1 lecture 11: cluster randomized and community trials clusters, groups, communities why allocate...
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3 Clusters, groups, communities Intervention directed at entire community vs individuals: –mass educational programs –immunization campaigns Targeting interventions to total population vs high risk group (e.g., hypertension): –population strategy aims to shift population blood pressure distribution –high-risk strategy targets those with HBPTRANSCRIPT
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Lecture 11:Cluster randomized and
community trials• Clusters, groups, communities• Why allocate clusters vs individuals?• Randomized vs nonrandomized designs• Methods of allocation of intervention• Design issues
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Clusters, groups, communities
• Intervention directed at entire community vs individuals:– mass educational programs– immunization campaigns
• Targeting interventions to total population vs high risk group (e.g., hypertension):– population strategy aims to shift population blood
pressure distribution– high-risk strategy targets those with HBP
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Clusters, groups, communities
• Intervention directed at entire community vs individuals:– mass educational programs– immunization campaigns
• Targeting interventions to total population vs high risk group (e.g., hypertension):– population strategy aims to shift population blood
pressure distribution– high-risk strategy targets those with HBP
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What is a community?
• “.. Group of people living in a defined geographic area who share a common culture, are arranged in a social structure and exhibit some awareness of their identity as a group” (Nutbeam, 1986)
• “A group of individuals organized into a unit, or manifesting some underlying trait or common interest; loosely, the locality or catchment area population for which a service is provided, or more broadly, the state, nation, or body politic.” (Last, 2001)
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What is a cluster?
• (Last) – CLUSTER/CLUSTERING: Aggregation of
relatively uncommon events .. In space and/or time … greater than expected by chance.
– CLUSTER ANALYSIS: Statistical methods to group variables or observations into strongly interrelated subgroups
– CLUSTER SAMPLING: Each unit selected is a group rather than individual
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What is a cluster?
• (Webster’s) – CLUSTER: a number of things growing
together OR of things or persons collected or grouped closely together
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Clustering - reasons
• Clustering:– individuals within clusters tend to be more
similar to each other than to individuals in other clusters
• Reasons:– selection– common exposures
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Examples of community-level interventions
• Screening or immunization programmes delivered to residents of a geographic area
• Health promotion programmes delivered to towns, schools
• Services provided to primary care practice populations
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Examples of group or cluster interventions
• Educational interventions• Group psychological interventions• Nutritional, environmental sanitation
interventions:– delivered to household, village etc– latrines, dietary supplements
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Rationale for community interventions
• Environmental change may be easier than voluntary behavior change (e.g, tax cigarettes vs stop smoking)
• Risk behaviors are socially influenced• Some interventions are not selective (e.g.,
fluoridation)
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Reasons for carrying out evaluations at group or cluster level
• More appropriate for interventions delivered to groups
• Individual randomization may not be feasible because all members of group are treated same way
• Individual randomization, although feasible, may result in substantial “contamination”
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Examples
• “Grass roots” intervention:– Nurse-midwife program for low-income
women in Colorado– Various needle exchange programs for IDUs
• Usually not true experiments– communities not randomly allocated– quasiexperimental “non-equivalent” control
group design
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Examples
• Social experiment:– COMMIT– 11 pairs of matched communities– intervention: multi-component smoking cessation
• media and community-wide events• health care providers• work-site and other organizations• cessation resources
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Community trial designs
• Single community:Before-after: O X OSingle (interrupted) time series: O O O X O O O
• One intervention and one control communityO X OO O
• One intervention and multiple control communities• Multiple intervention and control clusters
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To randomize or not?
• Complete randomization usually feasible only when large # clusters
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Allocation of intervention
• Allocation of communities:– in pairs– stratified– matching or stratification factors:
• known predictors/correlates of outcome• cluster size and other characteristics• matching can be ignored in analysis when matching
variable is weakly correlated with outcomes
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Study design
• Serial cross-sectional surveys vs follow-up of cohort– is intervention aimed at whole community of “stayers”
only?– individual or community-level change?– Testing effects– attrition
• Because blinding of subjects not possible, try to use objective outcome measures (e.g., serum cotinine vs self-reported smoking)
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Study design (cont)
• Community-level vs individual-level outcomes/indicators– e.g., tobacco sales to assess smoking prevention
intervention– cluster-level measures may be less biassed and
less costly than individual-level measures
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Study design (cont)
• Develop causal model (hypotheses about how program should work)– measure key elements of model to understand why
intervention was (or was not) successful– assess process and outcomes
• Formative evaluation:– feedback of results of process evaluation to help
improve intervention?• Qualitative (ethnographic) methods
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Ethical issues in cluster randomization
• Individual consent not possible prior to randomization (or other method of allocation)
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Analysis of community-level trials
• Failure to account for clustering in analysis is common in group-level interventions (Donner)
• Analysis that accounts for clustering will yield more conservative level of statistical significance
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10 Key Considerations(adapted from Ukoumunne et al, 1999)
• Recognize the cluster as the unit of intervention or allocation
• Justify the use of cluster as unit of intervention or allocation (these methods are not as powerful as individual designs)
• Include enough clusters (at least 4 per group)• Randomize clusters when possible• Allow for clustering when computing sample size
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10 Key Considerations(cont.)
• Consider the use of matching or stratification of clusters where appropriate (but matching methods limit the statistical analyses that can be done)
• Consider different approaches to repeated assessments in prospective evaluations:– cohort vs repeated cross-sections
• Allow for clustering at time of analysis • Allow for confounding by individual and cluster characteristics • Include estimates of intracluster correlations of key outcomes,
to aid in planning of future studies