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Multisystem Inflammatory Syndrome in children (MIS-C) is a rare and novel pediatric complication linked to COVID-19 exposure, which was first identified in April 2020. A small n, Sequential, Multiple Assignment, Randomized Trial (snSMART) was applied to the Multisystem Inflammatory Syndrome Therapies in Children Comparative Effectiveness Study (MISTIC) to efficiently evaluate multiple competing treatments. In the MISTIC snSMART study, participants are randomized to one of three interventions (steroids, infliximab or anakinra), and potentially re-randomized to the remaining two treatments depending on their response to the first randomized treatment. However, given the novelty and urgency of the MIS-C disease, in addition to patient welfare concerns, treatments were not always administered sequentially, but allowed to be administered concurrently if deemed medically necessary. We propose a pragmatic modification to the original snSMART design to address the analysis of concurrent versus sequential treatments in the MISTIC study. A modified Bayesian joint stage model is developed that can distinguish a concurrent treatment effect from a sequential treatment effect. A simulation study is conducted to demonstrate the improved accuracy and efficiency of the primary aim to estimate the first stage treatments' response rates and the secondary aim to estimate the combined first and second stage treatments' responses in the proposed model compared to the standard snSMART Bayesian joint stage model. We observed that the modified model has improved efficiency in terms of bias and rMSE under large sample size settings. [ABSTRACT FROM AUTHOR]

In today's data‐driven decision‐making era, acquiring accurate information is vital. However, survey research faces challenges with sensitive issues. To address this, the Composite Randomized Response Technique (CRRT) was introduced in estimating the proportion of respondents possessing sensitive attributes. This study revealed that as the model captures more and more people involved in the sensitive attributes (πs) from 0.1 to 0.4, the relative efficiency of CRRT increases from 2.2217 to 678.7843. Hence, CRRT was found to be more efficient than the conventional model, making it a robust approach for surveys targeting sensitive attributes, enhancing data accuracy, and supporting effective policy evaluation and resource allocation. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Randomized controlled trials (RCTs) are necessary to evaluate the efficacy of novel treatments for epilepsy. However, there have been concerning increases in the placebo responder rate over time. To understand these trends, we evaluated features associated with increased placebo responder rate.Using individual‐level data from 20 focal‐onset seizure trials provided by seven pharmaceutical companies, we evaluated associations with change in seizure frequency in participants randomized to placebo. We used multivariable logistic regression to evaluate participant and study factors associated with differing rates of 50% reduction in seizure frequency during blinded placebo treatment, as compared to pre‐randomization baseline seizure frequency. In addition, we focused on the association of placebo responder rate with pre‐randomization baseline seizure frequency and country of recruitment.In the pooled analysis of 1674 participants randomized to placebo, a higher 50% responder rate (50RR) was associated with a shorter duration of epilepsy (p = .006), lower baseline seizure rate (p = .002), fewer concomitant antiseizure medications (p = .004), absence of adverse events (p < .001), more trial arms (p = .006), and geographic region (p < .001). Mixture modeling indicated a significantly higher 50RR in Bulgaria, Croatia, India, and Canada (42% in the higher group vs 22% in the lower group comprising all 40 other countries, p < 10−15). In addition, there was a significantly higher 50RR in participants with a baseline seizure frequency of six or fewer seizures per 28 days (29% vs 21%, p = .00018).These results can assist future RCTs in estimating the expected placebo responder rate, which may lead to more reliable power estimates. Higher placebo responder rate was associated with markers of less‐refractory epilepsy. There were concerning significant differences in placebo responder rate by country and geographic region as well as an elevated placebo responder rate in participants with baseline seizure frequency close to the minimum eligibility criteria. [ABSTRACT FROM AUTHOR]

Gupta et al. suggested an improved estimator by using the Diana and Perri model in estimating the finite population variance using the single auxiliary variable. On the same lines, Saleem et al. proposed a new scrambled randomized response model (RRT) based on two auxiliary variables for estimating the finite population variance. Recently Azeem et al. presented a new randomized response model in estimating the finite population variance. It is observed that Bias and MSE of these estimators up to first order of approximation seem to lack sufficient information. In this study, we rectify the bias and MSE expressions of the estimators proposed by Gupta et al., Saleem et al. and Azeem et al. Additionally, we suggest a new generalized class of estimators that is more efficient in comparison to the previously considered estimators. A simulation study is conducted to establish the behavior of the estimators. The suggested estimator performs better than the estimators considered by the authors earlier. [ABSTRACT FROM AUTHOR]

AbstractIn this study, a mixed quantitative randomized response (RR) model has been proposed for estimating the population mean of a sensitive variable in the presence of scrambled response under simple random sampling. The properties of proposed model are described and verified its theoretical results with empirical analysis. An impartial comparison of proposed mixed quantitative randomized response model with some existing additive, multiplicative, and mixed quantitative models are made. The model efficiency and privacy are considered in comparison, the finding suggests that proposed RR model perform better than some existing models in term of efficiency. [ABSTRACT FROM AUTHOR]

Aim of the study: To systematically evaluate the herbal placebo response in randomized controlled trials (RCTs) of herbal medicine on irritable bowel syndrome (IBS). Materials and methods: We searched for RCTs with herbal placebo groups for IBS in PubMed, EMBASE, the Cochrane Library, the China National Knowledge Infrastructure (CNKI), the Wan Fang database and Sinomed database from 31 January 1994 to November 2023, and the quality of the literature was evaluated by the Cochrane risk of bias assessment criteria. The primary outcome indicators were response rate, abdominal pain and stool improvement rate, which were analyzed by single-group rate meta-analysis. Secondary outcomes were analyzed in subgroups based on diagnostic criteria, duration of treatment, subtype, research locations, placebo form, and presence of herbal ingredients to look for factors affecting respond rate. Results: The study included 24 papers, involving a total of 2,596 patients. Of these, 1151 IBS patients were treated with the herbal placebo. The placebo response rate in IBS patients in the herbal placebo group was 37% (P < 0.01,I2 = 75%). A total of 287 patients in five studies were given the herbal placebo, and the improvement rate of abdominal pain was 29% (P = 0.83, I2 = 0%). Four studies enrolled a total of 212 patients with IBS who received herbal placebo, and the stool improvement rate was 46% (P = 0.02 < 0.05, I2 = 71%). The research locations and treatment duration were sources of heterogeneity (P < 0.05). Conclusion: There is a significant herbal placebo response in patients with IBS. Different research locations and treatment durations are major sources of heterogeneity that may affect IBS patient response rates. The addition of a low dose of herbal ingredients when simulating an herbal placebo does not exaggerate the therapeutic effect of the placebo. There is a lack of uniformity and standardization in the preparation and evaluation of herbal placebos. [ABSTRACT FROM AUTHOR]

Measurement errors are an important consideration in sample surveys. Neglecting their influence can give misleading and inaccurate results. In this paper, we propose three variance estimators using Optional Randomized Response Technique (ORRT) in the presence of measurement error. Performance of the proposed estimators are evaluated through a simulation study and a numerical example. [ABSTRACT FROM AUTHOR]

Randomized response is an interview technique for sensitive questions designed to eliminate evasive response bias. Since this elimination is only partially successful, two models have been proposed for modeling evasive response bias: the cheater detection model for a design with two sub-samples with different randomization probabilities and the self-protective no sayers model for a design with multiple sensitive questions. This paper shows the correspondence between these models, and introduces models for the new, hybrid "ever/last year" design that account for self-protective no saying and cheating. The model for one set of ever/last year questions has a degree of freedom that can be used for the inclusion of a response bias parameter. Models with multiple degrees of freedom are introduced for extensions of the design with a third randomized response question and a second set of ever/last year questions. The models are illustrated with two surveys on doping use. We conclude with a discussion of the pros and cons of the ever/last year design and its potential for future research. [ABSTRACT FROM AUTHOR]

AbstractThe randomized response technique offers an effective way for reducing potential bias resulting from nonresponse and untruthful responses when asking questions about sensitive behaviors or beliefs. The technique is also used for conducting statistical disclosure control of public use data files released by statistical agencies such as the U.S. Census Bureau. In both cases, the technique works by randomizing the actual survey responses using some known randomization model. In the case of asking sensitive survey questions, the randomization of responses is done by the survey respondents and only the randomized responses are collected, whereas in the case of disclosure control, the survey agency implements the randomization of responses after collecting the survey data and prior to releasing it for public use. This paper considers estimating the finite population mean from a survey where randomized responses are available for the study variable along with complete non-randomized auxiliary information. We define and study a class of nonparametric model-assisted estimators that make efficient use of the available auxiliary information and account for the complex survey design. The asymptotic properties of the proposed estimators are derived and a bootstrap variance estimator is presented. The finite sample performance of the proposed estimators is studied via extensive simulations accounting for a wide range of forms for the relationship between the study variable and auxiliary variable. The empirical results support the theoretical analyses and suggest that our proposed estimators are superior to existing estimators in most cases. Furthermore, the proposed methods are illustrated using real data from the 2015 U.S. consumer expenditure survey. [ABSTRACT FROM AUTHOR]

When researchers conduct surveys seeking sensitive, socially stigmatized information, respondents, on average, modify their answers to represent themselves favorably. To overcome this issue, researchers may use Randomized Response Technique (RRT) models. Recently, Parker et al. proposed a model that incorporates some of the most critical recent quantitative RRT advancements—mixture, optionality, and enhanced trust—into a single model, which they called a Mixture Optional Enhanced (MOET) model. We now improve upon the MOET model by incorporating auxiliary information into the analysis. Positively correlated auxiliary information can improve the mean response estimation through use of a ratio estimator. In this study, we propose just such an estimator for the MOET model. Further, we investigate the conditions under which the ratio estimator outperforms the basic MOET estimator proposed by Parker et al. in 2024. We also consider the possibility that the collection of auxiliary information may compromise privacy; and we study the impact of privacy reduction on the overall model performance as assessed by the unified measure (UM) proposed by Gupta et al. in 2018. [ABSTRACT FROM AUTHOR]

Abstract.Auxiliary information is widely used in various studies to improve the precision of estimation. In this article, we extend the application of auxiliary information within the context of randomized response techniques (RRT), building upon the prior research on kernel density estimation (KDE) under additive RRT models. Inspired by Mostafa and Ahmad (2019), we proposed a kernel density estimator that incorporates an auxiliary variable to enhance the accuracy of estimating the distribution of a sensitive variable. Extensive simulations are conducted to evaluate the performance of this proposed methodology, highlighting the advantages of utilizing auxiliary information and the impact of factors such as noise levels, sample size, and the correlation between the auxiliary variable and the sensitive variable. [ABSTRACT FROM AUTHOR]

AbstractThe randomized response technique (RRT) is widely considered for estimating the sensitive attribute in sample surveys. Various randomized response methods have been formulated by the different researchers which provide suitable estimates and privacy protection to the respondents. In the present paper, a new randomized response technique is introduced for estimating a sensitive attribute by amalgamating forced answer strategy into optional RRT. The mean and variance of estimates of proportion of sensitive attribute and sensitivity level under proposed method are derived. The proposed procedure is found to be more efficient than the existing RRTs. The privacy of the respondents possessing the sensitive attribute is well protected under the proposed strategy. The superiority of the proposed procedure is established with the help of a numerical study. [ABSTRACT FROM AUTHOR]

AbstractThe randomized response technique has gained a wide popularity among survey researchers over the past few decades. Randomized response procedure is applicable in a variety of fields including sociology, psychology, business, and education, etc. It is a useful method which helps the researcher in dealing with the high non-response rates in sample surveys on sensitive issues such as monthly income, traffic rules violations, income tax amount, and expenditure on luxury items, etc. We introduce an optimal scrambling technique which uses forced responses. The algebraic properties of the estimator of population mean using the suggested technique have been derived. For the purpose of comparison with existing models, the different measures of model-evaluation have been obtained. Our analysis suggests that the new quantitative technique using forced responses is better than the available scrambling techniques not only in efficiency, but also in the combined metric of model’s efficiency and level of privacy, which make it more useful than the available models for practical problems. Using real-world example, we illustrate its practical implementation to sensitive sample surveys. [ABSTRACT FROM AUTHOR]

This article provides an introduction to randomized response polling, a technique which was designed to allow for questioning on sensitive issues while protecting the respondent's privacy and avoiding social desirability bias. It is described in terms that are suitable for presentation and use in any classroom environment. Instructions for plain users are included, along with the results of a small in-class implementation. The underpinnings of the method, which are laid out for the statistically savvy, illustrate the tradeoff between data acquisition and privacy protection. A few suitable references are also included for those who wish to dig further into the subject. for this article are available online. [ABSTRACT FROM AUTHOR]

Little is known about the scope of use-of-force incidents in carceral settings, nor the impact of efforts to control it. Correctional agencies have recently begun adopting body-worn cameras (BWCs) to mitigate such incidents and improve institutional management. We conducted a one-year randomized controlled trial of BWCs among the 12 housing units in the Loudoun County Adult Detention Center in Virginia. We collected data on the 97 response-to-resistance (RTR) events that occurred during our study period, including deputies' control methods (physical controls, restraints, weapons) and residents' resistance levels (passive, active, aggressive) during these events. Using negative binomial regression, we found a 40% decrease in RTR events in unit-months with BWCs. We also found 37% and 52% reductions in the use of physical controls by deputies and the occurrence of resident active resistance, respectively. We situate these findings in the correctional context and discuss their implications for policy and future research. [ABSTRACT FROM AUTHOR]

It is well-known that given survey data at hand by a given method, it is possible to examine how this method promises to fare vis- à -vis another one that might have been employed but not actually implemented. We illustrate how this may be extended to cover Randomized Response (RR) Techniques (RRT) by citing a few RR procedures in combination with a few sample selection methods generally in use. Developing the theory, simulated results are presented to demonstrate interesting numerical findings in this context. [ABSTRACT FROM AUTHOR]

The Extended Crosswise design is a randomized response design characterized by a sensitive and an innocuous question and two sub-samples with complementary randomization probabilities of the innocuous question. The response categories are 'One' with two different answers and 'Two' with two answers that are the same. Due to the complementary randomization probabilities, 'One' is the incriminating response in one sub-sample, and 'Two' in the other. The use of two sub-samples generates a degree of freedom to test for response biases with a goodness-of-fit test, but this test is unable to detect bias resulting from self-protective respondents giving the non-incriminating response when the incriminating response was required. This raises the question what a significant goodness-of-fit test measures? In this paper, we hypothesize that respondents are largely unaware which response is associated with the sensitive characteristic, and intuitively perceive 'One' as the safer response. We present empirical evidence for one-saying in six surveys among a total of 4 , 242 elite athletes, and present estimates of doping use corrected for it. Furthermore, logistic regression analyses are conducted to test the hypothesis that respondents who complete the survey in a short time are more likely to answer randomly, and therefore are less likely to be one-sayers. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, a new optional randomized response model has been proposed for estimating the population mean of a sensitive variable and sensitivity level in presence of scrambled response under simple random sampling. The properties of proposed model are described and verified its theoretical results with empirical analysis. Both efficiency and privacy measures are also explained and it is found that the proposed optional scrambled randomized response model outperform as compared to some existing additive and multiplicative models. [ABSTRACT FROM AUTHOR]

We develop a collection of unbiased estimators for the proportion of a population bearing a sensitive characteristic using a randomized response technique with two decks of cards for any choice of weights. The efficiency of the estimator depends on the weights, and we demonstrate how to find an optimal choice. The coefficients of skewness and kurtosis are introduced. We support our findings with a simulation study that models a real survey dataset. We suggest that a careful choice of such weights can also lead to all estimates of proportion lying between [0, 1]. In addition, we illustrate the use of the estimators in a recent study that estimates the proportion of students, 18 years and over, who had returned to the campus and tested positive for COVID-19. [ABSTRACT FROM AUTHOR]

The purpose of this study is to present a generalized class of estimators using the three-stage optional randomized response technique (RRT) in the presence of non-response and measurement errors on a sensitive study variable. The proposed estimator makes use of dual auxiliary information. The expression for the bias and mean square error of the proposed estimator are derived using Taylor series expansion. The proposed estimator's applicability is proven using real data sets. A numerical study is used to compare efficiency of the proposed estimator with adapted estimators of the finite population mean. The suggested estimator performs better than adapted ordinary, ratio, and exponential ratio-type estimators in the presence of both non-response and measurement errors. The efficiency of the proposed estimator of population mean declines as the inverse sampling rate, non-response rate, and sensitivity level of the survey question increase. [ABSTRACT FROM AUTHOR]

AbstractDifferent strategies have been developed in survey sampling to address the issue of missing observations and non response. Asking direct questions can be difficult in getting a real response. To get over this problem, we can utilize an indirect approach, such as the randomized response technique (RRT) which is frequently used to estimate the proportion of sensitive trait. In this work, when population level data on a non-sensitive auxiliary variable is available, we provide an enhanced class of estimators for sensitive traits. This class of estimators is based on a generalization of the well-known Kuk’s estimator and a usual difference estimator proposed by Diana and Perri (2009). We verify the findings with survey information obtained from the University of Wah in Pakistan and a data set previously utilized by Zaizai (2006). [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to decision-making based on a three-phase application of a new fuzzy logic model that embraces the principles of symmetry by balancing competing objectives in data collection and analysis. Our study, which employs a three-stage stratified random sample strategy with a randomized response technique, addresses the critical challenges of cost management and volatility reduction. Using the alpha-cut method, our model creates an effective allocation strategy that finds a balance between cost constraints and variance reduction objectives. We use numerical examples from real-world scenarios to demonstrate our approach's durability and practicality. Our revolutionary technique maintains data quality and cost-effectiveness while offering a game-changing answer to sensitive information acquisition concerns. By combining randomized response techniques and fuzzy logic, this study establishes a new standard for decision-making models that prioritizes both data-gathering precision and privacy preservation, encapsulating the essential principle of symmetry in balancing competing aims. [ABSTRACT FROM AUTHOR]

The key innovation lies in the incorporation of an adaptive cluster sampling strategy and a randomized response model based on the Poisson distribution. This integration aims to overcome shortcomings inherent in conventional models, providing a more robust framework for research area. In this paper, an adaptive cluster sampling randomized response model with Poisson distribution using a randomized response strategy was proposed. The proposed cluster randomized response model has improved efficiency and a large gain in precision. Conditions were obtained under which the proposed model is more efficient than the existing models. To validate the effectiveness of our approach, numerical computations were conducted, offering concrete illustrations of the model's performance. The results underscore the significant gains in efficiency and precision achieved by the proposed adaptive cluster sampling randomized response model. [ABSTRACT FROM AUTHOR]

Local soil conditions play a crucial role in shaping ground surface responses and impacting the intensity of ground shaking. In this study, the influence of different site profiles on computed ground motion was investigated using a 2D equivalent linear analysis approach. Four distinct site profiles: sand, clay, sand-clay-sand-clay, and clay-sand-clay-sand profiles were considered. The results were presented using multiple metrics, including surface acceleration, displacement, modulus decreasing ratio, and coherence analysis. Notably, the clay profile significantly influenced ground motion, while the sand profile exhibited relatively lower seismic activity. This suggests that softer sites significantly influence ground motion, leading to potentially high levels of shaking. [ABSTRACT FROM AUTHOR]

In the majority of sample surveys, the variable of interest might have sensitive characteristics. Since we frequently get unrealistic responses from respondents during interviews as employing the direct method is not seen logical in these situations. To tackle this difficulty, the randomized response technique is good substitute for the direct method. The respondent is reassured that there would not be any issues will your response when using the randomized response technique as it protects privacy and secrets. In this paper, we propose a novel quantitative three-stage randomized response technique. Using simulation with R software, we prove that the results obtained from the proposed method are ideal. We suggest ratio and product type estimators by using the auxiliary information to enhance the efficiency of estimators. It is demonstrated that these estimators are superior for the conventional ones in special situations and in applied task, the use of auxiliary variable improves the estimation of the average cheating of Shahid Chamran university of Ahvaz students. The main objective is to increase the average sensitive reaction by utilizing the auxiliary variable.

In this article, two new estimators of population proportion of a sensitive characteristic are introduced by using a method analogous to Analysis of Variance (ANOVA). Then, a new unbiased regression type estimator is developed by utilizing these two estimators. The proposed estimator is, then, compared with its competitor at the same level of protection of the respondents. Also included is a study, based on data collected during summer 2021, of the currently hot topic of estimating the proportion of students, 18 years and older, returning to schools in fall 2021, who tested positive for COVID-19. [ABSTRACT FROM AUTHOR]

Automated decision systems are increasingly used to make consequential decisions in people's lives. Due to the sensitivity of the manipulated data and the resulting decisions, several ethical concerns need to be addressed for the appropriate use of such technologies, particularly fairness and privacy. Unlike previous work, which focused on centralized differential privacy (DP) or on local DP (LDP) for a single sensitive attribute, in this paper, we examine the impact of LDP in the presence of several sensitive attributes (i.e., multi-dimensional data) on fairness. Detailed empirical analysis on synthetic and benchmark datasets revealed very relevant observations. In particular, (1) multi-dimensional LDP is an efficient approach to reduce disparity, (2) the variant of the multi-dimensional approach of LDP (we employ two variants) matters only at low privacy guarantees (high ϵ ), and (3) the true decision distribution has an important effect on which group is more sensitive to the obfuscation. Last, we summarize our findings in the form of recommendations to guide practitioners in adopting effective privacy-preserving practices while maintaining fairness and utility in machine learning applications. [ABSTRACT FROM AUTHOR]

Social Desirability Bias (SDB) often leads to low response rate or worse, untruthful responding during face-to-face surveys involving sensitive questions. Randomized Response Technique (RRT) is often used to circumvent SDB by allowing respondents to provide a scrambled response. However, if respondents do not trust the RRT model, significant bias can still be introduced in the estimates. Yet, none of the quantitative RRT models currently account for respondents' lack of trust. We propose an Optional Enhanced Trust (OET) quantitative RRT model that mitigates the effect of respondents' lack of trust by allowing respondents who do not trust the traditional additive RRT model to use an alternative scrambling technique. Using a combined measure of respondent privacy and model efficiency, we demonstrate both theoretically and empirically that the proposed OET model is superior to the traditional Warner's model. [ABSTRACT FROM AUTHOR]

In this paper, an improved two-stage and three-stage optional randomized response (ORR) models for quantitative variables that make the use of additive scrambling was proposed. These two-stage and three-stage models achieve efficient estimation of the mean and sensitivity level simultaneously in the single sample by using two questions. It is found that the proposed models perform better than the existing ORR models in terms of estimating sensitive attribute and sensitivity level simultaneously. It is found that the proposed three stage ORR model provides better estimates than the two-stage and one-stage ORR models and offers more privacy to the respondents with suitable choice of design parameters. The properties of the proposed models are demonstrated with the help of a numerical study. [ABSTRACT FROM AUTHOR]

In recent years, the research field of data collection under local differential privacy (LDP) has expanded its focus fromelementary data types to includemore complex structural data, such as set-value and graph data. However, our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection. Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key. Additionally, the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff. Recognizing the importance of obtaining accurate key frequencies and mean estimations for key-value data collection, this paper presents a novel framework: the Key-Strategy Framework forKey-ValueDataCollection under LDP. Initially, theKey-StrategyUnary Encoding (KS-UE) strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies; subsequently, the Key-Strategy Generalized Randomized Response (KS-GRR) strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration methods. Both strategies are adapted for scenarios in which users possess either a single or multiple key-value pairs. Theoretically, we demonstrate that the variance of KS-UE is lower than that of existing methods. These claims are substantiated through extensive experimental evaluation on real-world datasets, confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies. [ABSTRACT FROM AUTHOR]

Summary: The increasing availability of high‐dimensional data collected from numerous users has led to the need for multi‐dimensional data publishing methods that protect individual privacy. In this paper, we investigate the use of local differential privacy for such purposes. Existing solutions calculate pairwise attribute marginals to construct probabilistic graphical models for generating attribute clusters. These models are then used to derive low‐dimensional marginals of these clusters, allowing for an approximation of the distribution of the original dataset and the generation of synthetic datasets. Existing solutions have limitations in computing the marginals of pairwise attributes and multi‐dimensional distribution on attribute clusters, as well as constructing relational dependency graphs that contain large clusters. To address these problems, we propose LoHDP, a high‐dimensional data publishing method composed of adaptive marginal computing and an effective attribute clustering method. The adaptive local marginal calculates any k‐dimensional marginals required in the algorithm. In particular, methods such as sampling‐based randomized response are used instead of privacy budget splits to perturb user data. The attribute clustering method measures the correlation between pairwise attributes using an effective method, reduces the search space during the construction of the dependency graph using high‐pass filtering technology, and realizes dimensionality reduction by combining sufficient triangulation operation. We demonstrate through extensive experiments on real datasets that our LoHDP method outperforms existing methods in terms of synthetic dataset quality. [ABSTRACT FROM AUTHOR]

We propose two types of estimators that are analogous to Franklin's model. One estimator is derived by concentrating on the row averages of the responses, and another is obtained by concentrating on the column averages of the observed responses. In the latter case we have two responses per respondent from a bi‐variate normal distribution. The proposed estimator based on row averages, by making use of negatively correlated random numbers from a multivariate density, is always more efficient than the corresponding Franklin's estimator. In the case of the proposed estimator based on column averages, we found that the use of positively correlated random numbers from a bivariate density can lead to the most efficient estimator. We also discuss results which are observed by making use of three responses per respondent. When the three responses are recorded, three independent normal densities are derived from three correlated variables. The findings are supported based on analytical, numerical, and simulation studies. A simulation study was done to determine the minimum sample size required to produce nonnegative estimates of the population proportion of a sensitive characteristic, and to investigate the 95% nominal coverage by the interval estimates. Ultimately at the end, one best estimator is suggested. A very neat and clean derivations of theoretical results and discussion of numerical and simulation studies are documented in Data S1. [ABSTRACT FROM AUTHOR]

This study introduces a new qualitative randomized response technique for gathering trust-worthy sensitive data using a perfect (imperfect) ranked set sampling design. This model takes into account the sizes (weights) of various balls on the randomizing device to choose one of the two questions (out of which one is sensitive); so the respondent feels, liberty in choosing balls. In this way, the likelihood of an honest response increases. It is established that the procedure is easy to apply and the estimate under the proposed model is more precise than the original Warner's randomized response model. In addition, an application to real medical data is considered. Finally, a cost analysis of the proposed model is also presented. [ABSTRACT FROM AUTHOR]

The present work proposes a new two-stage unrelated randomized response model to estimate the mean number of individuals who possess a rare sensitive attribute in a given population by using Poisson probability distribution, when the proportion of rare non-sensitive unrelated attribute is known and unknown. The properties of the proposed model are examined. The variance of the proposed randomized response model smaller than Land et al. (Stat J Theor Appl Stat, 46(3):351–360, 2012) and Singh and Tarray (Model Assist Stat Appl, 10(2):129–138, 2015) to estimate sensitive characteristic under study. The proposed model provides a more efficient unbiased estimator of the mean number of individuals. The procedure also introduces the measure of privacy protection of respondents and compares randomized response models in terms of efficiency and privacy protection. Empirical illustrations are presented to support the theoretical results and suitable recommendations are put forward to the survey statisticians/practitioners. [ABSTRACT FROM AUTHOR]

The primary goal of the proposed study is to establish and validate a low‐cost, sensitive, reliable, and trouble‐free RP‐UPLC process with improved performance for the direct estimation of Lixisenatide in pure powder and fixed‐dose tablet form. The proposed method is optimized by implementing quality by design studies. The method is optimized using quadratic model of central composite design (CCD) of randomized response surface study. Successful separation of Lixisenatide is obtained by the use of Phenyl‐XBD (1.7 µm, 2.1 mm × 100 mm) column and a mobile phase of acetonitrile: 0.1% w/v OPA (60:40) with 0.5 mL min−1 flow rate. The separated Lixisenatide and its degradants are explorated with PDA detector which has the ability to provide comprehensive spectral information, rapid data acquisition, quantitative analysis, and peak purity analysis of all compounds simultaneously. The RT of Lixisenatide is found to be 1.34 min. The lowest concentration of Lixisenatide's limit of detection and limit of quantification makes guarantees regarding the method's sensitivity. For the provided range of linear concentrations (1.25–7.5 µg mL−1), the regression coefficient is found to be 0.999. The computed average percentage recoveries of Lixisenatide in spiked solutions ranged from 98.9% to 101.2%. The % degradation of Lixisenatide in the presence of a variety of forced conditions confirms the existing method's stability. Lixisenatide is significantly photo sensitive when compared to other stressful conditions such as acid, base hydrolysis, and oxidation. The proposed UPLC approach separates Lixisenatide with high sensitivity, a short retention time, and an affordable solvent system. [ABSTRACT FROM AUTHOR]

In this paper, we extend the optional randomized response technique (ORRT) developed by Chaudhuri and Mukerjee [Optionally randomized response techniques. Bull. Calcutta Statist. Assoc. 1985;34:225–230; Randomized response: theory and techniques. New York: Marcel Dekker, Inc.; 1988] to the situation of estimating the proportion of two sensitive characteristics and their overlap. Lee, Sedory and Singh [Estimating at least seven measures of qualitative variables from a single sample using randomized response technique. Stat Prob Lett. 2013;83(1):399–409; Estimation of odds ratio, attributable risk, relative risk, correlation coefficient and other parameters using randomized response techniques. Behaviormetrika. 2021;48:371–392.] have shown that their crossed model performs better than their simple model from an efficiency point of views. Here we investigated a further improvement in the crossed model along the lines of Chaudhuri and Mukerjee [Optionally randomized response techniques. Bull. Calcutta Statist. Assoc. 1985;34:225–230; Randomized response: theory and techniques. New York: Marcel Dekker, Inc.; 1988]. New unbiased estimators are proposed, their variance expressions are derived and estimators of variances are suggested. Lastly, we carry out a simulation study to investigate the behaviour of the proposed estimators with respect to their competitors. [ABSTRACT FROM AUTHOR]

This study examines the effect of measurement error on binary Randomized Response Technique models. We discuss a method for estimating and accounting for measurement error and untruthfulness in two basic models and one comprehensive model. Both theoretical and empirical results show that not accounting for measurement error leads to inaccurate estimates. We introduce estimators that account for the effect of measurement error. Furthermore, we introduce a new measure of model privacy using an odds ratio statistic, which offers better interpretability than traditional methods. [ABSTRACT FROM AUTHOR]

Virtual reality exposure therapy (VRET) has been recognized as an effective treatment for specific phobias and has the potential to overcome the limitations of traditional exposure therapy. The pursuit of non-invasive brain stimulation provides a practical means of augmenting VRET. Transcranial direct current stimulation (tDCS), a non-invasive neuromodulation technique, stimulates the medial prefrontal cortex (mPFC), with the potential to enhance the effects of exposure therapy. Therefore, we conducted a randomized controlled trial to examine whether tDCS enhanced the effects of VRET in acrophobia. This study recruited 64 college students with significant fear of height (based on the Acrophobia Questionnaire, AQ). Finally, 61 participants were randomly allocated to the tDCS active-stimulated group (n = 30) or the sham-stimulated group (n = 31). After stimulation, VRET was conducted, and clinical indices were recorded. The AQ was used as the first primary outcome, and Subjective Units of Distress (SUDS) and the Heights Interpretation Questionnaire (HIQ) were used as secondary outcomes. There was a significant reduction in psychometric and behavioral anxiety measurements from pre to post treatment as indicated by main effects for the factor time (AQ-Anxiety: F (2.60) = 139.55, p < 0.001, η2 = 0.83; AQ-Avoidance: F (2.60) = 53.73, p < 0.001, η2 = 0.69; HIQ: F (2.60) = 128.12, p < 0.001, η2 = 0.81; STAI-Y-S: F (2.60) = 15.44, p < 0.001, η2 = 0.34; BAI: F (2.60) = 73.81, p < 0.001, η2 = 0.71). Compared with the sham-stimulated group, the reduction of AQ-Anxiety and SUDS in the first exposure trial (F (2,60) = 8.56, p = 0.001, η2 = 0.23; t = 2.34, p = 0.024, d = 0.61) was significantly faster in the active group. At follow-up, there was also a further reduction in AQ anxiety and avoidance (Anxiety: M = 56.51 ± 27.19; main effect time F (1,60) = 25.16, p < 0.001, η2 = 0.35; Avoidance: M = 12.57 ± 7.97; main effect time F (1,60) = 31.40, p < 0.001, η2 = 0.45) without interaction time*group (Anxiety: F (1.60) = 0.12, p = 0.740, η2 = 0.00; Avoidance: F (1.60) = 0.64, p = 0.430, η2 = 0.02). Results could be explained tDCS could accelerate the effects of VRET on acrophobia by stimulating mPFC, indicating that tDCS may be used as an enhancement technique for exposure therapy for specific phobias. [ABSTRACT FROM AUTHOR]

Introduction Evidence suggests that e-liquid flavor and nicotine concentration are important factors in the initiation and maintenance of e-cigarette use (vaping). Flavors may increase the initiation and maintenance of vaping, and nicotine content is a factor in e-cigarette dependence and the efficacy of e-cigarettes for cigarette smoking cessation. Few human laboratory studies have assessed the joint and interactive effects of flavor and nicotine on subjective responses to e-cigarettes. Methods Regular e-cigarette users (N = 89) completed a multi-session study involving a paced vaping procedure with e-liquid cartridges containing their preferred flavor (berry, menthol, or tobacco) or no flavor, with or without nicotine (18 mg). Subjective effects of vaping (satisfaction, reward, aversion, airway sensations, and craving relief) were assessed. Results Nicotine significantly increased psychological reward and craving relief, whereas flavor significantly increased vaping satisfaction and taste. Nicotine dependence severity moderated the effect of nicotine on reward, such that those with the greatest dependence severity reported the greatest reward. Conclusions These findings support differential and noninteractive effects of e-liquid nicotine content and flavor on reinforcing effects of e-cigarettes. Implications E-liquid flavor and nicotine content have independent, non-interactive effects on subjective responses to vaping under controlled laboratory conditions. Among regular e-cigarette users, vaping a preferred flavor increased taste and satisfaction, but did not interact with nicotine to alter reward or craving. Further research on the ways in which these subjective effects may motivate vaping behavior among different populations of e-cigarette users would be useful to inform regulatory policy of ENDS products. [ABSTRACT FROM AUTHOR]

This study evaluated and characterized the pharmacological activity of the orally administered interleukin‐1 receptor‐associated kinase 4 (IRAK4) inhibitors BAY1834845 (zabedosertib) and BAY1830839 in healthy male volunteers. Participants received one of either IRAK4 inhibitors or a control treatment (prednisolone 20 mg or placebo) twice daily for 7 days. Localized skin inflammation was induced by topical application of imiquimod (IMQ) cream for 3 days, starting at Day 3 of treatment. The inflammatory response was evaluated by laser speckle contrast imaging (skin perfusion) and multispectral imaging (erythema). At Day 7, participants received 1 ng/kg intravenous lipopolysaccharide (LPS). Circulating inflammatory proteins, leukocyte differentiation, acute phase proteins, and clinical parameters were evaluated before and after the systemic LPS challenge. Treatment with BAY1834845 significantly reduced the mean IMQ‐induced skin perfusion response (geometric mean ratio [GMR] vs. placebo: 0.69 for BAY1834845, 0.70 for prednisolone; both p < 0.05). Treatment with BAY1834845 and BAY1830839 significantly reduced IMQ‐induced erythema (GMR vs. placebo: 0.75 and 0.83, respectively, both p < 0.05; 0.86 for prednisolone, not significant). Both IRAK4 inhibitors significantly suppressed the serum TNF‐α and IL‐6 responses (≥80% suppression vs. placebo, p < 0.05) and inhibited C‐reactive protein, procalcitonin, and IL‐8 responses to intravenous LPS. This study demonstrated the pharmacological effectiveness of BAY1834845 and BAY1830839 in suppressing systemically and locally induced inflammatory responses in the same range as prednisolone, underlining the potential value of these IRAK4 inhibitors as future therapies for dermatological or other immune‐mediated inflammatory diseases. [ABSTRACT FROM AUTHOR]

Randomized response (RR) is a well-known interview technique designed to eliminate evasive response bias that arises from asking sensitive questions. The most frequently asked questions in RR are either whether respondents were "ever" carriers of the sensitive characteristic, or whether they were carriers in a recent period, for instance, "last year". The present paper proposes a design in which both questions are asked, and derives a multinomial model for the joint analysis of these two questions. Compared to the separate analyses with the binomial model, the model makes a useful distinction between last year and former carriers of the sensitive characteristic, it is more efficient in estimating the prevalence of last year carriers, and it has a degree of freedom that allows for a goodness-of-fit test. Furthermore, it is easily extended to a multinomial logistic regression model to investigate the effects of covariates on the prevalence estimates. These benefits are illustrated in two studies on the use of anabolic androgenic steroids in the Netherlands, one using Kuk and one using both the Kuk and forced response. A salient result of our analyses is that the multinomial model provided ample evidence of response biases in the forced response condition. [ABSTRACT FROM AUTHOR]

Introduction: Despite improved treatment options for plaque psoriasis within the last decades, some patients still have an inadequate response to treatment. Direct clinical evaluation between therapies used after biologic failure could facilitate physicians' choice of treatment. Methods: COBRA (NCT04533737) was a randomized (1:1), blinded (patient and assessor), 28-week, active-comparator trial conducted in Europe from December 2020 to December 2022. The objective was to compare the efficacy and safety of brodalumab versus guselkumab in adults with moderate-to-severe plaque psoriasis and inadequate response to ustekinumab. Patients received either brodalumab 210 mg or guselkumab 100 mg. The primary [having Psoriasis Area and Severity Index (PASI)-100 response at week 16] and key secondary (time to PASI-100 response) endpoints were tested in a fixed sequence. Results: Due to delays and enrollment challenges, recruitment was terminated with 113 patients enrolled of 240 planned. The proportion of patients having PASI-100 at week 16 for brodalumab was 53.4% compared with 35.9% for guselkumab [odds ratio (OR) 2.05; 95% confidence interval (CI) 0.95, 4.44; p = 0.069]. As this was not statistically significant, the hierarchical testing procedure was stopped. All other secondary PASI endpoints had nominal p-values below 0.05 in favor of brodalumab. In the time to PASI response analyses, brodalumab separated from guselkumab in estimated cumulative incidence of patients achieving a response from week 2 onward, suggesting fast onset of action with brodalumab. Quality of life measures improved in both treatment groups. The safety findings were consistent with the known safety profiles. Conclusions: Brodalumab showed a tendency toward better and earlier effect than guselkumab in patients who had failed ustekinumab. Thus, this trial provides important information in assisting physicians in their choice of therapy for patients who have failed their prior anti-interleukin (IL)-12/23 treatment. Trial Registration: ClinicalTrials.gov identifier NCT04533737. [ABSTRACT FROM AUTHOR]

Randomized response techniques (RRTs) are applied to reduce response biases in self-report surveys on sensitive research questions (e.g., on socially undesirable characteristics). However, there is evidence that they cannot completely eliminate self-protecting response strategies. To address this problem, there are RRTs specifically designed to measure the extent of such strategies. Here we assessed the recently devised unrelated question model—cheating extension (UQMC) in a preregistered online survey on intimate partner violence (IPV) victimization and perpetration during the first contact restrictions as containment measures for the outbreak of the coronavirus disease 2019 pandemic in Germany in early 2020. The UQMC accounting for self-protecting responses described the data better than its predecessor model which assumes instruction adherence. The resulting three-month prevalence estimates were about 10% and we found a high proportion of self-protecting responses in the group of female participants queried about IPV victimization. However, unexpected results concerning the differences in prevalence estimates across the groups queried about victimization and perpetration highlight the difficulty of investigating sensitive research questions even using methods that guarantee anonymity and the importance of interpreting the respective estimates with caution. [ABSTRACT FROM AUTHOR]

Indirect questioning techniques such as the randomized response technique aim to control social desirability bias in surveys of sensitive topics. To improve upon previous indirect questioning techniques, we propose the new Cheating Detection Triangular Model. Similar to the Cheating Detection Model, it includes a mechanism for detecting instruction non-adherence, and similar to the Triangular Model, it uses simplified instructions to improve respondents' understanding of the procedure. Based on a comparison with the known prevalence of a sensitive attribute serving as external criterion, we report the first individual-level validation of the Cheating Detection Model, the Triangular Model and the Cheating Detection Triangular Model. Moreover, the sensitivity and specificity of all models was assessed, as well as the respondents' subjective evaluation of all questioning technique formats. Based on our results, the Cheating Detection Triangular Model appears to be the best choice among the investigated indirect questioning techniques. [ABSTRACT FROM AUTHOR]

In social surveys, the respondents often tend to refuse to provide truthful information on sensitive issues. The technique of randomized response is a useful tool to collect reliable data by ensuring the privacy protection of the respondents. This paper presents a new modified randomized response scrambling procedure which is found to be more efficient than the existing techniques. Besides efficiency, the joint measure of respondent-privacy and efficiency is also smaller than the existing models which indicates that the overall quality of the proposed model is better than the existing quantitative randomized response models. A real-life example of data collection through the proposed model is also presented. The improvement in percent relative efficiency and privacy protection is shown for various choices of parameters. [ABSTRACT FROM AUTHOR]

In recent decades, the randomized response technique has attracted researchers due to its usefulness in sensitive surveys. The randomized response procedure is used for the collection of responses on sensitive issues such as cheating in examination, income earned through illegal sources, expenditure on luxury items, and amount of tax paid, etc. This study introduces a new variant of quantitative randomized response models for use with sample surveys where the variable of interest is quantitative. The properties of a mean estimator based on the new technique have been studied. Further, the combined and separate evaluation metrics for efficiency and privacy level have also been derived and compared with those of the existing methods. Further, a simulation study has been conducted to prove the improvement in the degree of privacy protection and efficiency. The findings reveal that the suggested randomized response technique is not only more efficient than the existing techniques, but also improves the joint measure of efficiency and respondents’ privacy, making it preferable over the existing techniques. A real-world example of a sample survey through the suggested model is also presented which illustrates its usefulness in practical surveys on sensitive issues.