Multiple sensitive estimation and optimal sample size allocation in the item sum technique

For surveys of sensitive issues in life sciences, statistical procedures can be used to reduce nonresponse and social desirability response bias. Both of these phenomena provoke nonsampling errors that are difficult to deal with and can seriously flaw the validity of the analyses. The item sum technique (IST) is a very recent indirect questioning method derived from the item count technique that seeks to procure more reliable responses on quantitative items than direct questioning while preserving respondents' anonymity. This article addresses two important questions concerning the IST: (i) its implementation when two or more sensitive variables are investigated and efficient estimates of their unknown population means are required; (ii) the determination of the optimal sample size to achieve minimum variance estimates. These aspects are of great relevance for survey practitioners engaged in sensitive research and, to the best of our knowledge, were not studied so far. In this article, theoretical results for multiple estimation and optimal allocation are obtained under a generic sampling design and then particularized to simple random sampling and stratified sampling designs. Theoretical considerations are integrated with a number of simulation studies based on data from two real surveys and conducted to ascertain the efficiency gain derived from optimal allocation in different situations. One of the surveys concerns cannabis consumption among university students. Our findings highlight some methodological advances that can be obtained in life sciences IST surveys when optimal allocation is achieved.     

Development and Validation of the Self-Acceptance Scale for Persons with Early Blindness: The SAS-EB

Investigations of self-acceptance are critical to understanding the development and maintenance of psychological health. However, valid and reliable instruments for measuring self-acceptance in persons with early blindness have yet to be developed. The current research describes three studies designed to develop and validate the Self-acceptance Scale for Persons with Early Blindness (SAS-EB). In Study 1, we developed the initial item pool. Thirty-three items were generated, based on data from specialized literature and from 2 focus groups. Items were organized in a three-factor structure, theoretically predicted for SAS-EB - (1) body acceptance, (2) self-protection from social stigmas, and (3) feeling and believing in one''s capacities. In Study 2, information obtained from a panel of 9 experts and 22 persons with early blindness representing the target population was used to refine the initial item pool, generating a new pool of 27 items. In Study 3, 318 persons with early blindness (141 women and 177 men), between 18 and 60 years of age (M = 37.74 years, SD = 12.37) answered the new pool of 27 items. After the elimination of 9 items using confirmatory factor analysis, we confirmed the theoretical three-factor structure of the SAS-EB. Study 3 also provided support for the scale''s internal consistency and construct validity. Finally, the psychometric properties of the SAS-EB, its utility, and its limitations are discussed along with considerations for future research.     

Are Cape Ground Squirrels (Xerus inauris) Sensitive to Variation in the Pay‐offs from Their Caches?

For food caching to be adaptive, the benefits of recovery must outweigh the costs of storing an item. One of the costs to cachers is the risk of theft, and therefore, it is predicted that individuals may be sensitive to this theft and show various behavioural strategies to minimise it. In this study, we gave wild Cape ground squirrels (Xerus inauris) a choice between two different coloured items of the same food type: one item with a specific colour that was always artificially removed when cached and the other item with a different colour that was not removed when cached. During the choice presentations, subjects reduced the amount they consumed and cached of the food items with the colour that was experimentally removed when cached, despite the two items only differing in caching pay‐off. This avoidance to choose the food with the colour that was removed occurred over time, which suggests that subjects were using information about the item's pay‐off during cache recovery and this then impacted on successive decisions. This study highlights how the sensitivity to a food item's pay‐off can affect an individual's choice towards items that offer the greater overall reward.     

Influence of predation risk on diet selection: a simple example in the grey squirrel

Current models of the optimal diet are special cases of a more general (and complex) model which incorporates the effects of predation risk on diet selection; this follows from an assumption implicit in current models that all prey items are eaten where they are encountered. Relaxing this assumption so that a forager might carry a prey item to protective cover for consumption leads to the conclusion that the value of a prey item is a function of its distance to cover as well as its energy content and handling time. Such considerations can significantly alter the outcome of diet selection relative to that expected from simple diet theory. We found that grey squirrels (Sciurus carolinensis) may reject more energetically profitable, but small food items in favour of locating larger, less energetically profitable items that can be carried to protective cover for consumption without greatly sacrificing foraging efficiency. The squirrel's tendency to reject a more profitable item is a function of its distance from cover and the size of the less profitable items. Such behaviour is inconsistent with predictions of current diet models, but is consistent with our qualitative predictions based on a previously determined predation-risk-foraging-efficiency trade-off in the grey squirrel.     

Biomarker-Defined Subgroup Selection Adaptive Design for Phase III Confirmatory Trial with Time-to-Event Data: Comparing Group Sequential and Various Adaptive Enrichment Designs

Predictive and prognostic biomarkers play an important role in personalized medicine to determine strategies for drug evaluation and treatment selection. In the context of continuous biomarkers, identification of an optimal cutoff for patient selection can be challenging due to limited information on biomarker predictive value, the biomarker’s distribution in the intended use population, and the complexity of the biomarker relationship to clinical outcomes. As a result, prespecified candidate cutoffs may be rationalized based on biological and practical considerations. In this context, adaptive enrichment designs have been proposed with interim decision rules to select a biomarker-defined subpopulation to optimize study performance. With a group sequential design as a reference, the performance of several proposed adaptive designs are evaluated and compared under various scenarios (e.g., sample size, study power, enrichment effects) where type I error rates are well controlled through closed testing procedures and where subpopulation selections are based upon the predictive probability of trial success. It is found that when the treatment is more effective in a subpopulation, these adaptive designs can improve study power substantially. Furthermore, we identified one adaptive design to have generally higher study power than the other designs under various scenarios.     

Optimal adaptive two‐stage designs for phase II cancer clinical trials

In oncology, single‐arm two‐stage designs with binary endpoint are widely applied in phase II for the development of cytotoxic cancer therapies. Simon's optimal design with prefixed sample sizes in both stages minimizes the expected sample size under the null hypothesis and is one of the most popular designs. The search algorithms that are currently used to identify phase II designs showing prespecified characteristics are computationally intensive. For this reason, most authors impose restrictions on their search procedure. However, it remains unclear to what extent this approach influences the optimality of the resulting designs. This article describes an extension to fixed sample size phase II designs by allowing the sample size of stage two to depend on the number of responses observed in the first stage. Furthermore, we present a more efficient numerical algorithm that allows for an exhaustive search of designs. Comparisons between designs presented in the literature and the proposed optimal adaptive designs show that while the improvements are generally moderate, notable reductions in the average sample size can be achieved for specific parameter constellations when applying the new method and search strategy.     

De Cognitieve Screening Test (CST) getoetst aan het schaalmodel van Mokken

The Cognitive Screening Test (CST) is a twenty-item orientation questionnaire in Dutch, that is commonly used to evaluate cognitive impairment. This study applied Mokken Scale Analysis, a non-parametric set of techniques derived from item response theory (IRT), to CST-data of 466 consecutive participants in psychogeriatric day care. The full item set and the standard short version of fourteen items both met the assumptions of the monotone homogeneity model, with scalability coefficient H = 0.39, which is considered weak. In order to select items that would fulfil the assumption of invariant item ordering or the double monotonicity model, the subjects were randomly partitioned into a training set (50% of the sample) and a test set (the remaining half). By means of an automated item selection eleven items were found to measure one latent trait, with H = 0.67 and item H coefficients larger than 0.51. Cross-validation of the item analysis in the remaining half of the subjects gave comparable values (H = 0.66; item H coefficients larger than 0.56). The selected items involve year, place of residence, birth date, the monarch's and prime minister's names, and their predecessors. Applying optimal discriminant analysis (ODA) it was found that the full set of twenty CST items performed best in distinguishing two predefined groups of patients of lower or higher cognitive ability, as established by an independent criterion derived from the Amsterdam Dementia Screening Test. The chance corrected predictive value or prognostic utility was 47.5% for the full item set, 45.2% for the fourteen items of the standard short version of the CST, and 46.1% for the homogeneous, unidimensional set of selected eleven items. The results of the item analysis support the application of the CST in cognitive assessment, and revealed a more reliable 'short' version of the CST than the standard short version (CST14).     

Optimal foraging by the swallow (Hirundo rustica, L): Prey size selection

The foraging ecology of the swallow (Hirundo rustica) was investigated in the field and compared with predictions of optimal foraging theory. Prey items were selected primarily by size. The inclusion of an item in the diet depended on the absolute abundance of the high-ranking prey and not of the low-ranking ones. Small items of low profitability were, however, included when food was abundant and more small items were taken when they were relatively abundant. Energetics data are used to show that it is profitable in terms of net energy gain for the swallow to take a mixture of large and small items, whereas optimal foraging theory would predict exclusive specialization on large, high-ranking items. Other possible explanations for the inclusion of small items in the diet are discussed.     

Optimal promising zone designs

Clinical trials with adaptive sample size reassessment based on an unblinded analysis of interim results are perhaps the most popular class of adaptive designs (see Elsäßer et al., 2007). Such trials are typically designed by prespecifying a zone for the interim test statistic, termed the promising zone, along with a decision rule for increasing the sample size within that zone. Mehta and Pocock (2011) provided some examples of promising zone designs and discussed several procedures for controlling their type‐1 error. They did not, however, address how to choose the promising zone or the corresponding sample size reassessment rule, and proposed instead that the operating characteristics of alternative promising zone designs could be compared by simulation. Jennison and Turnbull (2015) developed an approach based on maximizing expected utility whereby one could evaluate alternative promising zone designs relative to a gold‐standard optimal design. In this paper, we show how, by eliciting a few preferences from the trial sponsor, one can construct promising zone designs that are both intuitive and achieve the Jennison and Turnbull (2015) gold‐standard for optimality.     

Development of an Item Scale to Assess Attitudes towards Domestic Dogs in the United Republic of Tanzania

ABSTRACT

Domestic dogs are important sources of rabies exposure for humans in the developing world. Control of the disease in endemic areas relies on the vaccination of owned dogs, and thus owners' attitudes and behavior towards household dogs may be of relevance to rabies control programs. However, none of the instruments used to evaluate attitudes towards companion animals have been validated for use in developing countries. In this paper, we present the development of an item scale to measure attitudes towards owned dogs in Tanzania. We assess the scale's reliability and validity, and conduct a preliminary analysis of factors affecting respondents' attitudes. Twelve 5-point Likert scale items were selected from an item pool during the pilot phase. Following administration of these items to 824 dog owners across 12 study sites in Tanzania, two subscales were derived, representing the acceptance of dogs as equals and physical interactions with household dogs. Both subscales showed acceptable levels of reliability and concurrent validity, although the latter estimates were found to be influenced by interviewer identity. Male respondents had significantly higher scores on both subscales than females, and Muslim respondents showed more positive attitudes towards dogs as equals than did Christians. Among those respondents who were also the heads of their households, those whose dogs were vaccinated against rabies had a more positive attitude towards dogs as equals. It is hoped that the derived item scale will serve as a basis to further understanding of the motivational considerations of attitudes towards dogs in developing countries, and how these may influence aspects of dog ownership, welfare, and disease control.     

科学确立奥林帕斯Au640 全自动生化分析仪校准周期

目的:利用校准周期图科学确立奥林帕斯Au640全自动生化分析仪校准周期。方法:生化分析仪定标校准后,每隔2小时分别测定三种浓度水平的质控血清一次,24小时后每天测定一次,总计测定30天。所有检测项目均重复测定四次取平均值。以积变异系数(CV)大于1/6 CLIA’88为评价标准,绘制校准周期图。结果:碱性磷酸酶(ALP)L1校准周期最短每12 h一次。酶类项目较稳定,校准周期可达30天以上,低水平质控血清必不可少,这样有利于测得更为准确的校准周期。结论:科学、合理地确立各检测项目的校准周期,对生化分析仪检测系统进行定期校准,既能够避免不必要的校准、又能够节省检验工作的时间和成本,保证生化检验项目检测结果可靠性。     

An Approximation to the Bayes Optimal Multi-Stage Design in the Colton Model for Clinical Trials

A simple and operationally convenient approximation is proposed for the Bayes optimal multistage design within the Colton decision-theoretic model for the comparison of two medical treatments. The two- and three-stage designs are developed in full; the latter is found to be superior to several existing designs including some involving sequential adaptive sampling.     

Robust and efficient design of experiments for the Monod model

In this paper the problem of designing experiments for the Monod model, which is frequently used in microbiology, is studied. The model is defined implicitly by a differential equation and has numerous applications in microbial growth kinetics, environmental research, pharmacokinetics, and plant physiology. The designs presented so far in the literature are local optimal designs, which depend sensitively on a preliminary guess of the unknown parameters, and are for this reason in many cases not robust with respect to their misspecification. Uniform designs and maximin optimal designs are considered as a strategy to obtain robust and efficient designs for parameter estimation. In particular, standardized maximin D- and E-optimal designs are determined and compared with uniform designs, which are usually applied in these microbiological models. It is demonstrated that maximin optimal designs are substantially more efficient than uniform designs. Parameter variances can be decreased by a factor of two by simply sampling at optimal times during the experiment. Moreover, the maximin optimal designs usually provide the possibility for the experimenter to check the model assumptions, because they have more support points than parameters in the Monod model.     

Conditional Point Estimation in Adaptive Group Sequential Test Designs

It is well known that point estimates in group sequential designs are biased. This also applies to adaptive designs that enable, e.g., data driven reassessments of group sample sizes. For triangular designs, Whitehead (1986) (Biometrika 73 , 573–581) proposed a bias adjusted estimate. But this estimate is not feasible in adaptive designs although it is in group sequential designs. Furthermore, there is a waste of information because it does not use the information at which stage the trial was stopped. We present a modification which does use this information and which is applicable to adaptive designs. The modified estimate achieves an improvement in group sequential designs and shows similar results in adaptive designs.     

A simple model of information use in the exploitation of patchily distributed food

In this paper we consider a simple model of an environment in which prey are distributed in patches. It is assumed that each patch contains at most one item, but items may vary in the ease with which they can be found. The time spent in unsuccessful search on a patch gives information of whether a patch contains an item, and if it does, how difficult that item is to find. We show how this information can be used to find the policy which maximizes the mean rate of reward for the environment. The analysis is illustrated by two examples.     

Measuring disease-specific quality of life in clinical trials.

While measurement of quality of life is a vital part of assessing the effect of treatment in many clinical trials, a measure that is responsive to clinically important change is often unavailable. Investigators are therefore faced with the challenge of constructing an index for a specific condition or even for a single trial. There are several stages in the development and testing of a quality-of-life measure: selecting an initial item pool, choosing the "best" items from that pool, deciding on questionnaire format, pretesting the instrument, and demonstrating the responsiveness and validity of the instrument. At each stage the investigator must choose between a rigorous, time-consuming approach to questionnaire construction that will establish the clinical relevance, responsiveness and validity of the instrument and a more efficient, less costly strategy that leaves reproducibility, responsiveness and validity untested. This article describes these options and outlines a pragmatic approach that yields consistently satisfactory disease-specific measures of quality of life.     

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (http://www.strobe-statement.org/) should be helpful resources to improve reporting of observational research.     

Complete allocation sampling: an efficient and easily implemented adaptive sampling design

Adaptive sampling designs are becoming increasingly popular in environmental science, particularly for surveying rare and aggregated populations. An adaptive sample is one in which the survey design is modified, or adapted, in some way on the basis of information gained during the survey. There are many different adaptive survey designs that can be used to estimate animal and plant abundance. In adaptive cluster sampling, additional sample effort is allocated during the survey to the immediate neighborhood in which the species is found. In adaptive stratified sampling, additional sample effort is allocated during the survey to strata of high abundance. The appealing feature of these adaptive designs is that the field biologist gets to do what innately seems sensible when working with rare and aggregated populations—field effort is targeted around where the species is observed in the first wave of the survey. However, there are logistical challenges of applying this principle of targeted field effort while remaining in the framework of probability-based sampling. We propose a simplified adaptive survey design that incorporates both targeting field effort and being logistically feasible. We show with a case study population of rockfish that complete allocation stratified sampling is a very efficient design.     

Attentive and pre-attentive processes in change detection and identification

In studies of change blindness, observers often have the phenomenological impression that the blindness is overcome all at once, so that change detection, localization and identification apparently occur together. Three experiments are described that explore dissociations between these processes using a discrete trial procedure in which 2 visual frames are presented sequentially with no intervening inter-frame-interval. The results reveal that change detection and localization are essentially perfect under these conditions regardless of the number of elements in the display, which is consistent with the idea that change detection and localization are mediated by pre-attentive parallel processes.In contrast, identification accuracy for an item before it changes is generally poor, and is heavily dependent on the number of items displayed. Identification accuracy after a change is substantially better, but depends on the new item's duration. This suggests that the change captures attention, which substantially enhances the likelihood of correctly identifying the new item. However, the results also reveal a limited capacity to identify unattended items. Specifically, we provide evidence that strongly suggests that, at least under these conditions, observers were able to identify two items without focused attention. Our results further suggest that spatial pre-cues that attract attention to an item before the change occurs simply ensure that the cued item is one of the two whose identity is encoded.     

Automated UMLS-Based Comparison of Medical Forms

Medical forms are very heterogeneous: on a European scale there are thousands of data items in several hundred different systems. To enable data exchange for clinical care and research purposes there is a need to develop interoperable documentation systems with harmonized forms for data capture. A prerequisite in this harmonization process is comparison of forms. So far – to our knowledge – an automated method for comparison of medical forms is not available. A form contains a list of data items with corresponding medical concepts. An automatic comparison needs data types, item names and especially item with these unique concept codes from medical terminologies. The scope of the proposed method is a comparison of these items by comparing their concept codes (coded in UMLS). Each data item is represented by item name, concept code and value domain. Two items are called identical, if item name, concept code and value domain are the same. Two items are called matching, if only concept code and value domain are the same. Two items are called similar, if their concept codes are the same, but the value domains are different. Based on these definitions an open-source implementation for automated comparison of medical forms in ODM format with UMLS-based semantic annotations was developed. It is available as package compareODM from http://cran.r-project.org. To evaluate this method, it was applied to a set of 7 real medical forms with 285 data items from a large public ODM repository with forms for different medical purposes (research, quality management, routine care). Comparison results were visualized with grid images and dendrograms. Automated comparison of semantically annotated medical forms is feasible. Dendrograms allow a view on clustered similar forms. The approach is scalable for a large set of real medical forms.