Bayesian Peer Calibration with Application to Alcohol Use

Peers are often able to provide important additional information to supplement self-reported behavioral measures. The study motivating this work collected data on alcohol in a social network formed by college students living in a freshman dormitory. By using two imperfect sources of information (self-reported and peer-reported alcohol consumption), rather than solely self-reports or peer-reports, we are able to gain insight into alcohol consumption on both the population and the individual level, as well as information on the discrepancy of individual peer-reports. We develop a novel Bayesian comparative calibration model for continuous, count, and binary outcomes that uses covariate information to characterize the joint distribution of both self and peer-reports on the network for estimating peer-reporting discrepancies in network surveys, and apply this to the data for fully Bayesian inference. We use this model to understand the effects of covariates on both drinking behavior and peer-reporting discrepancies. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Barnett Vd.  Simultaneous pairwise linear structural relationships. , 1969 .

[2]  M. Forina,et al.  Multivariate calibration. , 2007, Journal of chromatography. A.

[3]  Estimation in comparative calibration models with replicate measurement , 2005 .

[4]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[5]  Charles E Matthews,et al.  The role of measurement error in estimating levels of physical activity. , 2007, American journal of epidemiology.

[6]  Berkson measurement error in designed repeated measures studies with random coefficients , 2002 .

[7]  V D Barnett.  Simultaneous pairwise linear structural relationships. , 1969, Biometrics.

[8]  D. Kimura.  Functional comparative calibration using an EM algorithm , 1992 .

[9]  D Spiegelman,et al.  Measurement error correction for logistic regression models with an "alloyed gold standard". , 1997, American journal of epidemiology.

[10]  G. Gmel,et al.  Measuring alcohol consumption--should the 'graduated frequency' approach become the norm in survey research? , 2006, Addiction.

[11]  Xiao-Li Meng,et al.  POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES , 1996 .

[12]  P. Muellerleile,et al.  Collateral reports in the college setting: a meta-analytic integration. , 2005, Alcoholism, clinical and experimental research.

[13]  Alexander Kukush,et al.  Measurement Error Models , 2011, International Encyclopedia of Statistical Science.

[14]  Gregory J Welk,et al.  Modeling errors in physical activity recall data. , 2012, Journal of physical activity & health.

[15]  Raymond J Carroll,et al.  A mixed‐effects model approach for estimating the distribution of usual intake of nutrients: The NCI method , 2010, Statistics in medicine.

[16]  Wayne A. Fuller,et al.  Estimation in the Presence of Measurement Error , 1995 .

[17]  H K Genant,et al.  Comparative calibration without a gold standard. , 1997, Statistics in medicine.

[18]  Geert Molenberghs,et al.  Marginal correlation from an extended random-effects model for repeated and overdispersed counts , 2011 .

[19]  A. Carriquiry,et al.  A Semiparametric Transformation Approach to Estimating Usual Daily Intake Distributions , 1996 .

[20]  C. Davis,et al.  The utility of collateral informants in substance use research involving college students. , 2007, Addictive behaviors.

[21]  S Richardson,et al.  A Bayesian approach to measurement error problems in epidemiology using conditional independence models. , 1993, American journal of epidemiology.

[22]  W. Fuller,et al.  [Estimation in the Presence of Measurement Error]: Discussion , 1995 .

[23]  C. M. Theobald,et al.  Comparative Calibration, Linear Structural Relationships and Congeneric Measurements , 1978 .

[24]  Sylvia Richardson,et al.  A Bayesian calibration model for combining different pre-processing methods in Affymetrix chips , 2008, BMC Bioinformatics.

[25]  Graham K. Rand,et al.  Quantitative Applications in the Social Sciences , 1983 .

[26]  N. Beyler.  Statistical methods for analyzing physical activity data , 2010 .

[27]  J. Holt,et al.  Measuring average alcohol consumption: the impact of including binge drinks in quantity-frequency calculations. , 2006, Addiction.

[28]  Julie McIntyre,et al.  Regression‐assisted deconvolution , 2011, Statistics in medicine.

[29]  Drinking reports from collateral individuals. , 2003, Addiction.

[30]  L. Kraus,et al.  Measuring alcohol consumption and alcohol-related problems: comparison of responses from self-administered questionnaires and telephone interviews. , 2001, Addiction.

[31]  Miles Q. Ott,et al.  Peer associations for substance use and exercise in a college student social network. , 2014, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[32]  Brian Borsari,et al.  Descriptive and injunctive norms in college drinking: a meta-analytic integration. , 2003, Journal of studies on alcohol.

[33]  D. Midthune,et al.  Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. , 2006, Journal of the American Dietetic Association.

[34]  Christine Osborne,et al.  Statistical Calibration: A Review , 1991 .

[35]  J. Baer,et al.  Biases in the perceptions of the consequences of alcohol use among college students. , 1993, Journal of studies on alcohol.

[36]  M. Schwandt,et al.  The biometric measurement of alcohol consumption. , 2012, Alcoholism, clinical and experimental research.