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Practical Applications of Measurement to Addiction Research (“Why do we care?”) Michael L. Dennis, Ph.D. Chestnut Health Systems, Bloomington, IL Presentation.

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Presentation on theme: "Practical Applications of Measurement to Addiction Research (“Why do we care?”) Michael L. Dennis, Ph.D. Chestnut Health Systems, Bloomington, IL Presentation."— Presentation transcript:

1 Practical Applications of Measurement to Addiction Research (“Why do we care?”) Michael L. Dennis, Ph.D. Chestnut Health Systems, Bloomington, IL Presentation at NIH Pre-session of the International Conference on Outcome Measurement, September 10, 2008, Rockville, MD. This presentation supported by National Institute on Drug Abuse (NIDA) grant no R37 DA11323 and Center for Substance Abuse Treatment (CSAT), Substance Abuse and Mental Health Services Administration (SAMHSA) contract 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) ,

2 Objectives are to...  Examine why more traditional clinical trials type researchers need to care about measurement  Provide explicit practical examples of how addressing measurement in Addiction Research can help improve it

3 Since the early 1960s, Jacob Cohen and colleagues has suggest that clinical trials research should:  Focus on Statistical power, which is - the probability of finding what you are looking for given that it is there  Combine data from multiple clinical trials into meta analyses, which can be used as - a more stable estimate of truth - to evaluate the accuracy of our early estimates and how methods can be improved

4 In a review of over 200 meta analyses of medical, social and legal studies published between , Lipsey consistently found  Less than a third of the individual articles coded even mentioned - the statistical power of their core contrast - reliability, validity, or sensitivity of their outcome measure  That relative to final effect size estimated from the meta analysis, the studies averaged less than 50% power - in other words, it was more accurate to flip a coin than to use a statistical test the way they were being used “on average” in the published literature

5 Movement to Improve the Methodological Quality of Clinical Trials Research  In 1993 a group of 30 experts (medical journal editors, clinical trialists, epidemiologists, and methodologists) met in Ottawa to try to identify methodological gaps in the literature  In 1996 this growing group issued the Consolidated Standards of Reporting Trials (CONSORT;  Since 2000, NIH has required DSMB on all Phase 3 and multi-site phase 2 studies (Notice OD-00-38) – which also push CONSORT  Today virtually every major medical, psychiatric, psychological, criminological, and social journal has signed onto CONSORT

6 Basic ways to increase power  Increase sample size  Increase observations  Target a higher severity/less heterogeneous sample  Increase implementation  Reduce measurement error  Reduce unexplained variance (which may be systematic)  More accurately model error and unexplained variance in analysis While the most common approach, these are also the most expensive and logistically difficult to do Today’s focus

7 Observed Effect Size as a function of “True” effect size (Cohen’s d) and reliability of dependent variable No Measurement Error “Observed” Effect size goes down with lower reliability

8 Sample size required for 80% power as a function of “True” effect size (Cohen’s d) and reliability of dependent variable A reliability of.7 doubles sample size requirements Increasing reliability from.4 to.7 cuts sample size requirements by over 50%

9  Unclear time periods  Badly worded double negatives  Constantly changing response sets  Difficult to use (or time consuming) response sets  Behavior/trait that varied in a range (disturbance)  Abstract concepts not defined well by a single question Some of common source of discordant answers in test-retest questions that can be readily addressed are:

10 Proportion of Inconsistencies (100%)* Duration (in Minutes)* Denial/Misrepresentation (Staff Rating)* Context Effect (Staff Report) Proportion of Missing Data (100%) Atypicalness (Outfit in Logits) Randomness (Infit in Logits) <- Cohen's d a \a Cohen's d (Post Certification - Pre Certification)/Pooled STD * p<.05 Impact of Comprehensive Data Collection Protocol Certification on Measurement Issues Source: GAIN coordinating center

11 Major improvement over the first 15 interviews Most improvements have occurred by 60 interviews Source: GAIN coordinating center Staff Experience Matters as well

12 Impact of the Number of Observations on Reliability Across Observations by Initial Reliability in a Wave Two observations (e.g., pre & post test) more reliable than post only The lower the reliability, the longer it takes to reach a point of diminishing returns on more observations

13 Some examples of increasing reliability with multiple observations  Baseline observation to separate individual differences  Multiple observations to separate trajectories  Multiple observations nested within a hierarchical structure (e.g., patients within staff or site)  Blood pressure, lung capacity, motivation, readiness to change, attitudes or other things that tend to vary in a range (aka disturbance)  Redoing a urine or BAC test when unexpected reading or it is contested by participant  Redoing a positive HIV test for confirmation

14 Identify Cut Points Where a Question Like “Peak Use” Is Likely to Become Unreliable Peak Joints Reported at time 1 on GAIN Peak Joints Reported at Time 2 on Form 90 Source: Dennis et al 2004

15 Impact of Number of Items on Reliability (Alpha) Observed by Average Inter-item Correlation Generally target.7 to.9 Behavioral Measures (e.g., how many days, times) have high reliability and max out around 3-5 items Covert Scales (e.g., MMPI), summative indices, and other measures with low inter item R may take 30 items (or more) Symptom counts related to a syndrome or latent construct usually max out in 5-13 items

16 Structure of GAIN’s Psychopathology Measures and Validity Checks Example of how scales can also be inter-related and used for validation Higher scores associated with alcohol and drug abuse medication (methadone, naltrexone, antaabuse, buprenorphine) and/or substance induced legal, mental health, physical health, and withdrawal problems Higher scores associated with greater dysfunction (e.g., dropping out of school, unemployment, financial problems, homelessness) Higher scores associated with mental health treatment (e.g., anti depressants, seritonin reuptake inhibitors (SSRI), monoamine oxidase inhibitors (MAOI) sedatives) and/or a history of traumatic victimization, and/or high levels of stress Higher scores associated with mental health treatment (e.g., Ritalin, Adderall, lithium), special/alternative education, school or work problems, gambling and other evidence of impulse control problems, and/or anti-social/borderline personality disorders Higher scores associated with arrests, detention/jail time, probation, parole, size of drug habit

17 Key Advantages of Creating Scales and Indices for Clinical Research  One of the lowest cost ways to reduce measurement error and increase statistical power  Reduce clinical omissions and backtracking for validity checks  Increase conceptual robustness, interpretability and make it easier to explain to others  Facilitates profiling over a large number of items

18 Formal Measurement Models Can Be Used to  Place people along a more reliable/sensitive ruler (aka common or latent factor)  Look at the slope/ discrimination of items (primarily 2 parameter IRT)  Related items in terms of their average severity  Look at the match/mismatch of people and item locations (primarily Rasch / 1 parameter IRT)  Study real differences by primary substance, gender, race, age or other groups  Identify potential bias at the item and test level by gender, race or other groups  Identify atypical patterns of answers (e.g. outfit)  Identify random response patterns (e.g., infit)

19 Note you can also create a summary measures across different sources of data Source: Lennox et al 2006 (CFI=.98)

20 Impact of Item Discrimination (aka steepness of slope) on Sample Size Requirements IRT focuses on better use of items with low / range of discrimination Rasch focuses on finding high discrimination items so that differences between items can be ignored 16-36% reduction in sample size

21 Why Use Rasch and IRT?  Raw, Rasch and IRT scales generally correlated over.95 and vary by less than 5% in sample size requirements  The big advantage of going to Rasch and IRT are that they can be used to: - reduce scale length (aka cost) through computer adaptive interviewing (as just described by Dr. Riley) - explore and test assumptions about how items are related to each other - explore and test assumptions how items/ scales vary by subgroups - identify people with atypical presentations - identify people who appear to be responding randomly

22 Example: Evaluating 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” (e.g., ASI, SFS) or “problem” scales (e.g., MAST, DAST, CAGE) more commonly used in treatment research

23 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?)

24 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?)

25 On-Going Debates About SUD Concept Formal assumption that symptoms of “physiological dependence” (either tolerance or withdrawal) are markers of high severity Debate about whether “abuse” symptoms should be dropped, thought of as early dependence, or thought of as moderate/high severity markers that warrant treatment even in the absence of a full syndrome Debate about whether to treat diagnostic orphans (1-2 symptoms of dependence) as abuse or continue to ignore them Concern about whether the current symptoms (which were based primarily on adult data) are appropriate for use with adolescents Concern about the sensitivity to change

26 Conrad et al 2007 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

27 The GAIN’s Substance Problem Scale (SPS)  DSM-IV Clinical Diagnosis categories and courser specifiers (Kappa of.5 to.7)  Epidemiological Lifetime, Past Year and/or Past Month Diagnosis categories (Kappa of.5 to.7)  Dimensional Symptom counts for lifetime, past year and/or past month with internal consistencies of.8 to.9 (test retest of.7 to.9)

28 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

29 Item Relationships Across Substances 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)

30 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

31 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

32 Comparing Substances

33 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

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

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

36 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

37 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

38 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

39 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


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