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Income and wealth surveys are usually affected by unit non‐response and reporting errors, which contribute to a mismatch with macroeconomic figures from national accounts. In this paper, we develop a novel allocation method to address these two issues simultaneously, when only limited external information is available. The proposed approach combines information from a power law (Pareto) model with imputation procedures based on calibration. We apply the proposed simultaneous approach to produce distributional indicators for four Euro‐Area countries that are consistent with their national accounts. In particular, we use data from the 2014 Finnish, French, German, and Italian Household Finance and Consumption Survey and rich list data from Forbes or national press sources, along with household sector aggregates from national accounts, as auxiliary sources of information. A bootstrap procedure is also applied to evaluate the precision of the final estimates. [ABSTRACT FROM AUTHOR]

Background: Mobile Ecological Momentary Assessment (EMA) is increasingly used to gather intensive, longitudinal data on behavioral nutrition, physical activity and sedentary behavior and their underlying determinants. However, a relevant concern is the risk of non-random non-compliance with mobile EMA protocols, especially in older adults. This study aimed to examine older adults' compliance with mobile EMA in health behavior studies according to participant characteristics, and prompt timing, and to provide recommendations for future EMA research. Methods: Data of four intensive longitudinal observational studies employing mobile EMA to understand health behavior, involving 271 community-dwelling older adults (M = 71.8 years, SD = 6.8; 52% female) in Flanders, were pooled. EMA questionnaires were prompted by a smartphone application during specific time slots or events. Data on compliance (i.e. information whether a participant answered at least one item following the prompt), time slot (morning, afternoon or evening) and day (week or weekend day) of each prompt were extracted from the EMA applications. Participant characteristics, including demographics, body mass index, and smartphone ownership, were collected via self-report. Descriptive statistics of compliance were computed, and logistic mixed models were run to examine inter- and intrapersonal variability in compliance. Results: EMA compliance averaged 77.5%, varying from 70.0 to 86.1% across studies. Compliance differed among subgroups and throughout the day. Age was associated with lower compliance (OR = 0.96, 95%CI = 0.93–0.99), while marital/cohabiting status and smartphone ownership were associated with higher compliance (OR = 1.83, 95%CI = 1.21–2.77, and OR = 4.43, 95%CI = 2.22–8.83, respectively). Compliance was lower in the evening than in the morning (OR = 0.82, 95%CI = 0.69–0.97), indicating non-random patterns that could impact study validity. Conclusions: The findings of this study shed light on the complexities surrounding compliance with mobile EMA protocols among older adults in health behavior studies. Our analysis revealed that non-compliance within our pooled dataset was not completely random. This non-randomness could introduce bias into study findings, potentially compromising the validity of research findings. To address these challenges, we recommend adopting tailored approaches that take into account individual characteristics and temporal dynamics. Additionally, the utilization of Directed Acyclic Graphs, and advanced statistical techniques can help mitigate the impact of non-compliance on study validity. [ABSTRACT FROM AUTHOR]

Electronic forms of communication—including email, texting, and social media platforms—have increased the speed and ease of communication. Yet, a rise in non-response and ghosting (when someone ceases communication without an explanation) has been documented across contexts, from romantic dating to quantitative research studies. Surprisingly, the rise of electronic communication has received little attention in the methodological literature for qualitative researchers. Based on an analysis of virtual and face-to-face recruitment in two qualitative studies I conducted, I find that ghosting is a routine feature of digital recruitment. There are situational contexts in which ghosting is more common, including lags in the timing of communication and requests that are too overwhelming. "Old-school" methods, such as seeking sponsorship, strategizing outreach, and building rapport, can be adapted to help researchers capitalize on the benefits of electronic communication technologies in recruitment. [ABSTRACT FROM AUTHOR]

This study uses evidence from a longitudinal survey (PISA Plus, Germany) to examine the potential of bias in international large-scale assessments (ILSAs). In PISA Plus, participation was mandatory at the first measurement point, but voluntary at the second measurement point. The study provides evidence for relevant selection bias regarding student competencies and background variables when participation is voluntary. Sample dropout at the second measurement point was related to characteristics such as family background, achievement in mathematics, reading and science, and other student and school demographic variables at both the student and school levels, with lower performing students and those with less favorable background characteristics having higher dropout frequencies, from which higher dropout probabilities of such students can be inferred. We further contrast the possibilities for addressing non-response through weight adjustments in longitudinal surveys with those in cross-sectional surveys. Considering our results, we evaluate and confirm the validity and appropriateness of strict participation rate requirements in ILSAs. Likely magnitudes of bias in cross-sectional studies in varying scenarios are illustrated. Accordingly, if combined participation rates drop below 70%, a difference of at least one-fifth of a standard deviation in an achievement score between non-respondents and participants leads to relevant bias. When participation drops below 50%, even a very small difference (one-tenth of a standard deviation) will cause non-negligible bias. Finally, we conclude that the stringent participation rate requirements established in most ILSAs are fully valid, reasonable, and important since they ensure a relatively low risk of biased results. [ABSTRACT FROM AUTHOR]

AbstractIn the present study, we suggested a chi-square-type distance function that works well for non-response situations and we have derived a new calibration-based estimator using such a kind of distance function, which is used to estimate the mean of a stratified population. The proposed estimator is derived through the implementation of a reweighting technique referred to as the calibration approach, aimed at improving upon Hansen and Hurwitz’s (1946) estimator. Typically, in situations where some units do not respond, researchers adjust the stratum weights to obtain an estimator for the mean of a stratified population using the calibration approach. In this study, the weights given to the responding units in the sample and the units in the sub-sample taken from the non-responding sample were calibrated to derive the calibration estimator. We assume that the unit non-response problem impacts both study and auxiliary variables. To examine the performance of the proposed calibration estimator in scenarios involving non-response, we have carried out empirical studies. [ABSTRACT FROM AUTHOR]

HIV estimation using data from the demographic and health surveys (DHS) is limited by the presence of non‐response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non‐respondents, but their performance depends critically on the validity of the instrument. Using Manski's partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and compare our method against its competitors. We illustrate the proposed method using DHS data from Zambia, Malawi and Kenya. Our simulations show that imputation leads to seriously biased results even under mild violations of non‐random missingness. Using worst case identification bounds that do not make assumptions about the non‐response mechanism is robust but not informative. By taking the union of instrumental variable bounds balances informativeness of the bounds and robustness to inclusion of some invalid instruments. Non‐response and refusals are ubiquitous in population based HIV data such as those collected under the DHS. Partial identification bounds provide a robust solution to HIV prevalence estimation without strong assumptions. Union bounds are significantly more informative than the worst case bounds without sacrificing credibility. [ABSTRACT FROM AUTHOR]

Non-response poses a significant concern within survey sampling, occurring when individuals are either unavailable or unwilling to respond to survey questions. To address this issue, the application of non-response adjustment through auxiliary variables is widely employed to estimate missing data points. This study delves into the extensive possibilities presented by this approach, particularly focusing on calibration weighting. The study specifically investigates the challenge of non-response within the context of ranked set sampling (RSS). Within this study, we introduce two novel calibration estimators for population means. One estimator addresses scenarios of complete response, while the other pertains to cases of non-response under the ranked set sampling technique. To evaluate their performance, the Mean Square Error (MSE) and Bias expressions are derived. Furthermore, the performance of the proposed estimators is assessed through comprehensive simulation studies, encompassing both artificial and real-world datasets. For this purpose, the Mean Square Error and Bias for the ranked set samples of sizes 5, 8, 10, and 25, encompassing a total of 10,000 samples for each size category are calculated. Our findings reveal that the proposed calibration estimator, along with the imputation through calibration for non-response, consistently generates more efficient estimates compared to mean and median imputation techniques. This underscores the potential of the calibration approach in mitigating the impact of non-response, offering improved accuracy and robustness in survey sampling. [ABSTRACT FROM AUTHOR]

Active learning can improve the efficiency of training prediction models by identifying the most informative new labels to acquire. However, non-response to label requests can impact active learning's effectiveness in real-world contexts. We conceptualise this degradation by considering the type of non-response present in the data, demonstrating that biased non-response is particularly detrimental to model performance. We argue that biased non-response is likely in contexts where the labelling process, by nature, relies on user interactions. To mitigate the impact of biased non-response, we propose a cost-based correction to the sampling strategy–the Upper Confidence Bound of the Expected Utility (UCB-EU)–that can, plausibly, be applied to any active learning algorithm. Through experiments, we demonstrate that our method successfully reduces the harm from labelling non-response in many settings. However, we also characterise settings where the non-response bias in the annotations remains detrimental under UCB-EU for specific sampling methods and data generating processes. Finally, we evaluate our method on a real-world dataset from an e-commerce platform. We show that UCB-EU yields substantial performance improvements to conversion models that are trained on clicked impressions. Most generally, this research serves to both better conceptualise the interplay between types of non-response and model improvements via active learning, and to provide a practical, easy-to-implement correction that mitigates model degradation. [ABSTRACT FROM AUTHOR]

Background: The advent of the hepatitis B vaccine has achieved tremendous success in eradicating and reducing the burden of hepatitis B infection, which is the main culprit for hepatocellular carcinoma—one of the most fatal malignancies globally. Response to the vaccine is achieved in about 90–95% of healthy individuals and up to only 50% in immunocompromised patients. This review aimed to provide an overview of hepatitis B vaccine non-response, the mechanisms involved, B cell amnesia, and strategies to overcome it. Methods: Databases, including Google Scholar, PubMed, Scopus, Cochrane, and ClinicalTrials.org, were used to search and retrieve articles using keywords on hepatitis B vaccine non-response and B cell amnesia. The PRISMA guideline was followed in identifying studies, screening, selection, and reporting of findings. Results: A total of 133 studies on hepatitis B vaccine non-response, mechanisms, and prevention/management strategies were included in the review after screening and final selection. Factors responsible for hepatitis B vaccine non-response were found to include genetic, immunological factors, and B cell amnesia in healthy individuals. The genetic factors were sex, HLA haplotypes, and genetic polymorphisms in immune response markers (cytokines). Non-response was common in conditions of immunodeficiency, such as renal failure, haemodialysis, celiac disease, inflammatory bowel disease, hepatitis C co-infection, and latent hepatitis B infection. Others included diabetes mellitus and HIV infection. The mechanisms involved were impaired immune response by suppression of response (T helper cells) or induced suppression of response (through regulatory B and T cells). Discussion: A comprehensive and careful understanding of the patient factors and the nature of the vaccine contributes to developing effective preventive measures. These include revaccination or booster dose, vaccine administration through the intradermal route, and the use of adjuvants in the vaccine. [ABSTRACT FROM AUTHOR]

Abstract Background Mobile Ecological Momentary Assessment (EMA) is increasingly used to gather intensive, longitudinal data on behavioral nutrition, physical activity and sedentary behavior and their underlying determinants. However, a relevant concern is the risk of non-random non-compliance with mobile EMA protocols, especially in older adults. This study aimed to examine older adults’ compliance with mobile EMA in health behavior studies according to participant characteristics, and prompt timing, and to provide recommendations for future EMA research. Methods Data of four intensive longitudinal observational studies employing mobile EMA to understand health behavior, involving 271 community-dwelling older adults (M = 71.8 years, SD = 6.8; 52% female) in Flanders, were pooled. EMA questionnaires were prompted by a smartphone application during specific time slots or events. Data on compliance (i.e. information whether a participant answered at least one item following the prompt), time slot (morning, afternoon or evening) and day (week or weekend day) of each prompt were extracted from the EMA applications. Participant characteristics, including demographics, body mass index, and smartphone ownership, were collected via self-report. Descriptive statistics of compliance were computed, and logistic mixed models were run to examine inter- and intrapersonal variability in compliance. Results EMA compliance averaged 77.5%, varying from 70.0 to 86.1% across studies. Compliance differed among subgroups and throughout the day. Age was associated with lower compliance (OR = 0.96, 95%CI = 0.93–0.99), while marital/cohabiting status and smartphone ownership were associated with higher compliance (OR = 1.83, 95%CI = 1.21–2.77, and OR = 4.43, 95%CI = 2.22–8.83, respectively). Compliance was lower in the evening than in the morning (OR = 0.82, 95%CI = 0.69–0.97), indicating non-random patterns that could impact study validity. Conclusions The findings of this study shed light on the complexities surrounding compliance with mobile EMA protocols among older adults in health behavior studies. Our analysis revealed that non-compliance within our pooled dataset was not completely random. This non-randomness could introduce bias into study findings, potentially compromising the validity of research findings. To address these challenges, we recommend adopting tailored approaches that take into account individual characteristics and temporal dynamics. Additionally, the utilization of Directed Acyclic Graphs, and advanced statistical techniques can help mitigate the impact of non-compliance on study validity.

Abstract This paper considers the problem of estimation of the population total under probability proportional to size (PPS) sampling scheme when complete data is not available due to the presence of missing observations or non-response. The suggested estimators are developed based on available multi-auxiliary information for the response group and non-response group utilizing the calibration approach. The variances of the suggested estimators have been derived up to the first order of approximation. A simulation study done on a real dataset using R software also supports the performance of the suggested estimators. The empirical percentage absolute relative biases (%ARB) and percentage relative root mean squared errors (%RRMSE) are computed for the suggested estimators. The developed estimators are compared with the estimators of the population total due to the design-based Horvitz and Thompson (HT) estimator and calibration HT type estimator obtained on the available complete response units along with the design-based Hansen and Hurwitz (HH) estimator in the presence of non-response.

Sampling is crucial in basic statistics due to the large size of certain populations. Sampling theory focuses on selecting samples based on appropriate population characteristics and approaches to estimating population characteristics from the sample. Firstly, we use the ratio-type estimator proposed by Prasad (1989). We adapt this unbiased ratio-type estimator to two new different estimators using the Hartley-Ross and non-response approaches. Finally, we combine these approaches and propose a new estimator. The theoretical results were supported by COVID-19 data and a simulation study with various scenarios. Based on the COVID-19 data result, it was concluded that the new tN3 estimator is the most effective estimator among those proposed and compared, with the minimum MSE and maximum PRE. Upon examining the changes in the COVID-19 data set in detail, it is evident that the PRE value increases with an increase in sample size, non-response rate, and k values. As in the COVID-19 dataset, the tN3 estimator was the most effective in the simulation study results. It is also important to note that these results are not applicable to only one scenario in simulation study. The tN2 estimator was the most effective in the scenario with rho=0.90, T=1000, n=400, non-response rate=0.30 and k equal to 2, 4 and 5.

Background: Owing to the evidence that as many as 30–40% of patients with vulvar lichen sclerosus (VLS) fail to report a remission of symptoms with first-line corticosteroid treatment (TCS), especially as what regards dyspareunia, we aimed to analyze patients' satisfaction following vulvar injection of autologous platelet-rich plasma (PRP). This is intended as an adjunctive treatment, to be used following TCS, and appears to promote tissue repair. It may also possibly have immunomodulatory proprieties. Materials and methods: Patients with VLS were considered eligible for this pilot study if, despite having been treated with a 3-month TCS regimen, they reported a persistence of symptoms. PRP was produced in a referral center using a manual method and a standardized protocol. Each patient received three treatments 4 to 6 weeks apart. Results: A total of 50 patients with a median age of 53 years [IQR 38–59 years] were included in the study. 6 months after the last injection of PRP all patients were either satisfied or very satisfied with the treatment (100%; 95% CI 93–100%). Median NRS scores for itching, burning, dyspareunia and dysuria were significantly reduced (p < 0.05) and FSFI, HADS and SF-12 questionnaires revealed a significant improvement in sexual function, psychological wellbeing and quality of life (p < 0.05). The number of patients reporting the need for maintenance TCS treatment was reduced by 42% (p < 0.001) and an improvement in vulvar elasticity and color was reported in all patients. Conclusion: Following standard medical therapy, PRP may be effective not only in improving symptoms, but also in restoring function. [ABSTRACT FROM AUTHOR]

Objective: Clozapine is considered as the standard treatment for this subgroup, but the evidence is not unequivocal. There are several potential alternatives being used because of the possible adverse effects of clozapine. We aimed to examine the efficacy and adverse events of different antipsychotics in treatment-resistant schizophrenia by performing a network meta-analysis. Methods: We searched the Cochrane Schizophrenia Group register for randomized-controlled trials (up to March 06, 2022) and MEDLINE (up to January 20, 2023). We included blinded and open studies and participants with a broad definition of treatment resistance. The primary outcome was overall symptoms of schizophrenia; secondary outcomes were response to treatment, positive and negative symptoms of schizophrenia, discontinuation, side effects, quality of life, and functioning. The study was registered in Open Science Framework (https://osf.io/9nf2y/). Results: We included 60 studies involving 6838 participants in the network meta-analysis. In the primary outcome, clozapine and olanzapine were more efficacious than risperidone, haloperidol, fluphenazine, sertindole, chlorpromazine, and quetiapine (range of mean SMDs, − 0.11 to − 0.48). The difference between clozapine and olanzapine was trivial and uncertain (SMD − 0.05, 95% CI, − 0.21 to 0.11). The result of other efficacy outcomes as well as subgroup and sensitivity analyses were consistent with the primary analysis. Clozapine and olanzapine were associated with more weight gain, and clozapine was associated with more sedation events compared to many other antipsychotics. Conclusions: Clozapine remains the gold standard for patients with treatment-resistant schizophrenia. Olanzapine seems to be second-best and could be tried before switching to clozapine. [ABSTRACT FROM AUTHOR]

This paper focuses on estimating the product of two population means. Within this paper, we have introduced three distinct classes of estimators for product of two population means. These estimators take into account the known population mean of an auxiliary variable under the framework of stratified random sampling and the presence of non-response in the study variable. Basically, for case (I) we assume the non-response on the study variable and utilize the auxiliary information corresponding to the responding units of the study variable and in case (II), we utilize the complete dataset from the auxiliary variable while also accounting for non-response in the study variable. In case (III) we combined both the information of the auxiliary variable and assumed the non-response on the study variable. Expressions for bias and mean square error have been derived, extending up to the first-order derivative. We have also pinpointed some specific members of the proposed estimator. We have conducted a simulation study to evaluate the valuable insights into the performance of the suggested classes of estimators with the conventional estimator. [ABSTRACT FROM AUTHOR]

Treatment resistance in anxiety disorders represents a clinical challenge, contributes to the chronicity of the diseases as well as sequential comorbidities, and is associated with a significant individual and socioeconomic burden. This narrative review presents the operational definition of treatment resistance in anxiety disorders according to international consensus criteria (< 50% reduction in the Hamilton Anxiety Scale, HAM‑A, score or < 50% reduction in the Beck Anxiety Inventory, BAI, score or a clinical global impression-improvement, CGI‑I, score > 2). At least two unsuccessful guideline-based treatment attempts with pharmacological monotherapy or at least one unsuccessful treatment attempt with adequately delivered cognitive behavioral therapy are required. Pharmacotherapeutically, after excluding pseudo-resistance, switching the medication within one class or to another class and augmentation strategies with other antidepressants (mirtazapine, agomelatine), antipsychotics (quetiapine) or anticonvulsants (valproate) are recommended. Psychotherapeutically, third-wave therapies, psychodynamic therapy, systemic therapy and physical exercise can be considered for therapy resistance. In cases of no response to psychotherapy or pharmacotherapy, the respective other form of therapy or a combination of both should be offered. Compounds targeting the glutamatergic and endocannabinoid systems as well as neuropeptides are being tested as potential innovative pharmaceuticals for treatment-resistant anxiety disorders. There is an urgent need for further research to identify predictive markers and mechanisms as well as to develop innovative pharmacological and psychotherapeutic interventions for treatment-resistant anxiety disorders. [ABSTRACT FROM AUTHOR]

Treatments for anorexia nervosa (AN) remain ineffective for many patients. Processes that can account for differential treatment outcomes remain mostly unknown. We propose that the field test the role of associative learning in current psychological treatments. We hold that this line of research could yield actionable information for understanding non‐response and improving long‐term outcomes. To make this argument, we define associative learning and outline its proposed role in understanding psychiatric disorders and their treatment. We then briefly review data exploring associative learning in AN. We argue that associative learning processes are implicitly implicated in existing treatments; by this rationale, baseline differences in learning may interfere with treatment response. Finally, we outline future research to test our hypotheses. Altogether, future research aimed at better understanding how associative learning may contribute to AN symptom persistence has the potential to inform novel directions in intervention research. Public Significance: There is a pressing need to improve outcomes in treatments for anorexia nervosa (AN). We propose that individual differences in associative learning—the ability to form and update associations between cues, contexts, behaviors, and outcomes—may account for differential response to existing treatments. Undertaking this research could provide an understanding of how current treatments work and inform new approaches for those who may be at risk of poor outcomes. [ABSTRACT FROM AUTHOR]

Objective To analyze the status of non-response to hepatitis B vaccine and its related factors in children born to mothers infected with hepatitis B virus (HBV). Methods The children born to mothers with HBV infection reported in Hunan Province from January to December 2022 were selected. All the children received hepatitis B immunoglobulin injection and 3 doses of hepatitis B vaccination and were followed up to 7-12 months of age for detection of hepatitis B surface antigen (HB-sAg) and anti-HBs. Both HBsAg and anti-HBs were negative, indicating that there was no response to hepatitis B vaccine in children. Unconditional binary logistic regression model was used to analyze the related factors of non-response to hepatitis B vaccine in children. Results A total of 9 792 children born to mothers infected with HBV were analyzed. Among 9 792 children, the non-response rate of hepatitis B vaccine was 9.5% (928/9 792). Non-conditional binary Logistic regression analysis showed that the risk of non-response to hepatitis B vaccine in children born to HBeAg-positive mothers was 1.62 times higher than that in children born to HBeAg-negative mothers (OR=1.62, 95% CI: 1.40-1.88). The risk of non-response to hepatitis B vaccine in children with macrosomia (birth weight ≥ 4 000g) was 1.41 times higher than that in children with normal birth weight (OR = 1.41, 95% CI: 1.08-1.85). Maternal antiviral therapy (OR=0.59, 95% CI: 0.48-0.73) and child HepB standard vaccination (OR=0.66, 95% CI: 0.57-0.78) were the protective factors of non-response to hepatitis B vaccine in children. Conclusion The non-response rate of hepatitis B vaccine in children born to HBV infected mothers is high, and it is affected by maternal HBeAg positive, antiviral therapy during pregnancy, birth weight, and standard vaccination. [ABSTRACT FROM AUTHOR]

Given the increasing number of studies in various disciplines using experience sampling methods, it is important to examine compliance biases because related patterns of missing data could affect the validity of research findings. In the present study, a sample of 592 participants and more than 25,000 observations were used to examine whether participants responded to each specific questionnaire within an experience sampling framework. More than 400 variables from the three categories of person, behavior, and context, collected multi-methodologically via traditional surveys, experience sampling, and mobile sensing, served as predictors. When comparing different linear (logistic and elastic net regression) and non-linear (random forest) machine learning models, we found indication for compliance bias: response behavior was successfully predicted. Follow-up analyses revealed that study-related past behavior, such as previous average experience sampling questionnaire response rate, was most informative for predicting compliance, followed by physical context variables, such as being at home or at work. Based on our findings, we discuss implications for the design of experience sampling studies in applied research and future directions in methodological research addressing experience sampling methodology and missing data. [ABSTRACT FROM AUTHOR]

In the present work, we have proposed a novel general class of estimators for the estimation of population distribution function in two distinct situations of non-response. The estimation of distribution function (DF) may play an important role in environmental sciences such as in modelling the annual data of atmospheric NOx temporal concentration, drug removal rates from the body, to present the distribution of gene expression levels across cells etc. Similarly, it may also be useful in other environmental data like groundwater quality, rainfall, wind speed, river discharges, etc. for effective analysis leading to predictive modelling. In this study, it is shown that the suggested estimators of DF are among the best of all other considered estimators. Some estimators are also derived from the proposed family by choosing the suitable values of constants used. Theoretical comparisons of the proposed family of estimators with other family of estimators have been discussed. An empirical study based on two real data sets from literature has been conducted where it has been found that the performance of the proposed family of estimators is superior in terms of enhanced percentage relative efficiencies (PREs) than other family of estimators considered in this study. Further, a simulation study is also carried out which validates the competency behaviour of suggested estimators.

The advent of biologic drugs has revolutionized the treatment of Inflammatory Bowel Disease, increasing rates of response and mucosal healing in comparison to conventional therapies by allowing the treatment of corticosteroid-refractory cases and reducing corticosteroid-related side effects. However, biologic therapies (anti-TNFα inhibitors, anti-α4β7 integrin and anti-IL12/23) are still burdened by rates of response that hover around 40% (in biologic-naïve patients) or lower (for biologic-experienced patients). Moreover, knowledge of the mechanisms underlying drug resistance or loss of response is still scarce. Several cellular and molecular determinants are implied in therapeutic failure; genetic predispositions, in the form of single nucleotide polymorphisms in the sequence of cytokines or Human Leukocyte Antigen, or an altered expression of cytokines and other molecules involved in the inflammation cascade, play the most important role. Accessory mechanisms include gut microbiota dysregulation. In this narrative review of the current and most recent literature, we shed light on the mentioned determinants of therapeutic failure in order to pave the way for a more personalized approach that could help avoid unnecessary treatments and toxicities. [ABSTRACT FROM AUTHOR]

In this study, we introduced a problem for the estimation of the population mean of two sensitive variables using ORRT models. A class of estimators has been developed for the estimation of ratio and product estimators. Up to the first degree of approximation, properties of the proposed estimators including bias and mean square error have been studied. To show the efficacy of the proposed class of estimators, we compare it with the conventional estimators and the PRE of the proposed class of estimators is obtained with respect to the usual ratio and product estimator. The simulation study justified that our suggested estimator is more efficient than the existing estimator in terms of having higher PRE. [ABSTRACT FROM AUTHOR]

Background: Adverse childhood experiences (ACE) encompass traumatic events occurring before age 18, with lasting impacts on health. While ACE disclosure is important for understanding these effects, some individuals decline to respond to ACE-related survey items due to sensitivity, privacy concerns, or psychological distress. This study explores the relationship between non-response to ACE items and health outcomes, shedding light on the implications for those who choose not to disclose. Methods: We performed a secondary analysis of the 2021 Behavioral Risk Factor Surveillance System (BRFSS)—a national telephone survey querying health behaviors and conditions. Sociodemographic factors, ACE exposure, and non-response to ACE items were analyzed. Results: Individuals who decline to respond to ACE items exhibit similar patterns of health behaviors and conditions as those reporting ACE exposure. Non-response is linked to both healthier behaviors (lifetime HIV testing) and riskier behaviors (higher odds of smoking and e-cigarette use). Moreover, non-responders have higher odds of being underweight or obese, experiencing concentration difficulties, reporting poor self-rated health, and reporting multiple health diagnoses including depression, diabetes, high blood pressure, heart attack, and stroke. Conclusions: The study underscores the need to address health disparities associated with ACE, regardless of disclosure status. Healthcare interventions should target respondents and non-respondents of ACE screeners, tailoring strategies to promote healthier coping mechanisms and mitigate maladaptive behaviors. These results emphasize the importance of trauma-informed care, early intervention, and targeted public health initiatives for individuals affected by ACE, irrespective of their disclosure choices.

Throughout this article, a two-phase sampling (TPS) technique is employed to estimate the population mean of the sensitive variable. The current article endeavours to develop a chain ratio type estimator for the estimation of sensitive variable(s) in the presence of non-response and measurement error simultaneously by utilizing ORRT models under a two-phase sampling technique. The significant aspects associated with the suggested estimator characterized by bias and mean squared error have been evaluated. Besides this, the utterance for the minimum mean squared error for the optimal values has also been identified. The supremacy of the proposed estimator has been compared with the modified existing estimators under the TPS scheme by using two sensitive auxiliary variables. To clarify the theoretical findings, a simulation study along with a hypothetical generated population and a real population which is based on abortion rates from Statistical Abstract of the United States: 2011 are also addressed in this study.

Abstract This paper presents some efficient classes of estimators of population mean on current occasion in the presence of random non-response under a two-phase successive sampling set-up. The suggested classes of estimators are proposed for simple random sampling under various situations of non-response. The properties of proposed estimators have been discussed up to first order of approximation. The efficiency of the presented estimators has been contrasted with the estimators for the complete response scenarios. Two real and two artificially generated data sets are used. The efficacy of the proposed classes of estimators over the existing estimators is checked theoretically and empirically. The numerical comparison supports the proposed estimators.

Background: The advent of the hepatitis B vaccine has achieved tremendous success in eradicating and reducing the burden of hepatitis B infection, which is the main culprit for hepatocellular carcinoma—one of the most fatal malignancies globally. Response to the vaccine is achieved in about 90–95% of healthy individuals and up to only 50% in immunocompromised patients. This review aimed to provide an overview of hepatitis B vaccine non-response, the mechanisms involved, B cell amnesia, and strategies to overcome it. Methods: Databases, including Google Scholar, PubMed, Scopus, Cochrane, and ClinicalTrials.org, were used to search and retrieve articles using keywords on hepatitis B vaccine non-response and B cell amnesia. The PRISMA guideline was followed in identifying studies, screening, selection, and reporting of findings. Results: A total of 133 studies on hepatitis B vaccine non-response, mechanisms, and prevention/management strategies were included in the review after screening and final selection. Factors responsible for hepatitis B vaccine non-response were found to include genetic, immunological factors, and B cell amnesia in healthy individuals. The genetic factors were sex, HLA haplotypes, and genetic polymorphisms in immune response markers (cytokines). Non-response was common in conditions of immunodeficiency, such as renal failure, haemodialysis, celiac disease, inflammatory bowel disease, hepatitis C co-infection, and latent hepatitis B infection. Others included diabetes mellitus and HIV infection. The mechanisms involved were impaired immune response by suppression of response (T helper cells) or induced suppression of response (through regulatory B and T cells). Discussion: A comprehensive and careful understanding of the patient factors and the nature of the vaccine contributes to developing effective preventive measures. These include revaccination or booster dose, vaccine administration through the intradermal route, and the use of adjuvants in the vaccine.

Objectives: Ecological momentary assessment (EMA) has seen an explosion in popularity in recent years; however, an improved understanding of how to minimise (selective) non‐adherence is needed. Methods: We examined a range of respondent characteristics predictors of adherence (defined as the number of EMA surveys completed) in the D2M EMA study. Participants were a sample of n = 255 individuals drawn from the longitudinal z‐proso cohort who completed up to 4 EMA surveys per day for a period of 2 weeks. Results: In unadjusted analyses, lower moral shame, lower self‐control, lower levels of self‐injury, and higher levels of aggression, tobacco use, psychopathy, and delinquency were associated with lower adherence. In fully adjusted analyses with predictors selected using lasso, only alcohol use was related to adherence: beer and alcopops to higher adherence and spirits to lower adherence. Conclusions: These findings provide potential insights into some of the psychological mechanisms that may underlie adherence in EMA. They also point to respondent characteristics for which additional or tailored efforts may be needed to promote adherence. [ABSTRACT FROM AUTHOR]

In this article, we consider a new family of exponential type estimators for estimating the unknown population mean of the study variable. We propose estimators taking advantage of the auxiliary variable information under the first and second non-response cases separately. The required theoretical comparisons are obtained and the numerical studies are conducted. In conclusion, the results show that the proposed family of estimators is the most efficient estimator with respect to the estimators in literature under the obtained conditions for both cases. [ABSTRACT FROM AUTHOR]

Sample surveys frequently experience the problem of non-response. Because of this, strategies for overcoming the obstacle every time it arises must be developed. In this paper, we suggest a new generalized ratio type estimator of population mean of main variable utilizing the known information on the auxiliary parameters. The bias and the Mean Squared Error (MSE) of the proposed estimator are derived up to the approximation of order one. The optimum value of the scalar is obtained and the minimum MSE for this optimum value of the scalar is also obtained. The proposed estimator is compared with other existing estimators of population mean under the problem of non-response both theoretically and empirically. The results show that the proposed estimator is more efficient than the other mentioned existing estimators. [ABSTRACT FROM AUTHOR]

In this paper, two classes of estimators for the ratio of two population means utilizing auxiliary information in stratified random sampling in the presence of non-response with a known population mean of auxiliary variable have been proposed. For the suggested classes, the expressions for bias and mean square error have been obtained. The members of the suggested classes have been defined. With the help of a simulation study and a real data set, the comparison of the proposed classes of estimators with the relevant estimators has been explored which supports the proposed classes of estimators in comparison to the relevant estimators in terms of minimum mean square error (MSE) and high precision. [ABSTRACT FROM AUTHOR]

Throughout this article, a two-phase sampling (TPS) technique is employed to estimate the population mean of the sensitive variable. The current article endeavours to develop a chain ratio type estimator for the estimation of sensitive variable(s) in the presence of non-response and measurement error simultaneously by utilizing ORRT models under a two-phase sampling technique. The significant aspects associated with the suggested estimator characterized by bias and mean squared error have been evaluated. Besides this, the utterance for the minimum mean squared error for the optimal values has also been identified. The supremacy of the proposed estimator has been compared with the modified existing estimators under the TPS scheme by using two sensitive auxiliary variables. To clarify the theoretical findings, a simulation study along with a hypothetical generated population and a real population which is based on abortion rates from Statistical Abstract of the United States: 2011 are also addressed in this study. [ABSTRACT FROM AUTHOR]

Two commonly used approaches to capturing information about teachers are random probability surveys and teacher panels. This paper reviews the strengths and limitations of these two approaches in the context of capturing information about the teacher workforce. A case study is then presented drawing upon recent teacher survey data collections in England. Although both designs should continue to play an important role in generating evidence about the teaching profession, random probability surveys of teachers should be used sparingly, and only when they will be properly resourced. [ABSTRACT FROM AUTHOR]

This paper presents some efficient classes of estimators of population mean on current occasion in the presence of random non-response under a two-phase successive sampling set-up. The suggested classes of estimators are proposed for simple random sampling under various situations of non-response. The properties of proposed estimators have been discussed up to first order of approximation. The efficiency of the presented estimators has been contrasted with the estimators for the complete response scenarios. Two real and two artificially generated data sets are used. The efficacy of the proposed classes of estimators over the existing estimators is checked theoretically and empirically. The numerical comparison supports the proposed estimators. [ABSTRACT FROM AUTHOR]

Despite their general effectiveness, 14-50% of individuals do not fully respond to evidence-based treatments for posttraumatic stress disorder (PTSD). Although changes in negative posttrauma cognitions (NPCs) are considered a likely PTSD treatment mechanism, less is known about how NPCs change among individuals who continue to be symptomatic following treatment (non-optimal responders). The objective of this study was to examine NPC change trajectories among individuals who were determined to be non-optimally responsive to intensive PTSD treatment. Using a 3-week Cognitive Processing Therapy-based intensive PTSD treatment sample (ITP; N = 243), the present study examined the number of distinct NPC change trajectories among non-optimal responders via Group Based Trajectory Modeling and assessed predictors of non-optimal responders' NPC change trajectory membership. Analyses were replicated in a separate 2-week ITP sample (N = 215). In both non-optimal responder samples, two trajectories emerged; a no NPC change group which represented those with an overall lack of NPC change throughout treatment and an NPC change group which represented those with an overall reduction of NPCs occurring primarily later in treatment. Changes in PTSD symptom severity during treatment was the only consistent predictor of NPC change trajectory group membership among treatment non-optimal responders across ITPs. Findings suggest NPC change among non-optimal responders is nuanced and may inform subsequent intervention selection, resulting in testable hypotheses for future research. Throughout intensive PTSD treatment, non-optimal responders exhibited two distinct negative posttrauma cognition change trajectories: (1) no change, and (2) slow change. Changes in PTSD symptom severity during treatment consistently predicted non-optimal responders' trajectory of change in negative posttrauma cognitions. Additional research is needed to explore how nuances of negative posttrauma cognition change may inform subsequent treatment intervention in initial non-optimal responders. [ABSTRACT FROM AUTHOR]

Despite discussions about the replicability of findings in psychological research, two issues have been largely ignored: selection mechanisms and model assumptions. Both topics address the same fundamental question: Does the chosen statistical analysis tool adequately model the data generation process? In this article, we address both issues and show, in a first step, that in the face of selective samples and contrary to common practice, the validity of inferences, even when based on experimental designs, can be claimed without further justification and adaptation of standard methods only in very specific situations. We then broaden our perspective to discuss consequences of violated assumptions in linear models in the context of psychological research in general and in generalized linear mixed models as used in item response theory. These types of misspecification are oftentimes ignored in the psychological research literature. It is emphasized that the above problems cannot be overcome by strategies such as preregistration, large samples, replications, or a ban on testing null hypotheses. To avoid biased conclusions, we briefly discuss tools such as model diagnostics, statistical methods to compensate for selectivity and semi- or non-parametric estimation. At a more fundamental level, however, a twofold strategy seems indispensable: (1) iterative, cumulative theory development based on statistical methods with theoretically justified assumptions, and (2) empirical research on variables that a ect (self-) selection into the observed part of the sample and the use of this information to compensate for selectivity. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a regression-type compromised imputation methods free of unknown parameters. The properties (biases and MSEs) of the proposed class of estimators are derived up to first order approximation using Taylor series approach. Also, the conditions for which the proposed estimators are more efficient than other estimators considered in the study were established. Results of numerical illustration using both real and simulated data revealed that the proposed estimators are more efficient and practicable than exiting estimators considered in the study. [ABSTRACT FROM AUTHOR]

The unsuccessful attempt to add a citizenship question to the 2020 US Census has drawn attention to citizenship questions on other surveys. Simultaneously, researchers have noted a secular increase in Current Population Survey (CPS) non-response. We combine these topics, studying the effect of the CPS citizenship question, added in the 1994 CPS redesign, on refusals. Direct panel regressions show states with higher rates of non-citizenship have higher refusal rates. An event-study regression discontinuity shows a 20-40% increase in refusals attributable to the redesign. Moreover, a difference-in-differences research design shows states with larger non-citizen and Hispanic populations were more affected by the redesign. These results imply the question causes non-citizens and Hispanics to refuse to participate in the survey disproportionately. Given the question appears to threaten the representativeness of the survey, we recommend there be a randomized controlled trial to precisely determine the question's effects. [ABSTRACT FROM AUTHOR]

Many adolescent health surveys ask if respondents are male or female. Non-response may be due to fear of de-anonymisation or being a gender-nonconforming youth. The present study investigates the frequency of non-response and its potential reasons. To this end, data from 54,833 adolescents aged 11–18 from six countries, participating in the 2018 Health Behaviour in School-aged Children (HBSC) study, were analysed. Respondents were divided into three groups: (1) "Responders" who answered both questions on age and gender, (2) "Age non-responders" who did not answer the question on age, and (3) "Gender non-responders" who answered the question on age but not the one on gender. These groups were compared regarding their non-response to other questions and regarding their health. Overall, 98.0% were responders, 1.6% were age non-responders and 0.4% were gender non-responders. On average, age non-responders skipped more questions (4.2 out or 64) than gender non-responders (3.2) and responders (2.1). Gender non-responders reported more psychosomatic complaints, more frequent substance use and lower family support than responders. This study shows that age and gender non-responders differ in their response styles, suggesting different reasons for skipping the gender question. The health disparities found between the groups suggest that further research should use a more nuanced approach, informed by LGBT+ youth's insights, to measure sex assigned at birth and gender identity. [ABSTRACT FROM AUTHOR]

This study develops the theory of risk-averse importance sampling and explains its potential application to forest inventory estimation through the use of a heuristic simulation. When the risk-producing elements of the landscape are known, a risk-averse sampling strategy can be created that results in fewer samples in high-risk areas. Our simulation shows that for certain high-risk populations, risk-averse importance sampling can be highly effective at reducing both risk to field crew members (requiring only 10% of the plot visits in the riskiest category) and sample variance relative to simple random sampling. The method is shown to be especially helpful when a population of values of interest decreases with increasing risk, with a reduction in mean square error (MSE) of 84% to 99% in these cases. The simulation also showed the opposite effect on MSE can be expected when values of interest increase with increasing risk. By increasing field crew safety, risk-averse importance sampling should also improve the frequency and accuracy of field observations, potentially leading to even bigger gains in estimate precision. We recommend risk-averse importance sampling any time hazardous conditions can result in a high number of missing observations and reasonably accurate characterizations of landscape risks can be developed. [ABSTRACT FROM AUTHOR]

This paper addresses new exponential estimators for population mean in case of non-response on both the study and the concomitant variables using simple random sampling. The expressions for theoretical bias and mean square error of new estimators are derived up to first-order approximation and comparisons are made with the existing estimators. The proposed estimators are observed more efficient as compared to the considered estimators in the literature. For instance, the classical [4] unbiased estimator, the estimator of [9], and other existing estimators under the explained conditions. The theoretical results are supported numerically by using real-life data sets, under the criteria of bias, mean square error, percent relative efficiency and mathematical conditions. It is also clear from the numerical results that the suggested exponential estimators performed better than the estimators in the literature.

AbstractIn this study, we concern with the improved estimation of sensitive variable when there is non-response and measurement error on sensitive variable but the auxiliary variable is non sensitive in nature. For the purpose, we propose an improved estimator in the presence of non-response and measurement error using Optional randomized response technique (ORRT) under simple random sampling without replacement (SRSWOR). The properties of the estimator have been studied and the efficiency conditions are obtained in comparison to the mean estimator, ratio estimator and Zhang’s estimator. Simulation study based on hypothetical populations has been carried out to demonstrate the performance of the proposed estimator at its optimum among others. It has been observed that the proposed estimator is more efficient than other considered estimators in term of having higher Percent Relative Efficiency (PRE).

Abstract Background Declining response proportions in surveys have been observed internationally. Improving response proportions is important for the generalizability of the outcome. The aim of this study was to examine the potential of animation videos to improve response proportions and sample composition in health surveys. Methods A randomized trial was embedded in the Danish National Health Survey 2021 (n = 186,113) where the use of animation videos in the digital invitation letter was tested as a mean to increase response proportion. The effect of both demographic-targeted videos and a general video was tested. The sample was stratified into four subsamples; (1) individuals with non-western background and a non-Danish citizenship (n = 9,956), (2) men aged 16–24 years (n = 12,481), (3) women aged 75 years or older (n = 7,815) and (4) the remaining individuals (n = 155,861). The fourth subsample was randomized into two equal sized groups; a group receiving the general video and a control group receiving no video. Each of the first three subsamples was subsequently randomized into three subgroups with 25% receiving the target group video, 25% receiving the general video and 50% receiving no video. A total of four reminders (one digital and three postal) were sent to the eligible population. Results The use of animation videos resulted in similar or slightly lower overall response proportion compared to the control group. The different animation videos were found to have heterogeneous effects on response proportions. A positive effect was found among men aged 16–24 years before the delivery of the postal reminder for the targeted animation video compared to no video (odds ratio: 1.13; 95% confidence interval: 1.02–1.26). Overall, the targeted animation videos tended to produce higher response proportions than the general animation video. Conclusions The heterogeneous effects of the videos suggest that there is some potential for the use of animation videos to improve response proportions and sample composition. The content, target group and timing of evaluation seem to be important for the animation videos to be successful. This warrants further research to better identify in which contexts, in which subgroups and under which circumstances, animation videos are useful to increase response proportions. Trial registration ClinicalTrials.gov ID: NCT05520242, registered 08/26/2022.

Abstract Background This paper aims at analysing the impact of partial non-response in the association between urban environment and mental health in Brussels. The potential threats of the partial non-response are biases in survey estimates and statistics. The effect of non-response on statistical associations is often overlooked and evidence in the research literature is lacking. Methods Data from the Belgian Health Interview Survey 2008 and 2013 were used. The association between non-response and potential determinants was explored through logistic regressions. Results Participants with low income, low educational levels, lower or higher age or in households with children were less likely to respond. When adjusting for socio-economic variables, non-response was higher in areas which are less vegetated, more polluted or more urbanised. Because the determinants of non-response and depressive disorders were similar, it is reasonable to assume that there will be more people with mental health problems among the non-respondents. And because more non-responses were found in low vegetation areas, the protective association between green spaces and mental health may be underestimated. Conclusion Our capacity to measure the association between the urban environment and health is affected by non-response in surveys. The non-random spatial and socio-economic distribution of this bias affects the research findings.

Low survey participation from online panel members is a key challenge for market and social researchers. We identify 10 key drivers of panel members' online survey participation from a qualitative study and then determine empirically using a stated choice experiment the relative importance of each of those drivers at aggregate and segment levels. We contribute to knowledge on survey participation by (a) eliciting key drivers of survey participation by online panel members, (b) determining the relative importance of each driver, and (c) accounting for heterogeneity across panel members in the importance assigned to drivers. Findings offer immediate practical guidance to market and social researchers on how to increase participation in surveys using online panels. [ABSTRACT FROM AUTHOR]

Abstract One-fourth to one-third of women with endometriosis receiving first-line hormonal treatment lacks an adequate response in terms of resolution of painful symptoms. This phenomenon has been ascribed to “progesterone resistance”, an entity that was theorized to explain the gap between the ubiquity of retrograde menstruation and the 10% prevalence of endometriosis among women of reproductive age. Nevertheless, the hypothesis of progesterone resistance is not free of controversies. As our understanding of endometriosis is increasing, authors are starting to set aside the traditionally accepted tunnel vision of endometriosis as a strictly pelvic disease, opening to a more comprehensive perspective of the condition. The question is: are patients not responding to first-line treatment because they have an altered signaling pathway for such treatment, or have we been overlooking a series of other pain contributors which may not be resolved by hormonal therapy? Finding an answer to this question is evermore impelling, for two reasons mainly. Firstly, because not recognizing the presence of further pain contributors adds a delay in treatment to the already existing delay in diagnosis of endometriosis. This may lead to chronicity of the untreated pain contributors as well as causing adverse consequences on quality of life and psychological health. Secondly, misinterpreting the consequences of untreated pain contributors as a non-response to standard first-line treatment may imply the adoption of second-line medical therapies or of surgery, which may entail non-negligible side effects and may not be free of physical, psychological and socioeconomic repercussions. The current narrative review aims at providing an overview of all the possible pain contributors in endometriosis, ranging from those strictly organic to those with a greater neuro-psychological component. Including these aspects in a broader psychobiological approach may provide useful suggestions for treating those patients who report persistent pain symptoms despite receiving first-line hormonal medical treatment.