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

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

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

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

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

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: Average of multiple 24h recalls can be distorted

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

Summary 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