Randomized Designs for Person, Place,
and Time
For Effectiveness Trials and Beyond
C Hendricks Brown
University of South Florida
Peter Wyman U Rochester
Jing Guo USF Juan Peña U Rochester
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Support
R01-MH40859 (Brown)
Methodology for Mental Health / Drug Abuse Prevention & Early Intervention
Designs & 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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A. Types of Questions: Phases of prevention
Sustaining &
Disseminating
Systemwide
Sustaining
Systemwide
Going to Scale
Going-to-Scale
Sustainability
Effectiveness
Efficacy
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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 Evidencebased 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 Works
For 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)
2)
3)
4)
5)
6)
7)
8)
Efficacy – Impact in optimally controlled settings
Effectiveness – Impact in real world settings
Implementability – What level of intervention produces
what level of impact; what does it take to implement?
Adaptability – How does variation in intervention
delivered affect impact?
Extensibility – What impact is achieved when delivered to
different persons/settings?
Sustainability – Does impact continue after training ?
Scalability– What impact is achieved when the
intervention is expanded system wide, or in larger
contexts?
Disseminability/ Adoptability – What interventions are
effective in having new
communities adopt such a
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program?
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
Selection
Bias
Located
Contacted
Eligible
Consented
Assessed at
Baseline
Pre-Intervention Design
Randomized and Intervention Begins
Participation
Adherence
Intervention Ends
Followed Up
Participation
Bias
Attrition
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Adapted from Brown & Liao, 1999
Intervention Design
Post-Intervention Design
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, GW
Sample
Randomize
Intervention
Randomize to Invitation
Routine
Intensity
High
Intensity
Measure Participation and
Measure Outcomes
Control
Can participation
be increased, and if so,
how do these people
benefit compared to those
who would normally have
participated?
Model Participation and
Measure Outcomes
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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
Time
Place
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Three Descriptive Epidemiologic Concepts
Viewed Three Different Ways
Concepts
Epidemiology
Person
Person
Environment
Place
Developmental
Course
Time
Analytical
Methods
Multivariate
or Personlevel
Analyses
Multi-level
Analyses
Longitudinal
Analyses
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Randomized
Designs
RCT: Individual
Level Randomized
Trials
Group/Cluster/Place
Based Randomized
Trials
Wait-Listed
Designs,
Randomizations at
Different Times
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?
Treatment
Placebo 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 Pgm
Brown & 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
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A
B
A
B
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
School
Combinations of Interventions
Assign Available
Services
Classrm Universal No
No Available
Services
Universal
No
Universal
Moderate Moderate
Moderate
Moderate
High
High
High
Universal
Child
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)
Tried to kill self in last four
weeks
3%
6%
6%
7%
Tried to kill self in last year
10%
10%
Ever tried to kill self
0%
Eighth Grade
2%
4%
6%
Tenth Grade
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8% 10% 12%
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
Effectiveness
0.3
10
0.2
5
4
0.1
3
2
1.5
1
0.0
Probability of Referral
0.4
0.5
Probability of Someone Referring Suicidal Child
as Function of Proportion of Staff Trained and Training Effectiveness
0.0
0.2
0.4
0.6
Proportion Trained
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0.8
1.0
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 Design
Year
1
2
Time Block
Time
1
2
3
4
5
6
7
8
Wait-Listed Design
Intervention
16
Wait-Listed
16
32
0
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Dynamic Wait-Listed
Design
Intervention
Wait-Listed
4
28
8
24
12
20
16
16
20
12
24
8
28
4
32
0
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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Power
0.6
0.8
1.0
Added Power from Dynamic Wait
Listed Design
0.2
0.4
32 Time Blocks
16 Time Blocks
8 Time Blocks
6 Time Blocks
4 Time Blocks
Standard Wait List
1.0
1.1
1.2
1.3
Effect = Intervention Rate / Control Rate
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1.4
1.5
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
Intervention Conditions
Child
Intervention
Yes
No
Child Intervention First
Intervention Conditions
Child
Intervention
Yes
No
Universal Classroom Intervention
Yes
No
A
B
C
Universal Classroom Intervention
Yes
No
D
F
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
Intervention Conditions
Child
Intervention
Yes
No
Late Intervention
Intervention Condition
Child
Intervention
Yes
No
Reduce Environmental Risk
Yes
No
A*
B
C*
D
Reduce Environmental Risk
Yes
No
E **
F
G **
H
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Summary
For Effectiveness Trials and Beyond
Often Dictated by Community Needs
“Everyone Gets This by a Certain Date”
Randomization involving TIME
Dynamic Wait-Listed Designs are Efficient
Newer Designs often require randomization involving 2 or 3
of
PERSON, PLACE, and TIME
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