Cost-effective Sampling Strategy Research Articles

Improving estimation efficiency of case-cohort studies with interval-censored failure time data.

The case-cohort design is a commonly used cost-effective sampling strategy for large cohort studies, where some covariates are expensive to measure or obtain. In this paper, we consider regression analysis under a case-cohort study with interval-censored failure time data, where the failure time is only known to fall within an interval instead of being exactly observed. A common approach to analyzing data from a case-cohort study is the inverse probability weighting approach, where only subjects in the case-cohort sample are used in estimation, and the subjects are weighted based on the probability of inclusion into the case-cohort sample. This approach, though consistent, is generally inefficient as it does not incorporate information outside the case-cohort sample. To improve efficiency, we first develop a sieve maximum weighted likelihood estimator under the Cox model based on the case-cohort sample and then propose a procedure to update this estimator by using information in the full cohort. We show that the update estimator is consistent, asymptotically normal, and at least as efficient asthe original estimator. The proposed method can flexibly incorporate auxiliary variables to improve estimation efficiency. A weighted bootstrap procedure is employed for variance estimation. Simulation results indicate that the proposed method works well in practical situations. An application to a Phase 3 HIV vaccine efficacy trial is provided for illustration.

A cost-effective sampling strategy by variables inspection for lot disposition

A cost-effective sampling strategy by variables inspection for lot disposition

Determination of the pyrethroid inseticide metabolite 3-phenoxybenzoic acid in wastewater using polar organic integrative samplers and LC-MS/MS analysis

Wastewater-based epidemiology (WBE) is a methodological approach that aims to obtain epidemiological information through wastewater analysis, by identifying endogenous metabolism products. Pyrethroid insecticides are commonly used but just a few studies evaluated concentrations of pyrethroid metabolites in wastewater. A cost-effective and efficient sampling strategy for WBE studies is the use of polar organic chemical integrative samplers (POCIS). This study aimed to develop and evaluate a method for estimating the concentration of the pyrethroid insecticide 3-phenoxybenzoic acid (3-PBA) using POCIS sampling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The sampler sorbent was extracted with methanol, followed by a double solid phase extraction (SPE) clean-up. The extraction procedure had yields in the range of 95.7–100.4 %. There were 27 collection cycles, with POCIS samplers changed every 14 days. The analyte 3-PBA was measured in all collections. The concentration of 3-PBA in the POCIS sorbent was 208.6–2456.9 ng g−1. The sampling rate was 0.096 L/day and the resulting estimated concentration in wastewater was 24.3 to 335.2 ng/L. This is the first study evaluating POCIS sampling to measure a pyrethroid metabolite in water or wastewater samples.

National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations.

Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO2 models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO2. Our results suggest that street view imagery alone may provide sufficient information to explain NO2 variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10-fold cross-validation R2 of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)─a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intra-urban variation of NO2 pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

Joint semiparametric models for case-cohort designs.

Two-phase studies such as case-cohort and nested case-control studies are widely used cost-effective sampling strategies. In the first phase, the observed failure/censoring time and inexpensive exposures are collected. In the second phase, a subgroup of subjects is selected for measurements of expensive exposures based on the information from the first phase. One challenging issue is how to utilize all the available information to conduct efficient regression analyses of the two-phase study data. This paper proposes a joint semiparametric modeling of the survival outcome and the expensive exposures. Specifically, we assume a class of semiparametric transformation models and a semiparametric density ratio model for the survival outcome and the expensive exposures, respectively. The class of semiparametric transformation models includes the proportional hazards model and the proportional odds model as special cases. The density ratio model is flexible in modeling multivariate mixed-type data. We develop efficient likelihood-based estimation and inference procedures and establish the large sample properties of the nonparametric maximum likelihood estimators. Extensive numerical studies reveal that the proposed methods perform well under practical settings. The proposed methods also appear to be reasonably robust under various model mis-specifications. An application to the National Wilms Tumor Study isprovided.

Tree Size Drives Diversity and Community Structure of Microbial Communities on the Bark of Beech (Fagus sylvatica)

Tree bark constitutes an ideal habitat for microbial communities, because it is a stable substrate, rich in micro-niches. Bacteria, fungi, and terrestrial microalgae together form microbial communities, which in turn support more bark-associated organisms, such as mosses, lichens, and invertebrates, thus contributing to forest biodiversity. We have a limited understanding of the diversity and biotic interactions of the bark-associated microbiome, as investigations have mainly focused on agriculturally relevant systems and on single taxonomic groups. Here we implemented a multi-kingdom metabarcoding approach to analyze diversity and community structure of the green algal, bacterial, and fungal components of the bark-associated microbial communities of beech, the most common broadleaved tree of Central European forests. We identified the most abundant taxa, hub taxa, and co-occurring taxa. We found that tree size (as a proxy for age) is an important driver of community assembly, suggesting that environmental filtering leads to less diverse fungal and algal communities over time. Conversely, forest management intensity had negligible effects on microbial communities on bark. Our study suggests the presence of undescribed, yet ecologically meaningful taxa, especially in the fungi, and highlights the importance of bark surfaces as a reservoir of microbial diversity. Our results constitute a first, essential step toward an integrated framework for understanding microbial community assembly processes on bark surfaces, an understudied habitat and neglected component of terrestrial biodiversity. Finally, we propose a cost-effective sampling strategy to study bark-associated microbial communities across large spatial or environmental scales.

Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries

In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improving the model robustness by training with noise perturbations. Most existing studies assume a fixed perturbation level for all training examples, which however hardly holds in real tasks. In fact, excessive perturbations may destroy the discriminative content of an example, while deficient perturbations may fail to provide helpful information for improving the robustness. Motivated by this observation, we propose to adaptively adjust the perturbation levels for each example in the training process. Specifically, a novel active learning framework is proposed to allow the model interactively querying the correct perturbation level from human experts. By designing a cost-effective sampling strategy along with a new query type, the robustness can be significantly improved with a few queries. Both theoretical analysis and experimental studies validate the effectiveness of the proposed approach.

Spatiotemporal sampling strategy for characterization of hydraulic properties in heterogeneous soils

Accurate characterization and prediction of soil moisture distribution and solute transport in vadose zone require detailed knowledge of the spatial distribution of soil hydraulic properties. Since the direct measurements of these unknown properties are challenging, many studies invert the soil hydraulic parameters by incorporating observation data (e.g., soil moisture and pressure head) at selected point sampling locations into soil moisture flow models. However, a cost-effective sampling strategy for where and when to collect the data, which is vital for saving the costs for monitoring and data interpretation, is relatively rare compared to the direct parameter retrieving efforts. Here, an optimal spatial–temporal sampling strategy was proposed based on cross-correlation analysis between observed state variables and soil hydraulic parameters. Besides, the effects of meteorological condition, observation type, bottom boundary condition, and correlation scale of soil hydraulic parameters are also demonstrated. The proposed sampling strategy was assessed by both synthetic numerical experiments and a real-world case study. Results suggest the retrieval accuracy of heterogeneous soil is acceptable if the spatial/temporal sampling interval is set to be one spatial/temporal correlation length of soil moisture. Besides, surface observation contains the most plentiful information which could be used to derive root-zone soil moisture/parameters, but this ability depends on the correlation scale of soil hydraulic parameters. Besides, the temporal value of soil moisture depends on meteorological condition. It is not necessary to sample repeatedly during dry periods, but more attention should be paid to the observations after rainfall events.

Recruiting Research Participants via Traditional Snowball vs Facebook Advertisements and a Website.

This article describes the use of Facebook, a website targeting nurses, and snowball sampling for recruitment of registered nurse participants in a qualitative study exploring measurement-driven clinical behavior and metric-driven harm. Previous studies suggest that social media can be a successful and cost-effective sampling strategy, increasing the numbers of participants, their diversity, and their representativeness of the population of interest. This study, however, found traditional snowball sampling to be far more effective than advertisements via Facebook and a professional website. Lessons learned are detailed, including cost and technical issues encountered. Suggestions for nurse researchers considering using Facebook for participant recruitment are described. Methodological research that could enhance the empirical-base supporting effective social media recruitment of research participants is offered for consideration by nurse researchers.

Optimal Designs of Two-Phase Studies

The two-phase design is a cost-effective sampling strategy to evaluate the effects of covariates on an outcome when certain covariates are too expensive to be measured on all study subjects. Under such a design, the outcome and inexpensive covariates are measured on all subjects in the first phase and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase. Previous research on two-phase studies has focused largely on the inference procedures rather than the design aspects. We investigate the design efficiency of the two-phase study, as measured by the semiparametric efficiency bound for estimating the regression coefficients of expensive covariates. We consider general two-phase studies, where the outcome variable can be continuous, discrete, or censored, and the second-phase sampling can depend on the first-phase data in any manner. We develop optimal or approximately optimal two-phase designs, which can be substantially more efficient than the existing designs. We demonstrate the improvements of the new designs over the existing ones through extensive simulation studies and two large medical studies. Supplementary materials for this article are available online.

Cost-effective sampling for estimating species richness of ground beetles (Coleoptera: Carabidae) using pitfall traps: efficiency of non-parametric species richness estimators

Because it is extremely time-consuming and costly to evaluate species richness of highly diverse taxa such as insects, cost-effective sampling strategies to estimate species richness are needed, especially when long-term monitoring is conducted. We found a sampling method that reduced the number of traps per site when estimating species richness of ground beetles (Coleoptera: Carabidae) in six forest sites in Sorachi, Hokkaido, northern Japan. We set 40 pitfall traps per site and collected ground beetles for 1-week periods in June, July, and August 2013. We evaluated the performance of three non-parametric species richness estimators [Jackknife2, Chao1, and the abundance-based coverage estimator (ACE)] to estimate species richness. Rarefaction curves showed that ACE was the only valid estimator to estimate species richness for all sites. Using ACE, we can reduce the number of traps to 30 or 20 from 40 traps per site if we allow a bias of 5 or 10%, respectively. This study suggests that non-parametric species richness estimators are useful in estimating species richness of ground beetles with reduced sample size.

Incremental sampling methodology for petroleum hydrocarbon contaminated soils: volume estimates and remediation strategies

ABSTRACTCurrent environmental assessments for petroleum hydrocarbon (PHC) contaminated sites are dependent on discrete soil sampling to estimate the degree and extent of contamination, leading to unreliable and non-reproducible results. Incremental sampling methodology (ISM) involves collecting and combining samples within a targeted area and holds promise for being a cost-effective, representative, and reproducible sampling strategy for contaminated site characterization. We hypothesized that traditional Phase II Environmental Site Assessments (ESA) discrete and ISM sampling protocols were not mutually exclusive, and the two approaches can be used to formulate a responsible land management strategy. Results gathered through ISM were compared to those from Phase II ESA for two PHC contaminated sites in Canada. Both methods indicated the sites were impacted with PHC beyond Saskatchewan Tier I guidance, however, the delineation of the PHC plume differed by as much as 75% for the heavier hydrocarbons. The Phase II ESA methods had higher incidences of false positive results and an overestimation of contamination at depth. A laboratory experiment confirmed that ISM does not “dilute” the samples as to cause underestimation, whereby the hydrocarbon concentrations for a single combined sample was equivalent to the mean of 30 discrete samples. Based on our results, sites should undergo risk assessment based on the estimates of the Phase II ESA results using vapor phase logs to estimate contaminant extent. If exposure pathways cannot be eliminated through the risk assessment process, remediation planning based on the ISM results is justified given the demonstrated cost-effectiveness, representativeness, and reproducibility.

Thermographic cameras reveal high levels of crepuscular and nocturnal shore-based recreational fishing effort in an Australian estuary

Abstract Although recreational fishing at night is a popular activity, crepuscular and nocturnal fishing effort and catches are often unaccounted for in fisheries assessments. Here, we present a method for estimating 24-h shore-based recreational fishing effort involving the analysis of data from dual-lens thermographic cameras. Cameras were installed at three sites along the foreshore in Peel-Harvey Estuary, a Ramsar Wetland in Western Australia. Stratified random sampling was used to select days for image analysis between March 2015 and February 2016 and examination of images enabled fishing effort to be calculated for the 12-month period. Crepuscular recreational fishing effort at the three sites ranged between 4.5% and 11.3% and nocturnal recreational fishing effort ranged between 6.1% and 26.9% of total recreational fishing effort. Crepuscular and nocturnal recreational fishing were more prevalent between November and March and occurred on both weekdays and weekend days. The majority of recreational fishers identified from the day-time images (96.5%) were targeting blue swimmer crabs (Portunus armatus) using scoop nets. Multiple lines of evidence suggest that most crepuscular and nocturnal activity involved the same method of fishing. The results demonstrate that restricting onsite recreational fishing surveys to daylight hours can lead to large underestimates of total fishing effort and total recreational catches within a fishery. Our method can easily be applied to other fisheries to justify the inclusion of night-time onsite sampling and to design cost-effective sampling strategies.

Cost-effectiveness analysis of biopsy strategies for endometrial cancer diagnosis in women with postmenopausal bleeding: Pipelle sampling curette versus dilatation & curettage

BackgroundEndometrial sampling is widely used for accurate diagnosis of endometrial cancer (EC), which is the most common gynecologic cancer in US women. The objective of this study was to explore the cost-effectiveness of two endometrial sampling procedures for diagnosing EC: (1) Pipelle endometrial sampling (Pipelle), and (2) dilatation & curettage (D&C), while accounting for sampling procedure failure rates and diagnostic accuracy in women with postmenopausal bleeding (PMB). MethodThe decision analytic model was built to compare the cost-effectiveness of Pipelle and D&C strategies in a hypothetical cohort of PMB women. The analysis was performed from the perspective of a public healthcare payer (Medicare, US). We used 2017 Medicare reimbursement data for cost estimation. The effectiveness of these two diagnostic strategies was measured by analyzing the remaining life expectancy after EC diagnosis and subsequent treatment. ResultsThe base case analysis suggested that Pipelle was not only equally effective (32.11 vs. 32.11 years of life), but also less costly ($1897.80 vs. $2999.11) based on Medicare reimbursement when compared to D&C. In one-way sensitivity analyses and Monte Carlo probabilistic sensitivity analysis, the Pipelle remained the more cost-effective sampling strategy even after accounting for sampling failure rate associated with each sampling strategy. ConclusionThe Pipelle is the more cost-effective sampling strategy compared to D&C for EC diagnosis in women with PMB. From the cost-effectiveness perspective, the higher sampling failure rate of Pipelle should not be regarded as a limitation in its clinical application.

When should stream water be sampled to be most informative for event-based, multi-criteria model calibration?

AbstractIsotope data from streamflow samples taken during rainfall or snowmelt events can be useful for model calibration, particularly to improve model consistency and to reduce parameter uncertainty. To reduce the costs associated with stream water sampling, it is important to choose sampling times with a high information content. We used the Birkenes model and synthetic rainfall, streamflow and isotope data to explore how many samples are needed to obtain a certain model fit and which sampling times are most informative for model calibration. Our results for nine model parameterizations and three events, representing different streamflow behaviours (e.g., fast and slow response, with and without overflow), show that the simulation performance of models calibrated with isotope data from two selected samples was comparable to simulations based on isotope data for all 100 time steps. Generally, samples taken on the falling limb were most informative for model calibration, although the exact timing of the most informative samples was dependent on the runoff response. Samples taken on the rising limb and at peakflow were less informative than expected. These model results highlight the value of a limited number of stream water samples and provide guidance for cost-effective event-based sampling strategies for model calibration.

Sampling schemes for hillslope hydrologic processes and stability analysis based on cross-correlation analysis

Abstract Cost-effective sampling strategies to understand hydrologic processes in a hillslope are essential to the estimation of hillslope seepage and to the evaluation of slope stability. The objectives of this paper are (a) to introduce a stochastic cross-correlation analysis to hillslope hydrologic processes and stability investigation and (b) to develop a cost-effective sampling strategy to reduce the pore-water pressure uncertainty at the region of our interest. The concept of stochastic representation of spatial variabilities of soil hydraulic properties is introduced first. We then develop a first-order analysis method to determine spatiotemporal distribution of pore-water pressure head (P) variance (unconditional variance or uncertainty) due to spatial variability of saturated hydraulic conductivity (Ks) in a variably saturated hillslope during rainfall. Subsequently, a cross-correlation analysis is presented, which quantifies the spatial correlation between P at a given location and Ks at any part of a hillslope under a transient infiltration event. We afterward formulate a first-order approximation of residual P variance (conditional variance or uncertainty) due to inclusions of Ks measurements. The developed methodology is applied to synthetic, heterogeneous, two-dimensional hillslopes to investigate the effectiveness of several Ks sampling schemes. Results of this investigation show that boreholes and sampling locations should be placed at an interval of one correlation scale. They should be distributed over an area covering several correlation scales, rather than clustered together at the largest cross-correlation region or at regions where prediction uncertainty is large (where our interest and concern are). Further, the results demonstrate that increasing the sampling density is useful but inefficient. Layering structures of hillslopes reduce the number of boreholes and samples required. At last, a cost-effective sampling strategy for study of slope stability based on cross-correlation is suggested, which minimizes the prediction uncertainty of P at critical locations.

Achievements and Challenges in Anti-Doping Research.

The most important element in achieving athlete compliance with anti-doping rules is the certainty of detection. Thus, scientific research plays a mission critical role in achieving clean competition. Many factors contribute to the advances in detection. Incremental advances in the ability to detect prohibited substances and methods, and identification of long-lived metabolites continue to lengthen detection windows. While the athlete biological passport hematological and steroidal modules hold great promise, experience shows that new research is needed to improve the sensitivity and specificity of the approach for current doping techniques. Indirect detection strategies using biomarkers or transcriptomic techniques have been increasingly investigated. The incorporation of more cost-effective sampling strategies using dried blood and plasma spots, oral fluid, and breath analysis show great promise toward increasing the number of tests while remaining within testing budget constraints. Despite the importance of research to ensuring rule compliance, a major challenge for anti-doping research is achieving and maintaining sufficient funding in the reality of the myriad of new substances introduced for disease treatment but abused for performance enhancement. In addition, obtaining metabolism and population reference range data, particularly for new drugs or designer drugs that have not obtained approval for administration to human subjects, remains a significant problem. Nevertheless, research continues to contribute important data to support anti-doping efforts.

A safe, efficient and cost effective process for removing petroleum hydrocarbons from a highly heterogeneous and relatively inaccessible shoreline

A rocky, intractable and highly heterogeneous, intertidal zone, was contaminated from a diesel fuel spill that occurred during refuelling of a grader used in road construction, on an operational mine's shiploading facility. A practical, cost-effective, and safer (to personnel by avoiding drilling and earthworks), and non-invasive sampling and remediation strategy was designed and implemented since the location and nature of the impacted geology (rock fill) and sediment, precluded conventional ex-situ and any in-situ treatment where drilling would be required. Enhanced biostimulation with surfactant, available N & P (which were highly constrained), and increased aeration, increased the degradation rate from no discernable change for 2 years post-spill, to 170 mg/kg/day; the maximum degradation rate after intervention. While natural attenuation was ineffective in this application, the low-cost, biostimulation intervention proved successful, allowing the site owner to meet their regulatory obligations. Petroleum hydrocarbons (aliphatic fraction) decreased from ∼20,000 mg/kg to <200 mg/kg at the completion of 180 weeks of treatment.

Salinity Measurements Collected by Fishermen Reveal a “River in the Sea” Flowing Along the Eastern Coast of India

Being the only tropical ocean bounded by a continent to the north, the Indian Ocean is home to the most powerful monsoon system on Earth. Monsoonal rains and winds induce huge river discharges and strong coastal currents in the northern Bay of Bengal. To date, the paucity of salinity data has prevented a thorough description of the spreading of this freshwater into the bay. The potential impact of the salinity on cyclones and regional climate in the Bay of Bengal is, however, a strong incentive for a better description of the water cycle in this region. Since May 2005, the National Institute of Oceanography conducts a program in which fishermen collect seawater samples in knee-deep water at eight stations along the Indian coastline every 5 days. Comparison with open-ocean samples shows that this cost-effective sampling strategy is representative of offshore salinity evolution. This new dataset reveals a salinity drop exceeding 10 g kg−1 in the northern part of the bay at the end of the summer monsoon. This freshening signal propagates southward in a narrow (~100 km wide) strip along the eastern coast of India, and reaches its southern tip after 2.5 months. Satellite-derived alongshore-current data shows that the southward propagation of this “river in the sea” is consistent with transport by seasonal coastal currents, while other processes are responsible for the ensuing erosion of this coastal freshening. This simple procedure of coastal seawater samples collection could further be used to monitor phytoplankton concentration, bacterial content, and isotopic composition of seawater along the Indian coastline.

Development of rapid sampling procedures for an exploited bivalve in the swash zone on exposed ocean beaches

We assessed a time-based technique for estimating the relative abundance and size composition of populations of the beach clam, Donax deltoides, in the swash zone of exposed ocean beaches by comparing it with a standard quadrat-based method. The time-based method consisted digging small plots of sand by hand and scooping sediment and clams into a mesh bag attached to a rigid frame. We tested three digging times (30, 60 and 120s) and two mesh sizes (12 and 19mm). Compared to a standard box-quadrat, the time-based diggings were more effective and efficient in terms of numbers of clams collected per time taken to do a sample. The timed digging technique was also much simpler and less problematic to use in the swash zone, which is important when industry are involved in sampling. Although a greater total number of clams were collected in the 120s diggings, when the CPUE data were standardized to number per 30s, a greater proportion of clams were collected in the shortest time frame tested. This suggests most clams were captured in the first 30s of digging, with fewer caught per unit of time thereafter. A major benefit of using the shortest digging time is that a greater number of replicate samples and patches of clams on a beach can be sampled per given unit of time, potentially improving overall precision without large increases in costs. An optimal sampling design would involve sampling more patches on a beach than replicates within a patch. Given a sampling window of 3h either side of low tide, we suggest that future sampling should incorporate 6 replicate 30-s diggings at each of 8 patches on a beach. We further recommend that a 12mm mesh bag be used as it retained a greater proportion of small clams (<20mm). This study highlights the importance of doing pilot studies to develop appropriate sampling gears and for determining optimal, cost-effective sampling strategies for large-scale surveys.