SOME ADDITIONAL POINTS ON
MEASUREMENT ERROR IN
EPIDEMIOLOGY
Sholom
May 28, 2011
Supplement to Prof. Carroll’s talk II

Measurement error (ME) in epidemiology
• ME may be more important than confounding
• Examples from my work
• Best solution based on my experience:
– Avoid or minimize ME

• Molecular epidemiology and biomarkers
– Reduce Coefficient of Variation (CV) by reducing lab error
• Population variation remains
• Genetics
– GWAS: power loss depends on LD between markers and tagging SNPs
• Best characterized by
R
2
• No theorems on D’ or recombination fraction
– Kin cohort analysis
• Known Mendelian rules allow inference of genotype from relative’s

• Misclassification of disease
– E.g.: Screening leads to diagnosis
•  Misclassification of disease, differential by screening
•  Screening studies use mortality, not diagnosis, as endpoint

• Standard research practices minimize differential misclassification
– Occupational epidemiology: Industrial hygienists blinded to disease status
– Molecular epidemiology: Blinding in the lab
– Randomized controlled trial:
• Double blind treatment assignment
• Blinded (masked) ascertainment of disease

Differential misclassification:
Case-control
• Qx: “recall bias” is always a potential
• Direct evidence of recall bias is weak
• Molecular studies: biomarkers can be affected by disease progression
• Pre-diagnostic biospecimens needed
• Or perhaps from before disease initiation
• Cohort studies needed
• Genetics/genomics
– Genotype calling, QC
– DNA quality of cases and controls may be different
• Obtained from different sources
• Controls and cases from different studies
– As in studies of rare cancers using shared controls with previous genotypes
– Recall genotype from optical density?

Randomized Controlled Trials (1)
Differential misclassification of tx:
• Intention to treat (ITT) analysis
– Effect of dose assigned
– Controls confounding
– Estimate of effect often biased
• Effect of dose actually received
– May be more interesting
– Can be subject to confounding
• Solutions?
– ITT + understanding error model for dose received
– ITT + instrumental variable approach

Randomized Controlled Trials (2):
Differential misclassification
• HPV vaccine to prevent CIN2 due to HPV16/18
– CIN2 is cervical precancer
• Early trials used HPV assays only for HPV16/18
• Example of misclassification
– CIN2 caused by HPV type not affected by vaccine
– HPV16/18 found in lesion only in placebo arm
– Contributes to apparent benefit from vaccination
• NCI and other recent studies test for all oncogenic HPV types

• Use of alloyed gold standard in validation studies can lead to overcorrection for bias in regression calibration models
– Wacholder et al., 1993, PMID: 8322765
• Average of multiple 24h recalls can be distorted

• MD and ME: absence of true value of the variable
• ME: proxy variable available;
• MD: No proxy
• MD and ME: Statistical approaches available when you understand underlying mechanisms

1. Measurement error is pervasive
– Even in trials, not just epidemiology
2. Understand the causes of measurement error
– minimize bias;
– increase power, efficiency
– Understand mechanism generating observations via
– Pilot studies
– Validation studies
– Replication studies
– Inter-observer studies
1. Statistical insights can help at design and analysis stages