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Scale and shape issues in focused cluster power for count data.

ABSTRACT: BACKGROUND: Interest in the development of statistical methods for disease cluster detection has experienced rapid growth in recent years. Evaluations of statistical power provide important information for the selection of an appropriate statistical method in environmentally-related disease cluster investigations. Published power evaluations have not yet addressed the use of models for focused cluster detection and have not fully investigated the issues of disease cluster scale and shape. As meteorological and other factors can impact the dispersion of environmental toxicants, it follows that environmental exposures and associated diseases can be dispersed in a variety of spatial patterns. This study simulates disease clusters in a variety of shapes and scales around a centrally located single pollution source. We evaluate the power of a range of focused cluster tests and generalized linear models to detect these various cluster shapes and scales for count data. RESULTS: In general, the power of hypothesis tests and models to detect focused clusters improved when the test or model included parameters specific to the shape of cluster being examined (i.e. inclusion of a function for direction improved power of models to detect clustering with an angular effect). However, power to detect clusters where the risk peaked and then declined was limited. CONCLUSION: Findings from this investigation show sizeable changes in power according to the scale and shape of the cluster and the test or model applied. These findings demonstrate the importance of selecting a test or model with functions appropriate to detect the spatial pattern of the disease cluster.

SUBMITTER: Puett RC

PROVIDER: S-EPMC1079923 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThere is an abundance of guidance for the interim monitoring of individually randomised trials. While methodological literature exists on how to extend these methods to cluster randomised trials, there is little guidance on practical implementation. Cluster trials have many features which make their monitoring needs different. We outline the methodological and practical challenges of interim monitoring of cluster trials; and apply these considerations to a case study.Case studyThe E-MOTIVE study is an 80-cluster randomised trial of a bundle of interventions to treat postpartum haemorrhage. The proposed data monitoring plan includes (1) monitor sample size assumptions, (2) monitor for evidence of selection bias, and (3) an interim assessment of the primary outcome, as well as monitoring data completeness. The timing of the sample size monitoring is chosen with both consideration of statistical precision and to allow time to recruit more clusters. Monitoring for selection bias involves comparing individual-level characteristics and numbers recruited between study arms to identify any post-randomisation participant identification bias. An interim analysis of outcomes presented with 99.9% confidence intervals using the Haybittle-Peto approach should mitigate any concern regarding the inflation of type-I error. The pragmatic nature of the trial means monitoring for adherence is not relevant, as it is built into a process evaluation.ConclusionsThe interim analyses of cluster trials have a number of important differences to monitoring individually randomised trials. In cluster trials, there will often be a greater need to monitor nuisance parameters, yet there will often be considerable uncertainty in their estimation. This means the utility of sample size re-estimation can be questionable particularly when there are practical or funding difficulties associated with making any changes to planned sample sizes. Perhaps most importantly interim monitoring has the potential to identify selection bias, particularly in trials with post-randomisation identification or recruitment. Finally, the pragmatic nature of cluster trials might mean that the utility of methods to allow for interim monitoring of outcomes based on statistical testing, or monitoring for adherence to study interventions, are less relevant. Our intention is to facilitate the planning of future cluster randomised trials and to promote discussion and debate to improve monitoring of these studies.

Project description:BackgroundThe assessment of methods for analyzing over-dispersed zero inflated count outcome has received very little or no attention in stratified cluster randomized trials. In this study, we performed sensitivity analyses to empirically compare eight methods for analyzing zero inflated over-dispersed count outcome from the Vitamin D and Osteoporosis Study (ViDOS) - originally designed to assess the feasibility of a knowledge translation intervention in long-term care home setting.MethodForty long-term care (LTC) homes were stratified and then randomized into knowledge translation (KT) intervention (19 homes) and control (21 homes) groups. The homes/clusters were stratified by home size (<250/> = 250) and profit status (profit/non-profit). The outcome of this study was number of falls measured at 6-month post-intervention. The following methods were used to assess the effect of KT intervention on number of falls: i) standard Poisson and negative binomial regression; ii) mixed-effects method with Poisson and negative binomial distribution; iii) generalized estimating equation (GEE) with Poisson and negative binomial; iv) zero inflated Poisson and negative binomial - with the latter used as a primary approach. All these methods were compared with or without adjusting for stratification.ResultsA total of 5,478 older people from 40 LTC homes were included in this study. The mean (=1) of the number of falls was smaller than the variance (=6). Also 72% and 46% of the number of falls were zero in the control and intervention groups, respectively. The direction of the estimated incidence rate ratios (IRRs) was similar for all methods. The zero inflated negative binomial yielded the lowest IRRs and narrowest 95% confidence intervals when adjusted for stratification compared to GEE and mixed-effect methods. Further, the widths of the 95% confidence intervals were narrower when the methods adjusted for stratification compared to the same method not adjusted for stratification.ConclusionThe overall conclusion from the GEE, mixed-effect and zero inflated methods were similar. However, these methods differ in terms of effect estimate and widths of the confidence interval.Trial registrationClinicalTrials.gov: NCT01398527. Registered: 19 July 2011.

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Scale and shape issues in focused cluster power for count data.

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