Intervention Studies - Cluster (Randomized) Trials

•Intervention at the cluster level- What are clusters? - Why intervene in clusters (rather than in individuals)?

•Cluster trials - Study designs

- Randomized vs nonrandomized - Methods of allocation (randomization) of clusters to treatment group-Data collection

•Key issues in cluster trials - Homogeneity within clusters

What Are Clusters? (Last)

• Cluster/clustering - Aggregation of.. In space and/or time … greater than expected by chance

- families - GP practices - communities

• Cluster sampling - Each unit selected is a group rather than individual

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)

Why do Individuals Cluster?

• Selection into clusters (students accepted into epidemiology program; individuals choose MDs)

• Similar exposure leads to clustering (job loss results in move to low-income housing; education related to job potential)

• Individuals within clusters interact with each other- tend to be more similar to each other than to individuals

in other clusters - may respond similarly (tend to be positively correlated,

violating assumption of independence required for the application of standard statistical test)

• Paradigm shift (following Framingham) in early 70s from individual- to cluster-level interventions

Why Intervene at the Cluster Level?

• Targeting interventions to total population vs high risk group (e.g., hypertension): - population strategy aims to shift population

distribution - high-risk strategy targets those with HBP

• Risk behaviors are socially influenced - social learning theory (Bandura – observing

and modeling behavior) - denormalization of smoking

• Environmental change may be easier than voluntary behavior change (i.e., tax cigarettes vs stop smoking)

Why Intervene at the Cluster Level? (con’d)

• Administrative convenience (recruit all schools in one school board)

• Some interventions are naturally applied at the cluster level (i.e., fluoridation, media education)

• To avoid treatment group contamination (all patients in one MD practice assigned to same treatment group)

• To enhance subject adherence (laws on use of bicycle helmets)

Issues in Interventions Applied at

the Cluster Level

• Intervention applied at the group level with little attention to individuals

• Overly-optimistic expectations about treatment effects

• Beat the secular trend• Usually complex • Takes time• Sustainability of intervention

Application of Intervention

• 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 as well as outcomes (to avoid a

Type III error)

• Formative evaluation– feedback of results of process evaluation to help

improve intervention

• Qualitative methods

Examples of Cluster-Level Interventions

• Screening or immunization programs delivered to residents of a geographic area

• Health promotion programs delivered in towns, schools

• Services provided to primary care practice populations

• Mass education interventions

• Nutritional, environmental sanitation interventions– Delivered to household, village etc– Latrines, dietary supplements

Cluster (Randomized) Trial

• Experiments in which clusters rather than individuals are allocated to treatment groups

• Key feature: inferences are applied at the individual level while attribution of treatment is at the cluster-level

• Thus the unit of attribution is different from the unit of analysis

• Lack of independence among individuals in the cluster creates methodological challenges at the design and analysis (must also account for between cluster variation)

Cluster Trial Designs• Single cluster

Before-after O X OTime series O O O X O O O

• Multiple clusters - One intervention and one control cluster

O X OO O

- One intervention and multiple control clusters

- Multiple intervention and control clusters

To Randomize Or Not?

• Complete randomization usually feasible only when large # clusters

• Quasi-experimental (intervention studies in which the comparisons used to assess program effectiveness are not based on random assignment)

• Grass-root versus social experiments with more clusters

Allocation of Clusters to Treatment Group

• In pairs

• Stratify clusters and then allocate

• Match clusters and then allocate

• 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

Data Collection

• Serial cross-sectional surveys vs follow-up of cohort– Is intervention aimed at whole community or

“stayers” only?– Individual or community-level change?– Repeat testing effects– Attrition

• Because blinding of subjects not possible, try to use objective outcome measures (e.g., serum cotinine vs self-reported smoking)

Key Issue - Homogenity Within Clusters

• Reduction in effective sample size – extent depends on within-cluster correlation and on average cluster size

• Approaches to estimate sample size and to analyze the data must take this into account

- standard sample size calculations lead to an underpowered study

- standard analytic methods bias p-values downwards (leads to spurious statistical significance)

• Interest in computing the intra-cluster correlation coefficient (measure of within-cluster homogeneity) and then correcting for homogeneity with a “variance inflation factor” (“design effect”)

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 biased and

less costly than individual-level measures

• Entire cluster (rather than individuals) may be lost to follow-up

Ethical Issue in Cluster Trials

• Individual consent difficult/not possible prior to allocation of clusters to treatment groups

Strategies to Improve Precision in Cluster Trials

• Increase number of clusters (even if only in the control group)

• Match or stratify clusters in the design on baseline variables with prognostic value

• Obtain baseline information on important prognostic variables

• Take repeated measures over time

• Develop measure to assure adherence and minimize loss to follow-up

Analysis of Cluster 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 - Mixed models (fixed and random effects) - Multi-level modeling - GEE

True/False

• In a cluster trial of the effect of immunization, children in a daycare are randomized to receive or not receive vaccine

• Using a cluster as the unit of allocation results in a study that is not as powerful as if individuals had been allocated

• The intra-class correlation coefficient is a measure of within-cluster variation

• Sample size computation should account for both within and between-cluster variation in cluster trials

• Randomization should be used whenever possible in cluster trials

True/False (cont.)

• Use of matching or stratification of clusters increases precision in cluster trials

• Repeat cross-sectional surveys are less useful than data collection using a cohort approach in cluster trials

• p-values will decrease when clustering is taken into account in the analysis of a cluster trail

• Control for confounding is not possible is a cluster trial

• Interventions using a high risk approach will generally save more lives than a population-based approach