Sample size considerations for studies of intervention efficacy in the occupational setting.
Sample size considerations for studies of intervention efficacy in the occupational setting.
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职业环境中干预效果研究的样本量考虑因素。
DOI：
10.1093/annhyg/mef028
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Dugan,SiobhanK
中科院分区：
文献类型：
--
作者：
Lazovich,Deann;Murray,DavidM;Brosseau,LisaM;Parker,DavidL;Milton,FThomas;Dugan,SiobhanK
Objective.由于一个工作场所内工人之间的共享环境和相似性，评估减少职业健康或安全危害的干预措施的有效性的最强有力的分析设计是随机分配工作场所，而不是工人，干预和比较条件。当分配单位是一个群体时，统计方法被很好地描述用于估计样本量，但这些方法尚未应用于职业健康和安全干预措施的评价。我们回顾并应用统计方法组随机试验计划的研究，以评估技术/行为干预措施的有效性，以减少木材粉尘水平之间的小型木工企业。 方法.我们在五个小型木工企业进行了一项试点研究，以估计工地之间和工地内以及工人之间和工人内的方差分量。在每个工地，8小时的时间加权粉尘浓度，获得每个生产员工之间的两个和五个场合。利用这些数据，我们估计了计算粉尘浓度变化百分比所需的参数，我们可以检测到（α = 0.05，功效= 80%）各种条件下的一系列工地，每个工地的工人和每个工人的重复测量。 结果木工企业的平均木尘浓度为4.53 mg/m3。木工企业内工人之间的相似性的措施是大（组内相关= 0.5086）。工人内的重复测量弱相关（r= 0.1927），而工地内的重复测量强相关（r= 0.8925）。样本量计算的主导因素是每种条件下的工地数量，每个工地的工人数量起着较小的作用。我们还观察到，由于我们的数据中工人内部相关性较低，增加每人重复测量的次数几乎没有好处。我们发现，每种条件下30个工地，每个工地10名工人，将使我们有80%的能力检测到木尘水平降低30%（α = 0.05）。 结论.我们的研究结果表明，应用组随机试验方法来评估干预措施，以减少职业危害。该方法具有广泛的适用性，并不局限于减少木尘的情况。
Objective. Due to a shared environment and similarities among workers within a worksite, the strongest analytical design to evaluate the efficacy of an intervention to reduce occupational health or safety hazards is to randomly assign worksites, not workers, to the intervention and comparison conditions. Statistical methods are well described for estimating the sample size when the unit of assignment is a group but these methods have not been applied in the evaluation of occupational health and safety interventions. We review and apply the statistical methods for group-randomized trials in planning a study to evaluate the effectiveness of technical/behavioral interventions to reduce wood dust levels among small woodworking businesses. Methods. We conducted a pilot study in five small woodworking businesses to estimate variance components between and within worksites and between and within workers. In each worksite, 8 h time-weighted dust concentrations were obtained for each production employee on between two and five occasions. With these data, we estimated the parameters necessary to calculate the percent change in dust concentrations that we could detect (α = 0.05, power = 80%) for a range of worksites per condition, workers per worksite and repeat measurements per worker. Results. The mean wood dust concentration across woodworking businesses was 4.53 mg/m3. The measure of similarity among workers within a woodworking business was large (intraclass correlation = 0.5086). Repeated measurements within a worker were weakly correlated (r= 0.1927) while repeated measurements within a worksite were strongly correlated (r= 0.8925). The dominant factor in the sample size calculation was the number of worksites per condition, with the number of workers per worksite playing a lesser role. We also observed that increasing the number of repeat measurements per person had little benefit given the low within-worker correlation in our data. We found that 30 worksites per condition and 10 workers per worksite would give us 80% power to detect a reduction of ∼30% in wood dust levels (α = 0.05). Conclusions. Our results demonstrate the application of the group-randomized trials methodology to evaluate interventions to reduce occupational hazards. The methodology is widely applicable and not limited to the context of wood dust reduction.
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