Advances In Measurement Modeling: Bringing Genetic Information Into Preventive Interventions And Getting The Phenotype Right

Transcription

1 Advances In Measurement Modeling: Bringing Genetic Information Into Preventive Interventions And Getting The Phenotype Right Bengt Muthen, UCLA 1

2 Modeling The Influence On A Person s Behavior Source of Influence Psychometric Sophistication (Measurement Modeling) Environment Genes High (Latent variable and multilevel models) Low (Phenotype is a latent variable) Genes x Environment Low 2

3 Limitations Of Conventional Analyses For Diagnosis And Genetic Analysis Substantively-Based Approach: x out of y criteria fulfilled (categorical), sum of criteria (dimensional) Limited support from data analysis Assumes unidimensionality and relevance of equal weighting Categorical Analysis Approach: Latent Class, Latent Transition Analysis Ignores continuous within-category heterogeneity Lower power for genetic linkage analysis Dimensional Analysis Approach: Factor Analysis, Growth Modeling No model-derived classification Difficulty choosing cut points Ignores heterogeneity in the form of subtypes 3

4 Genetic Modeling Genetic information by design Example: Twin analysis Genetic information by DNA Example: QTL (Quantitative Trait Locus) linkage and association analysis using pair-specific information on alleles shared Identical By Descent 4

5 y1 y2 a c e a c e A1 C1 E1 A2 C2 E2 5

6 Phenotype As A Latent Variable Categorical latent variable: Latent Class Analysis Continuous latent variable: Factor Analysis Hybrids 6

7 Latent Class Analysis a. Item Profiles b. Model Diagram 1.0 Item Probability inatt1 inatt2 hyper1 hyper Class 1 Class Class 3 c Class 4 inatt1 inatt2 hyper1 hyper2 Item x 7

8 Examples Of LCA Applications To Health Schizophrenia: Nestadt et al (1994) Alcohol: Bucholz et al (1996), Muthén (2001) Aging (physical disability): Bandeen-Roche et al (1997) Antisocial behavior: Muthén & Muthén (2000) Cancer tumors: Albert et al (2001) ADHD: Rasmussen et al (2002) 8

9 Factor Analysis (IRT, Latent Trait) a. Item Response Curves b. Model Diagram Item Probability Item 1 Item 2 Item 3 Item 4 item1 item2 item3 item f Factor (f) x 9

10 DSM-IV Criteria In A National Sample Of 13,067 Male Current Drinkers Alcohol Dependence Tolerance Withdrawal Drinking in larger amounts over a longer period of time than intended Persistent desire or unsuccessful efforts to cut down or control drinking Great deal of time spent in activities to obtain alcohol, to drink, or to recover from its effects Important social, occupational, or recreational activities given up or reduced in favor of drinking Continued to drink despite knowledge of having a persistent or recurrent physical or psychological problem caused or exacerbated by drinking 10

11 DSM-IV Criteria In A National Sample Of 13,067 Male Current Drinkers (Continued) Alcohol Abuse Recurrent drinking resulting in failure to fulfill major role obligations at work, school, or home Recurrent drinking in situations where alcohol use is physically hazardous Recurrent alcohol-related legal problems Continued drinking despite persistent or recurrent social or interpersonal problems caused or exacerbated by drinking 11

12 Item Profiles: Latent Class Analysis (4 Classes) Class 1, 1.1% Class 2, 76.8% Class 3, 17.4% Class 4, 4.7% Item Probability Toler Withdraw Larger Cutdown TimeSpent GivenUp Criteria PhpsProblem MajorRole Hazard Legal SoinProblem 12

13 Model Testing For 11 Alcohol Criteria Male Current Drinkers logl #par s BIC Latent class analysis: 4 classes Factor analysis: -24, ,424 1 dimension -25, ,274 13

14 Factor Mixture Analysis a. Cluster Types b. Model Diagram for FMA FMA Item j item1 item2 item3 item4 Item j LCA Item k c f Generalized latent class and factor analysis Categories and dimensions Item k 14

15 Model Testing for 11 Alcohol Criteria Latent class analysis: 4 classes Factor analysis: 1 dimension logl -24,989-25,032 #par s BIC 50,424 50,274 Factor mixture analysis: 3 classes, 1 dimension -24, ,396 7% (highest overall), 2% (high Cutdown), 90% (low) 15

16 Item Profiles: Factor Mixture Analysis 16 Toler Withdraw Larger Cut down Time spent Given up Phps problem Major role Hazard Legal SocInt problem Class 1, 2.5% Class 2, 7.4% Class 3, 90.1% Criteria Item Probability

17 Conclusion I: Psychometric Modeling New types of measurement models need to be applied to research on Environment Genes Genes x Environment 17

18 Conclusion II: Design Implications for Preventive Interventions (Randomized, Longitudinal Studies) Different aims: Candidate gene DNA data from parents (and siblings) Phenotype data from parents (and siblings) Better measures of environment interacting with genes Genome-wide SNP array (Affymetrix 500K chip) 18