Measurement error correction for nutritional exposures with correlated measurement error: Use of the method of triads in a longitudinal setting
Nutritional exposures are often measured with considerable error in commonly used surrogate instruments such as the food frequency questionnaire (FFQ) (denoted by Qi for the ith subject). The error can be both systematic and random. The diet record (DR) denoted by Ri for the ith subject is considere...
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| Published in | Statistics in medicine Vol. 27; no. 18; pp. 3466 - 3489 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
15.08.2008
Wiley Subscription Services, Inc |
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| Online Access | Get full text |
| ISSN | 0277-6715 1097-0258 |
| DOI | 10.1002/sim.3238 |
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| Abstract | Nutritional exposures are often measured with considerable error in commonly used surrogate instruments such as the food frequency questionnaire (FFQ) (denoted by Qi for the ith subject). The error can be both systematic and random. The diet record (DR) denoted by Ri for the ith subject is considered an alloyed gold standard. However, some authors have reported both systematic and random errors with this instrument as well.
One goal in measurement error research is to estimate the regression coefficient of Ti (true intake for the ith subject) on Qi denoted by λTQ. If the systematic errors in Qi and Ri (denoted by qi and ri) are uncorrelated, then one can obtain an unbiased estimate of λTQ by λRQ obtained by regressing Ri on Qi. However, if Corr(qi, ri)>0, then λRQ>λTQ.
In this paper, we propose a method for indirectly estimating λTQ even in the presence of correlated systematic error based on a longitudinal design where Qi (surrogate measure of dietary intake), Ri (a reference measure of dietary intake), and Mi (a biomarker) are available on the same subjects at 2 time points. In addition, between‐person variation in mean levels of Mi among people with the same dietary intake is also accounted for. The methodology is illustrated for dietary vitamin C intake based on longitudinal data from 323 subjects in the European Prospective Investigation of Cancer (EPIC)‐Norfolk study who provided two measures of dietary vitamin C intake from the FFQ (Qi) and a 7‐day DR (Ri) and plasma vitamin C (Mi) 4 years apart. Copyright © 2008 John Wiley & Sons, Ltd. |
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| AbstractList | Nutritional exposures are often measured with considerable error in commonly used surrogate instruments such as the food frequency questionnaire (FFQ) (denoted by Qi for the ith subject). The error can be both systematic and random. The diet record (DR) denoted by Ri for the ith subject is considered an alloyed gold standard. However, some authors have reported both systematic and random errors with this instrument as well.
One goal in measurement error research is to estimate the regression coefficient of Ti (true intake for the ith subject) on Qi denoted by λTQ. If the systematic errors in Qi and Ri (denoted by qi and ri) are uncorrelated, then one can obtain an unbiased estimate of λTQ by λRQ obtained by regressing Ri on Qi. However, if Corr(qi, ri)>0, then λRQ>λTQ.
In this paper, we propose a method for indirectly estimating λTQ even in the presence of correlated systematic error based on a longitudinal design where Qi (surrogate measure of dietary intake), Ri (a reference measure of dietary intake), and Mi (a biomarker) are available on the same subjects at 2 time points. In addition, between‐person variation in mean levels of Mi among people with the same dietary intake is also accounted for. The methodology is illustrated for dietary vitamin C intake based on longitudinal data from 323 subjects in the European Prospective Investigation of Cancer (EPIC)‐Norfolk study who provided two measures of dietary vitamin C intake from the FFQ (Qi) and a 7‐day DR (Ri) and plasma vitamin C (Mi) 4 years apart. Copyright © 2008 John Wiley & Sons, Ltd. Nutritional exposures are often measured with considerable error in commonly used surrogate instruments such as the food frequency questionnaire (FFQ) (denoted by Q... for the ith subject). The I error can be both systematic and random. The diet record (DR) denoted by R... for the ith subject is considered an alloyed gold standard. However, some authors have reported both systematic and random errors with this instrument as well. One goal in measurement error research is to estimate the regression coefficient of T... (true intake for I the ith subject) on Q... denoted by ... If the systematic errors in Q... and R... (denoted by q... and r...) are uncorrelated, then one can obtain an unbiased estimate of by obtained by regressing R... on Q... However, if Corr(q..., r...)>0, then ... In this paper, we propose a method for indirectly estimating ... even in the presence of correlated systematic error based on a longitudinal design where Q... (surrogate measure of dietary intake), R... (a reference measure of dietary intake), and M... (a biomarker) are available on the same subjects at 2 time points. In addition, between-person variation in mean levels of M... among people with the same dietary intake is also accounted for. The methodology is illustrated for dietary vitamin C intake based on longitudinal data from 323 subjects in the European Prospective Investigation of Cancer (EPIC)-Norfolk study who provided two measures of dietary vitamin C intake from the FFQ (Q...) and a 7-day DR (R...) and plasma vitamin C (M...) 4 years apart. (ProQuest: ... denotes formulae/symbols omitted.) Nutritional exposures are often measured with considerable error in commonly used surrogate instruments such as the food frequency questionnaire (FFQ) (denoted by Q(i) for the ith subject). The error can be both systematic and random. The diet record (DR) denoted by R(i) for the ith subject is considered an alloyed gold standard. However, some authors have reported both systematic and random errors with this instrument as well.One goal in measurement error research is to estimate the regression coefficient of T(i) (true intake for the ith subject) on Q(i) denoted by lambda(TQ). If the systematic errors in Q(i) and R(i) (denoted by q(i) and r(i)) are uncorrelated, then one can obtain an unbiased estimate of lambda(TQ) by lambda(RQ) obtained by regressing R(i) on Q(i). However, if Corr(q(i), r(i))>0, then lambda(RQ)>lambda(TQ).In this paper, we propose a method for indirectly estimating lambda(TQ) even in the presence of correlated systematic error based on a longitudinal design where Q(i) (surrogate measure of dietary intake), R(i) (a reference measure of dietary intake), and M(i) (a biomarker) are available on the same subjects at 2 time points. In addition, between-person variation in mean levels of M(i) among people with the same dietary intake is also accounted for. The methodology is illustrated for dietary vitamin C intake based on longitudinal data from 323 subjects in the European Prospective Investigation of Cancer (EPIC)-Norfolk study who provided two measures of dietary vitamin C intake from the FFQ (Q(i)) and a 7-day DR (R(i)) and plasma vitamin C (M(i)) 4 years apart.Nutritional exposures are often measured with considerable error in commonly used surrogate instruments such as the food frequency questionnaire (FFQ) (denoted by Q(i) for the ith subject). The error can be both systematic and random. The diet record (DR) denoted by R(i) for the ith subject is considered an alloyed gold standard. However, some authors have reported both systematic and random errors with this instrument as well.One goal in measurement error research is to estimate the regression coefficient of T(i) (true intake for the ith subject) on Q(i) denoted by lambda(TQ). If the systematic errors in Q(i) and R(i) (denoted by q(i) and r(i)) are uncorrelated, then one can obtain an unbiased estimate of lambda(TQ) by lambda(RQ) obtained by regressing R(i) on Q(i). However, if Corr(q(i), r(i))>0, then lambda(RQ)>lambda(TQ).In this paper, we propose a method for indirectly estimating lambda(TQ) even in the presence of correlated systematic error based on a longitudinal design where Q(i) (surrogate measure of dietary intake), R(i) (a reference measure of dietary intake), and M(i) (a biomarker) are available on the same subjects at 2 time points. In addition, between-person variation in mean levels of M(i) among people with the same dietary intake is also accounted for. The methodology is illustrated for dietary vitamin C intake based on longitudinal data from 323 subjects in the European Prospective Investigation of Cancer (EPIC)-Norfolk study who provided two measures of dietary vitamin C intake from the FFQ (Q(i)) and a 7-day DR (R(i)) and plasma vitamin C (M(i)) 4 years apart. Nutritional exposures are often measured with considerable error in commonly used surrogate instruments such as the food frequency questionnaire (FFQ) (denoted by Q(i) for the ith subject). The error can be both systematic and random. The diet record (DR) denoted by R(i) for the ith subject is considered an alloyed gold standard. However, some authors have reported both systematic and random errors with this instrument as well.One goal in measurement error research is to estimate the regression coefficient of T(i) (true intake for the ith subject) on Q(i) denoted by lambda(TQ). If the systematic errors in Q(i) and R(i) (denoted by q(i) and r(i)) are uncorrelated, then one can obtain an unbiased estimate of lambda(TQ) by lambda(RQ) obtained by regressing R(i) on Q(i). However, if Corr(q(i), r(i))>0, then lambda(RQ)>lambda(TQ).In this paper, we propose a method for indirectly estimating lambda(TQ) even in the presence of correlated systematic error based on a longitudinal design where Q(i) (surrogate measure of dietary intake), R(i) (a reference measure of dietary intake), and M(i) (a biomarker) are available on the same subjects at 2 time points. In addition, between-person variation in mean levels of M(i) among people with the same dietary intake is also accounted for. The methodology is illustrated for dietary vitamin C intake based on longitudinal data from 323 subjects in the European Prospective Investigation of Cancer (EPIC)-Norfolk study who provided two measures of dietary vitamin C intake from the FFQ (Q(i)) and a 7-day DR (R(i)) and plasma vitamin C (M(i)) 4 years apart. Nutritional exposures are often measured with considerable error in commonly used surrogate instruments such as the food frequency questionnaire (FFQ) (denoted by Qi for the ith subject). The error can be both systematic and random. The diet record (DR) denoted by Ri for the ith subject is considered an alloyed gold standard. However, some authors have reported both systematic and random errors with this instrument as well. One goal in measurement error research is to estimate the regression coefficient of Ti (true intake for the ith subject) on Qi denoted by λTQ. If the systematic errors in Qi and Ri (denoted by qi and ri) are uncorrelated, then one can obtain an unbiased estimate of λTQ by λRQ obtained by regressing Ri on Qi. Howfever, if Corr(qi, ri) > 0, then λRQ > λTQ. In this paper, we propose a method for indirectly estimating λTQ even in the presence of correlated systematic error based on a longitudinal design where Qi (surrogate measure of dietary intake), Ri (a reference measure of dietary intake), and Mi (a biomarker) are available on the same subjects at 2 time points. In addition, between-person variation in mean levels of Mi among people with the same dietary intake is also accounted for. The methodology is illustrated for dietary vitamin C intake based on longitudinal data from 323 subjects in the European Prospective Investigation of Cancer (EPIC)-Norfolk study who provided two measures of dietary vitamin C intake from the FFQ (Qi) and a 7-day DR (Ri) and plasma vitamin C (Mi) 4 years apart. Nutritional exposures are often measured with considerable error in commonly used surrogate instruments such as the food frequency questionnaire (FFQ) (denoted by Q i for the i th subject). The error can be both systematic and random. The diet record (DR) denoted by R i for the i th subject is considered an alloyed gold standard. However, some authors have reported both systematic and random errors with this instrument as well. One goal in measurement error research is to estimate the regression coefficient of T i (true intake for the i th subject) on Q i denoted by λ TQ . If the systematic errors in Q i and R i (denoted by q i and r i ) are uncorrelated, then one can obtain an unbiased estimate of λ TQ by λ RQ obtained by regressing R i on Q i . However, if Corr( q i , r i )>0, then λ RQ >λ TQ . In this paper, we propose a method for indirectly estimating λ TQ even in the presence of correlated systematic error based on a longitudinal design where Q i (surrogate measure of dietary intake), R i (a reference measure of dietary intake), and M i (a biomarker) are available on the same subjects at 2 time points. In addition, between‐person variation in mean levels of M i among people with the same dietary intake is also accounted for. The methodology is illustrated for dietary vitamin C intake based on longitudinal data from 323 subjects in the European Prospective Investigation of Cancer (EPIC)‐Norfolk study who provided two measures of dietary vitamin C intake from the FFQ ( Q i ) and a 7‐day DR ( R i ) and plasma vitamin C ( M i ) 4 years apart. Copyright © 2008 John Wiley & Sons, Ltd. |
| Author | Day, Nicholas E. Michels, Karin B. Rosner, Bernard Chen, Ya-Hua |
| AuthorAffiliation | 3 Department of Epidemiology, Harvard School of Public Health, Boston, MA, U.S.A 4 Strangeways Research Laboratories, Institute of Public Health, University of Cambridge, Cambridge, U.K 2 Obstetrics and Gynecology Epidemiology Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, U.S.A 1 Channing Laboratory, Harvard Medical School, Boston, MA, U.S.A |
| AuthorAffiliation_xml | – name: 3 Department of Epidemiology, Harvard School of Public Health, Boston, MA, U.S.A – name: 1 Channing Laboratory, Harvard Medical School, Boston, MA, U.S.A – name: 2 Obstetrics and Gynecology Epidemiology Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, U.S.A – name: 4 Strangeways Research Laboratories, Institute of Public Health, University of Cambridge, Cambridge, U.K |
| Author_xml | – sequence: 1 givenname: Bernard surname: Rosner fullname: Rosner, Bernard email: rinkel@stat.harvard.edu organization: Channing Laboratory, Harvard Medical School, Boston, MA, U.S.A – sequence: 2 givenname: Karin B. surname: Michels fullname: Michels, Karin B. organization: Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, U.S.A – sequence: 3 givenname: Ya-Hua surname: Chen fullname: Chen, Ya-Hua organization: Channing Laboratory, Harvard Medical School, Boston, MA, U.S.A – sequence: 4 givenname: Nicholas E. surname: Day fullname: Day, Nicholas E. organization: Strangeways Research Laboratories, Institute of Public Health, University of Cambridge, Cambridge, U.K |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18416440$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1093/ajcn/65.4.1240S 10.1002/sim.4780080905 10.1002/sim.4780121005 10.1002/sim.4780130204 10.1093/aje/kwg092 10.1079/PHN2002394 10.1002/sim.4780121004 10.1016/j.annepidem.2004.12.012 10.1093/oxfordjournals.aje.a115715 10.1093/oxfordjournals.aje.a114086 10.1093/oxfordjournals.aje.a010063 10.1093/aje/kwh169 10.1002/sim.2055 10.1080/00401706.1983.10487848 |
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| References_xml | – reference: Plummer M, Clayton D. Measurement error in dietary assessment: an investigation using covariance structure models. Part II. Statistics in Medicine 1993; 12:937-948. – reference: Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Hennekens CH, Speizer FE. Reproducibility and validity of a semi-quantitative food frequency questionnaire. American Journal of Epidemiology 1985; 122:51-65. – reference: Plummer M, Clayton D. Measurement error in dietary assessment: an investigation using covariance structure models. Part I. Statistics in Medicine 1993; 12:925-935. – reference: Kipnis V, Carroll RJ, Freedman LS, Li L. Implications of a new dietary measurement error model for estimation and relative risk: application to four calibration studies. American Journal of Epidemiology 1999; 150(6):642-651. – reference: Subar AF, Kipnis V, Troiano RP, Midthune D, Schoeller DA, Bingham S, Sharbaugh CO, Trabulsi J, Runswick S, Ballard-Barbash R, Sunshine J, Schatzkin A. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study. American Journal of Epidemiology 2003; 158(1):1-13. – reference: Michels KB, Bingham SA, Luben R, Welch AA, Day NE. The effect of correlated measurement error in multivariate models of diet. American Journal of Epidemiology 2004; 160:59-67. – reference: Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Statistics in Medicine 1989; 8:1051-1069. – reference: Fraser GE, Butler TL, Shavlik DJ. Correlation between estimated and true dietary intakes: using two instrumental variables. Annals of Epidemiology 2005; 15:509-518. – reference: Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. American Journal of Epidemiology 1990; 132:134-145. – reference: Rosner B. Percentage points for a generalized ESD many-outlier procedure. Technometrics 1983; 25(2):165-172. – reference: Ocke MC, Kaaks RJ. Biochemical markers as additional measurements in dietary validity studies: application of the method of triads with examples from the European Prospective Investigation into Cancer and Nutrition. American Journal of Clinical Nutrition 1997; 65:1240S-1245S. – reference: Riboli E, Hunt KJ, Slimani N, Ferrari P, Norat T, Fahey M, Charrondière UR, Hémon B, Casagrande C, Vignat J, Overvad K, Tjønneland A, Clavel-Chapelon F, Thiébaut A, Wahrendorf J, Boeing H, Trichopoulos D, Trichopoulou A, Vineis P, Palli D, Bueno-de-Mesquita HB, Peeters PHM, Lund E, Engeset D, González CA, Barricarte A, Berglund G, Hallmans G, Day NE, Key TJ, Kaaks R, Saracci R. European prospective investigation into cancer and nutrition (EPIC): study population and data collection. Public Health Nutrition 2002; 5:1113-1124. – reference: Spiegelman D, Zhao B, Kim J. Correlated errors in biased surrogates: study designs and methods for measurement error correction. Statistics in Medicine 2005; 24(11):1657-1682. – reference: Kaaks R, Riboli E, Esteve J, Van Kappel A, Van Staveren W. Estimating the accuracy of dietary questionnaire assessments: validation in terms of structural equation models. Statistics in Medicine 1994; 13:127-142. – volume: 8 start-page: 1051 year: 1989 end-page: 1069 article-title: Correction of logistic regression relative risk estimates and confidence intervals for systematic within‐person measurement error publication-title: Statistics in Medicine – volume: 5 start-page: 1113 year: 2002 end-page: 1124 article-title: European prospective investigation into cancer and nutrition (EPIC): study population and data collection publication-title: Public Health Nutrition – volume: 132 start-page: 134 year: 1990 end-page: 145 article-title: Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error publication-title: American Journal of Epidemiology – volume: 25 start-page: 165 issue: 2 year: 1983 end-page: 172 article-title: Percentage points for a generalized ESD many‐outlier procedure publication-title: Technometrics – volume: 12 start-page: 925 year: 1993 end-page: 935 article-title: Measurement error in dietary assessment: an investigation using covariance structure models. Part I publication-title: Statistics in Medicine – volume: 122 start-page: 51 year: 1985 end-page: 65 article-title: Reproducibility and validity of a semi‐quantitative food frequency questionnaire publication-title: American Journal of Epidemiology – volume: 24 start-page: 1657 issue: 11 year: 2005 end-page: 1682 article-title: Correlated errors in biased surrogates: study designs and methods for measurement error correction publication-title: Statistics in Medicine – volume: 150 start-page: 642 issue: 6 year: 1999 end-page: 651 article-title: Implications of a new dietary measurement error model for estimation and relative risk: application to four calibration studies publication-title: American Journal of Epidemiology – volume: 65 start-page: 1240S year: 1997 end-page: 1245S article-title: Biochemical markers as additional measurements in dietary validity studies: application of the method of triads with examples from the European Prospective Investigation into Cancer and Nutrition publication-title: American Journal of Clinical Nutrition – volume: 160 start-page: 59 year: 2004 end-page: 67 article-title: The effect of correlated measurement error in multivariate models of diet publication-title: American Journal of Epidemiology – volume: 158 start-page: 1 issue: 1 year: 2003 end-page: 13 article-title: Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study publication-title: American Journal of Epidemiology – volume: 15 start-page: 509 year: 2005 end-page: 518 article-title: Correlation between estimated and true dietary intakes: using two instrumental variables publication-title: Annals of Epidemiology – volume: 12 start-page: 937 year: 1993 end-page: 948 article-title: Measurement error in dietary assessment: an investigation using covariance structure models. Part II publication-title: Statistics in Medicine – volume: 13 start-page: 127 year: 1994 end-page: 142 article-title: Estimating the accuracy of dietary questionnaire assessments: validation in terms of structural equation models publication-title: Statistics in Medicine – ident: e_1_2_1_7_2 doi: 10.1093/ajcn/65.4.1240S – ident: e_1_2_1_3_2 doi: 10.1002/sim.4780080905 – ident: e_1_2_1_5_2 doi: 10.1002/sim.4780121005 – ident: e_1_2_1_6_2 doi: 10.1002/sim.4780130204 – ident: e_1_2_1_8_2 doi: 10.1093/aje/kwg092 – ident: e_1_2_1_13_2 doi: 10.1079/PHN2002394 – ident: e_1_2_1_4_2 doi: 10.1002/sim.4780121004 – ident: e_1_2_1_12_2 doi: 10.1016/j.annepidem.2004.12.012 – volume: 132 start-page: 134 year: 1990 ident: e_1_2_1_9_2 article-title: Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error publication-title: American Journal of Epidemiology doi: 10.1093/oxfordjournals.aje.a115715 – ident: e_1_2_1_2_2 doi: 10.1093/oxfordjournals.aje.a114086 – ident: e_1_2_1_10_2 doi: 10.1093/oxfordjournals.aje.a010063 – ident: e_1_2_1_15_2 doi: 10.1093/aje/kwh169 – ident: e_1_2_1_11_2 doi: 10.1002/sim.2055 – ident: e_1_2_1_14_2 doi: 10.1080/00401706.1983.10487848 – reference: 19340846 - Stat Med. 2009 Apr 30;28(9):1423-4. doi: 10.1002/sim.3528. |
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| Title | Measurement error correction for nutritional exposures with correlated measurement error: Use of the method of triads in a longitudinal setting |
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