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Status on statistical methods in dietary assessment and Multiple Source Method Heiner Boeing German Institute of Human Nutrition PotsdamRehbrücke Department of Epidemiology Dietary assessment „Proper assessment of dietary intakes is critical in epidemiologic research that examines the relationships between diet and disease risk“ NCI Web site Long term average dietary assessment The concept of long-term average daily intake, or "usual intake," is important because diet-health hypotheses are based on dietary intakes over the long term. However, until recently, sophisticated efforts to capture this concept have been limited at best. NCI Web site Dietary assessment of usual intake for large-scale prospective studies „True“ individual dietary intake of 365 days d1 d2 d4 d3 d5 d6 d7 d8 d9 d10 d… Approximation d1 d4 d5 d10 24-h-recall/Food records FFQ Statistical modelling Estimated usual dietary intake d365 Snapshots Concepts of proper dietary assessment in cohort studies • Correction of the effect measures (Validation studies) • Use of a reference instrument in a subgroup to apply information to the full cohort (calibration/standardisation) • Best estimate of individual intake in the full cohort (reduction of assessment bias) Calibration Referenz Instrument Calibrated Instrument Consequences of calibration Assessment Instrument Exposition time Ranking Bias FFQ Long term Good ranking Large bias Reference instrument Short term Bad ranking Small bias Calibrated FFQ Long term Good ranking Small bias How to calibrate • Use of a reference instrument (R) • Selection of a subgroup with • simultaneous use of Q and R Determination of a mathematical function for the Q value that fit the R value (Calibration function) Calibration functions Calibration function 1) Linear regression calibration (1 day) R=Q+ 2) Linear regression calibration (2 days) Qcal = sqrt(q*VAR(R)/VAR(Q))*(Q-AM(Q))+AM(R) q = 1/sqrt(1+VARINTRA/VARINTER) 3) Use of standardisation functions (mean, variance, skewness, curtosis) f(Q) Calibration does not change ranking by Q Multiple Source Method Structure of the algorithm Observed consumption days pij Observed positive daily intake data Yi * Parallel steps First step Probability of consumption Y Second step Usual intake on consumption days ij p * i Third step Yi * p i* Usual intake = probability of a consumption day * usual intake on consumption day First step Observed consumption days pij • Partition of observations • Transformation • Shrinkage of transformed data • Back transformation • Composition of components Probability of consumption p i* Second step Observed positive daily intake data Yij • Partition of observations • Transformation • Shrinkage of transformed data • Back transformation • Composition of components Usual intake on consumption days Yi * Third step Usual intake on consumption days Yi Probability of consumption * p Yi * p i* Usual intake = usual intake on consumption day * probability of a consumption day * i Example fresh fruits Example fish Example breakfast cereals Summary • There are several strategies available to • • improve dietary assessment in cohort studies There is currently a lack of empirical data that have compared the strategies The Multiple Source Method is a new promising tool for dietary assessment in epidemiological studies
