Data quality/usability and population-based biobanks

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Data quality/usability and population-based
biobanks

• Paul Burton
• Dept of Health Sciences
• Dept of Genetics
• University of Leicester

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Structure of talk

• Why does data quality/usability matter?
• UK Biobank as an illustration
• Statistical power of nested case-control studies
• Expected event rates in UK Biobank
• Biobank harmonisation
• Conclusions

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Why does data quality/usability matter?
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Epidemiological analysisat its simplest

• Odds ratio (OR) (120240)/(200100) 1.44
  1.04 2.0
• May also adjust for a confounder
• e.g. high saturated fat intake y/n
• What is the impact of error in an outcome or an
  explanatory variable or in a confounder?

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Systematic error

• Some disease free smokers deny smoking
• Odds ratio (OR) (120250)/(190100) 1.58

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Random error

• At random, 10 of subjects state their exposure
  incorrectly
• Odds ratio (OR) (118236)/(204102) 1.34

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The impact of errors

• Systematic errors in outcome or explanatory
  variables ? systematic bias in either direction
• True OR 2 ? estimated OR e.g. 1.5 or 2.7
• Random errors in binary outcomes or any
  explanatory variables ? shrinkage bias
• True OR 2 ? estimated OR e.g. 1.5
• Random errors in confounding variables ?
  systematic bias in either direction
• True OR 2 ? estimated OR e.g. 1.5 or 2.7

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Errors in biobanks

• Random errors
• Loss of power is primary problem
• Biobank sample sizes very large, so why is there
  a problem?

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Errors in biobanks

• Random errors
• But why are biobank sample sizes so large?
• NB Biobanks very large not nested case-control
  studies
• Need to detect small relative risks (e.g. OR1.3)
• Power generally limited (see later)
• Small error effects catastrophic
• Apparent causal effects easily created or
  destroyed

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Errors in biobanks

• Systematic errors
• Small real effects a major issue again
• Must understand data collection protocols, and
  must attempt to optimise those protocols
• UK Biobank
• P3G Observatory

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What is UK Biobank?
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Basic design features

• A prospective cohort study
• 500,000 adults across UK
• Middle aged (40-69 years)
• A population-based biobank
• Not disease or exposure based
• Recruitment via electronic GP lists
• Broad spectrum not fully representative
• Individuals not families
• MRC, Wellcome Trust, DH, Scottish Executive
• 61M

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Basic design features

• Longitudinal health tracking
• Nested case-control studies
• Long time-horizon
• Owned by the Nation
• Central Administration Manchester
• PI Prof Rory Collins - Oxford
• 6 collaborating groups (RCCs) of university
  scientists

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Statistical powerand sample size
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Focus on power of nestedcase-control analyses

• Likely to be very common analyses
• Power limiting

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Issues that are often ignored in standard power
calculations

• Multiple testing/low prior probability of
  association
• Interactions
• Unobserved frailty
• Misclassification
• Genotype
• Environmental determinant
• Case-control status
• Subgroup analyses
• Population substructure

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Power calculations

• Work with least powerful setting
• Binary disease, binary genotype, binary
  environmental exposure
• Logistic regression analysis interactions
  departure from a multiplicative model
• Complexity (arbitrary but reasonable)

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Summarise power using Minimum Detectable Odds
Ratios (MDORs) calculated by iterative
simulation

• Estimate minimum ORs detectable with 80 power at
  stated level of statistical significance under
  specified scenario

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Genetic main effects
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Whole genome scan

• Genetic main effect, plt10-7

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Geneenvironment interaction

• 20,000 cases

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Summary rule of thumb

• 80 power for genotype frequency 0.1, (allele
  frequency ? 0.05 under dominant model)
• Genetic main effect ? 1.5, p10-4 ? 5,000 cases
• Genetic main effect ? 1.3, p10-4 ? 10,000 cases
• Genetic main effect ? 1.2, p10-4 ? 20,000 cases
• Genetic main effect ? 1.4, p10-7 ? 10,000 cases
• Genetic main effect ? 1.3, p10-7 ? 20,000 cases
• GE interaction with environmental exposure
• prevalance 0.2 ? 2.0, p10-4 ? 20,000
  cases

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Effect of realistic data errors
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Expected event ratesin UK Biobank
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Taking account of

• Age range at recruitment 40-69 years
• Recruitment over 5 years
• All cause mortality
• Disease incidence (healthy cohort effect)
• Migration overseas
• Comprehensive withdrawal (max 1/500 p.a.)

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No need to contact subjects
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Smaller sample sizes
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Interim conclusions

• Having taken account of realistic bioclinical
  complexity, UK Biobank is just large enough to be
  of great value as a stand-alone research
  infrastructure
• Data quality, in particular errors in outcome or
  explanatory variables, or in confounders is
  crucial
• Its value will be greatly augmented if it proves
  possible to set up a coherent and scientifically
  harmonized international network of Biobanks and
  large cohort studies

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Harmonising biobanks internationally
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Why harmonise?

• Basic aim is to enable and promote data pooling,
  in a manner that recognises and takes appropriate
  account of systematic differences between studies.

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Why harmonise?

• Investigate less common (but not rare) conditions
• UKBB Ca stomach 2,500 cases in 29 years
• 6 UKBB equivalents ? 10,000 cases in 20 years
• Investigate smaller ORs
• GME 1.5 ? 1.2 requires 5,000 ? 20,000
• 4 UKBB equivalents
• Analysis based on subsets homogeneous classes
  of phenotype, or e.g. by sex

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Why harmonise?

• Earlier analyses
• UKBB Alzheimers disease, 10,000 cases in 18 yrs
• 5 UKBB equivalents ? 9 years
• Events at younger ages
• Broad range of environmental exposures
• Aim for 4-6 UKBB equivalents
• 2M 3M recruits

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Harmonisation initiatives

• Public Population Program in Genomics (P3G)
• Canada Europe
• Tom Hudson, Bartha Knoppers, Leena Peltonen,
  Isabel Fortier ..
• Population Biobanks
• FP6 Co-ordination Action (PHOEBE Promoting
  Harmonisation Of Epidemiological Biobanks in
  Europe)
• Camilla Stoltenberg, Paul Burton, Leena Peltonen,
  George Davey Smith ..

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Harmonisation in the P3G Observatory(from Isabel
Fortier)

• Description
• Comparison
• Harmonisation
• Data quality crucial at every stage

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Final conclusions

• Power of individual biobanks is limited
• Minimisation of measurement error is crucial
• Harmonisation is crucial if we are to optimise
  the value of biobanks internationally
• Harmonisation depends on a full understanding of
  all aspects of data quality

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Extra slides
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Rarer genotypes

• Genetic main effects

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Geneenvironment interaction

• 10,000 cases

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Hattersley AT, McCarthy MI. A question of
standards what makes a good genetic association
study? Lancet 2005 in press.
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Summarise power using MDORs calculated by
iterative simulation

• Want minimum ORs detectable with 80 power at
  stated level of statistical significance
• 1. Guess starting values for ORs
• 2. Simulate population under specified scenario
• 3. Sample required number of cases and controls
• 4. Analyse resultant case-control study in
  standard way
• 5. Repeat 2,3,4 1,000 times
• 6. Use empirical statistical power results from
  the 1,000 analyses to update ORs to new values
  expected to generate a power of 80
• Repeat 2-6 till all ORs have 80 power

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Taking account of

• Age range at recruitment 40-69 years
• Recruitment over 5 years
• All cause mortality
• Disease incidence (healthy cohort effect)
• Migration overseas
• Comprehensive withdrawal (max 1/500 p.a.)
• Partial withdrawal (c.f. 1958 Birth Cohort)

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Necessary to contact subjects
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