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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