Optimizing cost-efficiency in mean exposure assessment - cost functions reconsidered

Abstract

BackgroundReliable exposure data is a vital concern in medical epidemiology and intervention studies. The present study addresses the needs of the medical researcher to spend monetary resources devoted to exposure assessment with an optimal cost-efficiency, i.e. obtain the best possible statistical performance at a specified budget. A few previous studies have suggested mathematical optimization procedures based on very simple cost models; this study extends the methodology to cover even non-linear cost scenarios.MethodsStatistical performance, i.e. efficiency, was assessed in terms of the precision of an exposure mean value, as determined in a hierarchical, nested measurement model with three stages. Total costs were assessed using a corresponding three-stage cost model, allowing costs at each stage to vary non-linearly with the number of measurements according to a power function. Using these models, procedures for identifying the optimally cost-efficient allocation of measurements under a constrained budget were developed, and applied on 225 scenarios combining different sizes of unit costs, cost function exponents, and exposure variance components.ResultsExplicit mathematical rules for identifying optimal allocation could be developed when cost functions were linear, while non-linear cost functions implied that parts of or the entire optimization procedure had to be carried out using numerical methods.For many of the 225 scenarios, the optimal strategy consisted in measuring on only one occasion from each of as many subjects as allowed by the budget. Significant deviations from this principle occurred if costs for recruiting subjects were large compared to costs for setting up measurement occasions, and, at the same time, the between-subjects to within-subject variance ratio was small. In these cases, non-linearities had a profound influence on the optimal allocation and on the eventual size of the exposure data set.ConclusionsThe analysis procedures developed in the present study can be used for informed design of exposure assessment strategies, provided that data are available on exposure variability and the costs of collecting and processing data. The present shortage of empirical evidence on costs and appropriate cost functions however impedes general conclusions on optimal exposure measurement strategies in different epidemiologic scenarios.

Data collection costs in industrial environments for three occupational posture exposure assessment methods

“…Most studies investigating this issue have considered only statistical efficiency, not measurement cost [11]. However, optimization based on both costs and statistical performance could yield a substantially different study design than what follows from optimizing only with respect to statistical performance [16,17,20]. For example, when within-worker variance is higher than between-worker variance and recruitment costs for engaging participants are high compared to costs for collecting more data from subjects already in the study, multiple measures on fewer workers may prove to be a more cost efficient sampling strategy than that suggested when only statistical performance is considered, i.e.…”

Cost-efficient assessment of biomechanical exposure in occupational groups, exemplified by posture observation and inclinometry

“…However, there have been few comprehensive efforts to report the empirical costs of biomechanical exposure assessment, and these preliminary efforts did not include any information on the quality or statistical properties of exposure information delivered (35)(36)(37). Recently some studies have combined empirical costs and precision of occupational exposure estimates, although these have addressed cost efficiency in general terms (20), or have focused on a narrow assessment of cost and compared sampling or analysis strategies just within a single method [in casu, observation; (19,21)]. …”

“…Although these articles have furthered inquiry into efficient exposure assessment from a statistical point of view, they have been criticized for rarely acknowledging the actual costs of exposure assessment. Thus, a 2010 systematic review of literature focusing on cost-efficient collection of exposure data (18) found only nine studies dealing with the trade-off between statistical performance and monetary resources invested in obtaining that performance, even if some studies have appeared after 2010 (19)(20)(21). Only some of the publications identified in the 2010 review dealt specifically with occupational or environmental exposures (7,(22)(23)(24)(25)(26); only two of these included empirical data to illustrate cost and efficiency (24,26), and none were devoted to assessment of biomechanical exposure.…”

Cost-efficient observation of working postures from video recordings – more videos, more observers or more views per observer?

“…Furthermore, different stages in a data collection process may entail different costs, and so the most efficient strategy in a statistical sense may not necessarily be the most cost-efficient [12]. Very little research has been devoted to investigating trade-offs between cost and statistical efficiency in data collection [17], and no studies have so far focused on the generic issue in video-based observation of whether resources -as constrained by a limited budget -should be allocated to collecting "many" video recordings and have them observed by "few" observers, or to a more meticulous observation of fewer video recordings.…”