Decision Rules Research Articles

Design and sample size determination for multiple-dose randomized phase II trials for dose optimization.

The U.S. Food and Drug Administration (FDA) has launched Project Optimus to shift dose selection from the maximum tolerated dose (MTD) to the dose that produces the optimal risk-benefit tradeoff. One approach highlighted in the FDA's guidance involves conducting a randomized phase II trial following the completion of a phase I trial, where multiple doses (typically including the MTD and one or two doses lower than the MTD) are compared to identify the optimal dose that maximizes the benefit-risk tradeoff. This article focuses on the design of such a multiple-dose randomized trial, specifically the determination of the sample size. We generalized the standard definitions of type I error and power to accommodate the unique characteristics of dose optimization and derived a decision rule along with an algorithm to determine the optimal sample size. The resulting design is referred to as MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy). Simulation studies demonstrate that MERIT has desirable operating characteristics, and a sample size between 20 and 40 per dosage arm often offers reasonable power and type I errors to ensure patient safety and benefit. To facilitate the implementation of the MERIT design, we provide software, available at https://www.trialdesign.org.

Random Regret Minimization Approach to Commuting Mode Choice in São Paulo, Brazil

The random regret minimization (RRM) approach has been widely used in transport literature, but its application in the Global South is still marginal. In this paper we discuss individual commuting mode choice in the city of São Paulo (Brazil) from the perspective of the RRM modeling approach and its variants found in the literature. We estimated several multinomial logit models (random utility maximization [RUM], classical RRM, [Formula: see text]RRM, and hybrid formulations of RUM-RRM models) and explored regret scale and decision rule heterogeneities using latent class models with specific [Formula: see text] parameters. The results showed that the RRM approach outperformed its RUM counterpart in relation to model fit and suggested that it better captured the mode choice behavior of individuals in the analyzed context. We also found that accounting for heterogeneity in scale and decision rules improved the results of the models, and the specific [Formula: see text] parameters indicated that individuals displayed different regret behavior for travel time and travel cost attributes.

National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series

Accurate and continuous maps of maize distribution are essential for food security and sustainable agricultural development. However, there are no continuous national-scale and fine-resolution maize maps and explicit updated information on the spatiotemporal dynamics of maize for most countries. Maize mapping at the national scale is challenging due to the spectral heterogeneity caused by crop growth conditions, cropping patterns, and inter-annual variations. To this end, this study developed a novel crop index-based algorithm for national-scale maize mapping. Compared to other crops, maize is characterized by large-leaf-dominated canopies and high photosynthetic efficiency. Maize shows significant changes in chlorophyll and anthocyanin content. Therefore, a robust maize index was established by exploring the temporal Variation of the Vegetation-Pigment index (VVP) during the growing period. A simple decision rule was coded on the Google Earth Engine (GEE) platform, which was used for maize mapping based on the Sentinel-2 time series in China and the contiguous United States (US) from 2018 to 2022. The national-scale 10 m annual maize maps for China and the contiguous US were developed and in good agreement with the corresponding agricultural statistics data for many years (R2 > 0.94) and 9,412 reference points (overall accuracy of 90.09 %). Compared with simply applying the vegetation index, the VVP index took account of spectral heterogeneity caused by variations in crop growth conditions, cropping patterns, and inter-annual, and the omission error of maize was reduced by over 20 %. Moreover, the VVP index can significantly improve the spatial transferability of the Random Forest (RF) classifier. The first 10 m annual maize maps for China revealed that the planted area trend decreased and then increased from 2018 to 2022. The year 2020 was the turning point. The maize planted area consisted of 68 % single maize and 32 % double cropping with maize in 2020, with the northern boundary for double cropping with maize in the Yanshan Mountains. The maize planted area in China consistently decreased by 39,352 km2 (about 9 %) from 2018 to 2020. This is mainly due to the adjustment of the maize-planted structure in the “Sickle Bend” region of China (the “Sickle Bend” policy). However, the maize planted area gradually recovered from 2020 to 2022, primarily concentrated in regions with ever-planted. This study will provide essential information for cropping structure adjustment and related agricultural policy formulation and contribute to sustainable agricultural development by mapping maize from a national to a global scale.

An interpretable framework to identify responsive subgroups from clinical trials regarding treatment effects: Application to treatment of intracerebral hemorrhage.

Randomized Clinical trials (RCT) suffer from a high failure rate which could be caused by heterogeneous responses to treatment. Despite many models being developed to estimate heterogeneous treatment effects (HTE), there remains a lack of interpretable methods to identify responsive subgroups. This work aims to develop a framework to identify subgroups based on treatment effects that prioritize model interpretability. The proposed framework leverages an ensemble uplift tree method to generate descriptive decision rules that separate samples given estimated responses to the treatment. Subsequently, we select a complementary set of these decision rules and rank them using a sparse linear model. To address the trial's limited sample size problem, we proposed a data augmentation strategy by borrowing control patients from external studies and generating synthetic data. We apply the proposed framework to a failed randomized clinical trial for investigating an intracerebral hemorrhage therapy plan. The Qini-scores show that the proposed data augmentation strategy plan can boost the model's performance and the framework achieves greater interpretability by selecting complementary descriptive rules without compromising estimation quality. Our model derives clinically meaningful subgroups. Specifically, we find those patients with Diastolic Blood Pressure≥70 mm hg and Systolic Blood Pressure<215 mm hg benefit more from intensive blood pressure reduction therapy. The proposed interpretable HTE analysis framework offers a promising potential for extracting meaningful insight from RCTs with neutral treatment effects. By identifying responsive subgroups, our framework can contribute to developing personalized treatment strategies for patients more efficiently.

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.

A semantic reasoning-based emergency rescue assistant decision method for maritime accidents involving chemicals

Maritime accidents involving chemicals (MAICs) constitute a unique category of marine emergency characterized by low frequency, high risk, and complex disposal challenges. These accidents often entail fires, explosions, and pollutant leakage, demanding effective emergency responses. The success of MAICs rescue operations relies on multiple factors, encompassing the structural features of the involved vessel, personnel, cargo, marine environment, and the availability of resources and response procedures. Proficiency in various professional fields, including ship transportation, chemistry, firefighting, and environmental science, is essential for informed decision-making. This paper addresses the need for a standardized description of knowledge in the MAICs emergency rescue decision-making process, aiming to enhance practical application frequency and decision-making efficiency. Employing ontology theory, the approach involves structurally modeling the knowledge of MAICs emergency rescue, constructing an ontology model, and establishing a decision rule base. Validating the proposed method, the emergency response to the “Longqing 1″ accident is examined as a case study, illustrating its potential for supporting MAICs emergency rescue decision-making. The application of ontology theory in this context holds both theoretical and practical significance. It facilitates the sharing, reuse, and expansion of knowledge in the emergency rescue field, providing crucial technical support for intelligent decision-making in MAICs emergencies.

Collaborative hunting in artificial agents with deep reinforcement learning.

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

An Ensemble Approach To Face Recognition In Access Control Systems

An Ensemble Approach To Face Recognition In Access Control Systems

Interval-valued intuitionistic fuzzy three-way conflict analysis based on cumulative prospect theory

 This paper makes a significant contribution to the field of conflict analysis by introducing a novel Interval-Valued Intuitionistic Fuzzy Three-Way Conflict Analysis (IVIFTWCA) method, which is anchored in cumulative prospect theory. The method’s key innovation lies in its use of interval-valued intuitionistic fuzzy numbers to represent an agent’s stance, addressing the psychological dimensions and risk tendencies of decision-makers that have been largely overlooked in previous studies. The IVIFTWCA method categorizes conflict situations into affirmative, impartial, and adverse coalitions, leveraging the evaluation of the closeness function and predefined thresholds. It incorporates a reference point, value functions and cumulative weight functions to assess risk preferences, leading to the formulation of precise decision rules and thresholds. The method’s efficacy and applicability are demonstrated through detailed examples and comparative analysis, and its exceptional performance is confirmed through a series of experiments, offering a robust framework for real-world decision-making in conflict situations.

Abstract PO2-19-11: NOBLE – Neoadjuvant Olaparib and Durvalumab for patients with BRCA-associated TripLE Negative Breast Cancer

Abstract Background The standard of care for patients with early triple-negative breast cancer (TNBC) is neoadjuvant chemotherapy followed by surgery. Recently, immune checkpoints inhibitors (ICI) added to neoadjuvant chemotherapy have shown efficacy. Among TNBC, BRCA mutations and other homologous recombination deficiency (HRD) signals are relatively frequent. As opposed to other types of breast cancer, TNBC show a high neoantigen load, a large number of tumour infiltrating lymphocytes (TILs), and a high PD-L1 expression. In BRCA/HRD breast cancer, PARP inhibitors (PARPi) exhibit a distinct anticancer activity. In addition, they show immunomodulatory effects by promoting PD-L1 expression and DNA damage, increasing neoantigen load. Chemotherapy-free regimens combining PARPi and ICI result in promising synergistic activity as shown in the metastatic setting, supporting investigation in the neoadjuvant setting. Trial design NOBLE is an EORTC phase 2, randomized, international, multicentre, open label, clinical trial. Eligible patients will undergo central screening for tumoral BRCA/HRD (Institute Curie, 455 patients), 152 patients will be randomized 1:1 between olaparib vs olaparib and durvalumab for 4 cycles (16 weeks) of neoadjuvant treatment. Thereafter, patients will undergo surgery. All patients will be followed-up to 2 years (y) after surgery. The trial will be conducted in 3 steps (screening/randomization): Step 1 – Feasibility (60/20): to assess the central testing failure rate, the turn-around time for central testing and the prevalence of tBRCA/HRD; Step 2 (193/64) – Futility; and Step 3 (202/152) – Expansion of recruitment. The primary endpoint is the rate of pathologic complete response (pCR) at the time of surgery, defined as ypT0/is ypN0. Secondary endpoints are response based on RECIST v1.1, surgery rate, 2-y event-free-survival, 2-y overall survival, safety, and quality of life. The study is expected to be activated in early autumn 2023 and the primary endpoint will be available approximately 5 years after the screening of the first patient. Main inclusion criteria Histologically confirmed primary TNBC, defined as: - ER and PgR ≤ 10% - HER2-negative per ASCO CAP guidelines Stage T1c-T2 N0-N1 M0 BRCA mutation and/or HRD based on methylation as determined by central testing ECOG status 0-1 Written informed consent Age ≥18 years No prior systemic therapy nor surgery for the current Main exclusion criteria Previous treatment with a PARPi or an immune-checkpoint inhibitor History of previous invasive breast cancer Bilateral and/or multifocal and/or multicentric BC Autoimmune or inflammatory disease Specific aims The aim of this study is to provide a safe and effective neoadjuvant treatment free of chemotherapy to a distinct subpopulation among patients with TNBC. We will evaluate the pCR after neoadjuvant treatment with olaparib alone and with olaparib in combination with durvalumab in patients with TNBC showing a BRCA1/2 mutation or HRD based on methylation. Statistical methods The sample size was calculated based on the minimax Simon two-stage design including an interim analysis for futility of the primary endpoint. Decision rules are pre-specified in the study protocol. Each treatment arm will be analyzed independently. There will be no formal comparison between the two experimental treatment arms. A minimization technique will be used for random treatment allocation stratifying for the type of HRD alteration, PD-L1 expression level and for TILs. Accrual We expect to start recruiting in September 2023. We aim to screen 455 patients to randomize 152 patients. Contact information for people with a specific interest in the trial Etienne Brain (study coordinator): etienne.brain@curie.fr Anne-Sophie Hamy-Petit (study co-coordinator): anne-sophie.hamy-petit@curie.fr Citation Format: Emanuel Buhrer, Eleni Xenophontos, Anne-Sophie Hamy-Petit, Jose Casas-Martin, Céline Callens, Thomas Meyskens, Coralie Poncet, Anastasia Lanzi, Etienne Brain. NOBLE – Neoadjuvant Olaparib and Durvalumab for patients with BRCA-associated TripLE Negative Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-19-11.

Computed tomography pulmonary angiogram ordering, adherence to decision rules and yield in the emergency department: An observational study.

Pulmonary embolism (PE) frequently requires diagnosis through CT pulmonary angiogram (CTPA). Appropriate application of evidence-based clinical decision tools can reduce unnecessary CTPAs. This study assessed adherence to and the efficacy of various aspects of the Queensland Health suspected PE diagnostic pathway, including Wells score, PE rule out criteria (PERC) and age-adjusted D-dimer interpretation. Retrospective study of CTPAs ordered from 1 January to 30 April 2023 in a tertiary and urban ED in Southeast Queensland. Data on clinical variables, D-dimer and CTPA results were collected through medical record and radiology database review. Descriptive analyses were used to determine adherence to Queensland guidelines and performance of D-dimer interpretation tools (including comparison of age-adjusted PE with a new pre-test probability [PTP]-based model using D-dimer cut-off <1000 ng/mL for Wells score ≤4 and 500 ng/mL for Wells score 4.5-6). A total of 573 CTPAs were available for analysis with a 12.4% (95% confidence interval 10.0-15.4) diagnostic yield. Stratification by Wells score showed yields of 4.0%, 18.5% and 41% for low-, moderate- and high-risk patients, respectively. Twenty-five patients with low-PTP who received CTPA could have been excluded with the PERC rule. Age-adjusted D-dimer interpretation may have prevented 26 CTPAs with no false negatives, whereas PTP approach may have prevented 128 CTPAs with four false negatives. Guideline adherence can be improved, and adherence to existing clinical decision tools may reduce unnecessary CTPA ordering and increase diagnostic yield. The use of the age-adjusted D-dimer had good sensitivity, whereas the new PTP approach will require further prospective research.

Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery

Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the glocal (aggregated local attributions over multiple samples) and global (concept based XAI) perspectives. We have established a set of sanity checks to identify saliency as the most sensible attribution method. We provide a dataset-wide analysis across entire patient subgroups, which goes beyond anecdotal evidence, to establish the first quantitative evidence for the alignment of model behavior with cardiologists’ decision rules. Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction. We believe that these proposed methods can serve as building blocks for a complementary assessment of the internal validity during a certification process, as well as for knowledge discovery in the field of ECG analysis.

SUSTAINABILITY TRANSPARENCY IN SOVEREIGN WEALTH FUNDS: BENCHMARK AND CASE STUDY APPROACH

The COVID-19 pandemic and the current food, energy, and safety crisis caused by the war in Ukraine strongly affected the existing gap in Sustainable Development Goals funding. Restoring global sustainability progress and the 2030 Agenda requires reshaping responsible investment architecture and rethinking the role of sovereign wealth funds as the largest investors in this process.The current study explores the ways in which the most transparent sovereign wealth funds act towards sustainability values and incorporate environmental, social, governance, and ethical criteria (ESGE) into their investment strategy, portfolio, and reporting. Also, 16 high-ranked funds by the Sustainability Transparency Index (Cavagnetto et al., 2022) were investigated by using the case study and benchmark analysis. Study results demonstrate the similarity of all funds disclosure within some questionnaire criteria like sustainability policy, scoring (voting) rules for decision-making, ESGE criteria, stakeholder engagements, and assurance of the financial information disclosed. Nevertheless, qualitative differences were identified between the sustainability transparency criteria of A and B-ranked funds. There is also huge variation in the implementation of funds’ ethical criteria, sustainability coherence, relevance of sustainability goals, disclosure of specific climate-related goals, and foundation membership criteria.Policy implications derived from the differences in sovereign funds' benchmarks are aimed at enhancing the sustainability context in funds’ operating frameworks, developing impact measurement metrics, prioritizing the most material stakeholder requests, sustainability verification procedures, and standardizing funds’ sustainability reporting. Specific channels, instruments, and regulatory measures towards greater funds’ sustainability transparency and their involvement in bridging the sustainable development funding gap may be the subject of further investigation.

Rawls on Just Savings and Economic Growth

In this paper, I address a controversial aspect of Rawls’s treatment of the question of justice between generations: how the parties in the original position could be motivated to select Rawls’s preferred principle of intergenerational savings, which he dubs the just savings principle. I focus on the explanation found in his later work where he proposes that the correct savings principle is the principle that any generation would have wanted preceding generations to have followed. By expanding upon this explanation, I respond to the objection that this explanation disregards the perspective of the first generation. I demonstrate that this objection ceases to be a concern when a proper account of the parties’ reasoning is developed. What is notable about the explanation I defend is that it relies on the parties adopting maximax – not maximin – as a decision-making rule. Having established this intermediary conclusion, I depart with Rawls and consider what savings principle the parties would choose if given more than one choice. Ultimately, I argue that the parties would select a principle that Rawls would have undoubtedly rejected if presented to him at face value. This is because the principle of savings I argue would be selected would require continual economic growth over generations to increase upwards indefinitely.

Fraud risk assessment in car insurance using claims graph features in machine learning

The article proposes a process for claims assessment in car insurance, which makes it possible to calculate the fraud rate on the annual set of claims using a reduced set of attributes and graph vertex properties. This approach improves the security of insurance companies’ assets against fraudulent attacks. A method for constructing a claims graph and extracting additional features from it for evaluation is described. It is shown that in order to build a graph, it is not necessary to have data on the connection of the claim participants. Two tests were carried out on a real opensource datasets with labelling of fraudulent cases. The results of the first one show the increase in classification metrics when using attributes obtained from the graph. The application of the proposed approach resulted in doubling the area under the Precision-Recall curve. The experimental results demonstrated high quality metrics for fraud detection, with a Recall rate of 83.33% and a Specificity rate of 91.05%. The second test confirmed the possibility of determining the insurance fraud level based on decision rule, which includes the condition of claims being connected to each other. The rule is able to detect claim groups with a high concentration of fraud, in which every second participant is a fraudster.

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

The United Nations Sustainable Development Goals (SDGs) agenda has stated the importance of green investment. Energy-related green investment involves intricate economic behavior and ecological objectives. Green investment definitely requires agile decisions, e.g., rule-based decisions, to respond to changes outside the country. The identification of significant rules with numerous result features and the assurance of the stability and robustness of the rules in decision-making are crucial for green energy investment. The rough set (RS) methodology works well for processing qualitative data that are difficult to examine with traditional statistical methods in order to induce decision rules. The RS methodology starts with the analysis of the limits of discernibility of a subset of objects belonging to the domain to induce rules. However, traditional RS methods cannot incrementally generate rules with outcome features when new objects are added, which frequently occurs in green energy investment with the inclusion of big data. In this paper, an intelligent RS approach is proposed. This approach effectively identifies the rules that either stay the same or are altered based on four classified cases after a new object is introduced; it is novel because it can deal with a complicated investment environment by imposing multiple outcome features, specifically when it is required to flexibly extract new decision rules via adding new data sets.

Integrating decision modeling and machine learning to inform treatment stratification.

There is increasing interest in moving away from "one size fits all (OSFA)" approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules ('policies') that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for "standard" and "intensive" blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.

HiSOMA: A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning

Multi-agent deep reinforcement learning (MADRL) has shown remarkable advancements in the past decade. However, most current MADRL models focus on task-specific short-horizon problems involving a small number of agents, limiting their applicability to long-horizon planning in complex environments. Hierarchical multi-agent models offer a promising solution by organizing agents into different levels, effectively addressing tasks with varying planning horizons. However, these models often face constraints related to the number of agents or levels of hierarchies. This paper introduces HiSOMA, a novel hierarchical multi-agent model designed to handle long-horizon, multi-agent, multi-task decision-making problems. The top-level controller, FALCON, is modeled as a class of self-organizing neural networks (SONN), designed to learn high-level decision rules as internal cognitive codes to modulate middle-level controllers in a fast and incremental manner. The middle-level controllers, MADRL models, in turn receive modulatory signals from the higher level and regulate bottom-level controllers, which learn individual action policies generating primitive actions and interacting directly with the environment. Extensive experiments across different levels of the hierarchical model demonstrate HiSOMA’s efficiency in tackling challenging long-horizon problems, surpassing a number of non-hierarchical MADRL approaches. Moreover, its modular design allows for extension into deeper hierarchies and application to more complex tasks with heterogeneous controllers. Demonstration videos and codes can be found on our project web page: https://smu-ncc.github.io.

Three-way decision method based on triangular norms in incomplete information systems and its applications in medical diagnosis

Three-way decision, as an extension of traditional two-way decision, was proposed by Yao in 2009, which can effectively avoid unnecessary losses caused by incorrect decisions in the decision-making process. Meanwhile, incomplete hybrid information systems represent the database of the relationship between objects and attributes, which refers to a system with multiple data and missing data. In this paper, based on the fact that many existing literatures involving incomplete hybrid information systems did not fully consider the impact of different conditional attributes on decision attributes and lacked effective aggregation methods to integrate weights and distances, we propose a new three-way decision method to deal with incomplete hybrid information systems with the help of triangular norms. First, in incomplete hybrid information systems, we redefine the distance between two objects based on conditional attributes and give the calculation formula of different data attributes in conditional attributes. At the same time, we define a new weight calculation method based on the relationship between conditional attributes and decision attributes. And then, by the distance between different conditional attributes and their corresponding weights, the hybrid distance is obtained using triangular norms. Furthermore, we use the hybrid distance to get the tolerance relation on the target set of an incomplete hybrid information system, thereby, using this tolerance relation, we get a new decision theoretic rough set model and the corresponding decision rules. Finally, by two experiments involving medical diagnosis, we demonstrate that our model has better classification ability, lower misclassification rate, and better stability compared to other corresponding models, thereby confirming that the proposed model provides a new and effective method for handling incomplete hybrid information systems in practical applications.

Estimating dynamic treatment regimes for ordinal outcomes with household interference: Application in household smoking cessation.

The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes, which are sequences of decision rules. At each decision point, the decision rules determine the next treatment according to the patient's baseline characteristics, the information on treatments and responses accrued by that point, and the patient's current health status, including symptom severity and other measures. However, dynamic treatment regime estimation with ordinal outcomes is rarely studied, and rarer still in the context of interference - where one patient's treatment may affect another's outcome. In this paper, we introduce the weighted proportional odds model: a regression based, approximate doubly-robust approach to single-stage dynamic treatment regime estimation for ordinal outcomes. This method also accounts for the possibility of interference between individuals sharing a household through the use of covariate balancing weights derived from joint propensity scores. Examining different types of balancing weights, we verify the approximate double robustness of weighted proportional odds model with our adjusted weights via simulation studies. We further extend weighted proportional odds model to multi-stage dynamic treatment regime estimation with household interference, namely dynamic weighted proportional odds model. Lastly, we demonstrate our proposed methodology in the analysis of longitudinal survey data from the Population Assessment of Tobacco and Health study, which motivates this work. Furthermore, considering interference, we provide optimal treatment strategies for households to achieve smoking cessation of the pair in the household.