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Int J Pers Cent Med. Author manuscript; available in PMC 2017 September 20. Published in final edited form as: Int J Pers Cent Med. 2016 ; 6(4): 260–273.
Item Response Theory Analysis to Assess Dimensionality of Substance Use Disorder Abuse and Dependence Symptoms Levent Kirisci, PhDa,b, Ralph E. Tarter, PhDa,b, Maureen Reynolds, PhDa, and Michael M. Vanyukov, PhDa,b,c aDepartment
of Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA
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bDepartment
of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
cDepartment
of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
Abstract 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.
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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 DSMIII-R SUD abuse and dependence symptoms.
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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.
Correspondence Address: Levent Kirisci, Ph.D., 807 Salk Hall, University of Pittsburgh, PA, 15261, USA; [email protected]. Disclosures: The authors declare no conflict of interest.
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Keywords item response theory; bi-factor model; substance use disorder; factor analysis; dimensionality
1. Introduction Substance use disorder (SUD) is the most important clinical phenotype in drug abuse research. Although diagnosis is dichotomous (present/absent), there are many advantages to quantifying SUD on a continuous scale[1] reflecting severity of the disorder. Typically, SUD severity is scaled as the sum of symptoms or diagnoses[2], even though this method yields biased results because liabilities to various drug-specific disorders substantially share variance, termed common (general) addiction liability[3,4] and these disorders are frequently comorbid.
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Item response theory (IRT) has been applied in recent years to address shortcomings associated with summing the number of substance types tried[1] and diagnoses[2]. Studies have been conducted on diverse samples, including the National Epidemiological Survey on Alcohol and Related Conditions[5], the Minnesota Family Twin project[6], the US general population[7], and National Longitudinal Study of Labor Market Experience in Youth[8]. The results of these studies reveal a dominant single factor for alcohol use disorder[7,9,10] and other specific disorders[11,12], as well as a unidimensional factor in patients receiving addictions treatment with co-occurring SUDs[13], and in an international sample in the WHO study of the DSM-IV substance use disorder criteria[14].
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IRT analyses additionally reveal that a unidimensional factor structure fits DSM-IV drug use, abuse, and dependence criteria for cannabis, sedatives, stimulants, cocaine and opiates[15,16]. A unidimensional factor structure has been observed in studies applying IRT models to DSM-V criteria[17,18,19] A main advantage of IRT methodology is the ability to simultaneously account for the individual phenotype and the statistical properties of each symptom (item parameters) comprising the diagnostic criteria. Accordingly, this methodology 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.
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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 multidimensional scale.
3. Method 3.1. Participants The participants in this study were part of a longitudinal research study examining the etiology of SUD in families, known as the Center for Education and Drug Abuse Research
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(CEDAR) funded by U.S. National Institute on Drug Abuse. The sample was composed of families of adult men (SUD+, n=349), who were the probands, and women (SUD+, n=173), their spouses/mates, 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). To be included in the study, at least one SUD symptom had to be present. The men and women were also required to have an IQ of 80 or higher, good health, and no lifetime psychosis.
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The proband men were identified using random digit telephone calls, newspaper and radio advertisements and public service announcements. Approximately 25% of the sample of affected (SUD+) men were identified following discharge from substance abuse treatment. The mean age of the men was 40 years. European–Americans comprised 77% and AfricanAmericans 22% of the sample. Mean family socioeconomic status according to Hollingshead criteria was 40, indicating that this sample was primarily middle class[20]. The mean age of the women was 38 years. The DSM-III-R taxonomy was employed because this research was initiated prior to publication of the DSM-IV manual. Table 1 presents the lifetime SUD diagnoses in the fathers and mothers. The most common SUD diagnoses are alcohol-, cannabis-, and cocaine-related in both males and females. 3.2. Structured Clinical Interview for DSM-III-R
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An expanded version of the Structured Clinical Interview for DSM-III-R (SCID)[21]was administered to characterize lifetime and current substance use disorders. Diagnoses were formulated during a clinical conference chaired by a psychiatrist certified in addiction psychiatry and attended by another psychiatrist or a psychologist, along with the clinical associates who conducted the interviews. The best estimate procedure was used to formulate the diagnoses[22]. In this procedure, the results of the diagnostic interview, along with medical, legal, and social history information obtained from other facets of the research protocol and official records, were considered in aggregate when formulating the diagnoses. In this study, abuse and dependence symptoms (N=11) related to lifetime use of alcohol, cannabis, cocaine, opioids, sedatives, and stimulants were used in the analyses. The abuse symptoms are: 1) failure to fulfill major role obligations at work, school, or home, 2) hazardous use, 3) legal problems, and 4) continued substance use despite having persistent or recurrent social or interpersonal problems, The dependence symptoms are: 1) tolerance, 2) withdrawal, 3) consumption of larger amounts or over a longer period than intended, 4) unsuccessful efforts to cut down, and 5) a great deal of time spent taking/using the substance, 6) reduced social, occupational and recreational activities, and 7) persisting consumption despite physical or psychological problems.
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3.3. Procedure Written informed consent was obtained from the participants prior to administering the protocols. All of the study participants were additionally informed that the findings from this research were protected by a Certificate of Confidentiality issued to the Center for Education and Drug Abuse Research (CEDAR) by the U.S. National Institute on Drug Abuse.
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3.4. Statistical Analysis
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First, exploratory factor analysis was conducted to estimate the variance explained by the first, second and the rest of the sizable factors. Followed by confirmatory factor analysis which tested whether the SUD abuse and dependence symptoms formed a unidimensional or multidimensional models. After showing the existence of the unidimensional general factor, item response theory (IRT) was employed to document the psychometric properties of the SUD symptoms and to derive the latent SUD severity trait. The advantages of IRT compared to traditional (or classical) psychometric approaches are well documented[23,24,25]. In particular, classical psychometric theory, although widely used in constructing and evaluating scales for measuring alcohol and drug use, is limited inasmuch as subject characteristics and scale characteristics are correlated and cannot be separated. Item threshold and item discrimination parameters as well as reliability and validity of a scale must also be interpreted in the context of a particular sample. Hence, scale and item parameters vary across samples. This limitation of classical measurement theory is referred to as group dependency. In addition, in classical measurement theory, reliability is quantified as the correlation between test scores on parallel scales, which is constant across the continuum of the trait. In contrast, IRT provides a reliability score for each trait level. Moreover, IRT informs on the relationship between responses to sets of items (endorsement/ nonendorsement of SUD symptoms) and the person's latent trait value (SUD severity phenotype). Contingent on satisfactory model data fit, the information obtained from IRT analyses enables, therefore, documenting SUD severity across the gradient of latent trait severity scores while taking into account the difference between items in capacity to be discriminative across the spectrum of trait level. A two-parameter (intercept and discrimination) IRT bi-factor logistic model, used in this study, is described as
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where θ is a continuous variable (latent SUD severity trait), Pi(θj) is the probability the subject endorses an SUD symptom, ai is the item discrimination parameter, di is the item intercept parameter, n is the number of subjects, p is the number of dimensions, and D=1.7 is the scaling constant used to approximate the logistic model to the normal ogive model. The probability of endorsing an SUD symptom item is related to the SUD severity scale as a monotonically increasing S-shaped item response function (IRF). The item discrimination parameter (a) is proportional to the slope of the item response function (IRF) at this trait value; that is, the rate at which the probability of endorsement of a symptom changes as the latent trait score increases at the point of inflection. Higher item discrimination values are associated with steeper IRFs. In other words, higher discrimination parameters indicate a stronger relationship between SUD severity and observed symptom. A symptom with a low value of the item discrimination parameter would result in an IRF that increases gradually as a function of SUD severity. The item threshold parameter (or item difficulty), which is defined as the of ratio of negative item intercept to item discrimination parameter, -di/aj, determines the position of the curve along the latent trait. The position of
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the curve on the SUD severity scale corresponds to the severity of the symptom. A higher threshold parameter indicates that fewer subjects endorse a particular symptom. In other words, a higher trait value (higher score on the continuum of the SUD severity scale) is required for the person to endorse the particular symptom; hence, individuals whose scores fall on the right of the scale are more severe cases compared to individuals whose scores are on the left of the scale.
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3.4.1. Item response model assumptions—Two testable assumptions need to be satisfied when applying IRT models. The unidimensionality assumption implies that only one latent trait is measured by the items used in developing the scale, i.e., the probability of endorsing a diagnosis is a function of only one latent trait. If the residual covariances among items are small (rather than zero) at a given trait level, it is possible that there exists a dominant factor to be measured by a set of items, even though other factors may be present, corresponding to essential unidimensionality[26]. The second assumption is that no relationship is present between the subject's responses to different items (or symptoms) after taking into account the subject's latent trait level. This is referred to as the local independence assumption[27]. Unidimensionality is a sufficient condition for satisfying the local independence assumption. 3.4.2. IRT-based reliability coefficient—We used the following formula[28], to estimate IRT-based reliability of the SUD severity index:
where
is the
observed scale score variance, and is the average measurement error of variance across the levels of the latent trait of SUD severity. In addition, an average IRT-based reliability for the entire trait is provided.
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3.4.3. Model comparisons—We used Mplus[29] to assess the dimensionality of the SUD symptoms. The intercept and discrimination parameters were estimated using IRTPRO[30]. This procedure utilizes the marginal maximum likelihood method to calibrate items, and the Bayesian expected a posteriori method to estimate latent trait scores. Model fit indices suggest a good fit if root mean square error of approximation (RMSEA) is less than .05, comparative fit index (CFI) is greater than .95, and/or Tucker-Lewis Index (TLI) is greater than .95[29]. In addition, -2log likelihood (-2logL), Akaike Information criterion (AIC)[31], Bayesian Information Criterion (BIC), and sample size-corrected BIC[32] are computed to determine the best fitting model. For AIC and BIC, a lower value indicates better model-data fit. Although sample size-corrected BIC outperforms all other indices[33], we also present AIC and BIC.
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3.5. Dimensionality of SUD abuse and dependence symptoms Four models representing the possible structure of the DSM-III-R SUD abuse and dependence symptoms were tested in this study: As presented in Figure 1, Model A assumes a unidimensional structure, whereas the other models have multidimensional structure. Model A assumes that substance use severity corresponds to a single factor that accounts for the covariance among symptoms. Model B assumes more than one uncorrelated common SUD severity factor encompassing alcohol, cannabis, cocaine opioids, sedative and stimulants. Model C assumes one common factor for each drug category, thus yielding six Int J Pers Cent Med. Author manuscript; available in PMC 2017 September 20.
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correlated factors. Model D assumes a bi-factor model, that is, a general factor explains the symptom intercorrelations while drug-specific group factors capture the covariation among symptoms of each drug category.
4 Results 4.1. Endorsement of abuse and dependence SUD symptoms
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Table 2 provides the frequencies of endorsements and corrected item-test correlations of the abuse and dependence symptoms for each SUD category. The most frequently endorsed dependence symptoms are “tolerance” for alcohol, “a great deal time spent taking and using it” for cannabis, and “need for larger amounts or over a longer period than intended” for cocaine in both men and women. The most frequently endorsed abuse items are “hazardous use” for alcohol and cannabis and “social and interpersonal problems” for cocaine in men, whereas “social and interpersonal problems” related to alcohol and cocaine use, and “failure to fulfill major role obligations” related to cannabis use, were most prominent in women. The majority of abuse and dependence symptoms have item-test correlations exceeding the . 2 threshold, indicating a significant item contribution to the severity scale. 4.2. Exploratory factor analysis Principal component analysis was conducted to explore the factor structure of the SUD abuse and dependence symptoms. The first 6 factors explained 20%, 12%, 10%, 8%, 6%, 4%, 2% of the variance in men, and 27%, 15%, 8%, 7%, 5%, 2% in women. The first factor explains 20% or higher of the variance in males and females suggests a unidimensional factor structure of SUD abuse and dependence symptoms in both sexes[34].
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4.3. Model comparisons
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Model data fit statistics are presented in Table 3. Model C (6 correlated substance-specific factors) had the best fit statistics in both sexes. Model D (the bi-factor model), assuming six substance-specific factors and one common factor had the second best model-data fit indices. However, the difference between χ2 values of two models was not significant in males and females. We chose the bi-factor model, because the significant and modest correlations between all six substance-specific classes (males: mean correlation=.33, sd=.17; females: mean correlation=.29, sd=.21) suggested the possibility of a general factor[35] and reflected the common variance among them, corresponding to general (common; non-drugspecific) liability to addiction. Furthermore, the difference between factor loadings of general factor of unidimensional and bi-factor models indicates that the bi-factor model recovers estimates more accurately than the unidimensional model[35], because forcing a unidimensional model to a multidimensional model results in parameter estimates which are highly unstable and inaccurate. The results also suggest that substance-specific class factors explain symptom covariation that is independent of the covariation due to the general factor. 4.3.1. Model parameters—The item discrimination and item intercept parameters of all 66 symptoms along with the factor loadings of the bi-factor model for the common factor and substance specific factors are presented in Table 4a, b and Table 5a, b, respectively. All symptoms indicating the global factor have high ability to discriminate between trait values
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(severity) in both males and females. The item threshold determines the location of a symptom on the SUD severity scale. The order of symptoms according to their severity in men and women is similar, except for stimulant drugs; however, the probability of endorsing any symptom is more likely in men than for women. Alcohol use disorder symptoms are the most likely ones to be endorsed in both males and females. Tables 5a and 5b present factor loadings for the bi-factor model in males and females. All symptoms load high on the global factor and substance-specific group factors. The factor loadings of the 66 symptoms on the global factor in the bi-factor model are all higher than .4 in males and, with few exceptions, in females. The discriminative power of 66 symptoms in a common factor varies; however, it does not vary within each substance category. All symptoms indicating the general liability factor have high ability to discriminate between the trait values in males and females.
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The correlation between the IRT-derived global score and observed score (sum of symptoms) is very high (r=.90), In addition, the severity score has a standard normal distribution with mean 0 and variance 1. Thus, it is well suited to clinical research requiring accurate measurement of individual differences as the conceptual foundation for personcentered medicine. 4.3.2. Aggregate criterion information function—The aggregate information function and standard error of measurement of SUD severity are presented in Figure 2. The
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standard error and information function are related: . A scale score was derived to constitute a continuum spanning -3 to +3 anchoring the least and the most severe scores on the scale. As can be seen in Figure 2, the SUD severity score in males has better precision at moderate to high levels of SUD severity (reaching its lowest level at half a standard deviation above the mean) whereas females are measured more precisely in the low to moderate range of SUD severity (reaching its lowest level at half a standard deviation below the mean). In other words, the SUD severity scale comprised of 66 symptoms is more discriminating at a half standard deviation above the mean in men and a half standard deviation below the mean in women. Concomitantly, the IRT-based reliability coefficient is in the good range for both men (ρ=95) and women (ρ=87).
5 Discussion
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This research complements and extends previous studies on the quantification of severity of substance use encompassing the various substance categories[1] and severity of SUD diagnoses[2], as well as general liability to addiction in the absence of SUD symptoms (in children) [3,4,36,37,38]. A bi-factor model provides the most 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. These data are consistent with the body of literature on both common mechanisms and manifestations of liabilities to drug-specific addictions (rev. in [4]) and the relationships among liabilities to drug-specific SUDs. In particular, despite the differences between the
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chemical classes, mechanisms of biotransformation and neurobiological response of various abused substances, and routes of administration, these liabilities have been shown to significantly covary [39,40], thus indicating common sources of variation. Moreover, these sources are to a large degree genetic, with liabilities to disorders related to licit and illicit substances forming two genetically distinct if correlated classes [41], and no substancespecific genetic variance estimated [42]. In addition to these consistently observed common sources of variance, drug-specific variance components are also unsurprisingly present [40,41,43,44]. Recently, however, a paper by Clark et al [45], modeling the relationships among the symptoms of abuse and dependence for illicit substances in confirmatory factor analysis asserted the lack of evidence for general addiction liability. It is possible that their conclusion, which is based purely on the statistics of model fit, is due to omitting the model that is most consistent with the psychopharmacological, neurobiological, phenogenetic and genetic association data – namely, with the presence of both general liability and drugspecific liabilities, i.e., the bifactor model. Indeed, the only model in their study that included general liability, thus taking into account correlations between the symptoms for different drugs, was the unifactor model. All the other models tested in the Clark et al. work specified correlations between the factors accounting for covariation of the drug-specific symptoms, which, to be sure, amounts to common variance between drug-specific groups of symptoms that corresponds to general liability, but those correlations were not presented or discussed.
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Several limitations of this study should be noted. In particular, a random sampling strategy was not employed; hence, the possibility of sampling bias cannot be ruled out. In addition, it is possible that the pattern and rate of SUD observed in women are, in part, due to the influence of homogamy, including the possibility of contagion [46]. These limitations notwithstanding, the present study demonstrated that applying IRT methodology to estimate SUD severity is heuristic for understanding variation within and between different populations, including cross-cultural investigations. One of the most essential properties of IRT methodology is sample independence; that is, probability of endorsing a symptom is the same in males and females at the same SUD severity level, a high correlation between item discrimination parameters in males and females and a high correlation between item intercept parameters (above r=.40) of the global factor in males and females confirm sample independence.
6. Conclusions
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The variation in the DSM-III-R symptoms of substance use disorders (SUD) was shown to have both substance-specific components and the common component corresponding to general liability to addiction (GLA). Item response theory (IRT) methodology was used to quantify the continuous latent GLA trait in individuals having SUD symptoms, corresponding to SUD severity. The results indicate that this methodology is effective for accurately measuring SUD severity in men and women, which is essential for the personcentered medicine to effectively decide the intensity of treatment. Accordingly, it is both feasible and practical to augment the current diagnostic procedures by the more precise quantitative measurement useful for understanding individual differences in an unbiased fashion. Int J Pers Cent Med. Author manuscript; available in PMC 2017 September 20.
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Acknowledgments This research received support from the US National Institute on Drug Abuse, Grant # P50 DA005605 and K02 DA018701.
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39. Tsuang MT, Lyons MJ, Meyer JM, Doyle T, Eisen SA, Goldberg J, True W, Lin N, Toomey R, Eaves L. Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Arch Gen Psychiatry. 1998; 55(11):967–972. [PubMed: 9819064] 40. Kendler KS, Jacobson KC, Prescott CA, Neale MC. 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. 2003; 160(4):687–695. [PubMed: 12668357] 41. Kendler KS, Myers J, Prescott CA. Specificity of genetic and environmental risk factors for symptoms of cannabis, cocaine, alcohol, caffeine, and nicotine dependence. Arch Gen Psychiatry. 2007; 64(11):1313–1320. [PubMed: 17984400] 42. Kendler KS, Prescott CA, Myers J, Neale MC. The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Arch Gen Psychiatry. 2003; 60(9):929–937. [PubMed: 12963675] 43. Haberstick BC, Zeiger JS, Corley RP, Hopfer CJ, Stallings MC, Rhee SH, Hewitt JK. Common and drug-specific genetic influences on subjective effects to alcohol, tobacco and marijuana use. Addiction. 2011; 106(1):215–224. [PubMed: 20955487] 44. Palmer RH, Button TM, Rhee SH, Corley RP, Young SE, Stallings MC, Hopfer CJ, Hewitt JK. Genetic etiology of the common liability to drug dependence: evidence of common and specific mechanisms for DSM-IV dependence symptoms. Drug Alcohol Depend. 2012; 123(1):S24–32. [PubMed: 22243758] 45. Clark SL, Gillespie NA, Adkins DE, Kendler KS, Neale MC. Psychometric modeling of abuse and dependence symptoms across six illicit substances indicates novel dimensions of misuse. Addict Behav. 2016; 53:132–40. [PubMed: 26517709] 46. Vanyukov M, Neale MC, Moss HB, Tarter R. Mating assortment and the liability to substance abuse. Drug and Alcohol Dependence. 1996; 42:1–10. [PubMed: 8889398]
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Figure 1. Four models tested
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Figure 2. Aggregate Information Function and Standard Error of Measurement
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Table 1
Lifetime SUD diagnoses in men and women
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Men (N=539)
Women (N=306)
Alcohol
52%
43%
Cannabis
41%
21%
Cocaine
30%
16%
Opioids
15%
12%
Sedative
5%
5%
Stimulants
8%
19%
Any SUD
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Author Manuscript Table 2
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34, .64
26, .56
31, .62
20, .59
25, .60
27, .59
46, .54
20, .51
39, .64
D3
D4
D5
D6
D7
A1
A2
A3
A4
17, .43
16, .37
32, .26
20, .36
10, .22
10, .36
22, .38
11, .32
19, .34
10, .30
25, .62
8, .35
18, .50
17, .55
14, .55
19, .55
23, .57
22, .55
25, .56
11, .47
11, .45
11, .40
7, .34
7, .34
6, .34
8, .44
12, .43
11, .37
11, .40
12, .40
10, .40
3, .35
3, .34
4, .32
2, .31
3, .32
2, .32
3, .37
2, .26
3, .31
3, .30
3, .29
4, .22
3, .20
7, .20
3, .19
5, .18
2, .16
5, .19
3, .19
5, .17
5, .24
4, .16
34, .48
8, .42
32, .34
25, .50
30, .43
18, .60
24, .53
20, .53
29, .49
12, .34
32, .57
9, .17
3, .05
10, .14
12, .18
8, .11
9, .25
11, .30
8, .20
10, .27
10, .18
6, .25
17, .76
6, .48
7, .64
14, .72
13, .70
13, .73
16, .77
17, .75
17, .75
10, .63
14, .73
Cocaine %, r
5, .29
4, .28
3, .26
3, .21
3, .30
3, .32
6, .38
4, .38
5, .37
6, .37
5, .33
Opioids %, r
2, .18
2, .10
2, .17
3, .22
4, .25
2, .19
1, .19
3, .20
3, .26
4, .22
4, .23
Sedative %, r
Note: Dependence symptoms. D1: Tolerance, D2: Withdrawal, D3: Need for larger amounts or over a longer period than intended, D4: Unsuccessful efforts to cut down, D5: A great deal of time spent taking/using it, D6: Reduced social, occupational and recreational activities, D7: Substance use is continued despite physical or psychological problems. Abuse Symptoms. A1: Failure to fulfill major role obligations at work, school, or home, A2: Hazardous use, A3: Substance use related legal problems, A4: Continued substance use despite having persistent or recurrent social or interpersonal problems.
15, .47
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D2
20, .52
15, .31
36, .62
Cannabis %, r
D1
Stimulants %, r
Alcohol %, r
Sedative %, r
Females (N=306) Opioids %, r
Alcohol %, r
Cocaine %, r
Cannabis %, r
Males (N=539)
Symptoms
17, .76
12, .65
8, .66
11, .64
16, .60
13, .74
17, .78
18, .76
18, .76
17, .75
15, .72
Stimulants %, r
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Percent of endorsement and corrected item-total test correlations (r) of lifetime DSM-III-R SUD dependence and abuse symptoms in males and females
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Author Manuscript 19150.58 132 4051.37 2079
Int J Pers Cent Med. Author manuscript; available in PMC 2017 September 20. Published in final edited form as: Int J Pers Cent Med. 2016 ; 6(4): 260–273.
Item Response Theory Analysis to Assess Dimensionality of Substance Use Disorder Abuse and Dependence Symptoms Levent Kirisci, PhDa,b, Ralph E. Tarter, PhDa,b, Maureen Reynolds, PhDa, and Michael M. Vanyukov, PhDa,b,c aDepartment
of Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA
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bDepartment
of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
cDepartment
of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
Abstract 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.
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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 DSMIII-R SUD abuse and dependence symptoms.
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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.
Correspondence Address: Levent Kirisci, Ph.D., 807 Salk Hall, University of Pittsburgh, PA, 15261, USA; [email protected]. Disclosures: The authors declare no conflict of interest.
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Keywords item response theory; bi-factor model; substance use disorder; factor analysis; dimensionality
1. Introduction Substance use disorder (SUD) is the most important clinical phenotype in drug abuse research. Although diagnosis is dichotomous (present/absent), there are many advantages to quantifying SUD on a continuous scale[1] reflecting severity of the disorder. Typically, SUD severity is scaled as the sum of symptoms or diagnoses[2], even though this method yields biased results because liabilities to various drug-specific disorders substantially share variance, termed common (general) addiction liability[3,4] and these disorders are frequently comorbid.
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Item response theory (IRT) has been applied in recent years to address shortcomings associated with summing the number of substance types tried[1] and diagnoses[2]. Studies have been conducted on diverse samples, including the National Epidemiological Survey on Alcohol and Related Conditions[5], the Minnesota Family Twin project[6], the US general population[7], and National Longitudinal Study of Labor Market Experience in Youth[8]. The results of these studies reveal a dominant single factor for alcohol use disorder[7,9,10] and other specific disorders[11,12], as well as a unidimensional factor in patients receiving addictions treatment with co-occurring SUDs[13], and in an international sample in the WHO study of the DSM-IV substance use disorder criteria[14].
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IRT analyses additionally reveal that a unidimensional factor structure fits DSM-IV drug use, abuse, and dependence criteria for cannabis, sedatives, stimulants, cocaine and opiates[15,16]. A unidimensional factor structure has been observed in studies applying IRT models to DSM-V criteria[17,18,19] A main advantage of IRT methodology is the ability to simultaneously account for the individual phenotype and the statistical properties of each symptom (item parameters) comprising the diagnostic criteria. Accordingly, this methodology 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.
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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 multidimensional scale.
3. Method 3.1. Participants The participants in this study were part of a longitudinal research study examining the etiology of SUD in families, known as the Center for Education and Drug Abuse Research
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(CEDAR) funded by U.S. National Institute on Drug Abuse. The sample was composed of families of adult men (SUD+, n=349), who were the probands, and women (SUD+, n=173), their spouses/mates, 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). To be included in the study, at least one SUD symptom had to be present. The men and women were also required to have an IQ of 80 or higher, good health, and no lifetime psychosis.
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The proband men were identified using random digit telephone calls, newspaper and radio advertisements and public service announcements. Approximately 25% of the sample of affected (SUD+) men were identified following discharge from substance abuse treatment. The mean age of the men was 40 years. European–Americans comprised 77% and AfricanAmericans 22% of the sample. Mean family socioeconomic status according to Hollingshead criteria was 40, indicating that this sample was primarily middle class[20]. The mean age of the women was 38 years. The DSM-III-R taxonomy was employed because this research was initiated prior to publication of the DSM-IV manual. Table 1 presents the lifetime SUD diagnoses in the fathers and mothers. The most common SUD diagnoses are alcohol-, cannabis-, and cocaine-related in both males and females. 3.2. Structured Clinical Interview for DSM-III-R
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An expanded version of the Structured Clinical Interview for DSM-III-R (SCID)[21]was administered to characterize lifetime and current substance use disorders. Diagnoses were formulated during a clinical conference chaired by a psychiatrist certified in addiction psychiatry and attended by another psychiatrist or a psychologist, along with the clinical associates who conducted the interviews. The best estimate procedure was used to formulate the diagnoses[22]. In this procedure, the results of the diagnostic interview, along with medical, legal, and social history information obtained from other facets of the research protocol and official records, were considered in aggregate when formulating the diagnoses. In this study, abuse and dependence symptoms (N=11) related to lifetime use of alcohol, cannabis, cocaine, opioids, sedatives, and stimulants were used in the analyses. The abuse symptoms are: 1) failure to fulfill major role obligations at work, school, or home, 2) hazardous use, 3) legal problems, and 4) continued substance use despite having persistent or recurrent social or interpersonal problems, The dependence symptoms are: 1) tolerance, 2) withdrawal, 3) consumption of larger amounts or over a longer period than intended, 4) unsuccessful efforts to cut down, and 5) a great deal of time spent taking/using the substance, 6) reduced social, occupational and recreational activities, and 7) persisting consumption despite physical or psychological problems.
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3.3. Procedure Written informed consent was obtained from the participants prior to administering the protocols. All of the study participants were additionally informed that the findings from this research were protected by a Certificate of Confidentiality issued to the Center for Education and Drug Abuse Research (CEDAR) by the U.S. National Institute on Drug Abuse.
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3.4. Statistical Analysis
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First, exploratory factor analysis was conducted to estimate the variance explained by the first, second and the rest of the sizable factors. Followed by confirmatory factor analysis which tested whether the SUD abuse and dependence symptoms formed a unidimensional or multidimensional models. After showing the existence of the unidimensional general factor, item response theory (IRT) was employed to document the psychometric properties of the SUD symptoms and to derive the latent SUD severity trait. The advantages of IRT compared to traditional (or classical) psychometric approaches are well documented[23,24,25]. In particular, classical psychometric theory, although widely used in constructing and evaluating scales for measuring alcohol and drug use, is limited inasmuch as subject characteristics and scale characteristics are correlated and cannot be separated. Item threshold and item discrimination parameters as well as reliability and validity of a scale must also be interpreted in the context of a particular sample. Hence, scale and item parameters vary across samples. This limitation of classical measurement theory is referred to as group dependency. In addition, in classical measurement theory, reliability is quantified as the correlation between test scores on parallel scales, which is constant across the continuum of the trait. In contrast, IRT provides a reliability score for each trait level. Moreover, IRT informs on the relationship between responses to sets of items (endorsement/ nonendorsement of SUD symptoms) and the person's latent trait value (SUD severity phenotype). Contingent on satisfactory model data fit, the information obtained from IRT analyses enables, therefore, documenting SUD severity across the gradient of latent trait severity scores while taking into account the difference between items in capacity to be discriminative across the spectrum of trait level. A two-parameter (intercept and discrimination) IRT bi-factor logistic model, used in this study, is described as
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where θ is a continuous variable (latent SUD severity trait), Pi(θj) is the probability the subject endorses an SUD symptom, ai is the item discrimination parameter, di is the item intercept parameter, n is the number of subjects, p is the number of dimensions, and D=1.7 is the scaling constant used to approximate the logistic model to the normal ogive model. The probability of endorsing an SUD symptom item is related to the SUD severity scale as a monotonically increasing S-shaped item response function (IRF). The item discrimination parameter (a) is proportional to the slope of the item response function (IRF) at this trait value; that is, the rate at which the probability of endorsement of a symptom changes as the latent trait score increases at the point of inflection. Higher item discrimination values are associated with steeper IRFs. In other words, higher discrimination parameters indicate a stronger relationship between SUD severity and observed symptom. A symptom with a low value of the item discrimination parameter would result in an IRF that increases gradually as a function of SUD severity. The item threshold parameter (or item difficulty), which is defined as the of ratio of negative item intercept to item discrimination parameter, -di/aj, determines the position of the curve along the latent trait. The position of
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the curve on the SUD severity scale corresponds to the severity of the symptom. A higher threshold parameter indicates that fewer subjects endorse a particular symptom. In other words, a higher trait value (higher score on the continuum of the SUD severity scale) is required for the person to endorse the particular symptom; hence, individuals whose scores fall on the right of the scale are more severe cases compared to individuals whose scores are on the left of the scale.
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3.4.1. Item response model assumptions—Two testable assumptions need to be satisfied when applying IRT models. The unidimensionality assumption implies that only one latent trait is measured by the items used in developing the scale, i.e., the probability of endorsing a diagnosis is a function of only one latent trait. If the residual covariances among items are small (rather than zero) at a given trait level, it is possible that there exists a dominant factor to be measured by a set of items, even though other factors may be present, corresponding to essential unidimensionality[26]. The second assumption is that no relationship is present between the subject's responses to different items (or symptoms) after taking into account the subject's latent trait level. This is referred to as the local independence assumption[27]. Unidimensionality is a sufficient condition for satisfying the local independence assumption. 3.4.2. IRT-based reliability coefficient—We used the following formula[28], to estimate IRT-based reliability of the SUD severity index:
where
is the
observed scale score variance, and is the average measurement error of variance across the levels of the latent trait of SUD severity. In addition, an average IRT-based reliability for the entire trait is provided.
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3.4.3. Model comparisons—We used Mplus[29] to assess the dimensionality of the SUD symptoms. The intercept and discrimination parameters were estimated using IRTPRO[30]. This procedure utilizes the marginal maximum likelihood method to calibrate items, and the Bayesian expected a posteriori method to estimate latent trait scores. Model fit indices suggest a good fit if root mean square error of approximation (RMSEA) is less than .05, comparative fit index (CFI) is greater than .95, and/or Tucker-Lewis Index (TLI) is greater than .95[29]. In addition, -2log likelihood (-2logL), Akaike Information criterion (AIC)[31], Bayesian Information Criterion (BIC), and sample size-corrected BIC[32] are computed to determine the best fitting model. For AIC and BIC, a lower value indicates better model-data fit. Although sample size-corrected BIC outperforms all other indices[33], we also present AIC and BIC.
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3.5. Dimensionality of SUD abuse and dependence symptoms Four models representing the possible structure of the DSM-III-R SUD abuse and dependence symptoms were tested in this study: As presented in Figure 1, Model A assumes a unidimensional structure, whereas the other models have multidimensional structure. Model A assumes that substance use severity corresponds to a single factor that accounts for the covariance among symptoms. Model B assumes more than one uncorrelated common SUD severity factor encompassing alcohol, cannabis, cocaine opioids, sedative and stimulants. Model C assumes one common factor for each drug category, thus yielding six Int J Pers Cent Med. Author manuscript; available in PMC 2017 September 20.
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correlated factors. Model D assumes a bi-factor model, that is, a general factor explains the symptom intercorrelations while drug-specific group factors capture the covariation among symptoms of each drug category.
4 Results 4.1. Endorsement of abuse and dependence SUD symptoms
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Table 2 provides the frequencies of endorsements and corrected item-test correlations of the abuse and dependence symptoms for each SUD category. The most frequently endorsed dependence symptoms are “tolerance” for alcohol, “a great deal time spent taking and using it” for cannabis, and “need for larger amounts or over a longer period than intended” for cocaine in both men and women. The most frequently endorsed abuse items are “hazardous use” for alcohol and cannabis and “social and interpersonal problems” for cocaine in men, whereas “social and interpersonal problems” related to alcohol and cocaine use, and “failure to fulfill major role obligations” related to cannabis use, were most prominent in women. The majority of abuse and dependence symptoms have item-test correlations exceeding the . 2 threshold, indicating a significant item contribution to the severity scale. 4.2. Exploratory factor analysis Principal component analysis was conducted to explore the factor structure of the SUD abuse and dependence symptoms. The first 6 factors explained 20%, 12%, 10%, 8%, 6%, 4%, 2% of the variance in men, and 27%, 15%, 8%, 7%, 5%, 2% in women. The first factor explains 20% or higher of the variance in males and females suggests a unidimensional factor structure of SUD abuse and dependence symptoms in both sexes[34].
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4.3. Model comparisons
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Model data fit statistics are presented in Table 3. Model C (6 correlated substance-specific factors) had the best fit statistics in both sexes. Model D (the bi-factor model), assuming six substance-specific factors and one common factor had the second best model-data fit indices. However, the difference between χ2 values of two models was not significant in males and females. We chose the bi-factor model, because the significant and modest correlations between all six substance-specific classes (males: mean correlation=.33, sd=.17; females: mean correlation=.29, sd=.21) suggested the possibility of a general factor[35] and reflected the common variance among them, corresponding to general (common; non-drugspecific) liability to addiction. Furthermore, the difference between factor loadings of general factor of unidimensional and bi-factor models indicates that the bi-factor model recovers estimates more accurately than the unidimensional model[35], because forcing a unidimensional model to a multidimensional model results in parameter estimates which are highly unstable and inaccurate. The results also suggest that substance-specific class factors explain symptom covariation that is independent of the covariation due to the general factor. 4.3.1. Model parameters—The item discrimination and item intercept parameters of all 66 symptoms along with the factor loadings of the bi-factor model for the common factor and substance specific factors are presented in Table 4a, b and Table 5a, b, respectively. All symptoms indicating the global factor have high ability to discriminate between trait values
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(severity) in both males and females. The item threshold determines the location of a symptom on the SUD severity scale. The order of symptoms according to their severity in men and women is similar, except for stimulant drugs; however, the probability of endorsing any symptom is more likely in men than for women. Alcohol use disorder symptoms are the most likely ones to be endorsed in both males and females. Tables 5a and 5b present factor loadings for the bi-factor model in males and females. All symptoms load high on the global factor and substance-specific group factors. The factor loadings of the 66 symptoms on the global factor in the bi-factor model are all higher than .4 in males and, with few exceptions, in females. The discriminative power of 66 symptoms in a common factor varies; however, it does not vary within each substance category. All symptoms indicating the general liability factor have high ability to discriminate between the trait values in males and females.
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The correlation between the IRT-derived global score and observed score (sum of symptoms) is very high (r=.90), In addition, the severity score has a standard normal distribution with mean 0 and variance 1. Thus, it is well suited to clinical research requiring accurate measurement of individual differences as the conceptual foundation for personcentered medicine. 4.3.2. Aggregate criterion information function—The aggregate information function and standard error of measurement of SUD severity are presented in Figure 2. The
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standard error and information function are related: . A scale score was derived to constitute a continuum spanning -3 to +3 anchoring the least and the most severe scores on the scale. As can be seen in Figure 2, the SUD severity score in males has better precision at moderate to high levels of SUD severity (reaching its lowest level at half a standard deviation above the mean) whereas females are measured more precisely in the low to moderate range of SUD severity (reaching its lowest level at half a standard deviation below the mean). In other words, the SUD severity scale comprised of 66 symptoms is more discriminating at a half standard deviation above the mean in men and a half standard deviation below the mean in women. Concomitantly, the IRT-based reliability coefficient is in the good range for both men (ρ=95) and women (ρ=87).
5 Discussion
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This research complements and extends previous studies on the quantification of severity of substance use encompassing the various substance categories[1] and severity of SUD diagnoses[2], as well as general liability to addiction in the absence of SUD symptoms (in children) [3,4,36,37,38]. A bi-factor model provides the most 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. These data are consistent with the body of literature on both common mechanisms and manifestations of liabilities to drug-specific addictions (rev. in [4]) and the relationships among liabilities to drug-specific SUDs. In particular, despite the differences between the
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chemical classes, mechanisms of biotransformation and neurobiological response of various abused substances, and routes of administration, these liabilities have been shown to significantly covary [39,40], thus indicating common sources of variation. Moreover, these sources are to a large degree genetic, with liabilities to disorders related to licit and illicit substances forming two genetically distinct if correlated classes [41], and no substancespecific genetic variance estimated [42]. In addition to these consistently observed common sources of variance, drug-specific variance components are also unsurprisingly present [40,41,43,44]. Recently, however, a paper by Clark et al [45], modeling the relationships among the symptoms of abuse and dependence for illicit substances in confirmatory factor analysis asserted the lack of evidence for general addiction liability. It is possible that their conclusion, which is based purely on the statistics of model fit, is due to omitting the model that is most consistent with the psychopharmacological, neurobiological, phenogenetic and genetic association data – namely, with the presence of both general liability and drugspecific liabilities, i.e., the bifactor model. Indeed, the only model in their study that included general liability, thus taking into account correlations between the symptoms for different drugs, was the unifactor model. All the other models tested in the Clark et al. work specified correlations between the factors accounting for covariation of the drug-specific symptoms, which, to be sure, amounts to common variance between drug-specific groups of symptoms that corresponds to general liability, but those correlations were not presented or discussed.
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Several limitations of this study should be noted. In particular, a random sampling strategy was not employed; hence, the possibility of sampling bias cannot be ruled out. In addition, it is possible that the pattern and rate of SUD observed in women are, in part, due to the influence of homogamy, including the possibility of contagion [46]. These limitations notwithstanding, the present study demonstrated that applying IRT methodology to estimate SUD severity is heuristic for understanding variation within and between different populations, including cross-cultural investigations. One of the most essential properties of IRT methodology is sample independence; that is, probability of endorsing a symptom is the same in males and females at the same SUD severity level, a high correlation between item discrimination parameters in males and females and a high correlation between item intercept parameters (above r=.40) of the global factor in males and females confirm sample independence.
6. Conclusions
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The variation in the DSM-III-R symptoms of substance use disorders (SUD) was shown to have both substance-specific components and the common component corresponding to general liability to addiction (GLA). Item response theory (IRT) methodology was used to quantify the continuous latent GLA trait in individuals having SUD symptoms, corresponding to SUD severity. The results indicate that this methodology is effective for accurately measuring SUD severity in men and women, which is essential for the personcentered medicine to effectively decide the intensity of treatment. Accordingly, it is both feasible and practical to augment the current diagnostic procedures by the more precise quantitative measurement useful for understanding individual differences in an unbiased fashion. Int J Pers Cent Med. Author manuscript; available in PMC 2017 September 20.
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Acknowledgments This research received support from the US National Institute on Drug Abuse, Grant # P50 DA005605 and K02 DA018701.
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Figure 1. Four models tested
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Figure 2. Aggregate Information Function and Standard Error of Measurement
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Table 1
Lifetime SUD diagnoses in men and women
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Men (N=539)
Women (N=306)
Alcohol
52%
43%
Cannabis
41%
21%
Cocaine
30%
16%
Opioids
15%
12%
Sedative
5%
5%
Stimulants
8%
19%
Any SUD
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Author Manuscript Table 2
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34, .64
26, .56
31, .62
20, .59
25, .60
27, .59
46, .54
20, .51
39, .64
D3
D4
D5
D6
D7
A1
A2
A3
A4
17, .43
16, .37
32, .26
20, .36
10, .22
10, .36
22, .38
11, .32
19, .34
10, .30
25, .62
8, .35
18, .50
17, .55
14, .55
19, .55
23, .57
22, .55
25, .56
11, .47
11, .45
11, .40
7, .34
7, .34
6, .34
8, .44
12, .43
11, .37
11, .40
12, .40
10, .40
3, .35
3, .34
4, .32
2, .31
3, .32
2, .32
3, .37
2, .26
3, .31
3, .30
3, .29
4, .22
3, .20
7, .20
3, .19
5, .18
2, .16
5, .19
3, .19
5, .17
5, .24
4, .16
34, .48
8, .42
32, .34
25, .50
30, .43
18, .60
24, .53
20, .53
29, .49
12, .34
32, .57
9, .17
3, .05
10, .14
12, .18
8, .11
9, .25
11, .30
8, .20
10, .27
10, .18
6, .25
17, .76
6, .48
7, .64
14, .72
13, .70
13, .73
16, .77
17, .75
17, .75
10, .63
14, .73
Cocaine %, r
5, .29
4, .28
3, .26
3, .21
3, .30
3, .32
6, .38
4, .38
5, .37
6, .37
5, .33
Opioids %, r
2, .18
2, .10
2, .17
3, .22
4, .25
2, .19
1, .19
3, .20
3, .26
4, .22
4, .23
Sedative %, r
Note: Dependence symptoms. D1: Tolerance, D2: Withdrawal, D3: Need for larger amounts or over a longer period than intended, D4: Unsuccessful efforts to cut down, D5: A great deal of time spent taking/using it, D6: Reduced social, occupational and recreational activities, D7: Substance use is continued despite physical or psychological problems. Abuse Symptoms. A1: Failure to fulfill major role obligations at work, school, or home, A2: Hazardous use, A3: Substance use related legal problems, A4: Continued substance use despite having persistent or recurrent social or interpersonal problems.
15, .47
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D2
20, .52
15, .31
36, .62
Cannabis %, r
D1
Stimulants %, r
Alcohol %, r
Sedative %, r
Females (N=306) Opioids %, r
Alcohol %, r
Cocaine %, r
Cannabis %, r
Males (N=539)
Symptoms
17, .76
12, .65
8, .66
11, .64
16, .60
13, .74
17, .78
18, .76
18, .76
17, .75
15, .72
Stimulants %, r
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Percent of endorsement and corrected item-total test correlations (r) of lifetime DSM-III-R SUD dependence and abuse symptoms in males and females
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Author Manuscript 19150.58 132 4051.37 2079
