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Click to edit Master title style,*,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,Chris Jackson,With Nicky Best and Sylvia Richardson,Department of Epidemiology and Public Health,Imperial College,London,NCRM BIAS node,:/bias-project.org.uk,Combining administrative and survey data,in a study of low birth weight and air pollution,BIAS:,Biases in observational studies,Promote principled methods for accounting for potential biases in observational data:,“non-response bias:,selection bias(participation in a study),missing data(on some variables for one individual),confounding(important variables not available),ecological bias(from aggregate/area-level data),measurement error,Nave methods not normally appropriate.,Alleviating biases,Suitable statistical,models,for the processes underlying the data,Express uncertainty about biases as probability distributions.,Uncertainty carries through to the results,Bayesian graphical models,Software,e.g.,WinBUGS,Using,multiple data sources,to inform about the potential biases,Application areas,Small area estimation,(with Virgilio Gmez Rubio),Using combination of aggregate(e.g.census)and individual survey data,Selection bias,in case-control and survey studies(with Sara Geneletti),Using directed acyclic graphs,Inference from,combining datasets,of different designs from different sources(with Chris Jackson,Jassy Molitor),Using Bayesian hierarchical/graphical models,See(:/bias-project.org.uk),Example:low birth weight and air pollution,Does exposure to air pollution during pregnancy increase the risk of low birth weight?,Example illustrates various biases.,Combine datasets with different strengths:,Survey data(Millennium Cohort Study),Small,great individual detail.,Administrative data(national births register),Large,but little individual detail.,Single underlying model assumed to govern both datasets:elaborate as appropriate to handle biases,Low birth weight,Important determinant of future health,population health indicator.,Established risk factors:,Tobacco smoking during pregnancy.,Ethnicity,(South Asian,issue for UK data),Maternal age,weight,height,number of previous births.,Role of environmental risk factors,such as air pollution,less clear.,Various studies around the world suggest a link.,Exposure to urban air pollution correlated with socioeconomic factors,ethnicity,tobacco smoking,confounding,Data sources(1):Millennium Cohort Study,About 15,000 births in the UK between Sep 2000 and August 2001,(we study only England and Wales,singleton births),Postcode made available to us under strict security,Match individuals with annual mean concentration of certain air pollutants(PM,10,NO,2,CO,SO,2,),(,NETCEN,),Birth weight,and reasonably complete set of confounder data available,Allows a reasonable analysis,but issues remain:,Low power to detect small effect,could be improved by incorporating other data.,Selection bias.,Selection of Millennium Cohort,ALL UK WARDS,ENGLAND,SCOTLAND,WALES,NORTHERN IRELAND,High child poverty,Low child poverty,High child poverty,Low child poverty,High child poverty,Low child poverty,High child poverty,Low child poverty,High ethnic minority,SELECTION PROBABILITY,0.04,0.02,0.11,0.07,0.04,0.18,0.06,0.16,0.08,Selection bias in the Millennium Cohort,Survey disproportionately represents population.,If selection probability related to exposure,and,outcome,then estimate of association biased.,Ethnicity/child poverty probably related to,both,pollution exposure and low birth weight.,Accounting for selection bias:,Adjust model,for all variables affecting selection,or,Weight cases,by inverse probability of selection,Cluster sampling,within-ward correlations,for correct standard errors,use a,hierarchical(multilevel)model,with groups defined by wards.,Data sources(2):National birth register,Every birth in the population recorded.,Individual data with postcode(,pollution exposure)and birth weight available to us under strict security.,Social class and employment status of parents also available for a 10%sample.,We study only this 10%sample:50,000 births between Sep 2000 and Aug 2001.,Larger dataset,no selection bias,but,no confounder information,especially ethnicity and smoking.,Data sources(3):Aggregate data,Ethnic composition of the population,2001 census,for census output areas(500 individuals),Tobacco expenditure,consumer surveys(CACI,who produce ACORN consumer classification data,),for census output areas.,linked by postcode to Millennium Cohort and national register data.,Birth weight and pollution(source:MCS),Birth weight and ethnicity(source:MCS),Birth weight and smoking(source:MCS),Pollution and confounders(source:MCS),Models for formally analysing combined data,Want estimate of the association between low birth weight and pollution,using all data,accounting for:,Selection bias,in MCS,Adjust models for all predictors of selection,Or weight by inverse probability of selection,Missing confounders,in register,Bayesian graphical mo
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