Sample size calculator for cluster randomized trials

ArticleinComputers in Biology and Medicine 34(2):113-25 · April 2004with244 Reads
DOI: 10.1016/S0010-4825(03)00039-8 · Source: PubMed
Cluster randomized trials, where individuals are randomized in groups are increasingly being used in healthcare evaluation. The adoption of a clustered design has implications for design, conduct and analysis of studies. In particular, standard sample sizes have to be inflated for cluster designs, as outcomes for individuals within clusters may be correlated; inflation can be achieved either by increasing the cluster size or by increasing the number of clusters in the study. A sample size calculator is presented for calculating appropriate sample sizes for cluster trials, whilst allowing the implications of both methods of inflation to be considered.
    • "Failing to account for clustering increases the risk of Type 1 error (e.g., finding a significant difference where there is not one). The need to address and report these additional considerations was formalized in the Consolidated Standards of Reporting Trials extension to cluster randomized trials (CONSORT extension ), which highlights the key areas to report on when utilizing this research design, over and above the reporting guidelines for a standard RCT (Campbell, Elbourne, & Altman, 2004). "
    [Show abstract] [Hide abstract] ABSTRACT: Growing use of cluster randomized control trials (RCTs) in health care research requires careful attention to study designs, with implications for the development of an evidence base for practice. The objective of this study is to investigate the characteristics, quality, and reporting of cluster RCTs evaluating occupational therapy interventions to inform future research design. An extensive search of cluster RCTs evaluating occupational therapy was conducted in several databases. Fourteen studies met our inclusion criteria; four were protocols. Eleven (79%) justified the use of a cluster RCT and accounted for clustering in the sample size and analysis. All full studies reported the number of clusters randomized, and five reported intercluster correlation coefficients (50%): Protocols had higher compliance. Risk of bias was most evident in unblinding of participants. Statistician involvement was associated with improved trial quality and reporting. Quality of cluster RCTs of occupational therapy interventions is comparable with those from other areas of health research and needs improvement.
    Full-text · Article · Dec 2015
    • "We considered a power of 80 % and a statistical significance of 5 %. To take into account, the cluster nature of the study, we inflated the standard sample size estimates by a factor, i.e. the design effect = 1 + ρ(m − 1), where m is the average cluster size and ρ is the estimated intracluster correlation coefficient (ICC) [31]. As Belgian data on the ICC for the primary outcome measure were not available, we made estimations for two extreme values, i.e. 0.05 and 0.5. "
    [Show abstract] [Hide abstract] ABSTRACT: Background: Ageing has become a worldwide reality and presents new challenges for the health-care system. Research has shown that potentially inappropriate prescribing, both potentially inappropriate medications and potentially prescribing omissions, is highly prevalent in older people, especially in the nursing home setting. The presence of potentially inappropriate medications/potentially prescribing omissions is associated with adverse drug events, hospitalisations, mortality and health-care costs. The Collaborative approach to Optimise MEdication use for Older people in Nursing homes (COME-ON) study aims to evaluate the effect of a complex, multifaceted intervention, including interdisciplinary case conferences, on the appropriateness of prescribing of medicines for older people in Belgian nursing homes. Methods/design: A multicentre cluster-controlled trial is set up in 63 Belgian nursing homes (30 intervention; 33 control). In each of these nursing homes, 35 residents (≥65 years) are selected for participation. The complex, multifaceted intervention comprises (i) health-care professional education and training, (ii) local concertation (discussion on the appropriate use of at least one medication class at the level of the nursing home) and (iii) repeated interdisciplinary case conferences between general practitioner, nurse and pharmacist to perform medication review for each included nursing home resident. The control group works as usual. The study period lasts 15 months. The primary outcome measures relate to the appropriateness of prescribing and are defined as (1) among residents who had at least one potentially inappropriate medication/potentially prescribing omission at baseline, the proportion of them for whom there is a decrease of at least one of these potentially inappropriate medications/potentially prescribing omissions at the end of study, and (2) among all residents, the proportion of them for whom at least one new potentially inappropriate medication/potentially prescribing omission is present at the end of the study, compared to baseline. The secondary outcome measures include individual components of appropriateness of prescribing, medication use, outcomes of the case conferences, clinical outcomes and costs. A process evaluation (focusing on implementation, causal mechanisms and contextual factors) will be conducted alongside the study. Discussion: The COME-ON study will contribute to a growing body of knowledge concerning the effect of complex interventions on the use of medicines in the nursing home setting, and on factors influencing their effect. The results will inform policymakers on strategies to implement in the near future. Trial registration: Current Controlled Trials ISRCTN66138978.
    Full-text · Article · Dec 2015
    • "To account for intracluster correlation, sample sizes required for cluster randomized trials must be increased to reach the desired power [2]. Sample size calculation formulas for cluster randomized trials are widely available [2,456. One method to account for clustering in sample size estimates , assuming constant cluster sizes, involves using the variance inflation factor (VIF) [2] . "
    [Show abstract] [Hide abstract] ABSTRACT: Few studies have comprehensively reported intracluster correlation coefficient (ICC) estimates for outcomes collected in primary care settings. Using data from a large primary care study, we aimed to: a) report ICCs for process-of-care and clinical outcome measures related to cardiovascular disease management and prevention, and b) investigate the impact of practice structure and rurality on ICC estimates. We used baseline data from the Improved Delivery of Cardiovascular Care (IDOCC) trial to estimate ICC values. Data on 5,140 patients from 84 primary care practices across Eastern Ontario, Canada were collected through chart abstraction. ICC estimates were calculated using an ANOVA approach and were calculated for all patients and separately for patient subgroups defined by condition (i.e., coronary artery disease, diabetes, chronic kidney disease, hypertension, dyslipidemia, and smoking). We compared ICC estimates between practices in which data were collected from a single physician versus those that had multiple participating physicians and between urban versus rural practices. ICC estimates ranged from 0 to 0.173, with a median of 0.056. The median ICC estimate for dichotomous process outcomes (0.088) was higher than that for continuous clinical outcomes (0.035). ICC estimates calculated for single physician practices were higher than those for practices with multiple physicians for both process (average 3.9-times higher) and clinical measures (average 1.9-times higher). Urban practices tended to have higher process-of-care ICC estimates than rural practices, particularly for measuring lipid profiles and estimated glomerular filtration rates. To our knowledge, this is the most comprehensive summary of cardiovascular-related ICCs to be reported from Canadian primary care practices. Differences in ICC estimates based on practice structure and location highlight the importance of understanding the context in which external ICC estimates were determined prior to their use in sample size calculations. Failure to choose appropriate ICC estimates can have substantial implications for the design of a cluster randomized trial.
    Full-text · Article · Dec 2015
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