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The Quantitative Randomized Response Technique (RRT) can be used by researchers to obtain honest answers to questions that, due to their sensitive (socially undesirable, dangerous, or even illegal) nature, might otherwise invoke partially or completely falsified responses. Over the years, Quantitative RRT models, sometimes called Scrambling models, have been developed to incorporate such advancements as mixture, optionality and enhanced trust, each of which has important benefits. However, no single model incorporates all of these features. In this study, we propose just such a unified model, which we call the Mixture Optional Enhanced Trust (MOET) model. After developing methodologies to assess MOET based on standard approaches and using them to explore the key characteristics of the new model, we show that MOET has superior efficiency compared to the Quantitative Optional Enhanced Trust (OET) model. We also show that use of the model's mixture capability allows practitioners to optimally balance the model's efficiency with its privacy, making the model adaptable to a wide variety of research scenarios. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative optimization model employing fuzzy logic to address the challenge of minimizing variance while considering costs in the context of a two-stage stratified random sampling model with a randomized response approach. The proposed model leverages the alpha-cut technique to establish an optimal allocation strategy, making it possible to effectively manage the trade-off between cost constraints and variance reduction objectives. To illustrate the practical application of the model, we provide numerical examples, demonstrating its efficacy in real-world scenarios. This research contributes to the field by offering a comprehensive framework for decisionmakers to enhance the quality of data collection processes, particularly in situations where preserving respondent privacy is crucial. The integration of fuzzy logic and randomized response techniques presents a novel approach to addressing the inherent challenges of collecting sensitive information while maintaining data integrity and cost-efficiency. [ABSTRACT FROM AUTHOR]

Surveys involving sensitive characteristics entail applying innovative tools such as Randomized Response Techniques (RRTs) to elicit responses from the sampled units. This manuscript proposes one such new two-stage RRT, involving two simple and common randomization devices, namely, a coin and a die. Empirical study has been conducted to show its efficacy over contemporary estimator in terms of both Percentage Relative Efficiency and Privacy Protection. Application of the proposed RRT to election polling has been discussed efficiently. The appropriateness of such application has been illustrated via detailed simulation study. The results have been tabulated and recommendations made for the use of the proposed estimation strategy. [ABSTRACT FROM AUTHOR]

Randomized response (RR) technique using the Negative Binomial distribution was proposed by Hussain et al. It showed numerically that the method performs better than some of the existing methods including Kuk. However, the method of comparison of the efficiencies used by them was not fair. A more appropriate method of comparison is, therefore, proposed in this article and it is found that Kuk’s RR is better than the Hussain et al.’s in terms of execution and efficiency. [ABSTRACT FROM AUTHOR]

Sensitive questions are frequently asked in medical, psychological, and sociological research. In the social science literature, it is widely accepted that when respondents are asked such questions, social desirability bias affects survey research. Different methods in the literature aimed to reduce measurement errors such as social desirability bias and increase the reliability of participants’ answers. These methods address the problem to increase the effectiveness of predictions by using indirect questioning techniques. The sensitive question methods evaluated in this study are randomized response technique, grouped answer method, unmatchedcount technique, and crosswise model. These methods were evaluated with a comprehensive simulation study. The performances of the methods were evaluated according to the sample size and the sensitive issue prevalence in the hypothetical population. In the second part, in order to reduce the small sample size and low prevalence effect of the crosswise model, the Uludag correction of the crosswise model was proposed in the study. As a result, the crosswise model performs quite well compared to the other methods. In the use of the crosswise model, it is recommended to use the Uludag correction of the model proposed in this study in cases where low prevalence and small samples are studied. [ABSTRACT FROM AUTHOR]

Process mining uses event logs to improve business processes, but such logs may contain privacy information. One popular research problem is the privacy protection of event logs. Publishing logs with differential privacy is one of major research directions. Existing research achieves privacy protection primarily by injecting random noise into event logs, or merging similar information. The former ignores the fact that injecting random noise will produce apparently unreasonable activity traces, and the latter will cause a loss of process information in the process mining perspective. To solve the above problems, this article proposes a differential algorithm based on randomized response to model Petri nets for the original event logs, select the important labels in the logs by the weak sequential relationship of control flow between activities, inject noise into the Petri net model based on the important labels using the randomized response approach, and establish a differential Petri net model. Experiments on public datasets show that the event logs produced by the approach proposed do not contain unreasonable traces. Compared with the baseline approach, the proposed approach performs better on Fitness metrics with consistent privacy requirements and retains more process variants, reducing the loss of original event log process information. [ABSTRACT FROM AUTHOR]

In this paper, a new scrambled randomized response (SRR) model has been proposed for estimating the population mean of a sensitive variable in presence of scrambled response under simple random sampling with replacement (SRSWR). The utility of proposed SRR model under two occasions successive sampling is also explored. It is found that the proposed SRR model is superior to the additive model of Gjestvang and Singh (2009) under SRSWR and successive sampling. We also proposed a composite class of estimators for estimating the population mean of sensitive variable under two occasions successive sampling. The proposed composite class of estimators under optimum conditions is shown to be more efficient than the classical ratio and exponential ratio type estimators respectively. [ABSTRACT FROM AUTHOR]

The article explores the issue of high placebo response rates in clinical trials for hidradenitis suppurativa (HS), a skin condition. It highlights the challenge this poses for drug development programs in distinguishing the effects of active treatments from placebos. The article provides data from 11 phase II/III trials, revealing a median placebo response rate of 31% using a specific measurement tool. This information is valuable for researchers and those interested in understanding the difficulties of developing effective treatments for HS. The text also discusses the challenges in accurately assessing disease activity and measuring treatment response in HS patients, due to the complex nature of the disease and the difficulty in identifying and counting specific lesion types. Efforts are being made to develop alternative measures that are more reliable and do not require lesion counting, such as the HS Investigator Global Assessment and a measure that assesses various aspects of the condition. Standardization of morphological terms and study assessments is also being pursued. Overcoming these challenges will be crucial for improving care for HS patients. [Extracted from the article]

In social surveys, the randomized response technique can be considered a popular method for collecting reliable information on sensitive variables. Over the past few decades, it has been a common practice that survey researchers develop new randomized response techniques and show their improvement over previous models. In majority of the available research studies, the authors tend to report only those findings which are favorable to their proposed models. They often tend to hide the situations where their proposed randomized response models perform worse than the already available models. This approach results in biased comparisons between models which may influence the decision of practitioners about the choice of a randomized response technique for real-life problems. We conduct a neutral comparative study of four available quantitative randomized response techniques using separate and combined metrics of respondents' privacy level and model's efficiency. Our findings show that, depending on the particular situation at hand, some models may be better than the other models for a particular choice of values of parameters and constants. However, they become less efficient when a different set of parameter values are considered. The mathematical conditions for efficiency of different models have also been obtained. [ABSTRACT FROM AUTHOR]

In this study, efficient Bayes estimators of sensitive proportion are proposed. It is documented that indirect reports increase variances of the estimates. To counteract this increase in variances we divided the total sample size, n = n1+n2, such that n1 individuals record direct responses and n2 individuals record indirect responses. The decision that a group of individuals should report indirect or direct responses would be based on distinct known factors. Bayes estimates and subsequent posterior risks are calculated taking into account different prior distributions, loss functions and a generalized randomized response technique. The impact of design parameters and the number of responses obtained using direct and indirect questioning techniques on the relative efficiencies are investigated. Graphical and numerical results indicate that the proposed estimators are better than the existing. [ABSTRACT FROM AUTHOR]

Increasing demand in surveys focused on quantitative characteristics (population mean among others) of controversial issues like corruption, tax evasion, drugs consumption of sensitive variables like spending on drugs or illegal sources of income which lead to lively research, are of randomized response techniques for quantitative variables. Therefore, we propose complex multicriteria evaluation methodology for randomized response techniques for population mean. Based on extensive review in the literature, following ranges of criteria were proposed: statistical properties of estimator, implementation and parameter choice, respondent burden and credibility and confidentiality protection of respondents' data. Finally, we evaluate in this setting standard techniques using scramble variables and recently proposed techniques of dichotomous question. [ABSTRACT FROM AUTHOR]

In sample surveys, refusals and untruthful reporting by the respondents are serious issues often faced by the researchers during the data collection process. Over the past few decades, randomized response models have gained wide popularity among survey statisticians as a tool to reduce the non‐response rate. The existing literature on quantitative randomized response models utilize the scrambling variable either as an additive term or as a multiplicative factor. This paper introduces the novel idea of the use of the exponential function of scrambling variable in quantitative randomized response models. Two new quantitative randomized response models are proposed based on exponential scrambling variable. It is found that the new randomized response models are more efficient than the existing quantitative randomized response models. The mathematical properties of the mean estimator of the sensitive variable under the new proposed methods have been discussed. Moreover, the joint and separate measures of privacy protection and efficiency of the proposed and existing models have also been discussed. Further, to illustrate the practical implementation of the suggested models, an example of data collection is also presented. [ABSTRACT FROM AUTHOR]

Background: Environmental co-exposure to allergen and traffic-related air pollution is common globally and contributes to the exacerbation of respiratory diseases. Individual responses to environmental insults remain variable due to gene-environment interactions. Objective: This study examined whether single nucleotide polymorphisms (SNPs) in lung cell surface receptor genes modifies lung function change and immune cell recruitment in allergen-sensitized individuals exposed to diesel exhaust (DE) and allergen. Methods: In this randomized, double-blinded, four-arm, crossover study, 13 allergen-sensitized participants underwent allergen inhalation challenge following a 2-hour exposure to DE, particle-depleted diesel exhaust (PDDE) or filtered air (FA). Lung function tests and bronchoscopic sample collection were performed up to 48 h after exposures. Transient receptor potential channel (TRPA1 and TRPV1) and toll-like receptor (TLR2 and TLR4) risk alleles were used to construct an unweighted genetic risk score (GRS). Exposure-by-GRS interactions were tested using mixed-effects models. Results: In participants with high GRS, allergen exposure was associated with an increase in airway hyperresponsiveness (AHR) when co-exposed to PDDE (p = 0.03) but not FA or DE. FA and PDDE also were associated with a relative increase in macrophages and decrease in lymphocytes in bronchoalveolar lavage. Conclusions: TRPs and TLRs variants are associated with increased AHR and altered immune cellularity in allergen-exposed individuals. This effect is blunted by DE exposure, suggesting greater influence of unmeasured gene variants as primary meditators of a particulate-rich co-exposure. Trial registration: The study was registered with ClinicalTrials.gov on December 20, 2013 (NCT02017431). [ABSTRACT FROM AUTHOR]

In survey sampling, we aspire to obtain sound and consistent responses, which are not achieved while dealing with sensitive issues. Frequently, respondents give elusive responses to sensitive questions, so we employ randomized response techniques that facilitate finding an appropriate proportion of socially sensitive characteristics. In the present study, we proposed a hidden logit estimation method using Huang, Warner, and Mangat's randomized response techniques. This study depicts that the estimates become closer to the standard logits as the values of p increase. We found that the hidden logit estimates obtained by the Huang randomized response technique were nearer to the parametric values, in contrast to the other existing techniques, and we demonstrate an increase in accuracy as well. The simulation-based AIC and SIC values are used to assess model performance. We found that the Huang model is the best model for the proposed hidden logit method. This paper contributes towards the application of logistic models in the case of sensitive or socially stigmatized issues. [ABSTRACT FROM AUTHOR]

Link prediction (LP) is an attractive research problem on social network data. Yet, the link information may be leaked by the untrusted collector. As countermeasures, there are a few methods specially designed for link prediction under local differential privacy (LDP), which allow the third-party data collector collects user data for link prediction while protecting user connection privacy. In this paper, we propose a C ommunity-based graph collection with P ersonalized sampling R andomized R esponse (CPRR) which is a novel graph collection algorithm with LDP and reaches decent trade-off between sensitive link protection and link prediction performance. The proposed mechanism adopts a personalized sampling technique for each connection of a user and then utlizes the randomized response mechanism on the sampled subset. Based on the personalized sampling technique, we can reduce the injected noise in LDP. Meanwhile, considering the different edge distributions of the different regions on the original graph, we propose a community-based sampling strategy. Then, we prove that the proposed CPRR satisfies LDP. Through extensive experiments on several real-life graph datasets, we demonstrate that CPRR can achieve better results in balancing privacy protection and link prediction performance than the state-of-art baselines. [ABSTRACT FROM AUTHOR]

In estimating the proportion of people bearing a stigmatizing characteristic in a community of people, randomized response techniques are plentifully available in the literature. They are implemented essentially using boxes of similar cards of two distinguishable types. In this paper, we propose a more general procedure using five different types of cards. A respondent-specific randomized response technique is also proposed, in which respondents are allowed to build up the boxes according to their own choices. An immediate objective for this change is to enhance, sense of protection of privacy of the respondents. But as by-products, higher efficiency in terms of actual coverage percentages of confidence intervals and related features are demonstrated by a simulation study, and superior jeopardy levels against divulgence of personal secrecy are also reported to be achievable. [ABSTRACT FROM AUTHOR]

Randomized response survey methods use noise to mask respondents' answers to stigmatizing questions in an attempt to elicit honest responses. Respondents weigh the preference for honesty against the disutility of stigmatization when deciding how to answer. Since the disutility of stigmatization depends on the degree of noise, the interviewer designs the survey to balance two goals: (i) honest reporting by respondents and (ii) maximization of the accuracy of estimates based on the survey. We fully characterize the non-linear set of design parameters that lead to truth-telling, as well as the interviewer's equilibrium survey design. [ABSTRACT FROM AUTHOR]

This article proposes two – stage stratified partial randomized response models based on Tracy and Osahan (1995) model and have large gain in precision. It is also shown that the proposed models are more efficient than Kim and Warde (2004) and Kim and Elam (2005) stratified randomized response models under the conditions presented in the case of completely truthful reporting and less than completely truthful reporting by the respondents. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper we have suggested a class of estimators of population mean of sensitive variable under optional randomized response technique as reported in Gupta et al (2014). We have obtained the mean squared error (MSE) of the suggested class of estimators up to the first order of approximation. The optimum conditions are obtained at which the (MSE) of the proposed class of estimators is minimum. An empirical study is carried out to show the performance of the suggested class of estimators over existing estimators .It is found that the performance of proposed class of estimators is better than the existing estimators including Grover and Kaur (2019) [ABSTRACT FROM AUTHOR]

Local differential privacy (LDP) is a promising technique to realize privacy-preserving data aggregation without a trusted aggregator. Normally, an LDP protocol requires each user to locally perturb his raw data and submit the perturbed data to the aggregator. Consequently, LDP is vulnerable to output poisoning attacks. Malicious users can skip the perturbation and submit carefully crafted data to the aggregator, altering the data aggregation results. Existing verifiable LDP protocols, which can verify the perturbation process and prevent output poisoning attacks, usually incur significant computation and communication costs, due to the use of zero-knowledge proofs. In this paper, we analyze the attacks on two classic LDP protocols for frequency estimation, namely GRR and OUE, and propose two verifiable LDP protocols. The proposed protocols are based on an interactive framework, where the user and the aggregator complete the perturbation together. By providing some additional information, which reveals nothing about the raw data but helps the verification, the user can convince the aggregator that he is incapable of launching an output poisoning attack. Simulation results demonstrate that the proposed protocols have good defensive performance and outperform existing approaches in terms of efficiency. [ABSTRACT FROM AUTHOR]

In this study, we introduce a mixture binary Randomized Response Technique (RRT) model by combining the elements of the Greenberg Unrelated Question model and the Warner Indirect Question model. We also account for untruthful responding in the proposed model. A unified measure of model efficiency and respondent privacy is also presented. Finally, we present a simulation study to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

Warner proposed a methodology called randomized response techniques, which, through the random scrambling of sensitive variables, allows the non-response rate to be reduced and the response bias to be diminished. In this document, we present a randomized response technique using simple random sampling. The scrambling of the sensitive variable is performed through the selection of a report Ri, i = 1,2,3. In order to evaluate the accuracy and efficiency of the proposed estimators, a simulation was carried out with two databases, where the sensitive variables are the destruction of poppy crops in Guerrero, Mexico, and the age at first sexual intercourse. The results show that more accurate estimates are obtained with the proposed model. [ABSTRACT FROM AUTHOR]

To develop more effective interventions, conservationists require robust information about the proportion of people who break conservation rules (such as those relating to protected species, or protected area legislation). Developed to obtain more accurate estimates of sensitive behaviors, including rule‐breaking, specialized questioning techniques such as Randomized Response Techniques (RRTs) are increasingly applied in conservation, but with mixed evidence of their effectiveness. We use a forced‐response RRT to estimate the prevalence of five rule‐breaking behaviors in communities living around the Ruaha–Rungwa ecosystem in Tanzania. Prevalence estimates obtained for all behaviors were negative or did not differ significantly from zero, suggesting the RRT did not work as expected and that respondents felt inadequately protected. To investigate, we carried out a second study to explore how topic sensitivity influenced respondents' propensity to follow RRT instructions. Results from this experimental study revealed respondents understood instructions well (~88% of responses were correct) but that propensity to follow RRT instructions was significantly influenced by the behavior asked about, and the type of answer they were required to provide. Our two studies highlight that even if RRTs are well understood by respondents, where topics are sensitive and respondents are wary of researchers, their use does not necessarily encourage more honest responding. [ABSTRACT FROM AUTHOR]

Background and Objective: Many randomized clinical trials (RCTs) for antidepressants in children and adolescents have failed to demonstrate efficacy due to a high placebo response. The aim of this study was to identify the potential factors affecting placebo response using meta-regression analysis of RCTs for antidepressants in children and adolescents using the Children's Depressive Rating Scale-Revised (CDRS-R) as the outcome. Methods: PubMed and ClinicalTrials.gov were searched for randomized, double-blind, placebo-controlled trials of antidepressants for the acute treatment of major depressive disorder in children and adolescents. The outcome used in the present study was the mean change of the CDRS-R total score from baseline to the last assessment for the primary efficacy in the placebo arm. Potential factors related to the placebo response, such as study design, operational, and patient factors, were explored using meta-regression. Results: The analyses included 23 trials. On multivariable meta-regression, setting up a placebo lead-in period was significantly associated with a smaller placebo response in the CDRS-R. Conclusion: Setting up a placebo lead-in period should be considered in future clinical trials of antidepressants in adolescents and children. [ABSTRACT FROM AUTHOR]

With the rapid development of point-of-interest (POI) recommendation services, how to utilize the multiple types of users' information safely and effectively for a better recommendation is challenging. To solve the problems of imperfect privacy-preserving mechanism and insufficient response-ability to complex contexts, this paper proposes a hybrid POI recommendation model based on local differential privacy (LDP). Firstly, we introduce randomized response techniques k-RR and RAPPOR to disturb users' ratings and social relationships, respectively and propose a virtual check-in time generation method to deal with the issue of missing check-in time after disturbance. Secondly, for simultaneously combining multiple types of information, we construct a hybrid model containing three sub-models. Sub-model 1 considers the effect of user preference, social relationship, forgetting feature, and check-in trajectory on similarity calculation. Sub-model 2 analyzes the geographical correlation of POIs. Sub-model 3 focuses on the categories of POIs. Finally, we generate the recommendation results. To test the performance of privacy-preserving and recommendation, we design three groups of experiments on three real-world datasets for comprehensive verifying. The experimental results show that the proposed method outperforms existing methods. Theoretically, our study contributes to the effective and safe usage of multidimensional data science and analytics for privacy-preserving POI recommender system design. Practically, our findings can be used to improve the quality of POI recommendation services. [ABSTRACT FROM AUTHOR]

Randomized response (RR) is a common privacy protection tool. It perturbs each true response using a probabilistic mechanism. Local differential privacy (LDP) is a rigorous privacy protection criterion that demands a guarantee that no intruder will get much new information about any respondent's true value from its perturbed value. Considering linear unbiased estimation of multinomial probabilities under LDP and squared error loss, we derive minimax RR methods. We address optimal choices for both the RR mechanism (or design) and the estimator. Our minimax design has a particular structure, which is used to define t-subset designs. We describe and study properties of t-subset designs including their practical implementation. We also study mixtures of t-subset designs and examine the RAPPOR method, which is used notably by Google and Apple. We note inadmissibility of the RAPPOR design and offer some suggestions for improving both the design and the customary estimator. [ABSTRACT FROM AUTHOR]

In this document is presented a review of the current literature on randomized responses for quantitative variables. The review is chronological ordered considering its origin, Warner (1965), up to recent proposals. The techniques are classified and some are selected for classification. A study, using real data is developed using data that may be considered as sensitive, evaluating the accuracy and efficiency of the best quantitative randomized response techniques. [ABSTRACT FROM AUTHOR]

In carrying out surveys involving sensitive characteristics, randomized response models have been considered among the best techniques since they provide the maximum privacy protection to the respondents and procure honest responses. Over the years, researchers have carried out studies on the estimation of proportions of the population possessing sensitive characteristics. However, there is a paucity of research studies that have addressed higher order interactions between these sensitive characters. In this article, we develop a new theory based on three proposed randomized response models which we name as: simple model, semi-crossed model, and fully crossed model. Twenty-one new unbiased estimators of seven parameters are introduced, their variance expressions are derived, and unbiased estimators of variances are developed. The three models are compared under various values of the parameters by computing the percent relative efficiency of one model over another model. The most efficient model is then applied to study the population proportions of three varieties of smoking habits among students, and their first- and second-order interactions. The last four sections (Ninth to Twelfth) are verifications of theoretical results using the Cramer–Rao lower bounds of variances for the developed 21 new estimators in randomized response sampling. [ABSTRACT FROM AUTHOR]

In much of the developing world, cooking accounts for the largest share of women's time in home production. Does relying on solid fuels drive this time burden? This study revisits a clean energy information experiment in rural India to assess the time savings' potential of cleaner cooking technologies. Treatment villages were randomly assigned to receive information about negative health effects of cooking with solid fuels and about public subsidies for cleaner liquid petroleum gas (LPG). Time-use data indicate that primary cooks spend almost 24 hours cooking each week. Cleaner fuel use is correlated with about 140 minutes less cooking time each week. Yet households only reduce their weekly cooking time by about 35 minutes in response to the randomized clean energy information nudge. Factors limiting the impact of clean energy nudges on the choice of home production technologies and time use are discussed and an avenue for future research is suggested. [ABSTRACT FROM AUTHOR]

The article discusses the integrity of randomized clinical trials (RCTs) and the need for validated integrity tests and checklists. The authors respond to comments from other researchers, emphasizing the importance of transparency and trustworthiness in RCTs. They highlight the challenges in validating integrity tests and checklists and call for cooperation among stakeholders to improve integrity assessment. The authors also address the use of the Carlisle test and advocate for the validation of integrity tools. They conclude by emphasizing the role of post-publication peer review and the need for validation research in RCT integrity. [Extracted from the article]

In survey sampling, the randomized response technique is a useful tool to collect reliable data in many fields including sociology, education, economics, and psychology etc. Over the past few decades, many variants of quantitative randomized response models have been developed by researchers. The existing literature on randomized response models lacks a neutral comparative study of different models to help the practitioners choose the appropriate model for a given practical problem. In most of the existing studies, the authors tend to show only the favorable results by hiding the cases where their suggested models are inferior to the existing models. This approach often leads to biased comparisons which may badly misguide the practitioners when choosing a randomized response model for a practical problem at hand. This paper attempts a neutral comparison of six existing quantitative randomized response models using separate as well as joint measures of respondent-privacy and model-efficiency. The findings suggest that one model may perform better than the other model in terms of efficiency but may perform worse when other metrics of model quality are taken into account. The current study guides practitioners in choosing the right model for a given problem under a particular situation. [ABSTRACT FROM AUTHOR]

The randomized response technique is one of the most commonly used indirect questioning methods to collect data on sensitive characteristics in survey research covering a wide variety of statistical applications including, e.g., behavioral science, socio-economic, psychological, epidemiology, biomedical, and public health research disciplines. After nearly six decades since the technique was invented, many improvements of the randomized response techniques have appeared in the literature. This work provides several different aspects of improvements of the original randomized response work of Warner, as well as statistical methods used in the RR problems. [ABSTRACT FROM AUTHOR]

Leachate concentrate is a highly complex effluent. This study compares the treatability of leachate concentrate by synergistic radical-based processes ((persulfate (PS) + O3 (ozone) + electrooxidation (EO)), (O2 state + zero-valent aluminum (ZVAl)) and (ZVAl + PS)). Process conditions for high color removal efficiency were optimized using the randomized response surface design. Under optimum conditions, operation cost (€/L wastewater), color and COD removal efficiency (%), and increase in BOD5/COD ratio (%) were determined as 1.1000, 84 and 50.66, and 195.20 for (PS + O3 + EO), 1.8425, 56.67 and 41.5, and 50 for (O2 state + ZVAl), and 4.1538, 70 and 64, and 147.61 for (O2 state + ZVAl + PS). Using the PROMETHEE approach, three processes were evaluated for operational cost, biodegradability, organic matter, and color removal criteria, and the most suitable process was selected. Process preference order was determined as (PS + O3 + EO) > (O2 state + ZVAl + PS) > (O2 state + ZVAl). The optimum operating conditions for (PS + O3 + EO) process were determined as pH of 9.96, duration of 146.69 min, PS of 4.02 g/L anion, electrode type of RuO2-IrO2, and ozone dose of 65.99%. [ABSTRACT FROM AUTHOR]

The notion of ratio-cum-product estimator was implemented in this work to estimate the population means of sensitive study variables in the existence of non-response and measurement error simultaneously under simple random sampling and two-phase sampling by employing ORRT models. The specifications (bias and MSE's) of the proposed and obtained estimators were derived up to the second-order approximation by using two auxiliary variable(s). The efficiency requirements of the suggested estimators explored in the study are provided. An operational study is analysed to ascertain the effectiveness of these estimators leveraging stimulated data sets. Ultimately, the findings revealed that the proposed ratio-cum-product estimators have the lowest mean squared error amongst all class of estimators in simple random sampling and two-phase sampling. [ABSTRACT FROM AUTHOR]

Repetitive transcranial magnetic stimulation (rTMS) is a promising alternative therapy for treatment-resistant depression, although its limited remission rate indicates room for improvement. As depression is a phenomenological construction, the biological heterogeneity within this syndrome needs to be considered to improve the existing therapies. Whole-brain modeling provides an integrative multi-modal framework for capturing disease heterogeneity in a holistic manner. Computational modelling combined with a probabilistic nonparametric fitting was applied to the resting-state fMRI data from 42 patients (21 women), to parametrize baseline brain dynamics in depression. All patients were randomly assigned to two treatment groups, namely active (i.e., rTMS, n = 22) or sham (n = 20). The active treatment group received rTMS treatment with an accelerated intermittent theta burst protocol over the dorsomedial prefrontal cortex. The sham treatment group underwent the identical procedure but with the magnetically shielded side of the coil. We stratified the depression sample into distinct covert subtypes based on their baseline attractor dynamics captured by different model parameters. Notably, the two detected depression subtypes exhibited different phenotypic behaviors at baseline. Our stratification could predict the diverse response to the active treatment that could not be explained by the sham treatment. Critically, we further found that one group exhibited more distinct improvement in certain affective and negative symptoms. The subgroup of patients with higher responsiveness to treatment exhibited blunted frequency dynamics for intrinsic activity at baseline, as indexed by lower global metastability and synchrony. Our findings suggested that whole-brain modeling of intrinsic dynamics may constitute a determinant for stratifying patients into treatment groups and bringing us closer towards precision medicine. Author summary: There are multiple therapeutic protocols with a limited remission rate, for treatment-resistant depression, using repetitive transcranial magnetic stimulation (rTMS). It is still unclear how we match different rTMS protocols to patients to optimize the therapy. Currently, the process of determining the best rTMS protocol for each individual involves trial and error. Whole-brain computational modelling alongside multimodal neuroimaging paves the way to find the optimal therapeutic protocol for each patient, by integrating multi-modal neuroimaging through theoretical models of brain dynamics. In this work, whole-brain modelling helped us identify two covert clinically relevant subtypes in our depression cohort, exhibiting different responses to the same rTMS therapy applied over the dorsomedial prefrontal cortex. Patients assigned to the subtype with blunted resting-state frequency dynamics improved more in specific affective and negative symptoms. Moreover, we further indicated that the summative scores of phenotypic behaviors for depression are not well-suited for dissociating the depression subtypes and measuring the treatment outcome. In conclusion, our results suggest that studying whole-brain dynamics could have profound implications for identifying reliable biomarkers and neurostimulation targets for the treatment of psychiatric disorders. [ABSTRACT FROM AUTHOR]

In social and behavioral surveys, researchers often have interest in obtaining separate estimates for the parameters of the study variable in specific domains, categorized according to some attribute(s) such as political affiliation, income status, residency, and ethnical affiliations etc. When complete information about such attributes are not available in advance, surveyors need to include questions in interviews which provide information about such attributes. The truthful response about these attributes can easily be collected when it is nonsensitive to the respondents, but not so for sensitive attributes. The respondents may either refuse to answer the question or provide evasive response about the sensitive attributes. In such cases, reliable estimates for the mean or total of even a nonsensitive quantitative variable in specific domains are quite challenging as there is no truthful information about the domain membership. Consequently, either we are left with unreliable estimates for small area parameters due to nonresponse, or get biased estimates following false responses from respondents. This article emphasizes on obtaining reliable estimates for subpopulation totals using Warner's randomized response technique. The suggested method enhances response rate by protecting confidentiality of the respondents in exposing their domain membership. A real-world data set from Pakistan Demographic Health Survey (2017–2018) is used to see the applicability of the proposed method followed by a simulation study. [ABSTRACT FROM AUTHOR]

In social surveys on sensitive characteristics, optional randomized response models give the respondents the option to either report the true response or report the scrambled response. If any respondent finds that the question being asked does not feel sensitive, he/she reports the true response. In the existing variants of optional randomized response models, the researcher doesn't know whether the respondent opted for the correct response or for a scrambled response. In practice, some of the respondents may have no problem in disclosing to the researcher that they are giving the true response and hence not opting for scrambling. This paper presents an alternative procedure to optional scrambling randomized response models, where each respondent has the choice whether or not to disclose to the researcher that he/she is giving the true response. Alternative modified versions of three existing scrambling randomized response models are presented. It is found that the efficiency of the quantitative randomized response models improves if the exact number of respondents who are opting for scrambling, is known to the researcher. Besides improvement in efficiency, the level of the respondent-privacy is the same as that of the existing models, thus resulting in an improvement in the overall quality of the existing models. [ABSTRACT FROM AUTHOR]

The finite population proportion of a sensitive characteristic is estimated indirectly by using Randomized Response (RR) Techniques (RRT's) pioneered by Warner (1965) followed by several other RRT's in the literature. The existing literature contains several RRT's for estimating the finite population mean of the sensitive quantitative variable. However, there might be a situation when the population proportion bearing the value of the stigmatizing variable below a threshold is of more concern than the exact population mean. The problem hence reduces to the estimation of the finite population distribution function of a quantitative sensitive variable. Following Chaudhuri and Saha (2004), a logistic regression approach has been used to estimate the finite population proportion bearing value of the stigmatizing variable below a threshold. As an alternative to this method, this article also attempts to provide suitable modifications for sensitive variables, in the estimation of distribution function proposed by Chaudhuri and Shaw (2020), when the variable of interest is innocuous. Numerical results based on a simulated population present interesting finding on the proposed methodologies. [ABSTRACT FROM AUTHOR]

Dalenius and Vitale's randomized response (RR) technique is limited to the simple random sampling with replacement (SRSWR) sampling design only. Chaudhuri, Chaudhuri and Christofides extended Dalenius and Vitale's RR techniques for complex survey designs. It is found that all the results derived by them are incorrect. The corrected results are presented in this paper. [ABSTRACT FROM AUTHOR]

The randomized response technique (RRT) is an effective method designed to obtain the stigmatized information from respondents while assuring the privacy. In this study, we propose a new two-stage RRT model to estimate the prevalence of sensitive attribute (π). A simulation study shows that the empirical mean and variance of proposed estimator are close to corresponding theoretical values. The utility of proposed two-stage RRT model under stratification is also explored. An efficiency comparison between proposed two-stage RRT model and some existing RRT models is carried out numerically under simple and stratified random sampling. [ABSTRACT FROM AUTHOR]

Randomized response techniques (RRTs) are useful survey tools for estimating the prevalence of sensitive issues, such as the prevalence of doping in elite sports. One type of RRT, the unrelated question model (UQM), has become widely used because of its psychological acceptability for study participants and its favorable statistical properties. One drawback of this model, however, is that it does not allow for detecting cheaters—individuals who disobey the survey instructions and instead give self-protecting responses. In this article, we present refined versions of the UQM designed to detect the prevalence of cheating responses. We provide explicit formulas to calculate the parameters of these refined UQM versions and show how the empirical adequacy of these versions can be tested. The Appendices contain R-code for all necessary calculations. [ABSTRACT FROM AUTHOR]

The Randomized Response Technique (Warner, Journal of the American Statistical Association, 60, 63-69, 1965) has been developed to control for socially desirable responses in surveys on sensitive attributes. The Crosswise Model (CWM; Yu et al., Metrika, 67, 251-263, 2008) and its extension, the Extended Crosswise Model (ECWM; Heck et al., Behavior Research Methods, 50, 1895-1905, 2018), are advancements of the Randomized Response Technique that have provided promising results in terms of improved validity of the obtained prevalence estimates compared to estimates based on conventional direct questions. However, recent studies have raised the question as to whether these promising results might have been primarily driven by a methodological artifact in terms of random responses rather than a successful control of socially desirable responding. The current study was designed to disentangle the influence of successful control of socially desirable responding and random answer behavior on the validity of (E)CWM estimates. To this end, we orthogonally manipulated the direction of social desirability (undesirable vs. desirable) and the prevalence (high vs. low) of sensitive attributes. Our results generally support the notion that the ECWM successfully controls social desirability bias and is inconsistent with the alternative account that ECWM estimates are distorted by a substantial influence of random responding. The results do not rule out a small proportion of random answers, especially when socially undesirable attributes with high prevalence are studied, or when high randomization probabilities are applied. Our results however do rule out that random responding is a major factor that can account for the findings attesting to the improved validity of (E)CWM as compared with DQ estimates. [ABSTRACT FROM AUTHOR]

Background Lysergic acid diethylamide (LSD) is currently being investigated in psychedelic-assisted therapy. LSD has a long duration of acute action of 8–11 hours. It produces its acute psychedelic effects via stimulation of the serotonin 5-hydroxytryptamine-2A (HT2A) receptor. Administration of the 5-HT2A antagonist ketanserin before LSD almost fully blocks the acute subjective response to LSD. However, unclear is whether ketanserin can also reverse the effects of LSD when administered after LSD. Methods We used a double-blind, randomized, placebo-controlled, crossover design in 24 healthy participants who underwent two 14-hour sessions and received ketanserin (40 mg p.o.) or placebo 1 hour after LSD (100 µg p.o.). Outcome measures included subjective effects, autonomic effects, acute adverse effects, plasma brain-derived neurotrophic factor levels, and pharmacokinetics up to 12 hours. Results Ketanserin reversed the acute response to LSD, thereby significantly reducing the duration of subjective effects from 8.5 hours with placebo to 3.5 hours. Ketanserin also reversed LSD-induced alterations of mind, including visual and acoustic alterations and ego dissolution. Ketanserin reduced adverse cardiovascular effects and mydriasis that were associated with LSD but had no effects on elevations of brain-derived neurotrophic factor levels. Ketanserin did not alter the pharmacokinetics of LSD. Conclusions These findings are consistent with an interaction between ketanserin and LSD and the view that LSD produces its psychedelic effects only when occupying 5-HT2A receptors. Ketanserin can effectively be used as a planned or rescue option to shorten and attenuate the LSD experience in humans in research and LSD-assisted therapy. Trial registry ClinicalTrials.gov (NCT04558294) [ABSTRACT FROM AUTHOR]

In recent years, decentralized social networks have gained increasing attention, where each client maintains a local view of a social graph. To provide services based on graph learning in such networks, the server commonly needs to collect the local views of the graph structure, which raises privacy issues. In this paper, we focus on learning graph neural networks (GNNs) on decentralized social graphs while satisfying local differential privacy (LDP). Most existing methods collect high-dimensional local views under LDP through Randomized Response, which introduces a large amount of noise and significantly decreases the usability of the collected graph structure for training GNNs. To address this problem, we present Structure Learning-based Locally Private Graph Learning (SL-LPGL). Its main idea is to first collect low-dimensional encoded structural information called cluster degree vectors to reduce the amount of LDP noise, then learn a high-dimensional graph structure from the cluster degree vectors via graph structure learning (GSL) to train GNNs. In SL-LPGL, we propose a Homophily-aware Graph StructurE Initialization (HAGEI) method to provide a low-noise initial graph structure as learning guidance for GSL. We then introduce an Estimated Average Degree Vector Enhanced Graph Structure Learning (EADEGSL) method to further mitigate the negative impact of LDP noise in GSL. We conduct experiments on four real-world graph datasets. The experimental results demonstrate that SL-LPGL outperforms the baselines. [ABSTRACT FROM AUTHOR]

ABSTRACT: In this work, a new method of Bayesian estimation for the Alternative Tripartite randomized response technique used in obtaining the proportion of people that belongs to sensitive character was proposed. The proposed approach accommodates other intrinsic parameter constraints in the posterior to improve statistical precision. The efficiency of the newly proposed Bayesian estimators was established for a wide interval of the values of the population proportion of the sensitive character (π). It was discovered that for any preset probabilities, the developed Bayesian estimators became better in capturing responses from respondents than any other classical estimators. Therefore, as the sample size increases and the proposed Bayesian models capture more and more sensitive characters, the Kumaraswamy prior estimator becomes more efficient while the Generalized beta prior estimator performs better when there are fewer people involved in the sensitive character. Applying the Bayesian methods to data on drug use disorder also confirmed their efficiency over the classical methods.