SOME ADDITIONAL POINTS ON MEASUREMENT ERROR IN EPIDEMIOLOGY

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

2. 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

3. Examples (1) • 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 • No theorems on D’ or recombination fraction • Kin cohort analysis • Known Mendelian rules allow inference of genotype from relative’s

4. Examples (2) • Misclassification of disease • E.g.: Screening leads to diagnosis •  Misclassification of disease, differential by screening • Screening studies use mortality, not diagnosis, as endpoint

5. Differential misclassification • 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

6. 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?

7. 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

8. 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

9. Alloyed gold standard • 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

10. Missing data (MD) vs. ME • 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

11. Summary • Measurement error is pervasive • Even in trials, not just epidemiology • 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 • Statistical insights can help at design and analysis stages