Optimal sampling strategies for weighted linear regression estimation

1992 ◽  
Vol 22 (2) ◽  
pp. 239-247 ◽  
Author(s):  
H.T. Schreuder ◽  
Z. Ouyang
Keyword(s):  
Linear Models ◽  
Sampling Strategy ◽  
Simple Random Sampling ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Variance Estimator ◽  
Unequal Probability ◽  
Regression Estimators ◽  
Efficient Sampling ◽  
Coverage Rates

Our strong effort to find an optimal sampling strategy that was clearly superior to other strategies for a range of linearity conditions and variance structures for linear models showed that several sampling strategies turned out to be equally efficient. Each of these stratified the population to the maximum extent feasible, i.e., used n strata based on a covariate. Which of two ways of stratification to use and how units in each stratum were selected (simple random sampling or sampling with probability proportional to size) did not seem to matter much. Two regression estimators, one considering both probability and variance weights (Ŷgr) and one considering only probability weights (Ŷpi), are preferred estimators with the five efficient sampling selection schemes that select one unit per stratum with either equal or unequal probability sampling. The bootstrap variance estimator is generally the least biased, yet conservative, variance estimator and yields reliable coverage rates with 95% confidence intervals for most populations studied.

scholarly journals Guidelines for depth data collection in rivers when applying interpolation techniques (kriging) for river restoration

2007 ◽  
Vol 4 (3) ◽  
pp. 1069-1094
Author(s):  
M. Rivas-Casado ◽  
S. White ◽  
P. Bellamy
Keyword(s):  
River Restoration ◽  
Sampling Strategy ◽  
Spatial Dimension ◽  
Sampling Strategies ◽  
Depth Data ◽  
Cyclic Patterns ◽  
Efficient Sampling ◽  
Before And After ◽  
Geostatistical Techniques ◽  
River Site

Abstract. River restoration appraisal requires the implementation of monitoring programmes that assess the river site before and after the restoration project. However, little work has yet been developed to design effective and efficient sampling strategies. Three main variables need to be considered when designing monitoring programmes: space, time and scale. The aim of this paper is to describe the methodology applied to analyse the variation of depth in space, scale and time so more comprehensive monitoring programmes can be developed. Geostatistical techniques were applied to study the spatial dimension (sampling strategy and density), spectral analysis was used to study the scale at which depth shows cyclic patterns, whilst descriptive statistics were used to assess the temporal variation. A brief set of guidelines have been summarised in the conclusion.

scholarly journals Development and evaluation of a Bayesian pharmacokinetic estimator and optimal, sparse sampling strategies for ceftazidime.

1996 ◽  
Vol 40 (8) ◽  
pp. 1860-1865 ◽  
Author(s):  
A D Kashuba ◽  
C H Ballow ◽  
A Forrest
Keyword(s):  
Sampling Strategy ◽  
Population Pharmacokinetic Analysis ◽  
Volume Of Distribution ◽  
Sparse Sampling ◽  
Pharmacokinetic Analysis ◽  
Population Pharmacokinetic ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Adaptive Feedback ◽  
Sampling Times

Data were gathered during an activity-controlled trial in which seriously ill, elderly patients were randomized to receive intravenous ceftazidime or ciprofloxacin and for which adaptive feedback control of drug concentrations in plasma and activity profiles was prospectively performed. The adaptive feedback control algorithm for ceftazidime used an initial population model, a maximum a posteriori (MAP)-Bayesian pharmacokinetic parameter value estimator, and an optimal, sparse sampling strategy for ceftazidime that had been derived from data in the literature obtained from volunteers. Iterative two-stage population pharmacokinetic analysis was performed to develop an unbiased MAP-Bayesian estimator and updated optimal, sparse sampling strategies. The final median values of the population parameters were follows: the volume of distribution of the central compartment was equal to 0.249 liter/kg, the volume of distribution of the peripheral compartment was equal to 0.173 liter/kg, the distributional clearance between the central and peripheral compartments was equal to 0.2251 liter/h/kg, the slope of the total clearance (CL) versus the creatinine clearance (CLCR) was equal to 0.000736 liter/h/kg of CL/1 ml/min/1.73 m2 of CLCR, and nonrenal clearance was equal to + 0.00527 liter/h/kg. Optimal sampling times were dependent on CLCR; for CLCR of > or = 30 ml/min/1.73 m2, the optimal sampling times were 0.583, 3.0, 7.0, and 16.0 h and, for CLCR of < 30 ml/min/1.73 m2, optimal sampling times were 0.583, 4.15, 11.5, and 24.0 h. The study demonstrates that because pharmacokinetic information from volunteers may often not be reflective of specialty populations such as critically ill elderly individuals, iterative two-stage population pharmacokinetic analysis, MAP-Bayesian parameter estimation, and optimal, sparse sampling strategy can be important tools in characterizing their pharmacokinetics.

scholarly journals Optimal Sampling Strategies for Detecting Zoonotic Disease Epidemics

2014 ◽  
Vol 10 (6) ◽  
pp. e1003668 ◽  
Author(s):  
Jake M. Ferguson ◽  
Jessica B. Langebrake ◽  
Vincent L. Cannataro ◽  
Andres J. Garcia ◽  
Elizabeth A. Hamman ◽  
...  
Keyword(s):  
Zoonotic Disease ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Disease Epidemics

scholarly journals General Relative Rate Models for the Analysis of Studies Using Case-Cohort Designs

2018 ◽  
Vol 188 (2) ◽  
pp. 444-450
Author(s):  
David B Richardson ◽  
Bryan Langholz ◽  
Kaitlin Kelly-Reif
Keyword(s):  
Information Needs ◽  
Linear Models ◽  
Broad Class ◽  
Relative Rate ◽  
Model Misspecification ◽  
Model Fitting ◽  
Simple Random Sampling ◽  
Additional Information ◽  
Cohort Design ◽  
Cohort Data

Abstract A standard approach to analysis of case-cohort data involves fitting log-linear models. In this paper, we describe how standard statistical software can be used to fit a broad class of general relative rate models to case-cohort data and derive confidence intervals. We focus on a case-cohort design in which a roster has been assembled and events ascertained but additional information needs to be collected on explanatory variables. The additional information is ascertained just for persons who experience the event of interest and for a sample of the cohort members enumerated at study entry. One appeal of such a case-cohort design is that this sample of the cohort may be used to support analyses of several outcomes. The ability to fit general relative rate models to case-cohort data may allow an investigator to reduce model misspecification in exposure-response analyses, fit models in which some factors have effects that are additive and others multiplicative, and facilitate estimation of relative excess risk due to interaction. We address model fitting for simple random sampling study designs as well as stratified designs. Data on lung cancer among radon-exposed men (Colorado Plateau uranium miners, 1950–1990) are used to illustrate these methods.

scholarly journals Optimal Sampling for the Population Coverage Survey of the New Italian Register Based Census

2021 ◽  
Vol 37 (3) ◽  
pp. 655-671
Author(s):  
Paolo Righi ◽  
Piero Demetrio Falorsi ◽  
Stefano Daddi ◽  
Epifania Fiorello ◽  
Pierpaolo Massoli ◽  
...  
Keyword(s):  
Sampling Design ◽  
Sample Selection ◽  
Sampling Strategy ◽  
Selection Method ◽  
Population Census ◽  
Optimal Sampling ◽  
Population Coverage ◽  
Sample Allocation ◽  
Population Counts ◽  
First Time

Abstract For the first time in 2018 the Italian Institute of Statistics (Istat) implemented the annual Permanent Population Census which relies on the Population Base Register (PBR) and the Population Coverage Survey (PCS). This article provides a general overview of the PCS sampling design, which makes use of the PBR to correct population counts with the extended dual system estimator (Nirel and Glickman 2009). The sample allocation, proven optimal under a set of precision constraints, is based on preliminary estimates of individual probabilities of over-coverage and under-coverage. It defines the expected sample size in terms of individuals, and it oversamples the sub-populations subject to the risk of under/over coverage. Finally, the article introduces a sample selection method, which to the greatest extent possible satisfies the planned allocation of persons in terms of socio-demographic characteristics. Under acceptable assumptions, the article also shows that the sampling strategy enhances the precision of the estimates.

scholarly journals Pharmacokinetic Modeling and Optimal Sampling Strategies for Therapeutic Drug Monitoring of Rifampin in Patients with Tuberculosis

2015 ◽  
Vol 59 (8) ◽  
pp. 4907-4913 ◽  
Author(s):  
Marieke G. G. Sturkenboom ◽  
Leonie W. Mulder ◽  
Arthur de Jager ◽  
Richard van Altena ◽  
Rob E. Aarnoutse ◽  
...  
Keyword(s):  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Mean Squared Error ◽  
Geometric Mean ◽  
Plasma Concentrations ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Wide Range

ABSTRACTRifampin, together with isoniazid, has been the backbone of the current first-line treatment of tuberculosis (TB). The ratio of the area under the concentration-time curve from 0 to 24 h (AUC0–24) to the MIC is the best predictive pharmacokinetic-pharmacodynamic parameter for determinations of efficacy. The objective of this study was to develop an optimal sampling procedure based on population pharmacokinetics to predict AUC0–24values. Patients received rifampin orally once daily as part of their anti-TB treatment. A one-compartmental pharmacokinetic population model with first-order absorption and lag time was developed using observed rifampin plasma concentrations from 55 patients. The population pharmacokinetic model was developed using an iterative two-stage Bayesian procedure and was cross-validated. Optimal sampling strategies were calculated using Monte Carlo simulation (n= 1,000). The geometric mean AUC0–24value was 41.5 (range, 13.5 to 117) mg · h/liter. The median time to maximum concentration of drug in serum (Tmax) was 2.2 h, ranging from 0.4 to 5.7 h. This wide range indicates that obtaining a concentration level at 2 h (C2) would not capture the peak concentration in a large proportion of the population. Optimal sampling using concentrations at 1, 3, and 8 h postdosing was considered clinically suitable with anr2value of 0.96, a root mean squared error value of 13.2%, and a prediction bias value of −0.4%. This study showed that the rifampin AUC0–24in TB patients can be predicted with acceptable accuracy and precision using the developed population pharmacokinetic model with optimal sampling at time points 1, 3, and 8 h.

scholarly journals Performance and uncertainties of TSS stormwater sampling strategies from online time series

2018 ◽  
Vol 78 (6) ◽  
pp. 1407-1416
Author(s):  
Santiago Sandoval ◽  
Jean-Luc Bertrand-Krajewski ◽  
Nicolas Caradot ◽  
Thomas Hofer ◽  
Günter Gruber
Keyword(s):  
Time Series ◽  
Suspended Solids ◽  
Sampling Strategy ◽  
Sampling Strategies ◽  
Sampling Errors ◽  
Time Step ◽  
Sewer System ◽  
Time Intervals ◽  
Combined Sewer ◽  
One Year

Abstract The event mean concentrations (EMCs) that would have been obtained by four different stormwater sampling strategies are simulated by using total suspended solids (TSS) and flowrate time series (about one minute time-step and one year of data). These EMCs are compared to the reference EMCs calculated by considering the complete time series. The sampling strategies are assessed with datasets from four catchments: (i) Berlin, Germany, combined sewer overflow (CSO); (ii) Graz, Austria, CSO; (iii) Chassieu, France, separate sewer system; and (iv) Ecully, France, CSO. A sampling strategy in which samples are collected at constant time intervals over the rainfall event and sampling volumes are pre-set as proportional to the runoff volume discharged between two consecutive sample leads to the most representative results. Recommended sampling time intervals are of 5 min for Berlin and Chassieu (resp. 100 and 185 ha area) and 10 min for Graz and Ecully (resp. 335 and 245 ha area), with relative sampling errors between 7% and 20% and uncertainties in sampling errors of about 5%. Uncertainties related to sampling volumes, TSS laboratory analyses and beginning/ending of rainstorm events are reported as the most influent sources in the uncertainties of sampling errors and EMCs.

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