Rank Variation Research Articles

Smartphone Addiction Assessment Using Pythagorean Fuzzy CRITIC-TOPSIS

Addiction to smartphones, particularly among adolescents, has reached alarming proportions, rivaling or perhaps exceeding internet addiction as the most widespread kind of dependence in modern culture. Evaluating the degree of problematic smartphone use habits by experts and identifying the vulnerable ones to steer to the right treatment program has become a critical issue. Since such a task may involve an abundance of criteria and candidates, as well as the inherent subjectivity of multiple decision experts participating in the process, the assessment of smartphone addiction can be framed as an uncertain multi-criteria decision-making (MCDM) problem. As an extension of intuitionistic fuzzy sets, Pythagorean fuzzy sets can be used to efficiently manage ambiguity and uncertainty during decision-making. This study provides an integrated fuzzy MCDM methodology based on Pythagorean fuzzy sets for evaluating the smartphone addiction level of adolescents. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to determine the importance levels of criteria in an objective manner, and smartphone addiction levels of the selected candidates are ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. A sensitivity analysis is conducted to examine the variations in candidate rankings caused by changes to the criteria and weights of the decision experts. Moreover, in the context of comparative analysis, the Evaluation based on Distance from Average Solution (EDAS) approach is used to validate the acquired findings.

Factors Influencing Community Participation in Internet Interventions Compared With Research Trials: Observational Study in a Nationally Representative Adult Cohort.

Digital mental health (DMH) programs can be effective in treating and preventing mental health problems. However, community engagement with these programs can be poor. Understanding the barriers and enablers of DMH program use may assist in identifying ways to increase the uptake of these programs, which have the potential to provide broad-scale prevention and treatment in the community. In this study, we aimed to identify and compare factors that may influence participation in DMH programs in practice and research trials, identify any respondent characteristics that are associated with these factors, and assess the relationship between intentions to use DMH programs and actual uptake. Australian adults aged ≥18 years were recruited from market research panels to participate in the study. The sample was representative of the Australian adult population based on age, gender, and location. Participants completed a cross-sectional web-based survey assessing demographic characteristics, mental health symptom measures, attitudes and use of DMH programs in practice and in research studies, and the factors influencing their use in both settings. Across both research and practice, trust in the organization delivering the service or trial was the top-ranked factor influencing participation, followed by anonymity or privacy and adequate information. There was little variation in rankings across demographic groups, including intentions to use DMH programs or mental health status. Intentions to use DMH programs were a strong predictor of both current (odds ratio 2.50, 99% CI 1.41-4.43; P<.001) and past (odds ratio 2.98, 99% CI 1.71-5.19; P<.001) use behaviors. Efforts to increase the uptake of DMH programs or participation in research trials should focus on clearly communicating the following to users: the legitimacy of the organization delivering the program, security and use of participant data, and effectiveness of DMH programs.

Managers’ rank & file employee coordination costs and real activities manipulation

We investigate the effect of managers' rank & file employee coordination costs on real activities manipulation (RAM). We identify exogenous variation in managers' rank & file employee coordination costs using the adoption of 99 wrongful dismissal laws across 47 U.S. states between 1970 and 1999. We first find that RAM declines when managers' rank & file employee coordination costs increase. We further document that managers’ rank & file employee coordination costs reduce decentralized RAM but affect neither centralized RAM nor centralized accrual-based earnings management, both of which are less likely to require rank & file employee coordination. Event-time tests corroborate and document the validity of the parallel trends assumption in our setting. Cross-sectional tests document predictable variation in our main result across firms and over time. Consistent with rank & file employee coordination costs constraining RAM, managers miss earnings thresholds more often after rank & file employee coordination costs increase. Our nuanced inferences inform the literature investigating factors that constrain RAM and the extent to which rank & file employees determine firm outcomes.

Translation of the Fugl-Meyer assessment into Romanian: Transcultural and semantic-linguistic adaptations and clinical validation.

The Fugl-Meyer Assessment (FMA) scale, which is widely used and highly recommended, is an appropriate tool for evaluating poststroke sensorimotor and other possible somatic deficits. It is also well-suited for capturing a dynamic rehabilitation process. The aim of this study was to first translate the entire sensorimotor FMA scale into Romanian using the transcultural and semantic-linguistic adaptations of its official afferent protocols and to then validate it using the preliminary clinical evaluation of inter- and intra-rater reliability and relevant concurrent validity. Through three main steps, we completed a standardized procedure for translating FMA's official afferent evaluation protocols into Romanian and their transcultural and semantic-linguistic adaptation for both the upper and lower extremities. For relevant clinical validation, we evaluated 10 patients after a stroke two times: on days 1 and 2. All patients were evaluated simultaneously by two kinesi-physiotherapists (generically referred to as KFT1 and KFT2) over the course of 2 consecutive days, taking turns in the roles of an examiner and observer, and vice versa (inter-rater). Two scores were therefore obtained and compared for the same patient, i.e., being afferent to an inter-rater assay by comparing the assessment outcomes obtained by the two kinesi-physiotherapists, in between, and respectively, to the intra-rater assay: based on the evaluations of the same kinesi-physiotherapist, in two consecutive days, using a rank-based method (Svensson) for statistical analysis. We also compared our final Romanian version of FMA's official protocols for concurrent validity (Spearman's rank correlation statistical method) to both of the widely available assessment instruments: the Barthel Index (BI) and the modified Rankin scale (mRS). Svensson's method confirmed overall good inter- and intra-rater results for the main parts of the final Romanian version of FMA's evaluation protocols, regarding the percentage of agreement (≥80% on average) and for disagreement: relative position [RP; values outside the interval of (-0.1, 0.1) in only two measurements out of the 56 comparisons we did], relative concentration [RC; values outside the interval of (-0.1, 0.1) in only nine measurements out of the same 56 comparisons done], and relative rank variation [RV; all values within an interval of (0, 0.1) in only five measurements out of the 56 comparisons done]. High correlation values were obtained between the final Romanian version of FMA's evaluation protocols and the BI (ρ = 0.9167; p = 0.0002) for FMA-upper extremity (FMA-UE) total A-D (motor function) with ρ = 0.6319 and for FMA-lower extremity (FMA-LE) total E-F (motor function) with p = 0.0499, and close to the limit, with the mRS (ρ = -0.5937; p = 0.0704) for FMA-UE total A-D (motor function) and (ρ = -0.6615; p = 0.0372) for FMA-LE total E-F (motor function). The final Romanian version of FMA's official evaluation protocols showed good preliminary reliability and validity, which could be thus recommended for use and expected to help improve the standardization of this assessment scale for patients after a stroke in Romania. Furthermore, this endeavor could be added to similar international translation and cross-cultural adaptations, thereby facilitating a more appropriate comparison of the evaluation and outcomes in the management of stroke worldwide.

A panel data analysis of the effect of state governance on the global competitiveness of BRICS nations

This research investigates the effect of state governance of BRICS nations on their global competitiveness and specific aspects of competitiveness factors, i.e., Knowledge, Technology, and Future Readiness, for the period of 2017-2021 using World Bank’s World Governance Indicators (WGI) as the independent variable and IMD’s World Competitiveness Ranking (WCR) as the dependent variable. The role of state governance emerged as an important source of global competitiveness and its factors for BRICS nations in the results which revealed certain specific crucial relationships. Overall, 37.68% variation in the WCR rankings of BRICS nations is determined by four key dimensions of WGI namely Government Effectiveness, Political Stability and Absence of Violence, Regulatory Quality, and Control of Corruption with the Control of Corruption being the most significant variable in the model. For the hypotheses relating the State Governance and specific factors of Knowledge, Technology, and Future Readiness, the highest contribution of 40.19% is revealed to be contributed by these four governance variables in Technology rankings, followed by 32.93% variation in Future Readiness and 27.03% in Knowledge. The results provide evidence for the role of the quality of governments and governance in securing global competitiveness by nations. The results are particularly useful for strategic state governance planning and implementations for BRICS nations to improve their competitive global rankings individually and collectively, particularly in the dimensions of control of corruption, government effectiveness, political stability, and absence of violence.

Complex Magnetic Anomaly Detection Using Structured Low-Rank Approximation With Total Variation Regularization

In the field of magnetic anomaly detection (MAD), the anomaly signal is easily submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low rank (SLR) and total variation (TV) regularization constraints is proposed in this letter. The noise suppression performance is improved by leveraging the structured low rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV method with four state-of-the-art methods with extensive field tests, the results demonstrate that the proposed SLR-TV method achieves the greatest SNR improvement by about 63.24% and the best structural similarity (SSIM) improvement by about 53.02% over other methods in the range from −40 to 0 dB, showing the utility and high fidelity of the proposed framework in low SNR.

A study of the evaluation metrics for generative images containing combinational creativity

AbstractIn the field of content generation by machine, the state-of-the-art text-to-image model, DALL⋅E, has advanced and diverse capacities for the combinational image generation with specific textual prompts. The images generated by DALL⋅E seem to exhibit an appreciable level of combinational creativity close to that of humans in terms of visualizing a combinational idea. Although there are several common metrics which can be applied to assess the quality of the images generated by generative models, such as IS, FID, GIQA, and CLIP, it is unclear whether these metrics are equally applicable to assessing images containing combinational creativity. In this study, we collected the generated image data from machine (DALL⋅E) and human designers, respectively. The results of group ranking in the Consensual Assessment Technique (CAT) and the Turing Test (TT) were used as the benchmarks to assess the combinational creativity. Considering the metrics’ mathematical principles and different starting points in evaluating image quality, we introduced coincident rate (CR) and average rank variation (ARV) which are two comparable spaces. An experiment to calculate the consistency of group ranking of each metric by comparing the benchmarks then was conducted. By comparing the consistency results of CR and ARV on group ranking, we summarized the applicability of the existing evaluation metrics in assessing generative images containing combinational creativity. In the four metrics, GIQA performed the closest consistency to the CAT and TT. It shows the potential as an automated assessment for images containing combinational creativity, which can be used to evaluate the images containing combinational creativity in the relevant task of design and engineering such as conceptual sketch, digital design image, and prototyping image.

A decision making framework for comparing sales and operational performance of firms in emerging market

Sales and operations planning translates the requirements of the customers at the market place (related to new and/or existing products and services) into actionable tactical plans to drive the activities of the value chain of the organization. The present work aims to provide a multi-period and multi-perspective evaluation framework to compare the sales and operational performance (SOP) of firms in an emerging market. SOP is one of the frontline KPIs that describes the efficiency and effectiveness of the sales and operations planning. There is a scantiness in the extant literature about well-defined indicators to measure SOP. The current work fills the gap in the literature by developing a hybrid multi-criteria decision making (MCDM) framework utilizing the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) and Evaluation based on Distance from Average Solution (EDAS) models for a novel application in assessing SOP. From the data analysis, it is also evident that there is a variations in the year wise ranking of the companies. However, all individual year wise rankings maintain statistically significant correlations with the aggregated ranking. For aggregation purpose, Borda Count Method is used. The companies like ITC Limited, Hindustan Unilever Ltd., Avanti Feeds Ltd., Britannia Industries Ltd., and Symphony Ltd. hold the top five positions on aggregate. The comparison with other MCDM models is made and sensitivity analysis is carried out. The present work is a first of its kind that would encourage the analysts and the policy makers to evaluate the sales and operational performance using a scientific way.

A Multi-Criteria Based Stock Selection Framework in Emerging Market

The present study aims to compare the stock performances of the Fast Moving Consumer Goods (FMCG) and Consumer Durables (CD) firms at the Bombay Stock Exchange (BSE), India. It is evident from the extant literature that investment in the stock market depends on two broad objectives such as maximization of return while minimization of risk. Besides, investment decisions are also influenced by the behavioral nature of the investors. To this end, the current work considers the earning prospect (average market return, return on net worth, earning per share, and yield), market-centric risk (beta), market perception (price to book value, shares traded), momentum (turnover) and benchmarked performance (alpha) to set the criteria for comparison. The study period considers seven consecutive financial years to discern the performance. For the comparative analysis, a combined multi-criteria decision-making (MCDM) framework of Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) (used to determine criteria weights) and Evaluation Based on Distance from Average Solution (EDAS) (for ranking) methods has been utilized. Borda Count Method (BC), Copeland Method, and Simple Additive Weighting (SAW) have been used to aggregate the year-wise rankings. The calculated weights show consistency to the modern portfolio theory as average return, beta, and return on net worth obtain higher weightage than others. It is observed that there are variations in the year-wise comparative ranking, while on aggregation, FMCG firms dominate the top positions. The analysis reveals that Avanti Feeds Ltd., Hindustan Unilever Ltd., Procter &amp; Gamble Hygiene &amp; Health Care Ltd., Britannia Industries Ltd., and Nestle India Ltd. are the top five performers, while Godfrey Phillips India Ltd., E I D-Parry (India) Ltd., United Breweries Ltd., Rajesh Exports Ltd., and Radico Khaitan Ltd. hold the bottom five positions during the same period. The results also indicate that, more or less, the firms having higher market capitalizations have performed well. The results obtained using the EDAS method and other popular MCDM models, such as multi-attributive border approximation area comparison (MABAC) and the Complex Proportional Assessment (COPRAS), show a significant correlation. Further, the outcome of the sensitivity analysis confirms the stability of the performance-based ranking results.

A Hybrid Hesitant Fuzzy Model for Healthcare Systems Ranking of European Countries

Ranking several countries on a specific area may require the consideration of various factors simultaneously. To obtain a ranking of countries, the development of analytical approaches, which can aggregate opinions of a group of people on various criteria, is essential. The main aim of this study was to propose such a ranking approach for European countries in terms of healthcare services. To this end, a hybrid group decision-making model based on Hesitant Fuzzy Linguistic Terms Set (HFLTS) and Hesitant Fuzzy Technique of Order Preference by Similarity to Ideal Solution (HF-TOPSIS) is presented in this study. Importance degree of indicators were determined by the HFLTS-based group decision-making approach, and then HF-TOPSIS was used to obtain the rank of countries. According to the results obtained by the proposed model, Austria, Sweden and Finland are the best European countries in terms of healthcare services. Moreover, two comparative analyses, one for the utilization of different hesitant fuzzy distance measures in HF-TOPSIS and one for the ranking of countries obtained by utilizing TOPSIS, return some variations in country rankings. While Austria remained the best country for all distance measures in the hesitant fuzzy environment, Luxemburg was found to be the best for the deterministic case of TOPSIS.

Modelling the importance of social responsibility standards (ISO 26000:2010) practices adoption in freight transport industry

The United Nations agenda for sustainable development attracts governments, supply chain actors, researchers, and societies to promote sustainability practices in their businesses. Most of the scholarly work focusing on the economic and environmental sustainability of supply chain and logistics processes. Most developed countries have incorporated the social responsibility of their businesses to improve the nation's social wellbeing. There is less exploration of the social responsibility of logistics operations in developing nations. In the above connection, the International Organization for Standardization (ISO) proposes a global standard, i.e., ISO26000, in 2010 (ISO26000:2010). Even minimal freight logistics firms are adopting the ISO26000 standard for improving sustainable performance. To bridge the above research gap, this research analyses the importance of ISO 26000 practices in the freight transport industry. The significance of the practices is assessed with a multi-criteria decision-making method under a fuzzy environment, i.e., the fuzzy best-worst method (FBWM). The advantage of using fuzzy set theory is to include the uncertainty and ambiguity of the decision-makers. The ISO26000 practices are ranked to make appropriate managerial decisions for improving the social responsibility of the Indian freight transport industry. Fair operating practices are obtained top rank, followed by labour and workforce practices. The overall analysis shows that health and safety at the workplace is a top practice to improve sustainability performance. The rank variation of the ISO26000 practices are tested with sensitivity analysis. The obtained results are less sensitive to weight variations, which is claimed as the model robustness.

Reducing echo state network size with controllability matrices.

Echo state networks are a fast training variant of recurrent neural networks excelling at approximating nonlinear dynamical systems and time series prediction. These machine learning models act as nonlinear fading memory filters. While these models benefit from quick training and low complexity, computation demands from a large reservoir matrix are a bottleneck. Using control theory, a reduced size replacement reservoir matrix is found. Starting from a large, task-effective reservoir matrix, we form a controllability matrix whose rank indicates the active sub-manifold and candidate replacement reservoir size. Resulting time speed-ups and reduced memory usage come with minimal error increase to chaotic climate reconstruction or short term prediction. Experiments are performed on simple time series signals and the Lorenz-1963 and Mackey-Glass complex chaotic signals. Observing low error models shows variation of active rank and memory along a sequence of predictions.

Online Nonnegative and Sparse Canonical Polyadic Decomposition of Fluorescence Tensors

The NonNegative Canonical Polyadic Decomposition (NN-CPD) is used in many fields such as in chemistry, biology and medicine. The data coming from these fields can be dynamic which lead to use real-time or “online” decomposition. Even though there are a variety of online tensor decomposition algorithms, the main assumption of all these algorithms is that the rank of the decomposition is known and/or does not vary over time. However this should not be the case in experimental conditions. In this work, we propose three algorithms to compute the online NN-CPD based on sparse dictionary learning for tracking chemical components in water by using a set of Emission and Excitation Matrices (EEMs) of fluorescence. The methods developed in this work is not limited to this application field and it addresses the major challenges posed by the variation of the CPD rank in real-time. First, the algorithms take into account the unknown factors and the variation of tensor rank. Second, previous extracted information are used to decompose upcoming new tensors. In addition to the development of these algorithms, one of the contributions of this paper is the real-time acquisition of fluorescence data in a semi-controlled environment. These algorithms were applied on these real datasets and compared to state of the art algorithms.

Sensitivity of environmental performance index based on stochastic dominance

The Environmental Performance Index (EPI) is a popular sustainability index. It is a composite index which ranks 180 countries based on their environmental performance in 32 indicators. Using the EPI data and stochastic dominance efficiency methodology, this paper examines the sensitivity to the subjective weights assigned to the indicators and categories of environmental performance. The findings show a remarkable variation in environmental performance based on alternative weights which are selected using Stochastic Dominance criteria. Except for 2020, the environmental health category in EPI gets relatively higher importance in the optimal scenario, and ecosystem vitality gets relatively higher weights in the inferior scenario, suggesting that the environmental health category achievements have been relatively higher for most countries over time. The ranking analysis also shows major variations in country rankings with alternative weights. Two countries, Maldives and Gabon, would have experienced more than 100 position changes in their rankings with alternative weights. Furthermore, 67 countries would have experienced 30 or more position changes in their rankings and 37 countries experienced an EPI score change of more than 50 (out of 100) with alternative weights. Overall, the results illustrate the importance of sensitivity analysis of composite indices to increase reliability and transparency.

Optimal site selection for wind power plant using multi-criteria decision-making methods: A case study in eastern Iran

Abstract Sustainable and renewable energy resources will play a significant role in the future of the 21st century. One of the most significant resources of sustainable energy is wind energy. Therefore, detection of the potential locations and selection of the optimal place to find a wind farm are a primary step toward using sustainable energy resources. In this research, the feasibility of establishing wind farms in three provinces in the east and north-east parts of Iran is studied using multiple criteria decision-making methods. Fifty proposed options to build a wind farm are ranked based on 13 effective criteria, including area, distance to power lines, distance to the substation, distance to a road, estimated capacity, distance to the city, altitude, population, population density, annual rainfall, average temperature, average wind speed and relative humidity by applying Shannon entropy weighting technique and using three methods of the simple additive weighting (SAW), technique for order of preference by similarity to ideal solution (TOPSIS) and elimination and choice translating reality (ELECTRE). Shannon entropy weighting technique reveals that the criteria of distance to power transmission lines and relative humidity will have the most and least weights, respectively. The ranking results show that, based on the TOPSIS and ELECTRE methods, Gezi Bojnourd option is the best site and Neishabour Hesar Yazdan case is the worst site. Furthermore, Bashirabad Torbatjam case is the most desirable site in the SAW method. The sensitivity analysis results show that the minimum sensitivity to the criteria weights is recorded in the SAW method. On the contrary, ELECTRE method offers the most sensitivity and variation in ranking.

Revisiting Feature Ranking Methods using Information-Centric and Evolutionary Approaches: Survey

Abstract: Feature ranking can have a severe impact on the feature selection problem. Feature ranking methods refer to the structure of features that can accept the designed data and have a positive effect on the quality of features. Moreover, accessing useful features helps in reducing cost and improving the performance of a feature ranking algorithm. There are numerous methods for ranking the features that are available in the literature. The developments of the past 20 years in the domain of knowledge research have been explored and presented in terms of relevance and various known concepts of feature ranking problems. The latest developments are mostly based on the evolutionary approaches which broadly include variations in ranking, mutual information, entropy, mutation, parent selection, genetic algorithm, etc. For a variety of algorithms based on differential evolution, it has been observed that although the suitability of the mutation operator is extremely important for feature selection yet other operators can also be considered. Therefore, the special emphasis of various algorithms is observing and reviewing the algorithms and finding new research directions: The general approach is to review a rigorous collection of articles first and then obtain the most accurate and relevant data followed by the narrow down of research questions. Research is based on the research questions. These are reviewed in four phases: designing the review, conducting the review, analyzing, and then writing the review. Threats to validity is also considered with research questions. In this paper, many feature ranking methods have been discussed to find further direction in feature ranking and differential evolution. A literature survey is performed on 93 papers to find out the performance in relevance, redundancy, correlation with differential evolution. Discussion is suitable for cascading the direction of differential evolution in integration with information-theoretic, entropy, and sparse learning. As differential evolution is multiobjective in nature so it can be incorporated with feature ranking problems. The survey is being conducted on many renowned journals and is verified with their research questions. Conclusions of the survey prove to be essential role models for multiple directions of a research entity. In this paper, a comprehensive view on the current-day understanding of the underlying mechanisms describing the impact of algorithms and review current and future research directions for use of evolutionary computations, mutual information, and entropy in the field of feature ranking is complemented by the list of promising research directions. However, there are no strict rules for the pros and cons of alternative algorithms.

Publication Perception of Faculty Researchers in a State University

The objective of the study is to create further insights in the level of publication Stress, Attitude and Resources Perception of faculty researchers to find out potential solutions and innovation that would help further research publication in Kalinga State UniversityThis is necessary, as variations in ranks and experience could indicate the necessity for different interventions.The Publication Pressure Questionnaire was used to measure perceived publication pressure inresearchers which consists of three subscales covering Stress, Attitude and Resources. The findings in the study suggest that respondents are optimistic about publishing in their field, and has access to multiple supporting resources as reflected in their subscale scores but perceive their level of performance as overall unsatisfactory. It is revealed thatfaculty researchers who holds higher ranks have lower attitude and resources perception scores but have higher rate in performance. They have equal level of perceived stress attitude and resources across experience but those who have more experience tend to do better in research.To improve the faculty researcher’s performance,the University is recommended to come up with a thorough plan and program interventiontaking into consideration the rank and experience of the faculty researchers to improve their research and publication skills and productivity.

In silico performance analysis of web tools for CRISPRa sgRNA design in human genes

Angiogenic gene overexpression has been the main strategy in numerous vascular regenerative gene therapy projects. However, most have failed in clinical trials. CRISPRa technology enhances gene overexpression levels based on the identification of sgRNAs with maximum efficiency and safety. CRISPick and CHOP CHOP are the most widely used web tools for the prediction of sgRNAs. The objective of our study was to analyze the performance of both platforms for the sgRNA design to angiogenic genes (VEGFA, KDR, EPO, HIF-1A, HGF, FGF, PGF, FGF1) involving different human reference genomes (GRCH 37 and GRCH 38). The top 20 ranked sgRNAs proposed by the two tools were analyzed in different aspects. No significant differences were found on the DNA curvature associated with the sgRNA binding sites but the sgRNA predicted on-target efficiency was significantly greater when CRISPick was used. Moreover, the mean ranking variation was greater for the same platform in EPO, EGF, HIF-1A, PGF and HGF, whereas it did not reach statistical significance in KDR, FGF-1 and VEGFA. The rearrangement analysis of the ranking positions was also different between platforms. CRISPick proved to be more accurate in establishing the best sgRNAs in relation to a more complete genome, whereas CHOP CHOP showed a narrower classification reordering.

OP236 Evidence Synthesis Of Time-To-Event Outcomes In The Presence Of Non-Proportional Hazards

IntroductionSynthesis of clinical effectiveness is a well-established component of health technology assessment (HTA) combining data from multiple trials to obtain an overall pooled estimate of clinical effectiveness, which may inform an associated economic evaluation. Time-to-event outcomes are often synthesized using effect measures from Cox proportional hazards models assuming a constant hazard ratio over time. However, where treatment effects vary over time an assumption of proportional hazards is not always valid. Several methods have been proposed for synthesizing time-to-event outcomes in the presence of non-proportional hazards. However, guidance on choosing between these methods and the implications for HTA is lacking.MethodsWe applied five methods for estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption to a network of melanoma trials, reporting overall survival: restricted mean survival time, an accelerated failure time generalized gamma model, piecewise exponential, fractional polynomial and Royston-Parmar models. We conducted a simulation study to compare these five methods. Simulated individual patient data was generated from a mixture Weibull distribution assuming a treatment-time interaction. Each simulated meta-analysis consisted of five trials with varying numbers of patients and length of follow-up across trials. For each model fitted to each dataset, we calculated the restricted mean survival time at the end of observed follow-up and following extrapolation to a 20-year time horizon.ResultsAll models fitted the melanoma data reasonably well with some variation in the treatment rankings and differences in the survival curves. The simulation study demonstrated the potential for different conclusions from different modelling approaches.ConclusionsThe restricted mean survival time, generalized gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can all accommodate non-proportional hazards and differing lengths of trial follow-up within an evidence synthesis of time-to-event outcomes. Further work is needed in this area to extend the simulation study to the network meta-analysis setting and provide guidance on the key considerations for informing model choice for the purposes of HTA.

Rank-invariant estimation of inbreeding coefficients

The two alleles an individual carries at a locus are identical by descent (ibd) if they have descended from a single ancestral allele in a reference population, and the probability of such identity is the inbreeding coefficient of the individual. Inbreeding coefficients can be predicted from pedigrees with founders constituting the reference population, but estimation from genetic data is not possible without data from the reference population. Most inbreeding estimators that make explicit use of sample allele frequencies as estimates of allele probabilities in the reference population are confounded by average kinships with other individuals. This means that the ranking of those estimates depends on the scope of the study sample and we show the variation in rankings for common estimators applied to different subdivisions of 1000 Genomes data. Allele-sharing estimators of within-population inbreeding relative to average kinship in a study sample, however, do have invariant rankings across all studies including those individuals. They are unbiased with a large number of SNPs. We discuss how allele sharing estimates are the relevant quantities for a range of empirical applications.