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ABSTRACT Missing data arise in most applied settings and are ubiquitous in electronic health records (EHR). When data are missing not at random (MNAR) with respect to measured covariates, sensitivity analyses are often considered. These solutions, however, are often unsatisfying in that they are not guaranteed to yield actionable conclusions. Motivated by an EHR‐based study of long‐term outcomes following bariatric surgery, we consider the use of double sampling as a means to mitigate MNAR outcome data when the statistical goals are estimation and inference regarding causal effects. We describe assumptions that are sufficient for the identification of the joint distribution of confounders, treatment, and outcome under this design. Additionally, we derive efficient and robust estimators of the average causal treatment effect under a nonparametric model and under a model assuming outcomes were, in fact, initially missing at random (MAR). We compare these in simulations to an approach that adaptively estimates based on evidence of violation of the MAR assumption. Finally, we also show that the proposed double sampling design can be extended to handle arbitrary coarsening mechanisms, and derive nonparametric efficient estimators of any smooth full data functional. [ABSTRACT FROM AUTHOR]

Summary: Binary data subject to one type of misclassification exist in various fields. It is collected in a double‐sampling scheme that includes a gold standard test and a fallible test. The main parameter of interest for this type of data is the positive probability p$$ p $$ of the gold standard test. Existing intervals are unreliable because the given nominal level 1−α$$ 1-\alpha $$ is not achieved. In this paper, we construct an exact interval by inverting the E+M score tests and improve it by the general h$$ h $$‐function method. We find that the total length of the improved interval is shorter than the exact intervals that are also the improved intervals when we apply the h$$ h $$‐function to several existing approximate intervals, including the score and Bayesian intervals. Therefore, it is recommended for practice. We are also interested in two other parameters: p∗$$ {p}^{\ast } $$—the difference between the two positive rates for the fallible and gold standard tests—and ξ$$ \xi $$—the false positive rate for the fallible test. To the best of our knowledge, the research on these two parameters is limited. For p∗$$ {p}^{\ast } $$, we find that any interval for p$$ p $$ can be converted to an interval for p∗$$ {p}^{\ast } $$. So, the interval converted from the aforementioned recommended interval for p$$ p $$ is recommended for inferring p∗$$ {p}^{\ast } $$. For ξ$$ \xi $$, the improved interval by the h$$ h $$‐function method over the E+M score interval is derived. We use an example to illustrate how the intervals are computed and provide a real data analysis. [ABSTRACT FROM AUTHOR]

In most applications, parametric monitoring schemes are used to monitor the majority of industrial and nonindustrial processes in order to improve the quality of the outputs or services. However, parametric monitoring schemes are known to underperform when the normality assumption is not met or when there is not enough information about the symmetry or asymmetry nature of the process underlying distribution. Hence, in this paper, a new nonparametric Phase II Shewhart-type double-sampling (DS) monitoring scheme based on the precedence statistic is proposed in order to efficiently monitor quality processes when the underlying process distribution departs from normality. The performance is investigated using the average run length (ARL), standard deviation of the run length (SDRL), expected ARL (EARL) and expected average number of observations to signal (EANOS), and the average sample sizes (ASS) metrics. The latter metrics are computed using Monte Carlo simulation and exact formulae. In general, it is shown that the new DS precedence scheme outperforms the existing basic Shewhart precedence scheme with and without supplementary runs rules in many situations. A real-life illustrative example based on a filling process of milk bottles is provided to demonstrate the application and implementation of the new DS precedence monitoring scheme. [ABSTRACT FROM AUTHOR]

In this study, we propose a design for a control chart for the normal distribution using a modified double sampling scheme. The probability of in control, out of control, the average run length (ARL), and the standard deviation of run length (SDRL) of the proposed control chart is derived. The proposed control chart using the modified double sampling scheme is found to be more efficient in detecting small shifts of a process in comparison with existing, single, and double sampling control charts. The efficiency of the proposed control chart is investigated in terms of ARL and SDRL via simulation and application to a real data set. [ABSTRACT FROM AUTHOR]

We derive and examine the interval width and coverage properties of an integrated‐likelihood‐ratio confidence interval for the binomial parameter p using a double‐sampling scheme. The data consist of a relatively large fallible sample containing underreported data and a relatively small infallible subsample. Via Monte Carlo simulations, we determine that the new integrated‐likelihood‐ratio interval estimator displays slightly conservative to moderately conservative coverage properties for small to medium sample sizes and can have shorter average‐interval width than two previously proposed confidence intervals when p < 0.10 or p > 0.90. We also apply the integrated‐likelihood‐ratio confidence interval to a real‐data set and determine that the integrated‐likelihood‐ratio interval has superior performance when contrasted to two properties of two competing confidence intervals. [ABSTRACT FROM AUTHOR]

Many clinical or prevention studies involve missing or censored outcomes. Maximum likelihood (ML) methods provide a conceptually straightforward approach to estimation when the outcome is partially missing. Methods of implementing ML methods range from the simple to the complex, depending on the type of data and the missing-data mechanism. Simple ML methods for ignorable missing-data mechanisms (when data are missing at random) include complete-case analysis, complete-case analysis with covariate adjustment, survival analysis with covariate adjustment, and analysis via propensity-to-be-missing scores. More complex ML methods for ignorable missing-data mechanisms include the analysis of longitudinal dropouts via a marginal model for continuous data or a conditional model for categorical data. A moderately complex ML method for categorical data with a saturated model and either ignorable or nonignorable missing-data mechanisms is a perfect fit analysis, an algebraic method involving closed-form estimates and variances. A complex and flexible ML method with categorical data and either ignorable or nonignorable missing-data mechanisms is the method of composite linear models, a matrix method requiring specialized software. Except for the method of composite linear models, which can involve challenging matrix specifications, the implementation of these ML methods ranges in difficulty from easy to moderate. [ABSTRACT FROM AUTHOR]

Abstract: In this article, two adaptive multivariate charts, which combine the double sampling (DS) and variable sampling interval (VSI) features, called the adaptive multivariate double sampling variable sampling interval T2 (AMDSVSI T2) and the adaptive multivariate double sampling variable sampling interval combined T2 (AMDSVSIC T2) charts, are proposed. The real purpose of using the proposed charts is to provide flexibility by enabling the sampling interval length of the DS T2 chart to be varied so that the chart's sensitivity can be enhanced. The fundamental difference between the two proposed charts is that when a second sample is taken, the AMDSVSI T2 chart uses the information of the combined sample mean vectors while the AMDSVSIC T2 chart uses the information of the combined T2 statistics, in deciding about the process status. This research is motivated by existing combined DS X ¯ and VSI X ¯ charts in the literature, which show convincing performance improvement over the standard DS X ¯ chart. Consequently, it is believed that adopting this existing approach in the multivariate case will enable superior multivariate DS charts to be proposed. Numerical results show that the proposed charts outperform the existing standard T2 and other adaptive multivariate charts, in detecting shifts in the mean vector, for the zero‐state and steady‐state cases. The performances of both charts when the shift sizes in the mean vector are unknown are also measured. The application of the AMDSVSI T2 chart is illustrated with an example. [ABSTRACT FROM AUTHOR]

The R chart and the S2 chart are the usual charts for monitoring process dispersion; however, the double sampling ( DS) scheme has only be used with the S2 chart. The difficulty in obtaining the properties of the DS R chart might explain the lack of papers dealing with this type of DS chart. The S2 chart has the advantage of being more efficient than the R chart, but the same is not always observed with their DS versions. Depending on the size of the 2 samples, the DS R chart performs better. The trade-off between operational simplicity and power of detection might lead the practitioner to choose the DS R chart, even with the DS S2 chart signaling faster. [ABSTRACT FROM AUTHOR]

In this work, we describe a two-stage sampling design to estimate the infection prevalence in a population. In the first stage, an imperfect diagnostic test was performed on a random sample of the population. In the second stage, a different imperfect test was performed in a stratified random sample of the first sample. To estimate infection prevalence, we assumed conditional independence between the diagnostic tests and develop method of moments estimators based on expectations of the proportions of people with positive and negative results on both tests that are functions of the tests' sensitivity, specificity, and the infection prevalence. A closed-form solution of the estimating equations was obtained assuming a specificity of 100% for both tests. We applied our method to estimate the infection prevalence of visceral leishmaniasis according to two quantitative polymerase chain reaction tests performed on blood samples taken from 4756 patients in northern Ethiopia. The sensitivities of the tests were also estimated, as well as the standard errors of all estimates, using a parametric bootstrap. We also examined the impact of departures from our assumptions of 100% specificity and conditional independence on the estimated prevalence. [ABSTRACT FROM AUTHOR]

An index-calibration experiment involves rigorous estimation of animal abundance at a small scale to calibrate a less rigorously derived index of abundance. The efficacy of such index-calibration experiments has been a matter of much controversy. In this study, we develop theoretical models and test them with empirical data on large-scale index-calibration experiments on tigers Panthera tigris to advance our understanding of this controversy., We propose two models that describe the sampling processes involved in typical index-calibration experiments. Using analytical derivations and some simulations, we evaluate the relative roles of these sampling parameters on the [ABSTRACT FROM AUTHOR]

In this article, we construct two likelihood-based confidence intervals (CIs) for a binomial proportion parameter using a double-sampling scheme with misclassified binary data. We utilize an easy-to-implement closed-form algorithm to obtain maximum likelihood estimators of the model parameters by maximizing the full-likelihood function. The two CIs are a naïve Wald interval and a modified Wald interval. Using simulations, we assess and compare the coverage probabilities and average widths of our two CIs. Finally, we conclude that the modified Wald interval, unlike the naïve Wald interval, produces close-to-nominal CIs under various simulations and, thus, is preferred in practice. Utilizing the expressions derived, we also illustrate our two CIs for a binomial proportion parameter using real-data example. [ABSTRACT FROM AUTHOR]

The steady-state average run length is used to measure the performance of the recently proposed synthetic double sampling [ABSTRACT FROM AUTHOR]

Most studies that follow subjects over time are challenged by having some subjects who dropout. Double sampling is a design that selects and devotes resources to intensively pursue and find a subset of these dropouts, then uses data obtained from these to adjust naïve estimates, which are potentially biased by the dropout. Existing methods to estimate survival from double sampling assume a random sample. In limited-resource settings, however, generating accurate estimates using a minimum of resources is important. We propose using double-sampling designs that oversample certain profiles of dropouts as more efficient alternatives to random designs. First, we develop a framework to estimate the survival function under these profile double-sampling designs. We then derive the precision of these designs as a function of the rule for selecting different profiles, in order to identify more efficient designs. We illustrate using data from the United States President's Emergency Plan for AIDS Relief-funded HIV care and treatment program in western Kenya. Our results show why and how more efficient designs should oversample patients with shorter dropout times. Further, our work suggests generalizable practice for more efficient double-sampling designs, which can help maximize efficiency in resource-limited settings. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper gives a detail presentation of a fully pseudo-differential open-loop BiCMOS track-and-hold amplifier (THA) for 9-b operation up to 1 GSample/s. The proposed THA not only uses a double sampling technique to increase the achievable sampling frequency by a factor of two, but also employs a linearization technique to reduce the gain dependence of the THA input stage upon the input level. Moreover, timing mismatch between the clock signals of the two interleaved paths is minimized by means of a timing mismatch insensitive clock generator controlled by a common master sampling clock. The post-layout simulation results using TSMC 75 GHz fT, 0.35-µm SiGe BiCMOS technology show that the proposed architecture achieve a signal to noise and distortion ratio of 53.92 dB, equivalent to the effective number of bits of 8.66-b for 58.11 MHz input frequency at 1 GSample/s. The power dissipation of the whole THA is 161.1 mW. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Population estimates, often difficult to acquire, warrantee the use of an index as an economical substitute for rapid assessments of populations. We estimated population size of the little known social, semi-fossorial Indian desert jird ( Meriones hurrianae) in Kachchh, Gujarat, India under closed population capture-mark-recapture (CMR) framework to calibrate a burrow count index for the species. A total of 147 individuals were trapped in 16 colonies using baited Sherman traps and the number of burrow entrances at each colony was recorded. Data from colonies with low number of captures were pooled to estimate capture probability using Huggins heterogeneity models with gender, site, body weight and age category as covariates in Program MARK. Colony sizes ranged from 2 to 46 individuals. The number of burrow entrances was calibrated against CMR-based population estimates using least squares regression ( n = 16, adjusted R = 0.96, t = 18.18, P < 0.001). The index was further validated using Jackknife (JK) analysis where JK-predicted population estimates strongly correlated with CMR estimates ( r = 0.96, P < 0.001). In habitats and climatic conditions similar to Kachchh and within the range of colony sizes sampled, our calibrated index can be a valuable and effective tool for large scale surveys of the desert jird, which occupies a keystone trophic level in the semi-arid ecosystem. [ABSTRACT FROM AUTHOR]

This study, carried out for the United States Bureau of Reclamation (BOR), demonstrated methods for surveying large landscapes using small-scale, habitat-based stratification, a common problem that has heretofore received little attention. The goal was to design a sampling plan for detecting change in the density of breeding birds of 6 species occurring along the Colorado River around and south of Lake Mead in the southwestern United States. The main problem in designing the study was that the focal species were concentrated in small, irregularly shaped patches of habitat. We partitioned the study area into >15,000 plots configured to enclose the high-quality habitat in the fewest possible plots with the constraint that plots could be surveyed in 1 morning by 1 person. Because of the irregular plot shapes and extremely dense vegetation, we used area searches to carry out the surveys. We used double-sampling, including a large sample of plots surveyed with a rapid method and a subsample of plots surveyed intensively, to estimate detection ratios. A simulation study helped allocate effort between rapid and intensive surveys and indicated that conducting 80 surveys per year would achieve high power to detect a 50% decline occurring during 20 years. This is one of the first studies to show how large landscapes can be sampled using small-scale stratification so that effort can be concentrated in the habitats of greatest interest. © 2012 The Wildlife Society. [ABSTRACT FROM AUTHOR]

Estimates of wildlife population sizes are frequently constructed by combining counts of observed animals from a stratified survey of aerial sampling units with an estimated probability of detecting animals. Unlike traditional stratified survey designs, stratum-specific estimates of population size will be correlated if a common detection model is used to adjust counts for undetected animals in all strata. We illustrate this concept in the context of aerial surveys, considering 2 cases: 1) a single-detection parameter is estimated under the assumption of constant detection probabilities, and 2) a logistic-regression model is used to estimate heterogeneous detection probabilities. Naïve estimates of variance formed by summing stratum-specific estimates of variance may result in significant bias, particularly if there are a large number of strata, if detection probabilities are small, or if estimates of detection probabilities are imprecise. [ABSTRACT FROM AUTHOR]

This article reports that moist-soil managers manipulate hydrology, soils, and vegetation to provide habitat and foods for waterfowl and other wildlife in seasonally flooded herbaceous wetlands. Increasing seed availability for waterfowl is a priority, but managers also provide resources such as invertebrates and tubers. Several methods have been used to quantify seed availability in moist-soil habitats. Harvesting seeds from inflorescences has been the most common method of estimating seed production of individual plant species. Double sampling for stratification potentially increases precision of estimates but does no assume that the stratum membership of plots or the sizes of strata are known. Successful stratification reduces sampling costs by decreasing the size of the second sample needed to achieve the desired level of precision to inform management decisions. Double sampling can be an effective strategy for increasing precision or decreasing costs of estimating moist-soil seeds available to waterfowl over entire management units.

Abstract Maximum likelihood estimation is investigated in the context of linear regression models under partial independence restrictions. These restrictions aim to assume a kind of completeness of a set of predictors Z in the sense that they are sufficient to explain the dependencies between an outcome Y and predictors X: L(Y |Z , X ) = L(Y |Z ), where L(·|·) stands for the conditional distribution. From a practical point of view, the former model is particularly interesting in a double sampling scheme where Y and Z are measured together on a first sample and Z and X on a second separate sample. In that case, estimation procedures are close to those developed in the study of double-regression by Engel & Walstra (1991) and Causeur & Dhorne (1998). Properties of the estimators are derived in a small sample framework and in an asymptotic one, and the procedure is illustrated by an example from the food industry context. [ABSTRACT FROM AUTHOR]

A novel adaptive filter is proposed that adapts to the sample spacing as well as to the tap weights. Although in adaptive digital processing, the tapped-delay line usually has uniformly spaced taps, with the spacing equal to symbol period or a fraction of symbol period, an adaptive filter with a few taps should be adaptively arranged for nonuniform spacing for better convergence as the sampling timing varies. The conventional non-uniformly spaced filters are not adaptively changed or not arranged under the constraint of the acceptable mean-square error. In this paper, a least-mean square (LMS)- type algorithm is derived in which an adaptive filter adaptively changes the amount of delay, as well as the filter coefficients. The performance of the proposed algorithm is investigated by computer simulation. [ABSTRACT FROM AUTHOR]

We present a general framework for treating categorical data with errors of observation. We show how both latent class models and models for doubly sampled data can be treated as exponential family nonlinear models. These are extended generalized linear models with the link function substituted by an observationwise defined non-linear function of the model parameters. The models are formulated in terms of structural probabilities and conditional error probabilities, thus allowing natural constraints when modelling errors of observation. We use an iteratively reweighted least squares procedure for obtaining maximum likelihood estimates. This is faster than the traditionally used EM algorithm and the computations can be made in GLIM. As examples we analyse three sets of categorical data with errors of observation which have been analysed before by Ashford and Sowden,² Goodman³ and Chen,&sup4; respectively. [ABSTRACT FROM AUTHOR]