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Applications of IRT/Rasch Measurement in Substance Abuse Treatment Beverly Pringle, National Institute on Drug Abuse Ken Conrad, School of Public Health,

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Presentation on theme: "Applications of IRT/Rasch Measurement in Substance Abuse Treatment Beverly Pringle, National Institute on Drug Abuse Ken Conrad, School of Public Health,"— Presentation transcript:


2 Applications of IRT/Rasch Measurement in Substance Abuse Treatment Beverly Pringle, National Institute on Drug Abuse Ken Conrad, School of Public Health, University of Illinois at Chicago Michael Dennis, Chestnut Health Systems, Bloomington, IL

3 Three Parts Of This Workshop 1.The Need to Improve Measurement in Health Services Research Related to Substance Abuse Treatment (Beverly Pringle) 2.The Basics of Rasch (Kendon Conrad) 3.Example: Validation of DSM-IV Substance Use Disorder by Substance and Age Using Rasch (Michael Dennis)

4 Part 1. The Need to Improve Measurement in Health Services Research Related to Substance Abuse Treatment Beverly Pringle, National Institute on Drug Abuse

5 What is the Problem? “There is a pressing need to better quantify clinically important symptoms and outcomes, including pain, fatigue, and quality of life...” Zerhouni, 2003

6 Measurement Science and Substance Abuse Need to better quantify over the life course: Symptoms (hazardous use, severity) Diagnoses (abuse, dependence, disorder) Outcomes (recovery, quality of life)

7 NIDA should… Support the development and refinement of methods to address critical treatment intervention questions. Provide technical assistance and funding to develop methodologies for conducting research. Blue Ribbon Task Force

8 What is Measurement? “The value of a reading taken of a phenomenon” - University of Leicester Statistical Glossary “The process of creating a correspondence between a concept and data in specifying that concept” - Northern Arizona University

9 “To Measure” > “To Count”

10 Why Now? Field of measurement has grown tremendously over past 15 years. New technologies facilitate: –Clinical assessment –Item development –Measurement calculations –Data analysis

11 Why Care about Measurement? Measurement quality affects treatment delivery: Amount of time and other resources clinicians devote to assessment The burden of assessment for clients Ability to determine clients’ treatment needs Program delivery costs

12 Why Care about Measurement? Measurement quality affects research: How well we measure what we are interested in Whether our classifications are meaningful Whether we find important differences that exist How well we understand concepts like health disparities

13 And now…

14 Part 2. The Basics of Rasch Kendon Conrad, University of Illinois at Chicago

15 Outline Differences between traditional/classical test theory and IRT/Rasch measurement Why and how Rasch analysis seeks to create linear, interval measures Evaluating Differential Item Functioning Evaluating Differential Test Functioning Facets analysis across multiple factors

16 Classical Testing Theory (CTT) assumes all items are created equal But we know that is not true. Is that how we measure potatoes? How about spelling? Items actually range from: Easy->hard Like addition -> division E.g., Guttman: Lack of recent practice on item 5: Educated guess on item 8: Slow, nervous start:

17 Requirements for Measurement Test items are the operational definition of the underlying trait. Test items can be ordered from easy to hard like increasing height for high jumping. The Rasch model expects: Test takers can be ordered from less able to more able.

18 Sample Free Measures Interval measures are not sample dependent. Children high jumping is analogous to the Rasch method. –A height of 2’ does not change –So we can define ability using that stable standard. –The children can then be defined in terms of their jumping ability using that standard. –Actually, every child can be placed quite exactly and reliably at their ability level on the interval height measure. It’s the point where their chance of getting over is 50/50.

19 Measuring Attributes To measure an attribute, we: Bring to the fore the idea of the variable we want to measure Determine what observations it will be useful to consider as informative manifestations of that variable Construct agents, write items, intended to elicit singular instances of this “made-to-be” unidimensional “ability” variable. These items are the “bars” that subjects try to “jump”. They enable us to home in on the person’s level on the construct.

20 How Scores Depend on the Difficulty of Test Items Very Easy Test 18 Expected Score 8 Person Very Hard Test 18 Expected Score 0 Person Medium Test 1 Expected Score 5 Person 8 Reprinted with permission from: Wright, B.D. & Stone, M. (1979) Best test design, Chicago: MESA Press, p. 5.

21 How Differences Between Person Ability and Item Difficulty Ought to Affect the Probability of a Correct Response Person Ability Item Difficulty p >. 5 Person Ability Item Difficulty p =. 5 Person Ability Item Difficulty p <. 5 Reprinted with permission from: Wright, B.D. & Stone, M. (1979) Best test design, Chicago: MESA Press, p. 13.

22 Probability of Success on an Item Rasch (1960) -The probability of a successful outcome is governed by the combination of the person’s ability and the item’s difficulty.

23 Fechner’s Law (1860): The relationship between the stimulus and the response (body and soul) is predictable and mathematical, but not additive The relationship is a logarithmic function Light intensity, electric shock The cumulative normal curve Thermometer to measure health

24 e (ability-item_difficulty) 1 + e (ability-item_difficulty) P 1,0 = Rasch (1960) formula for the probability of endorsing an item … “e” is a constant, 2.71…., that describes growth curves. It is like Pi, 3.14…., a constant that describes circumference.

25 P 1,0 = e (0) 1 + e (0) = 1 2 =.50 When Person Ability equals Item Difficulty

26 The Rasch Yardstick The concatenated odds of passing an item can be represented by distances on a map of persons and items. The resulting map of the variable is no less a “ruler” than one constructed for measuring length. It can be applied in a similar way to produce measures as useful as those of any yardstick.

27 Unidimensionality Requirement We can only measure one thing at a time. The ideal of the Rasch model is that all the information in the data be explained by one latent construct. Then, the unexplained part, the residuals, is, by intention, random noise. If there is a 2 nd or 3 rd rival factor, we will need to construct more than one measure. If the data fit the model, we have a map or ruler of the variable.

28 PERSONS MAP OF ITEMS | 2 TRUNCATED.### | ## |.## |. | HlthProbs.## |T 1.## +.## S|.### |.### |S Withdrawal/ill.#### | ProbW/Law.###### | Unsafe GiveUpActs DespiteMedPsyProbs.#### | DepressedNervous NeededMoreAOD UnableCutDown 0.###### +M.###### | ResponNotMet LargerAmnt/more.####### |.############ M| HideWhenUseAOD Fights/trouble.###### |S SpentTimeGetting.####### |.###### | ParentComplained -1.###### +.##### |T WeeklyAOD. |.###### |.#### |. S|.###### | #### |.##### | -3 TRUNCATED + -4.############ + EACH '#' is 24 Figure 1. GAIN Substance Problems Scale

29 Computerized Adaptive Test Having a difficulty parameter enables us to home in on a person’s trait level without using all of the items. People with less of the trait will not need to take a lot of items that are too difficult for them; while people with more of the trait will not be bored with items that are too easy. Reduces testing time. Enables more privacy, e.g., in carrels or via Internet Immediate data entry Immediate feedback

30 A person fits the Guttman model if their answers look like this: A person fits the Rasch model if their answers look like this: What if their answers look like a: b: c: The Question of Fit

31 |ENTRY RAW MODEL| INFIT | OUTFIT |PTMEA| |NUMBER SCORE COUNT MEASURE S.E. |MNSQ ZSTD|MNSQ ZSTD|CORR.| ITEM | | | |A.51| CHideWhenUseAOD | | | |B.42| KProbW/Law | | | |C.62| DParentComplainedAOD | | | |D.49| GHlthProbs | | | |E.62| MFights/trouble | | | |F.60| PWithdrawl/ill | | |1.03.8|G.60| JUnsafe | | |1.02.6|H.68| EWeeklyAOD | | | |h.64| FDepressedNervous | | | |g.65| RUnableCutDownAOD | | | |f.65| UCausingProbs | | | |e.66| NNeededMoreAOD | | | |d.67| TGiveUpActs | | | |c.71| SSpentTimeGet/UseAOD | | | |b.69| HResponNotMet | | | |a.69| QLargerAmnt | ITEM STATISTICS: MISFIT ORDER

32 Differential Item Functioning (DIF) Tendency of a subtype of respondents systematically to answer in a way that differs from another subtype even though they are at the same level on the construct, e.g., gender, age, race, country, culture, language, diagnosis. DIF can be a form of multidimensionality (Stout, 1987; Lange et al., 2000) such that removal of DIF in biased items decreases dimensionality. Removal of DIF, in addition to creating unbiased measures, may also improve their quality. Alternatively, DIF may identify REAL differences among subgroups that have implications for clinical practice

33 Differential Item Functioning Do the items mean the same thing to men and women? Whites and non- whites? Youth and adults? In the tables below, items that are harder for adults to endorse are indicated by a positive value. Our threshold for a clinically significant DIF is.5 logit.

34 DIF Contrasts of Youth (1) with Young Adults (2) and with Adults (3) | PERS DIF DIF PERS DIF DIF DIF JOINT ITEM | CLAS MEAS S.E.CLAS MEAS S.E. CONTRAST S.E. t Number Name | | | HideWhenUseAOD | CHideWhenUseAOD | DParentComplained | DParentComplained | EWeeklyAOD | EWeeklyAOD | FDepressedNervous | FDepressedNervous | GHlthProbs | GHlthProbs | HResponNotMet | HResponNotMet | JUnsafe | JUnsafe | KProbW/Law | KProbW/Law | MFights/trouble | MFights/trouble

35 DIF Contrasts of Youth (1) with Young Adults (2) and with Adults (3) | PERS DIF DIF PERS DIF DIF DIF JOINT ITEM | CLAS MEAS S.E.CLAS MEAS S.E. CONTRAST S.E. t Number Name | | | NNeededMoreAOD | NNeededMoreAOD | PWithdrawl/ill | PWithdrawl/ill | QLargerAmnt/more| | QLargerAmnt/more| | RUnableCutDownAOD | RUnableCutDownAOD | SSpentTimeGet/use | SSpentTimeGet/use | TGiveUpActs | TGiveUpActs | UDespiteMedPsyProbs | UDespiteMedPsyProbs

36 DIF Contrasts of Youth (1) with Adults (3) | PERS DIF DIF PERS DIF DIF DIF JOINT ITEM | Teen MEAS S.E.AdultMEAS S.E. CONTRAST S.E. t Number Name | | | CHideWhenUseAOD | DParentComplained | EWeeklyAOD | FDepressedNervous | GHlthProbs | HResponNotMet | JUnsafe | KProbW/Law | MFights/trouble | NNeededMoreAOD | PWithdrawl/ill | QLargerAmnt/more| | RUnableCutDownAOD | SSpentTimeGet/use | TGiveUpActs | UDespiteMedPsyProbs

37 Differential Test Functioning (DTF) While there may be differences in individual items, over a larger pool of items these differences may balance out and still produce a reliable measure Significant DTF suggest the need for different norms for the subgroups and/or that the test may not “mean” the same thing

38 Evaluating DTF 1.Put teens and adults on the same ruler by anchoring rating scale categories and 8 common (non-DIF) items using their common calibrations. 2.Do separate Winsteps runs for each group letting the unanchored, 8 DIF items “float,” i.e., calibrate themselves within the subgroup. 3.Given a cutoff score based on common clinical practice estimate the changes before and after DIF adjustment.

39 Creating A Common Ruler to Evaluate DTF 8 items fixed to be equal and used to make a common ruler Remaining 8 items allowed to vary

40 DTF Group Means and High Need Cut-off Results Before and After DIF Adjustment

41 Analyzing Facets: Beyond Persons and Items There are factors, a.k.a. facets, beyond persons and items that influence measures. For example, when raters are involved, the scoring severity of the rater influences how highly the person will be ranked. With the SPS, we have clients attribute 11 substance abuse and dependence symptoms to 1 to 14 DSM substance classes. Like logistic regression, Facets allows us to simultaneously look at difference in multiple dimensions (e.g.., age, gender, race, time, substance)

42 Part 3. Example: Validation of DSM-IV Substance Use Disorder by Substance and Age Using Rasch Michael Dennis, Chestnut Health Systems

43 This presentation was supported by analytic runs provided Substance Abuse and Mental Health Services Administration's (SAMHSA's) Center for Substance Abuse Treatment (CSAT) under Contracts , , and using data provided by the following grantees: CSAT (TI11320, TI11324, TI11317, TI11321, TI11323, TI11874, TI11424, TI11894, TI11871, TI11433, TI11423, TI11432, TI11422, TI11892, TI11888, TI013313, TI013309, TI013344, TI013354, TI013356, TI013305, TI013340, TI130022, TI03345, TI012208, TI013323, TI14376, TI14261, TI14189,TI14252, TI14315, TI14283, TI14267, TI14188, TI14103, TI14272, TI14090, TI14271, TI14355, TI14196, TI14214, TI14254, TI14311, TI15678, TI15670, TI15486, TI15511, TI15433, TI15479, TI15682, TI15483, TI15674, TI15467, TI15686, TI15481, TI15461, TI15475, TI15413, TI15562, TI15514, TI15672, TI15478, TI15447, TI15545, TI15671, TI11320, TI12541, TI00567); NIAAA (R01 AA 10368); NIDA (R37 DA11323; R01 DA ); Illinois Criminal Justice Information Authority (95-DB-VX-0017); Illinois Office of Alcoholism and Substance Abuse (PI 00567); Intervention Foundation’s Drug Outcome Monitoring Study (DOMS), Robert Woods Johnson Foundation’s Reclaiming Futures. Any opinions about this data are those of the authors and do not reflect official positions of the government or individual grantees. The opinions are those of the author and do not reflect official positions of the consortium or government. Available on line at or by contacting Joan Unsicker at 720 West Chestnut, Bloomington, IL 61701, phone: (309) , fax: (309) , Acknowledgement

44 Goals for Part 3 1.Examine the origins, definitions and current debates surrounding the Diagnostic and Statistical Manual IV TR (DSM-IV-TR) substance use disorder (SUD) construct 2.Use Rasch analysis of the GAIN’s Substance Problem Scale (SPS) data to inform current debates related to SUD 3.Discuss the implications of the findings for further refinement of the SUD concept.

45 Evolution of the Substance Use Disorders (SUD) Concept Much of our conceptual basis of addiction comes from Jellnick’s 1960 “disease” model of adult alcoholism Edwards & Gross (1976) codified this into a set of bio- psycho-social symptoms related to a “dependence” syndrome In practice, they are typically complemented by a set of separate “abuse” symptoms that represent other key reasons why people enter treatment DSM 3, 3R, 4, 4TR, ICD 8, 9, & 10, and ASAM’s PPC1 and PPC2 all focus on this syndrome Note that these symptoms are only correlated about.4 to.6 with use or problem scales more commonly used in evaluation

46 DSM (GAIN) Symptoms of Dependence (3+ Symptoms) Physiological n. Tolerance (you needed more alcohol or drugs to get high or found that the same amount did not get you as high as it used to?) p. Withdrawal (you had withdrawal problems from alcohol or drugs like shaking hands, throwing up, having trouble sitting still or sleeping, or that you used any alcohol or drugs to stop being sick or avoid withdrawal problems?) Non-physiological q.Loss of Control (you used alcohol or drugs in larger amounts, more often or for a longer time than you meant to?) r.Unable to Stop (you were unable to cut down or stop using alcohol or drugs?) s.Time Consuming (you spent a lot of your time either getting alcohol or drugs, using alcohol or drugs, or feeling the effects of alcohol or drugs?) t.Reduced Activities (your use of alcohol or drugs caused you to give up, reduce or have problems at important activities at work, school, home or social events?) u.Continued Use Despite Personal Problems (you kept using alcohol or drugs even after you knew it was causing or adding to medical, psychological or emotional problems you were having?)

47 DSM (GAIN) Symptoms of Abuse (1+ symptoms) h.Role Failure (you kept using alcohol or drugs even though you knew it was keeping you from meeting your responsibilities at work, school, or home?) j.Hazardous Use (you used alcohol or drugs where it made the situation unsafe or dangerous for you, such as when you were driving a car, using a machine, or where you might have been forced into sex or hurt?) k.Legal problems (your alcohol or drug use caused you to have repeated problems with the law?) m.Continued Use after Legal/Social Problems (you kept using alcohol or drugs even after you knew it could get you into fights or other kinds of legal trouble?) Source: Dennis et al 2003

48 Do abuse and dependence symptoms vary along the same or different dimensions? Are physiological symptoms (tolerance and withdrawal) good markers of high severity? Are abuse symptoms good markers of low severity? Does the average and pattern of symptom severity vary by substance? Are there differential item function by age? (Note: there was no adolescent data considered at the time DSM-IV was created). Are diagnostic orphans (1-2 symptoms of dependence without abuse) similar to abuse or lower? Unresolved Questions from DSM’s Substance Use Disorder Criteria

49 Data Source and Methods Data from 2474 Adolescents, 344 Young Adults and 661 Adults interviewed between 1998 and 2005 with the Global Appraisal of Individual Needs (GAIN; Dennis et al 2003) Participants recruited at intake to Early Intervention, Outpatient, Intensive Outpatient, Short, Moderate & Long term Residential, Corrections Based and Post Residential Outpatient Continuing Care as part of 72 local evaluations around the U.S. and pooled into a common data set Analysis here focuses on the GAIN Substance Use Disorder Scale (SUDS) with symptoms of dependence and abuse overall and by substance. The rating scale is 3=past month, 2=past 2-12 months, 1=more than a year ago and 0=never. Analyses done with a combination of Winsteps and Facets

50 Sample Characteristics Adolescents: <18 (n=2474) Young Adult: (n=344) Adults: 26+ (n=661) Male 74%58%47% Caucasian 48%54%29% African American 18%27%63% Hispanic 12%7%2% Average Age Substance Disorder 85%82%90% Internal Disorder 53%62%67% External Disorder 63%45%37% Crime/Violence 64%51%34% Residential Tx 31%56%74% Current CJ/JJ invol. 69%74%45% Note: all significant, p <.01

51 Differences in Symptom Severity by Drug Rasch Severity Measure Desp.PH/MH (+0.10) Give up act. (+0.05) Can't stop (+0.05) Time Cons. (-0.21) Loss of Contro (-0.10) Hazardous (-0.03) Despite Legal (+0.10) Role Failure (-0.12) Fights/troub. (0.17) Time Cons Role Failure Fights/troub. Loss of Control Hazardous Tolerance Can't stop Give up act. Desp.PH/MH Despite Legal Withdrawal Tolerance (0.00) Withdrawal (+0.34) Physiological Sx: While Withdrawal is High severity, Tolerance is only Moderate Dependence Sx: Other dependence Symptoms spread over continuum Abuse Sx: Abuse Symptoms are also spread over continuum 1 st dimension explains 75% of variance (2 nd explains 1.2%) Average Item Severity (0.00)

52 Symptom Severity Varied by Drug Easier to endorse hazardous use for ALC/CAN Rasch Severity Measure ALC AMP CAN COC OPI ALC AMP CAN COC OPI Time Cons. Role Failure Fights/troub. Loss of Control Hazardous Tolerance Can't stop Give up act. Desp.PH/MHDespite Legal Withdrawal AVG (0.00) ALC (-0.44) AMP (+0.89) CAN (-0.67) COC (-0.22) OPI (+0.44) Easier to endorse fighting/ trouble for ALC/CAN Easier to endorse time consuming for CAN Easier to endorse moderate Sx for COC/OPI Easier to endorse despite legal problem for ALC/CAN Easier to endorse Withdrawal for AMP/OPI Withdrawal much less likely for CAN

53 Symptom Severity Varied Even More By Age Rasch Severity Measure < < Time Cons. Role Failure Fights/troub. Loss of Control Hazardous Tolerance Can't stop Give up act. Desp.PH/MH Despite Legal Withdrawal < Age Adults more likely to endorse most symptoms More likely to lead to fights among Adol/YA Hazardous use more likely among Adol/YA Continued use in spite of legal problems more likely among Adol/YA

54 Lifetime Pattern of Substance Use Disorders

55 Past Month Status

56 Rasch Severity by Past Month Status NoneDiagnostic Orphan in early remission Diagnostic Orphan Lifetime SUD in early remission Lifetime SUD in CE 45+ days Abuse Only Dependence Only Both Abuse and Dependence Rasch Severity Measure Diagnostic Orphans (1-2 dependence symptoms) are lower, but still overlap with other clinical groups

57 Severity by Past Year Symptom Count Rasch Severity Measure 1. Better Gradation 2. Still a lot of overlap in range

58 Severity by Number of Past Year SUD Diagnoses Rasch Severity Measure Better Gradation 2. Less overlap in range

59 Severity by Weighted (past month=2, past year=1) Number of Substance x SUD Symptoms Rasch Severity Measure Better Gradation 2. Much less overlap in range

60 Average Severity by Age Adolescent (<18)Young Adult (18-25)Adult (26+) 1. Average goes up with age 2. Complete overlap in range 3. Narrowing of distribution on higher severity at older ages

61 Construct Validity (i.e., does it matter?) FrequencyOf Use Past Week WithdrawalEmotionalProblemsRecovery Environment Social Risk DSM diagnosis \a Symptom Count Continuous \b Weighted Drug x Symptom \c,d \a Categorized as Past year physiology dependence, non-physiological dependence, abuse, other \b Raw past year symptom count (0-11) \c Symptoms weighted by recency (2=past month, 1=2-12 months ago, 0=other) \d Symptoms by drug (alcohol, amphetamine, cannabis, cocaine, opioids) Past year Symptom count did better than DSM Weighted Symptom Rasch \c Rasch does a little Better still Weighted symptom by drug count severity did WORSE

62 Implications for SUD Concept “Tolerance” is not a good marker of high severity; withdrawal (and substance induced health problems are) “Abuse” symptoms are consistent with the overall syndrome and represent moderate severity or “other reasons to treat in the absence of the full blown syndrome” Diagnostic orphans are lower severity, but relevant Pattern of symptoms varies by substance and age, but all symptoms are relevant “Adolescents” experienced the same range of symptoms, though they (and young adults) were particularly more likely to be involved with the law, use in hazardous situations, and to get into fights at lower severity Symptom Counts appear to be more useful than the current DSM approach to categorizing severity While weighting by recency & drug delineated severity, it did not improve construct validity

63 Other Progress Will work to submit a paper on this analysis this fall Also submitting papers on –Differential item functioning by age, gender, & race –Differential item functioning over time –Computer adaptive testing to shorten the GAIN Started doing Rasch analyses of other scales: –Internal Mental Distress Scale (somatic, depression, suicide, anxiety, trauma) –Behavior Complexity Scale (ADHD, CD, and other impulse control disorders) –Crime/Violence Scale (violence, property, interpersonal, and drug related crime) –General Individual Severity Scale (total symptom count for above and substance problems scale)

64 References Dennis, M. L., Titus, J. C., White, M. K., Unsicker, J., & Hodgkins, D. (2003). Global Appraisal of Individual Needs: Administration Guide for the GAIN and Related Measures. Bloomington, IL: Chestnut Health Systems. Retrieved from Fechner, G.T. (1860). Elemente der Psychophysik. Leipzig: Breitkopf & Hartel. For a brief, useful discussion, see Nunnally, J. & Bernstein, I. (1994). Psychometric Theory, 3 rd Ed. New York: McGraw-Hill, pp Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danmarks Paedogogiske Institut. (Republished Chicago: The University of Chicago Press: 1980). Weisner, C., McLellan, T., Barthwell, A., Blitz, C., Catalano, R., Chalk, M., Chinnia, L., Collins, R. L., Compton, W., Dennis, M. L., Frank, R., Hewitt, W., Inciardi, J. A., Lightfoot, M., Montoya, I., Sterk, C. E., Wood, J., Pintello, D., Volkow, M., & Michaud, S. E. (2004). Report of the Blue Ribbon Task Force on Health Services Research at the National Institute on Drug Abuse. Rockville, MD: National Institute on Drug Abuse. Retrieved on 2/14/04 from Zerhouni, E. (2003). NIH Roadmap. Science, 32(3),

65 Copies of these handouts are available… On line at or by contacting Joan Unsicker at 720 West Chestnut, Bloomington, IL 61701, phone: (309) , fax: (309) , e- Mail:

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