Related Covariates Research Articles

A Bayesian Approach for Population Pharmacokinetic Modeling of Alcohol in Japanese Individuals

BackgroundBlood alcohol concentration data that were previously obtained from 34 healthy Japanese subjects with limited sampling times were reanalyzed. Characteristics of the data were that the concentrations were obtained from only the early part of the time-concentration curve. ObjectiveTo explore significant covariates for the population pharmacokinetic analysis of alcohol by incorporating external data using a Bayesian method, and to estimate effects of the covariates. MethodsThe data were analyzed using a Markov chain Monte Carlo Bayesian estimation with NONMEM 7.3 (ICON Clinical Research LLC, North Wales, Pennsylvania). Informative priors were obtained from the external study. ResultsA 1-compartment model with Michaelis-Menten elimination was used. The typical value for the apparent volume of distribution was 49.3 L at the age of 29.4 years. Volume of distribution was estimated to be 20.4 L smaller in subjects with the ALDH2*1/*2 genotype than in subjects with the ALDH2*1/*1 genotype. ConclusionsA population pharmacokinetic model for alcohol was updated. A Bayesian approach allowed interpretation of significant covariate relationships, even if the current dataset is not informative about all parameters. This is the first study reporting an estimate of the effect of the ALDH2 genotype in a PPK model.

Generation of correlated pseudorandom varibales

When Monte Carlo method is used to study many problems, it is sometimes necessary to sample correlated pseudorandom variables. Previous studies have shown that the Cholesky decomposition method can be used to generate correlated pseudorandom variables when the covariance matrix satisfies the positive eigenvalue condition. However, some covariance matrices do not satisfy the condition. In this study, the theoretical formula for generating correlated pseudorandom variables is deduced, and it is found that Cholesky decomposition is not the only way to generate multidimensional correlated pseudorandom variables. The other matrix decomposition methods can be used to generate multidimensional relevant random variables if the positive eigenvalue condition is satisfied. At the same time, we give the formula for generating the multidimensional random variable by using the covariance matrix, the relative covariance matrix and the correlation coefficient matrix to facilitate the later use. In order to verify the above theory, a simple test example with 33 relative covariance matrix is used, and it is found that the correlation coefficient results obtained by Jacobi method are consistent with those from the Cholesky method. The correlation coefficients are more close to the real values with increasing the sampling number. After that, the antineutrino energy spectra of Daya Bay are generated by using Jacobi matrix decomposition and Cholesky matrix decomposition method, and their relative errors of each energy bin are in good agreement, and the differences are less than 5.0% in almost all the energy bins. The above two tests demonstrate that the theoretical formula for generating correlated pseudorandom variables is corrected. Generating correlated pseudorandom variables is used in nuclear energy to analyze the uncertainty of nuclear data library in reactor simulation, and many codes have been developed, such as one-, two-and three-dimensional TSUNAMI, SCALE-SS, XSUSA, and SUACL. However, when the method of generating correlated pseudorandom variables is used to decompose the 238U radiation cross section covariance matrix, it is found that the negative eigenvalue appears and previous study method cannot be used. In order to deal with the 238U radiation cross section covariance matrix and other similar matrices, the zero correction is proposed. When the zero correction is used in Cholesky diagonal correction and Jacobi eigenvalue zero correction, it is found that Jacobi negative eigenvalue zero correction error is smaller than that with Cholesky diagonal correction. In future, the theory about zero correction will be studied and it will focus on ascertaining which correction method is better for the negative eigenvalue matrix.

Cerebrospinal fluid penetration of meropenem in neurocritical care patients with proven or suspected ventriculitis: a prospective observational study.

BackgroundVentriculitis is a complication of temporary intraventricular drains. The limited penetration of meropenem into the cerebrospinal fluid (CSF) is well known. However, ventricular CSF pharmacokinetic data in patients with ventriculitis are lacking. The aim of this study was to evaluate meropenem pharmacokinetics in the serum and CSF of neurocritical care patients with proven or suspected ventriculitis.MethodsWe conducted an observational pharmacokinetic study of neurocritical care patients with proven or suspected ventriculitis receiving meropenem. Multiple blood and CSF samples were taken and were described using nonparametric pharmacokinetic modelling with Pmetrics.ResultsIn total, 21 patients (median age 52 years, median weight 76 kg) were included. The median (range) of peak and trough concentrations in serum were 20.16 (4.40–69.00) mg/L and 2.54 (0.00–31.40) mg/L, respectively. The corresponding peak and trough concentrations in CSF were 1.20 (0.00–6.20) mg/L and 1.28 (0.00–4.10) mg/L, respectively, with a median CSF/serum ratio (range) of 0.09 (0.03–0.16). Median creatinine clearance ranged from 60.7 to 217.6 ml/minute (median 122.5 ml/minute). A three-compartment linear population pharmacokinetic model was most appropriate. No covariate relationships could be supported for any of the model parameters. Meropenem demonstrated poor penetration into CSF, with a median CSF/serum ratio of 9 % and high interindividual pharmacokinetic variability.ConclusionsAdministration of higher-than-standard doses of meropenem and therapeutic drug monitoring in both serum and CSF should be considered to individualise meropenem dosing in neurocritical care patients with ventriculitis.

Regional differentiation of felid vertebral column evolution: a study of 3D shape trajectories

Recent advances in geometric morphometrics provide improved techniques for extraction of biological information from shape and have greatly contributed to the study of ecomorphology and morphological evolution. However, the vertebral column remains an under-studied structure due in part to a concentration on skull and limb research, but most importantly because of the difficulties in analysing the shape of a structure composed of multiple articulating discrete units (i.e. vertebrae). Here, we have applied a variety of geometric morphometric analyses to three-dimensional landmarks collected on 19 presacral vertebrae to investigate the influence of potential ecological and functional drivers, such as size, locomotion and prey size specialisation, on regional morphology of the vertebral column in the mammalian family Felidae. In particular, we have here provided a novel application of a method—phenotypic trajectory analysis (PTA)—that allows for shape analysis of a contiguous sequence of vertebrae as functionally linked osteological structures. Our results showed that ecological factors influence the shape of the vertebral column heterogeneously and that distinct vertebral sections may be under different selection pressures. While anterior presacral vertebrae may either have evolved under stronger phylogenetic constraints or are ecologically conservative, posterior presacral vertebrae, specifically in the post-T10 region, show significant differentiation among ecomorphs. Additionally, our PTA results demonstrated that functional vertebral regions differ among felid ecomorphs mainly in the relative covariation of vertebral shape variables (i.e. direction of trajectories, rather than in trajectory size) and, therefore, that ecological divergence among felid species is reflected by morphological changes in vertebral column shape.

Survival model construction guided by fit and predictive strength.

Survival model construction can be guided by goodness-of-fit techniques as well as measures of predictive strength. Here, we aim to bring together these distinct techniques within the context of a single framework. The goal is how to best characterize and code the effects of the variables, in particular time dependencies, when taken either singly or in combination with other related covariates. Simple graphical techniques can provide an immediate visual indication as to the goodness-of-fit but, in cases of departure from model assumptions, will point in the direction of a more involved and richer alternative model. These techniques appear to be intuitive. This intuition is backed up by formal theorems that underlie the process of building richer models from simpler ones. Measures of predictive strength are used in conjunction with these goodness-of-fit techniques and, again, formal theorems show that these measures can be used to help identify models closest to the unknown non-proportional hazards mechanism that we can suppose generates the observations. Illustrations from studies in breast cancer show how these tools can be of help in guiding the practical problem of efficient model construction for survival data.

Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19.

BackgroundThe Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed for GAW19, which focused on analysis of human genomic and transcriptomic data.MethodsGAW19 data were donated by the T2D-GENES Consortium and the San Antonio Family Heart Study and included whole genome and exome sequences for odd-numbered autosomes, measures of gene expression, systolic and diastolic blood pressures, and related covariates in two Mexican American samples. These two samples were a collection of 20 large families with whole genome sequence and transcriptomic data and a set of 1943 unrelated individuals with exome sequence. For each sample, simulated phenotypes were constructed based on the real sequence data. ‘Functional’ genes and variants for the simulations were chosen based on observed correlations between gene expression and blood pressure. The simulations focused primarily on additive genetic models but also included a genotype-by-medication interaction. A total of 245 genes were designated as ‘functional’ in the simulations with a few genes of large effect and most genes explaining < 1 % of the trait variation. An additional phenotype, Q1, was simulated to be correlated among related individuals, based on theoretical or empirical kinship matrices, but was not associated with any sequence variants. Two hundred replicates of the phenotypes were simulated. The GAW19 data are an expansion of the data used at GAW18, which included the family-based whole genome sequence, blood pressure, and simulated phenotypes, but not the gene expression data or the set of 1943 unrelated individuals with exome sequence.

The pharmacokinetics of propofol in ICU patients undergoing long-term sedation.

The aim of this study was to characterize the pharmacokinetics (PK) of propofol in ICU patients undergoing long-term sedation and to assess the influence of routinely collected covariates on the PK parameters. Propofol concentration-time profiles were collected from 29 patients. Non-linear mixed-effects modelling in NONMEM 7.2 was used to analyse the observed data. The propofol pharmacokinetics was best described with a three-compartment disposition model. Non-parametric bootstrap and a visual predictive check were used to evaluate the adequacy of the developed model to describe the observations. The typical value of the propofol clearance (1.46l/min) approximated the hepatic blood flow. The volume of distribution at steady state was high and was equal to 955.1l, which is consistent with other studies involving propofol in ICU patients. There was no statistically significant covariate relationship between PK parameters and opioid type, SOFA score on the day of admission, APACHE II, predicted death rate, reason for ICU admission (sepsis, trauma or surgery), gender, body weight, age, infusion duration and C-reactive protein concentration. The population PK model was developed successfully to describe the time-course of propofol concentration in ICU patients undergoing prolonged sedation. Despite a very heterogeneous group of patients, consistent PK profiles were observed. Copyright © 2016 John Wiley & Sons, Ltd.

A study of students’ reasoning about probabilistic causality: Implications for understanding complex systems and for instructional design

Understanding complex systems requires reasoning about causal relationships that behave or appear to behave probabilistically. Features such as distributed agency, large spatial scales, and time delays obscure co-variation relationships and complex interactions can result in non-deterministic relationships between causes and effects that are best understood statistically. Causal Bayesian Research (e.g. Gopnik and Schulz in Causal learning: psychology, philosophy, and computation, Oxford University Press, New York, 2007) suggests that summing across probabilistic instances is inherent to human causal induction, yet other research (e.g. Schulz and Sommerville in Child Development 77(2):427–442, 2006) suggests a human tendency to assume deterministic relationships. Classroom science learning often stresses the replicability of outcomes, putting this learning in tension with understanding probabilistic patterns in complex systems. This investigation examined students’ reasoning patterns on tasks with probabilistic causal features. Microgenetic studies were conducted in multiple sessions over a school year with students in kindergarten, second, fourth and sixth grades (n = 16) to assess their assumptions when dealing with tasks from four domains: social; games; machines; and biology. Later sessions attempted to scaffold students’ understanding using connection making and analogical reasoning. This paper reports on the overall patterns and trends in the data. Most students held a deterministic stance at the outset; however, at least one student at each grade level reasoned probabilistically from the start. All students except one eventually revealed at least one topic for which they held a primarily probabilistic stance. The results have implications for how students reason about complex systems and for how patterns of co-variation and evidence in science are discussed.

Relative Position Error Analysis for Mars Probes

Abstract: The relative angular position error covariance model of two deep space probes is propagated based on the error covariance analysis method, and is used for the relative angular position error analysis for multiple deep space probes. The differential time delay model of Same Beam Interferometry (SBI) about the relative right ascension and relative declination of two deep space probes is established, and the information matrixes of two ground-based baseline differential time delay measurements are obtained. The relative angular position error covariance is established, and the influence of measurement error and measurement geometry on relative angular position error is analyzed. The results show that in appropriate measurement geometry the relative right ascension and relative declination error of Mars probe are in order of 0.1nrad when the measurement error is in order of pico-second.

Initial Relative Orbit Determination Analytical Covariance and Performance Analysis for Proximity Operations

This research furthers the development of a closed-form solution to the angles-only initial relative orbit determination problem for close-in proximity operations when the camera offset from the vehicle center of mass allows for range observability. Emphasis is placed on developing closed-form error covariance equations for the initial relative orbit state solution and verification of the analytic covariance equations through systematic nonlinear Monte Carlo simulation of typical rendezvous missions. Closed-form analytic estimates of the relative state error covariance based on angle measurement errors, attitude knowledge errors, and camera center-of-mass offset uncertainties for three or more observations are obtained. A two-body Monte Carlo simulation system is used to evaluate the performance of the closed-form relative state estimation algorithms and associated closed-form covariance equations. The sensitivity of the solution accuracy to spacecraft trajectories, camera offset, camera accuracy, attitude knowledge, and the time interval between measurements is presented and discussed.

Run‐length method for determining the segmentation length based on the segmentation long‐code blind estimation

In this study, the blind despreading of long-code direct sequence spread spectrum signals (LC-DSSS) is considered. Qiu et al. presented a heuristic segmentation-based method to estimate the spreading code of LC-DSSS signals, which divided the received signal into far shorter temporal windows than the symbol duration, then spreading code was reconstructed from these short windows by computing the related covariance matrices. However, Qiu et al. did not consider the criterion of determining the segmentation length. By combining the matrix perturbation theory and run length property, the authors propose a run-length method for determining the segmentation length based on the segmentation idea, and this method can also be applied to long-code direct-sequence code division multiple access (LC-DS-CDMA) signals. Monte Carlo simulations for both LC-DSSS signals and LC-DS-CDMA signals are presented to demonstrate the effectiveness of the proposed method.

A Tutorial on Pharmacodynamic Scripting Facility inSimcyp.

The Simcyp Simulator provides a framework for mechanistic Physiologically‐Based Pharmacokinetic/Pharmacodynamic modeling of potentially interacting drugs. It also provides a scripting facility, using the Lua language, for developing customized pharmacodynamic and toxicity models driven by drug concentrations at the site of action. We present an overview of the scripting facility including the scripting language, the editor, and how scripts are embedded within the Simulator. Examples incorporating differential equations and including inter‐individual variability on parameters are presented.

A simulation study on matched case-control designs in the perspective of causal diagrams.

BackgroundIn observational studies, matched case-control designs are routinely conducted to improve study precision. How to select covariates for match or adjustment, however, is still a great challenge for estimating causal effect between the exposure E and outcome D.MethodsFrom the perspective of causal diagrams, 9 scenarios of causal relationships among exposure (E), outcome (D) and their related covariates (C) were investigated. Further various simulation strategies were performed to explore whether match or adjustment should be adopted. The “do calculus” and “back-door criterion” were used to calculate the true causal effect (β) of E on D on the log-odds ratio scale. 1:1 matching method was used to create matched case-control data, and the conditional or unconditional logistic regression was utilized to get the estimators ( overset{frown }{beta } ) of causal effect. The bias ( overset{frown }{beta}hbox{-} beta ) and standard error ( SEleft(overset{frown }{beta}right) ) were used to evaluate their performances.ResultsWhen C is exactly a confounder for E and D, matching on it did not illustrate distinct improvement in the precision; the benefit of match was to greatly reduce the bias for β though failed to completely remove the bias; further adjustment for C in matched case-control designs is still essential. When C is associated with E or D, but not a confounder, including an independent cause of D, a cause of E but has no direct causal effect on D, a collider of E and D, an effect of exposure E, a mediator of causal path from E to D, arbitrary match or adjustment of this kind of plausible confounders C will create unexpected bias. When C is not a confounder but an effect of D, match or adjustment is unnecessary. Specifically, when C is an instrumental variable, match or adjustment could not reduce the bias due to existence of unobserved confounders U.ConclusionsArbitrary match or adjustment of the plausible confounder C is very dangerous before figuring out the possible causal relationships among E, D and their related covariates.

Sympathoadrenal Activation is Associated with Acute Traumatic Coagulopathy and Endotheliopathy in Isolated Brain Injury.

ABSTRACTBackground:Acute coagulopathy after traumatic brain injury (TBI) involves a complex multifactorial hemostatic response that is poorly characterized. Objectives: To examine early posttraumatic alterations in coagulofibrinolytic, endothelial, and inflammatory blood biomarkers in relation to sympathetic nervous system (SNS) activation and 6-month patient outcomes, using multivariate partial least-squares (PLS) analysis.Patients and Methods:A multicenter observational study of 159 adult isolated TBI patients admitted to the emergency department at an urban level I trauma center, was performed. Plasma concentrations of 6 coagulofibrinolytic, 10 vascular endothelial, 19 inflammatory, and 2 catecholamine biomarkers were measured by immunoassay on admission and 24 h postinjury. Neurological outcome at 6 months was assessed using the Extended Glasgow Outcome Scale. PLS-discriminant analysis was used to identify salient biomarker contributions to unfavorable outcome, whereas PLS regression analysis was used to evaluate the covariance between SNS correlates (catecholamines) and biomarkers of coagulopathy, endotheliopathy, and inflammation.Results:Biomarker profiles in patients with an unfavorable outcome displayed procoagulation, hyperfibrinolysis, glycocalyx and endothelial damage, vasculature activation, and inflammation. A strong covariant relationship was evident between catecholamines and biomarkers of coagulopathy, endotheliopathy, and inflammation at both admission and 24 h postinjury.Conclusions:Biomarkers of coagulopathy and endotheliopathy are associated with poor outcome after TBI. Catecholamine levels were highly correlated with endotheliopathy and coagulopathy markers within the first 24 h after injury. Further research is warranted to characterize the pathogenic role of SNS-mediated hemostatic alterations in isolated TBI.

Developmental course of non-medical use of prescription drugs from adolescence to adulthood in the United States: national longitudinal data.

To identify the developmental course of non-medical use of four separate prescription drug classes (opioids, sedatives, stimulants and tranquilizers) by examining the general functional growth and related covariates during the transition from adolescence to adulthood in the United States. Nationally representative probability samples of high school seniors were followed longitudinally across five waves (waves 1, 2, 3, 4 and 5: modal ages 18, 19/20, 21/22, 23/24 and 25/26years, respectively). Data were collected via self-administered questionnaires to high school seniors and young adults in the United States. The sample consisted of nearly 72 000 individuals in 30 cohorts (high school senior years of 1977-2006) who participated in at least one wave. Self-reports of annual non-medical use of prescription opioids, sedatives, stimulants, and tranquilizers. The annual non-medical use of prescription opioids, sedatives, stimulants and tranquilizers was highest at wave 1 over the five waves. There was a consistent descending path (linear and quadratic slopes, P<0.001) in annual non-medical use from baseline across all four prescription drug classes (e.g. opioids linear slope=-0.043 and opioids quadratic slope=0.034, P<0.001). While the annual non-medical use of stimulants declined over time (linear slope=0.063, P<0.01; quadratic slope=-0.133, P<0.001), the same decrease was not observed for the annual non-medical use of prescription opioids, sedatives or tranquilizers when controlling for socio-demographic and substance use behaviors at baseline. The covariates associated with the general functional growth differed across the four prescription drug classes. The non-medical use of prescription opioids, sedatives, stimulants and tranquilizers appears to peak during late adolescence, suggesting preventive intervention efforts should be initiated in early adolescence. The developmental course of non-medical use is not the same among all four classes of prescription drugs, suggesting that each drug class warrants individual research.

Probable insomnia is associated with future total energy intake and diet quality in men1–3

Insomnia is associated with several adverse health outcomes. Small clinical studies have suggested that an inferior nutrition status is a potential explanation, but to our knowledge, this possibility has not been examined in a large-scale, population-based cohort study. We examined whether individuals with probable insomnia and individual insomnia symptoms had greater energy intake and a lower diet quality as assessed with the use of the Alternate Healthy Eating Index (AHEI) 2 y later. A cohort study of 15,273 US men aged 58-93 y who were free of cancer, cardiovascular diseases, and diabetes and were participating in the Health Professionals Follow-Up Study reported information on insomnia symptoms in 2004. Dietary intake was assessed with the use of a food-frequency questionnaire in 2002 and 2006. We calculated the adjusted mean differences of total energy intake in 2006 and the AHEI-component scores and their 95% CIs between subjects with and without probable insomnia in 2004 and also across categories for each insomnia symptom while adjusting for related covariates. After dietary intake in 2002, major chronic conditions, and other potential confounders were controlled for, men with probable insomnia had a mean higher consumption of 35.8 kcal/d (95% CI: 17.4, 54.1 kcal/d) and had lower scores in 3 individual AHEI components (trans fat, vegetables, and sodium), which denoted higher consumption of trans fat and sodium and lower intake of vegetables (P ≤ 0.01 for all). For individual insomnia symptoms, nonrestorative sleep and a difficulty maintaining sleep were associated with higher energy intake (P-trend ≤ 0.007 for both). A similar trend was observed in men who had difficulty initiating sleep (P-trend = 0.07). We also observed a significant association between the difficulty of initiating sleep and a lower AHEI score 2 y later (P-trend = 0.004). Probable insomnia is associated with higher intakes of total energy, trans fat, and sodium and lower intake of vegetables.

Second‐neighbor interactions in classical field theories: invariance of the relative power and covariance

We propose a model‐building framework unifying those continuum models of condensed matter accounting for second‐neighbor interactions. A notion of material isomorphism justifies restrictions that we impose to changes in observers on the material manifold. In the presence of dissipation due to evolution of inhomogeneities, we extend the notion of relative power including hyperstresses and derive pertinent balance equations by exploiting an invariance axiom. The scheme presented here permits an extension of the multi‐field model‐building framework for complex materials to account at a gross scale for second‐neighbor microstructural interactions. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Multi-timescale data assimilation for atmosphere–ocean state estimates

Abstract. Paleoclimate proxy data span seasonal to millennial timescales, and Earth's climate system has both high- and low-frequency components. Yet it is currently unclear how best to incorporate multiple timescales of proxy data into a single reconstruction framework and to also capture both high- and low-frequency components of reconstructed variables. Here we present a data assimilation approach that can explicitly incorporate proxy data at arbitrary timescales. The principal advantage of using such an approach is that it allows much more proxy data to inform a climate reconstruction, though there can be additional benefits. Through a series of offline data-assimilation-based pseudoproxy experiments, we find that atmosphere–ocean states are most skillfully reconstructed by incorporating proxies across multiple timescales compared to using proxies at short (annual) or long (∼ decadal) timescales alone. Additionally, reconstructions that incorporate long-timescale pseudoproxies improve the low-frequency components of the reconstructions relative to using only high-resolution pseudoproxies. We argue that this is because time averaging high-resolution observations improves their covariance relationship with the slowly varying components of the coupled-climate system, which the data assimilation algorithm can exploit. These results are consistent across the climate models considered, despite the model variables having very different spectral characteristics. Our results also suggest that it may be possible to reconstruct features of the oceanic meridional overturning circulation based on atmospheric surface temperature proxies, though here we find such reconstructions lack spectral power over a broad range of frequencies.

Brain network changes and memory decline in aging.

One theory of age-related cognitive decline proposes that changes within the default mode network (DMN) of the brain impact the ability to successfully perform cognitive operations. To investigate this theory, we examined functional covariance within brain networks using regional cerebral blood flow data, measured by 15O-water PET, from 99 participants (mean baseline age 68.6±7.5) in the Baltimore Longitudinal Study of Aging collected over a 7.4year period. The sample was divided in tertiles based on longitudinal performance on a verbal recognition memory task administered during scanning, and functional covariance was compared between the upper (improvers) and lower (decliners) tertile groups. The DMN and verbal memory networks (VMN) were then examined during the verbal memory scan condition. For each network, group differences in node-to-network coherence and individual node-to-node covariance relationships were assessed at baseline and in change over time. Compared with improvers, decliners showed differences in node-to-network coherence and in node-to-node relationships in the DMN but not the VMN during verbal memory. These DMN differences reflected greater covariance with better task performance at baseline and both increasing and declining covariance with declining task performance over time for decliners. When examined during the resting state alone, the direction of change in DMN covariance was similar to that seen during task performance, but node-to-node relationships differed from those observed during the task condition. These results suggest that disengagement of DMN components during task performance is not essential for successful cognitive performance as previously proposed. Instead, a proper balance in network processes may be needed to support optimal task performance.

Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping

AbstractMost calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples to cope with usually scarce resources for field sampling and laboratory analysis. The study focuses on the development and application of an efficiency improved DSM sampling design that (1) applies an optimized sample set size, (2) compensates for limited field accessibility, and (3) enables the integration of legacy soil samples. The proposed sampling design represents a modification of conditioned Latin Hypercube Sampling (cLHS), which originally returns distinct sample sites to optimally cover a soil related covariate space and to preserve the correlation of the covariates in the sample set. The sample set size was determined by comparing multiple sample set sizes of original cLHS sets according to their representation of the covariate space. Limited field accessibility and the integration of legacy samples were incorporated by providing alternative sample sites to replace the original cLHS sites. We applied the modified cLHS design (cLHSadapt) in a small catchment (4.2 km2) in Central China to model topsoil sand fractions using Random Forest regression (RF). For evaluating the proposed approach, we compared cLHSadapt with the original cLHS design (cLHSorig). With an optimized sample set size n = 30, the results show a similar representation of the cLHS covariate space between cLHSadapt and cLHSorig, while the correlation between the covariates is preserved (r = 0.40 vs. r = 0.39). Furthermore, we doubled the sample set size of cLHSadapt by adding available legacy samples (cLHSadapt+) and compared the prediction accuracies. Based on an external validation set cLHSval (n = 20), the coefficient of determination (R2) of the cLHSadapt predictions range between 0.59 and 0.71 for topsoil sand fractions. The R2‐values of the RF predictions based on cLHSadapt+, using additional legacy samples, are marginally increased on average by 5%.