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Hopkins PSMG Meeting
Randomized Designs for Person, Place, and Time
For Effectiveness Trials and Beyond
C Hendricks BrownUniversity of South Florida
Peter Wyman U RochesterJing Guo USF Juan Peña U Rochester
Hopkins PSMG Meeting
Support
R01-MH40859 (Brown) Methodology for Mental Health / Drug Abuse Prevention & Early InterventionDesigns & Analyses for Mental Health Preventive Trials
NIMH and NIDA
3R01MH040859-15S1 (Brown)Methodology for Population-Based Approaches to the Prevention of Suicide
NIMH Office of Rare Diseases CDC Injury Prevention Center
R34MH071189-01 (Wyman) RCT of Gatekeeper Training for Suicide Prevention
NIMH
P20MH071897-01 (Caine) Developing Center On Public Health and Population Interventions For The Prevention Of Suicide
NIMH
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Brown C.H., Wyman P. A., Guo J, and Peña J. (2005). Dynamic wait-listed designs for randomized trials: New designs for prevention of youth suicide. Submitted for publication.
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Outline
A. Types of Scientific Questions
B. Typology of Trials Matching these Questions
C. Randomization involving Person, Place, and Time
D. Dynamic Wait-Listed Design for suicide prevention
E. Randomizations of Person/Place/Time
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Efficacy
Effectiveness
Sustainability
Going to Scale
Sustaining Systemwide
Efficacy
Effectiveness
Sustainability
Going-to-Scale
Disseminating & Sustaining Systemwide
A. Types of Questions: Phases of prevention
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Do we need random assignment as we move to effectiveness and beyond?
Nonrandomized designs Blueprints Project U Colorado Denver
Alternative Study Designs for Evidence-based Practice: Harnessing Natural Variation for Effectiveness Research
Agency for Healthcare Research and Quality (AHRQ),
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Prevention Field Trials Needed to Determine Precise Information
about
What WorksFor Whom
Under What Conditions
Because …
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There are very few Broad Street Pump Handles Left to Remove
John Snow’s Map of London
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To Obtain Precise Answers
• Need to rely on Very Carefully Designed Intervention Studies
• Random Assignment needed,
• In novel ways ……
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B. Typology of Research Questions/Designs
1) Efficacy – Impact in optimally controlled settings2) Effectiveness – Impact in real world settings3) Implementability – What level of intervention produces
what level of impact; what does it take to implement?4) Adaptability – How does variation in intervention
delivered affect impact?5) Extensibility – What impact is achieved when delivered to
different persons/settings?6) Sustainability – Does impact continue after training ?7) Scalability– What impact is achieved when the
intervention is expanded system wide, or in larger contexts?
8) Disseminability/ Adoptability – What interventions are effective in having new communities adopt such a program?
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Extensibility Trials
• Selection Bias -- ASU Braver
• Participation Bias – UM, ASU, GW
Price/Vinokur/Sandler/Howe
-- U Miami Szapocznik
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Three Stages of a Trial Design
Target Population
Located Contacted Eligible Consented Assessed at Baseline
Randomized and Intervention Begins
Intervention Ends
Selection Bias
Participation Bias
Attrition
Participation Adherence
Followed Up
Adapted from Brown & Liao, 1999
Pre-Intervention Design
Intervention Design
Post-Intervention Design
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Extensibility Trials
Participation Trials
Can participation be increased and if so, how do these people benefit compared to those who would normally have participated?
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Extensibility Trial with Two Randomizations
Trial Inside a Trial U Michigan, GWSample
Intervention Control
Routine Intensity
High Intensity
Randomize
Randomize to Invitation
Measure Participation and
Measure Outcomes
Model Participation and Measure Outcomes
Can participation be increased, and if so, how do these people benefit compared to those who would normally have participated?
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Newer Examples of Experimental Manipulations
Extensibility Trial –
Intervening to Increase Participation
Effectiveness Trial --
Combinations of Intervention Components
Sustainability Trial –
Schedule for Supervision of a defined intervention
Scalability Trial –
Systems level intervention
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C. A General Framework for Randomizing Using Person Place,
and Time
Person
PlaceTime
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Three Descriptive Epidemiologic Concepts Viewed Three Different Ways
Concepts Epidemiology Analytical Methods
Randomized Designs
Person Person Multivariate or Person-level Analyses
RCT: Individual Level Randomized Trials
Environment Place Multi-level Analyses
Group/Cluster/Place Based Randomized Trials
Developmental Course
Time Longitudinal Analyses
Wait-Listed Designs, Randomizations at Different Times
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Units that can be Randomly Assigned in a Trial
• Persons – Inclusion/Exclusion Criteria • Places/Groups – Random assignment at
the group level Group/Cluster Randomized Trials- Murray
Place-Based Randomized Trials -- Boruch
• Time -- when to intervene
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Person Level Assignments
Persons Target Population
Inclusion / Exclusion Criteria Universal, Selective, Indicated, Treated
Extensibility Trials: Does intervention work effectively for Prevention
Lower risk subjects?Less likely to be identified or participate?
TreatmentPlacebo Nonresponders?Genetic Risk?
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Place-Based Randomized Trials
Places/Groups – Classroom, Schools, Child Welfare Settings, MH/Drug Abuse Clinics
Schools -- Sloboda 88 HS’s New DARE 7th & 9th
Classrooms within Schools – Balt Prev PgmBrown & Liao (1999) Am J Commun Psychol
Rural Communities – PROGRESA, Mexico
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Time Assignments
Time -- when to intervene
Wait-listed design: Half initially half later
Crossover Design A-B B-A
Multiple Baseline Designs
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Time (Persons)Randomized Clinical Trial with Blocking on Time
A B B A A B B A A B A B
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Person-Place Randomizations
Interventions at Two Levels
Example: Linking Classroom Prevention and an individually based Service Intervention within Schools
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Person/Place
Unit Combinations of Interventions
School Assign Available Services
No Available Services
Classrm Universal No Universal
Universal No Universal
Child Moderate
High
Moderate
High
Moderate
High
Moderate
High
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Place and Time
Group Based, Wait-Listed Design
Group Based, Multiple Baseline Design
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Randomization in Place and Time:
D. Dynamic Wait-Listed Designs
• Divide a set of schools into replicates
• Randomly assign WHEN a replicate converts from wait-listed to active intervention
Brown et al. (under review)
Wyman, Brown Ga Gatekeeper Trial: NIMH
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Randomization in Place and Time:School-Based Gatekeeper Training
for Suicide Prevention QPR: (Quinnett, 1995): Question a person (showing warning signs) about
suicide Persuade the person to get help Refer the person to the appropriate resource
Identify recognizable behaviors (“threats of suicide”)
Develop knowledge and skills to take action
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Primary Endpoint
Rate of referral of youth by school staff for suicidality
QPR should
• Increase rate of referral in those schools that have been trained
• Maintain this increase rate over the study
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Prevalence of Suicidal Behavior in Middle and High School
SmartTrack Survey Results(2003-2004 / 3291 Surveys)
10%
7%
6%
10%
6%
3%
0% 2% 4% 6% 8% 10% 12%
Ever tried to kill self
Tried to kill self in last year
Tried to kill self in last fourweeks
Eighth Grade Tenth Grade
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Proportion of Suicidal Attempters Identified by School System
6%
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Probability that a Single Staff Member Would be able to Identify
and Refer a Suicidal Child
0.03%
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Probability of Referral as Function of Proportion Trained and Training
EffectivenessProbability of Someone Referring Suicidal Child
as Function of Proportion of Staff Trained and Training Effectiveness
Proportion Trained
Pro
babi
lity
of R
efer
ral
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.1
0.2
0.3
0.4
0.5
1
1.52
3
4
5
10
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Comparison of Classic versus Dynamic Wait-Listed Designs
Standard Wait-List Design
Half are assigned immediately, half later
Dynamic Wait-Listed Design
Divide into replicates, then assign intervention or wait-listed control within each replicate
Train in 1st replicate early intervention schools, then in 2nd replicate, etc.
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Classic and Dynamic Wait-Listed DesignYear Time Block Wait-Listed Design Dynamic Wait-Listed
Design Time Intervention Wait-Listed Intervention Wait-Listed
1 4 28 2 8 24 3 12 20
1
4
16 16
16 16 5 20 12 6 24 8 7 28 4
2
8
32 0
32 0
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Comparing Power of Classic vs Dynamic Wait-Listed Design
Assumptions• Poisson Rates of Referral that vary randomly
over time but are proportional to time interval• Intervention Effect is Linear on the rate• General least squares analysis
• Efficiency in Asymptotic Variance fctn of Intervals 2 / (Intervals 2 – 1) 3 18% 4 25%Limit 33%
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More Realistic Setting
Poisson counts for each interval with a varying multiplicative rate parameter that has a Gamma distribution
Log-Linear model for intervention impact
Maximum likelihood analysis
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Efficiency Increases with Number of Time Blocks and Low Variability
in Rates over Time
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Recommendation for the Current Trial
Currently completed first half of trial with half receiving intervention, half wait-listed.
Switch to a dynamic wait-listed design in the second phase, 4 groups of 4 schools
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Added Power from Dynamic Wait Listed Design
Effect = Intervention Rate / Control Rate
Po
we
r
1.0 1.1 1.2 1.3 1.4 1.5
0.2
0.4
0.6
0.8
1.0
32 Time Blocks16 Time Blocks8 Time Blocks6 Time Blocks4 Time BlocksStandard Wait List
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E. Randomizations involving Person Place and Time
Timing of Multi Level Interventions
Testing of Intervention in the Presence of Gene by Environment Interactions
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Timing of Universal Classroom and Individual Level Interventions
Universal Classroom First Universal Classroom Intervention Intervention Conditions Yes No
Yes A Child Intervention No B C Child Intervention First
Universal Classroom Intervention Intervention Conditions Yes No
Yes D F Child Intervention No E
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Serotonin Transporter Gene SLC6A4 Long and Short Forms
Short form associated with increased depression, bipolar disorder, violent suicide
Low CSF serotonin occurs among short allele monkeys who are raised in deprived environments, but not in mother raised environments
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Comparison of Interventions for Those with Genetic Risk
(Short Alleles)Early Intervention
Reduce Environmental Risk Intervention Conditions Yes No
Yes A * B Child Intervention No C * D Late Intervention
Reduce Environmental Risk Intervention Condition Yes No
Yes E ** F Child Intervention No G ** H
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Summary
For Effectiveness Trials and BeyondOften Dictated by Community Needs
“Everyone Gets This by a Certain Date”
Randomization involving TIMEDynamic Wait-Listed Designs are Efficient
Newer Designs often require randomization involving 2 or 3 ofPERSON, PLACE, and TIME
Recommended