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 270-07-019. The opinions are those of the author and do not reflect official positions of the consortium or government. Available on line at www.chestnut.org/LI/Posters or by contacting Joan Unsicker at 720 West Chestnut, Bloomington, IL 61701, phone: (309) 827-6026, fax: (309) 829-4661, e-Mail: junsicker@Chestnut.Org

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

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

In a review of over 200 meta analyses of medical, social and legal studies published between 1960-1990, 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

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; www.consort-statement.org)

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

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

Observed Effect Size as a function of “True” effect size (Cohen’s d) and reliability of dependent variable

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.901.

00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

Reliability of Dependent Variable

Obs

erve

d E

ffec

t Siz

e (O

bser

ved

d)

d=.2d=.4d=.8

True Effect Size

No Measurement Error

“Observed” Effect size goes down with lower

reliability

Sample size required for 80% power as a function of “True” effect size (Cohen’s d) and reliability of dependent variable

0100200300400500600700800900

10001.

00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

Reliability of Dependent Variable

n pe

r gr

oup

for

80%

pow

er

d=.2d=.4d=.8

True Effect Size

A reliability of .7 doubles sample size

requirements

Increasing reliability

from .4 to .7 cuts sample

size requirements by over 50%

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:

-0.39

-0.25

-0.24

-0.10

-0.04

-0.03

-0.03

-0.6 -0.4 -0.2 0

Proportion of Inconsistencies (100%)*

Duration (in Minutes)*

Denial/Misrepresentation (Staff Rating)*

Context Effect (Staff Report)

Proportion of MissingData (100%)

Atypicalness (Outfit in Logits)

Randomness (Infit in Logits)

<- Cohen's da

\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

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0 10 20 30 40 50 60 70 80 90 100

Staff Experience

Goo

d <

--Z

-Sco

re -

-> B

ad

Inconsistencies

Missing

Randomness

Atypicalness

Duration

Denial/Misrep.

Major improvement over the first 15

interviews

Most improvements have occurred by 60

interviews

Source: GAIN coordinating center

Staff Experience Matters as well

Impact of the Number of Observations on Reliability Across Observations by Initial Reliability in a Wave

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.01 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 18

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

R in wave

Observations

Rel

iabi

lity

Acr

oss

OIb

serv

atio

ns

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

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

Identify Cut Points Where a Question Like “Peak Use” Is Likely to Become Unreliable

Below Cut Point, r2=22,

Above Cut Point, r2=.050

10

20

30

40

0 10 20 30 40

Above Cut point

Below 20 JointCut Point

Linear (Below 20Joint Cut Point)

Linear (Above Cutpoint)

Peak Joints Reported at time 1 on GAIN

Peak Joints Reported at Time 2 on Form 90

Source: Dennis et al 2004

Impact of Number of Items on Reliability (Alpha) Observed by Average Inter-item Correlation

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 3 5 7 9 11 13 15 18 20 22 24 26 28 30

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Avg Item R

Number of Items

Rei

labl

ity

(Alp

ha)

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

Structure of GAIN’s Psychopathology Measures and Validity Checks

Example of how scales can also be inter-related and used for validation

S u bs ta nce Issu es Ind e x (S II)S u b sta n ce A b u se S ca le (S A S )S u b sta n ce D ep e n de n ce S ca le (S D S )

S u b sta nce P ro b le m S ca le

S o m atic S ym p to m Ind e x (S S I)D e p re ss io n S ym p to m S ca le (D S S )H o m icid a l/S u ic id a l T h ou g h t In d e x (H S T I)A n x ie ty/F e a r S ym pto m S ca le (A F S S )T ra u m a tic D is tress S ca le (T D S )

In te rna l M e n ta l D is tre ss S ca le

In a tte n tive n ess D iso rd e r S ca le (ID S )H yp e ra c tiv ity-Im p lu s iv ity S ca le (H IS )C o n d u c t D is o rd e r S ca le (C D S )

B e h av io r C o m p le x ity S c a le

G e n e ra l C o n flic t T ac tic S ca le (G C T S )P ro p e rty C rim e S ca le (P C S )In te rp e rso n a l C rim e S ca le (IC S )D ru g C rim e S ca le (D C S )

C rim e /V io le n ce S ca le

G e n e ra l In d iv id u a l S e verity S ca le (G IS S )

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

Key Advantages of Creating Scalesand 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

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)

Note you can also create a summary measures across different sources of data

Source: Lennox et al 2006 (CFI=.98)

Impact of Item Discrimination (aka steepness of slope) on Sample Size Requirements

0100200300400500600700800900

1000

0.5 1.0 1.5 2.0 2.5

n pe

r gr

oup

for

80%

pow

er

d=.2 (50 items)

d=.4 (10 items)

d=.8 (10 items)

True Effect Size(number of items)

Flat<-Average Item Discrmination/slope -> Steep 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

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

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

DSM (GAIN) Symptoms of Dependence (3+ Symptoms)

Physiologicaln. 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-physiologicalq. 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?)

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?)

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

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

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)

Sample Characteristics

Adolescents: <18 (n=2474)

Young Adult: 18-25

(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 15.6 20.2 37.3

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

Item Relationships Across SubstancesR

asch

Sev

erit

y M

easu

re

Des

p.P

H/M

H (

+0.

10)

Giv

e u

p a

ct. (

+0.

05)

Can

't s

top

(+

0.05

)

Tim

e C

on

s. (

-0.2

1)

Lo

ss o

f C

on

tro

(-0

.10)

Haz

ard

ou

s (-

0.03

)

Des

pit

e L

egal

(+

0.10

)

Ro

le F

ailu

re (

-0.1

2)

Fig

hts

/tro

ub

. (0.

17)

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80Tim

e Con

s

Role F

ailur

e

Fights/

troub

.

Loss

of C

ontro

l

Hazar

dous

Tolera

nce

Can't s

top

Give u

p ac

t.

Desp.

PH/MH

Despit

e Le

gal

With

draw

al

To

lera

nce

(0.

00)

Wit

hd

raw

al (

+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

1st dimension explains 75% of variance (2nd explains 1.2%)Average Item Severity (0.00)

Symptom Severity Varied by Drug

Easier to endorse

hazardous use for

ALC/CAN

Ras

ch S

ever

ity

Mea

sure

ALC

ALC

ALC

ALCALC

ALC

ALC

AMP

AMP

AMPAMP

AMP

AMP

CAN

CAN

CAN

CAN

COC COC

COC

OPI

OPIOPI

OPI

OPI

ALC

ALC ALC

ALC

AMP

AMP

AMP

AMP

AMP

CAN

CAN

CAN

CAN

CAN

CAN

CAN

COC

COCCOC

COC

COC

COCCOC

COC

OPI

OPI OPIOPI

OPI

OPI

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80Tim

e Con

s.

Role F

ailur

e

Fights/

troub

.

Loss

of C

ontro

l

Hazar

dous

Tolera

nce

Can't s

top

Give u

p ac

t.

Desp.

PH/MH

Despit

e Le

gal

With

draw

al

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

Symptom Severity Varied Even More By AgeR

asch

Sev

erit

y M

easu

re

<18 <18

<18

<18

<18

18-25

18-25

18-25

18-25

18-25

18-25

26+

26+

26+

26+

26+

26+

26+

26+

26+

26+

26+<18<18

<18

<18

<18

<18

18-25

18-25

18-25

18-25

18-25

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8Tim

e Con

s.

Role F

ailur

e

Fights/

troub

.

Loss

of C

ontro

l

Hazar

dous

Tolera

nce

Can't s

top

Give u

p ac

t.

Desp.

PH/MH

Despit

e Le

gal

With

draw

al

<18

18-25

26+

Age

Adults more likely to endorse most symptomsMore likely to lead to

fights among Adol/YAHazardous use more

likely among Adol/YA

Continued use in spite of legal problems more likely among Adol/YA

Comparing Substances

Amp 0.88

Opi 0.43

Coc -0.21

Alc -0.44

Can -0.66

-1.2-1.0-0.8-0.6-0.4-0.20.00.20.40.60.81.01.21.41.6

TimeG

et

Respo

n

Troub

le

Longe

r

Unsafe

Mor

eAOD

Cutdow

n

GivupA

cs

Med

prob

s

Law With

draw

l

Avg.S

x. Sev

.

Rasch Severity by Past Month Status

-3.50

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

None DiagnosticOrphan in early

remission

DiagnosticOrphan

Lifetime SUD

in early remission

LifetimeSUD in CE

45+ days

Abuse Only

DependenceOnly

BothAbuse

and Dependence

Ras

ch S

ever

ity

Mea

sure

Diagnostic Orphans (1-2 dependence symptoms)

are lower, but still overlap with other clinical groups

Severity by Past Year Symptom Count

-4.00-3.50-3.00-2.50-2.00-1.50-1.00-0.500.000.501.001.502.00

0 1 2 3 4 5 6 7 8 9 10 11

Ras

ch S

ever

ity

Mea

sure

1. Better Gradation2. Still a lot of overlap in range

Severity by Number of Past Year SUD Diagnoses

Ras

ch S

ever

ity

Mea

sure

-4.00

-3.50

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

0 1 2 3 4 5

1. Better Gradation2. Less overlap in range

Severity by Weighted (past month=2, past year=1)

Number of Substance x SUD Symptoms

Ras

ch S

ever

ity

Mea

sure

-4.00-3.50-3.00-2.50-2.00-1.50-1.00-0.500.000.501.001.502.00

0 1-4 5-8 9-12 13-16 17-20 21-24 25-30 31-40 41+

1. Better Gradation2. Much less overlap in range

Average Severity by Age

-4.00

-3.50

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

Adolescent (<18) Young Adult (18-25) Adult (26+)

1. Average goes up with age2. Complete overlap in range3. Narrowing of distribution on higher severity at older ages

Construct Validity (i.e., does it matter?)

Fre

que

ncy

Of U

se

Pa

st W

eek

With

dra

wal

Em

otio

na

l P

robl

em

s

Re

cove

ry

En

viro

nmen

t

So

cia

l Ris

k

DSM diagnosis \a 0.47 0.40 0.32 0.30 0.30

Symptom Count Continuous \b 0.48 0.43 0.39 0.32 0.31

Weighted Drug x Symptom \c,d 0.26 0.27 0.19 0.29 0.09

\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 Symptomcount didbetter than

DSM

Weighted Symptom Rasch \c 0.57 0.46 0.39 0.39 0.32

Rasch does

a little Betterstill

Weighted symptom by drug count severity did

WORSE

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