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      Accuracy and precision of variance components in occupational posture recordings: a simulation study of different data collection strategies

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          Abstract

          Background

          Information on exposure variability, expressed as exposure variance components, is of vital use in occupational epidemiology, including informed risk control and efficient study design. While accurate and precise estimates of the variance components are desirable in such cases, very little research has been devoted to understanding the performance of data sampling strategies designed specifically to determine the size and structure of exposure variability. The aim of this study was to investigate the accuracy and precision of estimators of between-subjects, between-days and within-day variance components obtained by sampling strategies differing with respect to number of subjects, total sampling time per subject, number of days per subject and the size of individual sampling periods.

          Methods

          Minute-by-minute values of average elevation, percentage time above 90° and percentage time below 15° were calculated in a data set consisting of measurements of right upper arm elevation during four full shifts from each of 23 car mechanics. Based on this parent data, bootstrapping was used to simulate sampling with 80 different combinations of the number of subjects (10, 20), total sampling time per subject (60, 120, 240, 480 minutes), number of days per subject (2, 4), and size of sampling periods (blocks) within days (1, 15, 60, 240 minutes). Accuracy (absence of bias) and precision (prediction intervals) of the variance component estimators were assessed for each simulated sampling strategy.

          Results

          Sampling in small blocks within days resulted in essentially unbiased variance components. For a specific total sampling time per subject, and in particular if this time was small, increasing the block size resulted in an increasing bias, primarily of the between-days and the within-days variance components. Prediction intervals were in general wide, and even more so at larger block sizes. Distributing sampling time across more days gave in general more precise variance component estimates, but also reduced accuracy in some cases.

          Conclusions

          Variance components estimated from small samples of exposure data within working days may be both inaccurate and imprecise, in particular if sampling is laid out in large consecutive time blocks. In order to estimate variance components with a satisfying accuracy and precision, for instance for arriving at trustworthy power calculations in a planned intervention study, larger samples of data will be required than for estimating an exposure mean value with a corresponding certainty.

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          Most cited references34

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          Bootstrapping clustered data

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            Restricted Maximum Likelihood (REML) Estimation of Variance Components in the Mixed Model

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              Diversity and variation in biomechanical exposure: what is it, and why would we like to know?

              Trends in global working life suggest that the occurrence of jobs characterized by long-lasting low-level loads or repetitive operations is increasing. More physical "variation" is commonly believed to be a remedy against musculoskeletal disorders in such jobs. One aim of the present paper was to shortly review the validity of this conviction. An examination of the available epidemiologic literature pointed out that the effectiveness of initiatives like job rotation or more breaks is weakly supported by empirical evidence, and only for short-term psychophysical outcomes. Only a limited number of studies have been devoted to physical variation, and concepts and metrics for variation in biomechanical exposure are not well developed. Thus, as a second objective, the paper proposes a framework for investigating and evaluating aspects of exposure variation, based on explicit definitions of variation as "the change in exposure across time" and diversity as "the extent that exposure entities differ". Operational methods for assessing these concepts are also discussed.
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                Author and article information

                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central
                1471-2288
                2012
                18 June 2012
                : 12
                : 58
                Affiliations
                [1 ]Centre for Musculoskeletal Research, University of Gävle, Gävle, Sweden
                [2 ]Occupational & Environmental Medicine, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
                [3 ]Danish Ramazzini Centre, Department of Occupational Medicine, Herning Hospital, Herning, Denmark
                Article
                1471-2288-12-58
                10.1186/1471-2288-12-58
                3377541
                22533627
                b92307f7-3902-4bcb-aec3-491d8299f244
                Copyright ©2012 Liv et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 October 2011
                : 25 April 2012
                Categories
                Research Article

                Medicine
                Medicine

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