1. 1 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

2. 2 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

3. 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 HBP

4. 4 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)

5. 5 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

6. 6 What is a cluster? (Webster’s) –CLUSTER: a number of things growing together OR of things or persons collected or grouped closely together

7. 7 Clustering - reasons Clustering: –individuals within clusters tend to be more similar to each other than to individuals in other clusters Reasons: –selection –common exposures

8. 8 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

9. 9 Examples of group or cluster interventions Educational interventions Group psychological interventions Nutritional, environmental sanitation interventions: –delivered to household, village etc –latrines, dietary supplements

10. 10 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)

11. 11 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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13. 13 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

14. 14 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

15. 15 Community trial designs Single community: Before-after:O X O Single (interrupted) time series: O O O X O O O One intervention and one control community O X O O One intervention and multiple control communities Multiple intervention and control clusters

16. 16 To randomize or not? Complete randomization usually feasible only when large # clusters

17. 17 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

18. 18 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)

19. 19 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

20. 20 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

21. 21 Ethical issues in cluster randomization Individual consent not possible prior to randomization (or other method of allocation)

22. 22 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

23. 23 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

24. 24 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