie A. Simpson
Keyword(s):  
Sensitivity Analysis ◽  
Multiple Imputation

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

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.

A multiple imputation‐based sensitivity analysis approach for data subject to missing not at random

2020 ◽  
Vol 39 (26) ◽  
pp. 3756-3771
Author(s):  
Chiu‐Hsieh Hsu ◽  
Yulei He ◽  
Chengcheng Hu ◽  
Wei Zhou
Keyword(s):  
Sensitivity Analysis ◽  
Multiple Imputation ◽  
Analysis Approach ◽  
Missing Not At Random ◽  
Data Subject

scholarly journals Sensitivity analysis in multiple imputation in effectiveness studies of psychotherapy

2015 ◽  
Vol 6 ◽  
Author(s):  
Aureliano Crameri ◽  
Agnes von Wyl ◽  
Margit Koemeda ◽  
Peter Schulthess ◽  
Volker Tschuschke
Keyword(s):  
Sensitivity Analysis ◽  
Multiple Imputation ◽  
Effectiveness Studies
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