Item Response Theory Analysis to Assess Dimensionality of Substance Use Disorder Abuse and Dependence Symptoms

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Levent Kirisci
Ralph Tarter
Maureen Reynolds
Michael Vanyukov


Background. Item response theory (IRT) based studies conducted on diverse samples showed a single dominant factor for DSM-III-R and DSM-IV substance use disorder (SUD) abuse and dependence symptoms of alcohol, cannabis, sedative, cocaine, stimulants, and opiates use disorders. IRT provides the opportunity, within a person-centered framework, to accurately gauge each person’s severity of disorder that, in turn, informs required intensiveness of treatment. Objectives. The aim of this study was to determine whether the SUD symptoms indicate a unidimensional trait or instead need to be conceptualized and quantified as a multidimensional scale. Methods. The sample was composed of families of adult SUD+ men (n=349), and SUD+ women (n=173), who qualified for DSM-III-R diagnosis of substance use disorder (abuse or dependence) and families of adult men and women who did not qualify for a SUD diagnosis (SUD- men: n=190, SUD- women: n=133). An expanded version of the Structured Clinical Interview for DSM-III-R (SCID) was administered to characterize lifetime and current substance use disorders. Item response theory methodology was used to assess the dimensionality of DSM-III-R SUD abuse and dependence symptoms.Results. A bi-factor model provided the optimal representation of the factor structure of SUD symptoms in males and females. SUD symptoms are scalable as indicators of a single common factor, corresponding to general (non-drug-specific, common) liability to addiction, combined with drug-specific liabilities. Conclusions. IRT methodology used to quantify the continuous general liability to addiction (GLA) latent trait in individuals having SUD symptoms was found effective for accurately measuring SUD severity in men and women. This may be helpful for person-centered medicine approaches to effectively address intensity of treatment.

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Author Biography

Levent Kirisci, Professor of Pharmaceutical Sciences and Psychiatry, University of Pittsburgh; and Co-Investigator and Director of the Statistics Core of the U.S. NIDA-funded Center for Education and Drug Abuse Research.

I received an M.S. degree in applied statistics (1982) and M.A. and Ph.D. degrees (1985 and 1990) in mathematical and applied statistics, respectively.  Following a staff position at the University of Pittsburgh Medical School (1991-1994), I was appointed to the faculty in 1995 as Assistant Professor of Psychiatry in the University of Pittsburgh.  I was promoted to Associate Professor of Pharmaceutical Sciences and Psychiatry in 2003 and Professor in 2009.   I have been a recipient of Independent Scientist Award from U.S. National Institute on Drug Abuse (NIDA). I am also Co-Investigator and Director of the Statistics Core of the U.S. NIDA-funded Center for Education and Drug Abuse Research.  My primary expertise and research focus are devising and evaluating psychometric tools. I have been actively engaged in researching the applications of item response theory methodology and the properties of this methodology. I have spearheaded development of an interval scale to measure the psychological components of SUD liability common across the DSM-IV categories.  Employing item response theory, in conjunction with longitudinal multivariate modeling, a scale termed the liability index has been provisionally validated to quantify SUD risk at ages 10-12, 12-14, 16 and 19 in boys and girls.


. Kirisci L, Vanyukov M, Dunn M, Tarter R: (2002). Item response theory modeling of substance use: An index based on 10 drug categories. Psychology of Addictive Behaviors 16: 290-298.

. Kirisci L, Tarter R, Vanyukov M, Martin C, Mezzich A, Brown S: (2006).Application of item response theory to quantify substance use disorder. Addictive Behaviors 31: 1035-1049.

. Vanyukov M, Kirisci L, Tarter R, Simkevitz HF, Kirillova GP, Maher BS: (2003a). Liability to substance use disorders: 2. A measurement approach. Neuroscience and Biobehavioral Reviews 27: 507-515.

. Vanyukov M, Tarter R, Kirisci L, Kirillova GP, Maher BS, Clark DB: (2003b). Liability to substance use disorders: 1. Common mechanisms and manifestations. Neuroscience and Biobehavioral Reviews 27: 517-526.

. Saha TD, Chou SP, Grant BF: (2006). Toward an alcohol use disorder continuum using item response theory: Results from the national epidemiological Survey on Alcohol and Related Conditions. Psychological Medicine 36: 931-941.

. Krueger RF, Nicho, PE, Hicks BM, Markon KE, Patrick CJ, Iacono WG, McGue M: (2004).Using latent trait modeling to conceptualize an alcohol problems continuum. Psychological Assessment 16: 107-119.

. Hasin DS, Muthen BO, Wisnicci KS, Grant B: (1994). Validity of the bi-axial dependence concept: A test in the US general population. Addiction 89: 573-579.

. Harford TC, Muthen BO: (2001).The Dimensionality of alcohol abuse and dependence: A multivariate analysis of DSM-IV symptoms items in the National Longitudinal Survey of Youth. Journal of Studies on Alcohol 62: 150-157.

. Kahler CW, Strong DR: (2006). A Rasch model analysis of DSM-IV alcohol abuse and dependence items in the national Epidemiological Survey on Alcohol and related Conditions. Alcoholism: Clinical and Experimental Research 30: 1165-1175.

. Hagman BT, Cohn AM: (2011). Toward DSM-V: mapping the alcohol use disorder continuum in college students. Drug and Alcohol Dependence 118: 202-208.

. Gillespie NA, Kendler KS, Neale MC: (2011). Psychometric modeling of cannabis initiation and use and the symptoms of cannabis abuse, dependence and withdrawal in a sample of male and female. Drug and Alcohol dependence 118: 166-172.

. Gillespie NA, Legrand LN, Iacono WG, McGue M, Neale MC: (2012). Are the symptoms of cannabis use disorder best accounted for by dimensional, categorical, or factor mixture models? A comparison of male and female young adults. Psychology of Addictive Behaviors 26: 68-77.

. Langenbucher JW, Labouvie E, Martin CS, Sanjuan PM, Bavly l, Kirisci L, Chung T: (2004). An application of item response theory analysis to alcohol, cannabis, and, cocaine criteria in DSM-IV. Journal of Abnormal Psychology 113: 72-80.

. Nelson CB, Rehm J, Ustun TB, Grant B, Chatterji S: (1999). Factor structures for DSM-IV substance disorder criteria endorsed by alcohol, cannabis, cocaine and opioid users: results from the WHO reliability and validity study. Addiction 94: 843-855.

. Gillespie NA, Neale MC, Prescott CA, Aggen SH, Kendler KS: (2007). Factor and item response analysis DSM-IV criteria for abuse of and dependence on cannabis, cocaine, hallucinogens, sedatives, stimulants and opioids. Addiction 102: 920-930.

. Lynskey MT, Agrawal A: (2007). Psychometric properties of DSM assessments of illicit drug abuse and dependence: results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Psychological Medicine 37: 1345-1355.

. Hasin DS, Fenton MC, Beseler C, Park JY, Wall MM: (2012). Analyses related to the development of DSM-5 criteria for substance use related disorders: 2. Proposed DSM-5 criteria for alcohol, cannabis, cocaine and heroin disorders in 663 substance abuse patients. Drug and Alcohol Dependence 122: 28-37.

. Kerridge BT, Saha TD, Smith S., Chou PS, Pickering RP, Huang B: (2011). Dimensionality of hallucinogen and inhalant/solvent abuse and dependence criteria: Implications for the diagnostic and statistical manual of mental disorders-fifth edition. Addictive Behaviors 36: 912-918.

. Saha TD, Compton WM, Chou SP, Smith S, Ruan WJ, Huang B, Pickering RP, Grant BF: (2012). Analyses related to the development of DSM-5 criteria for substance use related disorders: 1. Toward amphetamine, cocaine and prescription drug use disorder continua using Item Response Theory. Drug and Alcohol Dependence 122: 38-46.

. Hollingshead A: (1975).Four-index of social status. Hillsdale, NJ: Department of Sociology, Yale University.

. Spitzer R, Williams B, Gibbons M, First M: (1990). User’s guide for structured clinical interview for DSM-III-R. New York, NY: New York State Psychiatric Institute.

. Leckman J, Sholomaskas D, Thompson W: (1982). Best estimate of lifetime psychiatric diagnosis: A methodological study. Archives of General Psychiatry 39: 879-883.

. Embretson SE, Reise, SP: (2000). Item Response Theory: Principals and Applications. Boston: Kluwer-Nijhoff Publishing.

. Hambleton RK, Swaminathan H, Rogers HJ: (1991). Fundamentals of Item Response Theory. Newsburry Park: Sage..

. Van der Linden WJ, Hambleton PK: (1997). Handbook of Modern Item Response Theory. New York: Springer.

. Stout W, Habing B, Dougles J, Kim HR, Rousses L, Zhang J: (1996). Conditional covariance-based nonparametric multidimensionality assessment. Applied Psychological Measurement 20: 331-354.

. Lord FM: (1980). Application of Item Response Theory to Practical Testing Problems. Hillsdale, NJ: Lawrence Erlbaum.

. Samejima F: (1977). A use of the information function in tailored testing. Applied Psychological Measurement 1: 233-247.

. Muthen LK, Muthen BO: (1998-2015).Mplus user’s guide, version 7, computer program). Los Angeles, CA: Muthen & Muthen.

. Cai L, du Toit S. H. C, Thissen D: (2011). IRTPRO: Flexible, multidimensional, multi categorical

a. IRT modeling (Version 2.1) [Computer software]. Skokie, IL: Scientific Software International.

. Akaike H: (1987). Factor analysis and AIC. Psychometrika 52: 317-332.

. Schwarz G: (1978). Estimating the dimension of a model. Annals of Statistics 6: 461-464.

. Nylund KL, Asparouhov T, Muthen B: (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling, a Monte Carlo simulation study. Structure Equation Modeling 14: 535-569.

. Reckase MD: (1979). Unifactor latent trait models applied to multifactor tests: Results and implications. Journal of Educational Statistics 4: 207-230.

. Reise SP, Morizot J, Hayes RD: (2007). The role of the bi-factor model in resolving dimensionality issues in health outcomes measures. Quality Life Research 16: 19-31.

. Vanyukov MM, Kirisci L, Moss L, Tarter RE, Reynolds MD, Maher BS, Kirillova GP, Ridenour T, Clark DB: (2009). Measurement of the risk for substance use disorders: phenotypic and genetic analysis of an index of common liability. Behav Genet. 39(3):233-244.

. Vanyukov M, Kim K, Irons D, Kirisci L, Neale M, Ridenour T, Hicks B, Tarter R, Reynolds M, Kirillova G, McGue M, Iacono W: (2015). Genetic relationship between the addiction diagnosis in adults and their childhood measure of addiction liability. Behav Genet. 45(1):1-11.

. Kirisci L, Tarter R, Mezzich A, Ridenour T, Reynolds M, Vanyukov M: (2009). Prediction of cannabis use disorder between boyhood and young adulthood: Clarifying the phenotype and environtype. The American Journal on Addictions 18: 36-47.

. Tsuang MT, Lyons MJ, Meyer JM, Doyle T, Eisen SA, Goldberg J, True W, Lin N, Toomey R, Eaves L: (1998). Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Arch Gen Psychiatry 55(11):967-972.

. Kendler KS, Jacobson KC, Prescott CA, Neale MC: (2003). Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry 160(4):687-695.

. Kendler KS, Myers J, Prescott CA: (2007). Specificity of genetic and environmental risk factors for symptoms of cannabis, cocaine, alcohol, caffeine, and nicotine dependence. Arch Gen Psychiatry 64(11):1313-1320.

. Kendler KS, Prescott CA, Myers J, Neale MC: (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Arch Gen Psychiatry 60(9):929-937.

. Haberstick BC, Zeiger JS, Corley RP, Hopfer CJ, Stallings MC, Rhee SH, Hewitt JK: (2011). Common and drug-specific genetic influences on subjective effects to alcohol, tobacco and marijuana use. Addiction 106(1):215-224.

. Palmer RH, Button TM, Rhee SH, Corley RP, Young SE, Stallings MC, Hopfer CJ, Hewitt JK: (2012). Genetic etiology of the common liability to drug dependence: evidence of common and specific mechanisms for DSM-IV dependence symptoms. Drug Alcohol Depend 123 Suppl 1:S24-32.

. Clark SL, Gillespie NA, Adkins DE, Kendler KS, Neale MC: (2016). Psychometric modeling of abuse and dependence symptoms across six illicit substances indicates novel dimensions of misuse. Addict Behav 53:132-40.