Title: Estimating Drug Use Prevalence Using Latent Class Models with Item Count Response as One Indicator
1Estimating Drug Use Prevalence Using Latent Class
Models with Item Count Response as One Indicator
- Paul Biemer
- RTI International and
- University of North Carolina
2Presentation Outline
- Describe the item count (IC) method
- Present standard IC estimates of cocaine use and
compare them with direct estimates - Describe a method for adjusting the standard
estimates for measurement bias - Present the bias corrected estimates
- Implications for future applications of IC
3What is the item count method?
- Used for estimating the prevalence of sensitive
behaviors - Sensitive behavior is one of a small number of
behaviors in a list - Respondents indicate only how many behaviors in
the list apply, not which ones - If the average number of other behaviors is
known, prevalence of the sensitive behavior can
be estimated
4Illustration One Pair of Lists
ICQ (short list)
ICQ (long list)
Long list short list sensitive item
5Illustration One Pair of Lists
ICQ (short list)
ICQ (long list)
Long list short list sensitive item
6Prevalence Estimate for Single Pair Design
Prevalence avg count for long list avg count
for short list
7Example of Youth ICQ ICQ1 Short
- Next is a list of things that you may or may not
have done in the past 12 months. How many of the
things on this list did you do in the past 12
months, that is since DATE 12 MONTHS AGO. - Rode with a drunk driver
- Walked alone after dark through a dangerous
neighborhood - Rode a bicycle without a helmet
- Went swimming or played outdoor sports when it
was lightning -
8Example of Youth ICQ ICQ1 Long
- Next is a list of things that you may or may not
have done in the past 12 months. How many of the
things on this list did you do in the past 12
months, that is since DATE 12 MONTHS AGO. - Rode with a drunk driver
- Walked alone after dark through a dangerous
neighborhood - Rode a bicycle without a helmet
- Went swimming or played outdoor sports when it
was lightning - Used cocaine, in any form, one or more times
-
9Results Using the Standard IC Estimator
10Item Count Estimates by Age and Gender
11Pseudo IC Variable
- Recall each of the 4 IC short-list item was asked
separately - Form a pseudo- IC variable corresponding to the
IC short-list response where - Pseudo-IC number of positive responses to the
- 4 IC short-list questions asked separately
12Item Count Response by Pseudo-Item Count Response
for Both Short IC Questions
13Objective of the Modeling Approach
- Combine all data on cocaine use including
- Direct question
- Item count pair of questions
- Pseudo-item count data
- Apply latent class models to predict cocaine use
- Why latent class models?
- Accounts for measurement error in all the
observations - Model assumptions are plausible for the current
application
14Central Idea for the Modeling Approach
Let A short form response D long form
response A is an indicator of X (latent
variable) D is an indicator of Z (latent
variable)
Standard IC estimator is
Use LCA to estimate Z and X and form
Repeat this for each of the two IC pairs
15Path Model for One IC Pair of Questions
Grouping variable
Short IC Question
Pseudo Short IC Question
Cocaine
Long IC Question
16Path Model for One IC Pair of Questions
Grouping variable
Short IC Question
Pseudo Short IC Question
Cocaine
Long IC Question
17Path Model for One IC Pair of Questions
Grouping variable
Short IC Question
Pseudo Short IC Question
Cocaine
Long IC Question
18Data Likelihood
Random split half-sample
MAR
Subsample I
Subsample II
19where
denotes summation over x, y and z xy.
20Estimation of Cocaine Use Prevalence
Parameters
Estimators
from LCA
Cocaine prevalence
21Corrected Estimator of Cocaine Prevalence
Corrected cocaine use prevalence
NSDUH Estimate
Correction estimated from LCM
22Results Using the LCM-based Estimator
23Estimates of Classification Accuracy from LCM
24NSDUH and Model-based IC Estimates of Past Year
Cocaine Use Prevalence by Gender and Age
25Summary
- Despite careful design and large sample size, the
standard item count method failed - Estimates of cocaine use prevalence were less
than direct estimates from NSDUH - Major cause appeared to be measurement error
- Difficult response task
- IC masking may be ineffective for eliciting
truthful counts - IC direct questions may be interpreted
differently - Latent class model corrections were successful at
reducing downward bias - NSDUH estimates were increased by 40 on average
- Standard errors were much larger
26Further reading -
- Biemer, P. and Brown, G. (2005). Model-based
Estimation of Drug Use Prevalence with Item Count
Data, Journal of Official Statistics, Vol. 21,
No. 3. - Biemer, P., B.K. Jordan, M. Hubbard, and D.
Wright (2005). A Test of the Item Count
Methodology for Estimating Cocaine Use
Prevalence. In Kennet, J., and J. Gfroerer
(Eds.), Evaluating and Improving Methods Used in
the National Survey on Drug Use and Health.
Rockville, MD SAMHSA