Hannemann, Tina

Shlomo, Natalie

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Compensating for Non-responsein Biosocial Research: Simulation Study from a Cross-sectional AnalysisDr Tina Hannemann Biomarkers in Longitudinal Studies in the UK• Understanding Society• Millennium Cohort Study• British Cohort Study• Child Development Study• English Longitudinal Study of AgeingEnglish Longitudinal Study of Ageing (ELSA)• Data from 11,391 core sample members of men and women over 50 years living in private households  • Multistage stratified probability sample from the Health Survey of England (HSE) with refreshment samples • Further health examinations including blood samples (W2, W4 & W6) and hair samples (W6)Missing Information in ELSA• Potential non-response at three different stages: • Main interview (CAPI)• Health examination (Nurse visit)• Blood/Hair sample collection Patterns of Missing information• Missing completely at random (MCAR)• Missing at random (MAR)• Missing not at random (MNAR) Patterns of Missing information• Missing completely at randomXY MX  explanatory variableY  depend variableM  missingness of YPatterns of Missing information• Missing at randomXY MX  explanatory variableY  depend variableM  missingness of YPatterns of Missing information• Missing not at randomXY MX  explanatory variableY  depend variableM  missingness of YAccounting for missing data bias• Several methods for missing data compensation• Confusion over effectiveness of methods• Problem of missing data is ignored (assumption of MAR)Objective• Test and evaluate several analytical approaches• Simulation study based on data from ELSA (wave 2)• Comparison of five different approaches• Evaluation over all three missingness patterns StrategyStep 1Choose substantive modelStep 2Select data in ELSA, expand sample sizeStep 3Run baseline model on “true” valuesStep 4Create missing data for MCAR, MAR and MNARStep 5Test five different compensation approachesStep 6Compare with “true” model and evaluate effectivenessStep 1 – Substantive modelImpact of socio-economic status on biomarker level• SES • Education, occupational class and wealth quintile• Biomarker• C-reactive protein (CRP)• Confounding variables• Age, sex, physical activity, BMI, Longstanding illness, pain, general health, BP, in paid workStep 2 – ELSA missing information8.780 Wave 2 core memberStep 2 – ELSA missing information2.881Non-response and missing5.899   Eligible blood samples32.8 %67.2%Step 2 – ELSA missing information2881Non-response and missing5.899   Eligible blood samples3328395961.114Unusable blood sampleNo blood sampleRefuse blood sampleRefuse of nurse visit3.8 %9.6 %6.8 %12.7 %67.2%Step 3 – Model on expanded full dataset• Logistic regression on logged CRP values (10.000 sample)• Coefficients for all three SES variables as baseline for simulation studyBaseline ModelEducation(ref. high)med. lowOccupation(ref. professional)Wealth quintiles(ref. lowest)IntermediateLower sup. and techSemi-routine and otherNo info2nd 5th3rd 4thStep 4 – Creating missing data patterns• MCAR: Missingness is completely random• MAR: Missingness dependent on age, sex, wealth and health• MNAR: Missingness dependent also on level of CRP• 100 simulation datasets for each missing patternsStep 5 – Five compensation methods• Complete Case (CC)• Inverse Propensity Weighting (IPW)• Selection model using Mills Ratio (MR)• Multiple Imputation (MI)• Multiple Imputation with Mills Ratio (MI+MR)Step 6 – Run and evaluate simulationsCC IPW MR MI MI+MRMCARMARMNARExpectationCC IPW MR MI MI+MRMCARMARMNARComplete Case –Wealth QuintilesMCAR MAR MNAR2nd quintile (-0.09)3rd quintile (-0.07)4th quintile (-0.23)highest quintile (-0.31)IPW –Wealth QuintilesMCAR MAR MNAR2nd quintile (-0.09)3rd quintile (-0.07)4th quintile (-0.23)highest quintile (-0.31)MR –Wealth QuintilesMCAR MAR MNAR2nd quintile (-0.09)3rd quintile (-0.07)4th quintile (-0.23)highest quintile (-0.31)MI –Wealth QuintilesMCAR MAR MNAR2nd quintile (-0.09)3rd quintile (-0.07)4th quintile (-0.23)highest quintile (-0.31)MI+MR –Wealth QuintilesMCAR MAR MNAR2nd quintile (-0.09)3rd quintile (-0.07)4th quintile (-0.23)highest quintile (-0.31)MI+MR –Wealth QuintilesMCAR MAR MNAR2nd quintile (-0.09)3rd quintile (-0.07)4th quintile (-0.23)highest quintile (-0.31)ExpectationCC IPW MR MI MI+MRMCARMARMNARObservationCC IPW MR MI MI+MRMCARMARMNARConclusions• No clear patterns• Multiple Imputation and Mills Ratio method has no advantage• MCAR and MAR rather robust• MNAR bias not compensated with these methodsFuture Research• Expand the simulation study to larger iteration number• Simplify substantial model• Test and evaluate effectiveness of methods depending on the proportion of missingness in dataset• Longitudinal analysis including wave 4 and wave 6 using growth curve modelling32Thank you for your attentionTina.Hannemann@manchester.ac.uk

Compensating for Non-response in Biosocial Research: Simulation Study from a Cross-sectional Analysis

https://eprints.ncrm.ac.uk/4185/1/NCRM_Methods_festival_Tina_2018_2.pdf

Compensating for Non-response in Biosocial Research: Simulation Study from a Cross-sectional Analysis

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