Methods for non-proportional hazards in clinical trials: A systematic review.

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.

High-fidelity intravoxel incoherent motion parameter mapping using locally low-rank and subspace modeling.

PURPOSE: Intravoxel incoherent motion (IVIM) is a quantitative magnetic resonance imaging (MRI) method used to quantify perfusion properties of tissue non-invasively without contrast. However, clinical applications are limited by unreliable parameter estimates, particularly for the perfusion fraction (f) and pseudodiffusion coefficient (D*). This study aims to develop a high-fidelity reconstruction for reliable estimation of IVIM parameters. The proposed method is versatile and amenable to various acquisition schemes and fitting methods. METHODS: To address current challenges with IVIM, we adapted several advanced reconstruction techniques. We used a low-rank approximation of IVIM images and temporal subspace modeling to constrain the magnetization dynamics of the bi-exponential diffusion signal decay. In addition, motion-induced phase variations were corrected between diffusion directions and b-values, facilitating the use of high SNR real-valued diffusion data. The proposed method was evaluated in simulations and in vivo brain acquisitions in six healthy subjects and six individuals with a history of SARS-CoV-2 infection and compared with the conventionally reconstructed magnitude data. Following reconstruction, IVIM parameters were estimated voxel-wise. RESULTS: Our proposed method reduced noise contamination in simulations, resulting in a 60%, 58.9%, and 83.9% reduction in the NRMSE for D, f, and D*, respectively, compared to the conventional reconstruction. In vivo, anisotropic properties of D, f, and D* were preserved with the proposed method, highlighting microvascular differences in gray matter between individuals with a history of COVID-19 and those without (p = 0.0210), which wasn't observed with the conventional reconstruction. CONCLUSION: The proposed method yielded a more reliable estimation of IVIM parameters with less noise than the conventional reconstruction. Further, the proposed method preserved anisotropic properties of IVIM parameter estimates and demonstrated differences in microvascular perfusion in COVID-affected subjects, which weren't observed with conventional reconstruction methods.

Exercise and osteoimmunology in bone remodeling.

Bones can form the scaffolding of the body, support the organism, coordinate somatic movements, and control mineral homeostasis and hematopoiesis. The immune system plays immune supervisory, defensive, and regulatory roles in the organism, which mainly consists of immune organs (spleen, bone marrow, tonsils, lymph nodes, etc.), immune cells (granulocytes, platelets, lymphocytes, etc.), and immune molecules (immune factors, interferons, interleukins, tumor necrosis factors, etc.). Bone and the immune system have long been considered two distinct fields of study, and the bone marrow, as a shared microenvironment between the bone and the immune system, closely links the two. Osteoimmunology organically combines bone and the immune system, elucidates the role of the immune system in bone, and creatively emphasizes its interdisciplinary characteristics and the function of immune cells and factors in maintaining bone homeostasis, providing new perspectives for skeletal-related field research. In recent years, bone immunology has gradually become a hot spot in the study of bone-related diseases. As a new branch of immunology, bone immunology emphasizes that the immune system can directly or indirectly affect bones through the RANKL/RANK/OPG signaling pathway, IL family, TNF-α, TGF-ß, and IFN-γ. These effects are of great significance for understanding inflammatory bone loss caused by various autoimmune or infectious diseases. In addition, as an external environment that plays an important role in immunity and bone, this study pays attention to the role of exercise-mediated bone immunity in bone reconstruction.

Two-stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers.

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care. We propose to use the information from the observed biomarker status strata to construct adjusted log-rank statistics for true biomarker status strata. These adjusted log-rank statistics are then used to develop sequential tests for the global (composite) hypothesis and component-wise hypothesis. We discuss the power analysis with the control of the type-I error rate by using the correlations between the adjusted log-rank statistics within and between the design stages. Our method is illustrated with examples of the recent successful development of immunotherapy in nonsmall-cell lung cancer.

Cell Membrane-Camouflaged Chitosan-Polypyrrole Nanogels Co-Deliver Drug and Gene for Targeted Chemotherapy and Bone Metastasis Inhibition of Prostate Cancer.

The development of functional nanoplatforms to improve the chemotherapy outcome and inhibit distal cancer cell metastasis remains an extreme challenge in cancer management. In this work, we report a human-derived PC-3 cancer cell membrane-camouflaged chitosan-polypyrrole nanogel (CH-PPy NG) platform, which can be loaded with chemotherapeutic drug docetaxel (DTX) and RANK siRNA for targeted chemotherapy and gene silencing-mediated metastasis inhibition of late-stage prostate cancer in a mouse model. The prepared NGs with a size of 155.8 nm show good biocompatibility, pH-responsive drug release profile, and homologous targeting specificity to cancer cells, allowing for efficient and precise drug/gene co-delivery. Through in-vivo antitumor treatment in a xenografted PC-3 mouse tumor model, we show that such a CH-PPy NG-facilitated co-delivery system allows for effective chemotherapy to slow down the tumor growth rate, and effectively inhibits the metastasis of prostate cancer to the bone via downregulation of the RANK/RANKL signaling pathway. The created CH-PPy NGs may be utilized as a promising platform for enhanced chemotherapy and anti-metastasis treatment of prostate cancer. This article is protected by copyright. All rights reserved.

Imputed mean tensor regression for near-sited spatial temporal data.

Modern spatial temporal data are often collected from sensor networks. Missing data problems are common to this kind of data. Making accurate imputation is important for many applications. In the unsupervised setting, one technique is to minimize the rank of a tensor or matrix. If we add related covariates, can we get more accurate imputation results? To address this, we transform the original sensor×time measurements to high order tensors by adding additional temporal dimensions and then integrate tensor regression with tensor completion using nuclear norm penalty. One advantage is we can simultaneously estimate parameters and impute missing values due to clear spatial consistency for near-sited spatial-temporal data. The proposed method doesn't assume missing mechanism of the response. Theoretical properties of the proposed estimator are investigated. Simulation studies and real data analysis are conducted to verify the efficiency of the estimation procedure.

Aggressiveness predicts dominance rank in greylag geese: mirror tests and agonistic interactions.

Individual differences in aggressiveness, if consistent across time and contexts, may contribute to the long-term maintenance of social hierarchies in complex animal societies. Although agonistic interactions have previously been used to calculate individuals' positions within a dominance hierarchy, to date the repeatability of agonistic behaviour has not been tested when calculating social rank. Here, we examined the consistency and social relevance of aggressiveness as a personality trait in a free-flying population of greylag geese (Anser anser). For each individual, we quantified (i) aggressiveness using a standardized mirror stimulation test and (ii) dominance ranking based on the number of agonistic interactions won and lost in a feeding context. We found that individual differences in aggressiveness were significantly repeatable and that individuals' aggressiveness predicted their dominance rank position. The flock showed a robust and intermediately steep dominance hierarchy. Social rank was higher in paired birds, males and older birds, and most agonistic interactions occurred between individuals with moderate rank differences. We suggest that selection favours aggressiveness as a personality trait associated with resource acquisition and social rank, whereby a dominance hierarchy may increase the benefits of group living and reduce costs over conflict within dyads.

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms.

INTRODUCTION: The incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community. OBJECTIVES: This study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant. METHODS: Our research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm. RESULTS: The results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques. CONCLUSION: The constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.

Environmental sustainability practice of sewage sludge and low-rank coal co-pyrolysis: A comparative life cycle assessment study.

This study attempts to bridge the current research gaps related to the environmental burdens of low-rank coal (LRC) and sewage sludge (SS) co-pyrolysis potentially. The life cycle assessment (LCA), energy recovery and sensitivity analysis were investigated for different proportions of LRC and SS (co-)pyrolysis. The results showed that the LRC/SS pyrolysis mitigated the environmental burden with an average improvement of 43 % across 18 impact categories compared with SS pyrolysis. The best net values of energy and carbon credits were identified in SL-4 with -3.36 kWh/kg biochar and -1.10 CO2-eq/kg biochar, respectively. This study firstly proposed an optimal LRC/SS co-feed proportion at 3 to 7, which achieves the acceptable environmental burden and satisfactory energy recovery. Moreover, sensitivity analysis demonstrated this proportion is robust and adaptable. LRC/SS co-pyrolysis is a promising and sustainable alternative for SS disposal, which could meet the imperative of carbon emission mitigation and resource recycling.

Projection domain decomposition denoising algorithm based on low rank and similarity-based regularization.

BACKGROUND: Projection Domain Decomposition (PDD) is a dual energy reconstruction method which implements the decomposition process before image reconstruction. The advantage of PDD is that it can alleviate beam hardening artifacts and metal artifacts effectively as energy spectra estimation is considered in PDD. However, noise amplification occurs during the decomposition process, which significantly impacts the accuracy of effective atomic number and electron density. Therefore, effective noise reduction techniques are required in PDD. OBJECTIVE: This study aims to develop a new algorithm capable of minimizing noise while simultaneously preserving edges and fine details. METHODS: In this study, a denoising algorithm based on low rank and similarity-based regularization (LRSBR) is presented. This algorithm incorporates the low-rank characteristic of tensors into similarity-based regularization (SBR) framework. This method effectively addresses the issue of instability in edge pixels within the SBR algorithm and enhances the structural consistency of dual-energy images. RESULTS: A series of simulation and practical experiments were conducted on a dual-layer dual-energy CT system. Experiments demonstrate that the proposed method outperforms exiting noise removal methods in Peak Signal-to-noise Ratio (PSNR), Root Mean Square Error (RMSE), and Structural Similarity (SSIM). Meanwhile, there has been a notable enhancement in the visual quality of CT images. CONCLUSIONS: The proposed algorithm has a significantly improved noise reduction compared to other competing approach in dual-energy CT. Meanwhile, the LRSBR method exhibits outstanding performance in preserving edges and fine structures, making it practical for PDD applications.

Multiblock partial least squares and rank aggregation: Applications to detection of bacteriophages associated with antimicrobial resistance in the presence of potential confounding factors.

Urban environments, characterized by bustling mass transit systems and high population density, host a complex web of microorganisms that impact microbial interactions. These urban microbiomes, influenced by diverse demographics and constant human movement, are vital for understanding microbial dynamics. We explore urban metagenomics, utilizing an extensive dataset from the Metagenomics & Metadesign of Subways & Urban Biomes (MetaSUB) consortium, and investigate antimicrobial resistance (AMR) patterns. In this pioneering research, we delve into the role of bacteriophages, or "phages"-viruses that prey on bacteria and can facilitate the exchange of antibiotic resistance genes (ARGs) through mechanisms like horizontal gene transfer (HGT). Despite their potential significance, existing literature lacks a consensus on their significance in ARG dissemination. We argue that they are an important consideration. We uncover that environmental variables, such as those on climate, demographics, and landscape, can obscure phage-resistome relationships. We adjust for these potential confounders and clarify these relationships across specific and overall antibiotic classes with precision, identifying several key phages. Leveraging machine learning tools and validating findings through clinical literature, we uncover novel associations, adding valuable insights to our comprehension of AMR development.

Pathological progression of osteoarthritis: a perspective on subchondral bone.

Osteoarthritis (OA) is a degenerative bone disease associated with aging. The rising global aging population has led to a surge in OA cases, thereby imposing a significant socioeconomic burden. Researchers have been keenly investigating the mechanisms underlying OA. Previous studies have suggested that the disease starts with synovial inflammation and hyperplasia, advancing toward cartilage degradation. Ultimately, subchondral-bone collapse, sclerosis, and osteophyte formation occur. This progression is deemed as "top to bottom." However, recent research is challenging this perspective by indicating that initial changes occur in subchondral bone, precipitating cartilage breakdown. In this review, we elucidate the epidemiology of OA and present an in-depth overview of the subchondral bone's physiological state, functions, and the varied pathological shifts during OA progression. We also introduce the role of multifunctional signal pathways (including osteoprotegerin (OPG)/receptor activator of nuclear factor-kappa B ligand (RANKL)/receptor activator of nuclear factor-kappa B (RANK), and chemokine (CXC motif) ligand 12 (CXCL12)/CXC motif chemokine receptor 4 (CXCR4)) in the pathology of subchondral bone and their role in the "bottom-up" progression of OA. Using vivid pattern maps and clinical images, this review highlights the crucial role of subchondral bone in driving OA progression, illuminating its interplay with the condition.

A robust Spearman correlation coefficient permutation test.

In this work, we show that Spearman's correlation coefficient test about H0:ρs=0 found in most statistical software is theoretically incorrect and performs poorly when bivariate normality assumptions are not met or the sample size is small. There is common misconception that the tests about ρs=0 are robust to deviations from bivariate normality. However, we found under certain scenarios violation of the bivariate normality assumption has severe effects on type I error control for the common tests. To address this issue, we developed a robust permutation test for testing the hypothesis H0:ρs=0 based on an appropriately studentized statistic. We will show that the test is asymptotically valid in general settings. This was demonstrated by a comprehensive set of simulation studies, where the proposed test exhibits robust type I error control, even when the sample size is small. We also demonstrated the application of this test in two real world examples.

bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

Bone Remodeling and Bone Structural Genes in Legg-Calvé-Perthes Disease: The OPG rs2073618 and IL-6 rs1800795 Are Associated with High Risk in Mexican Patients.

Legg-Calve-Perthes disease (LCPD) is an idiopathic avascular necrosis of the pediatric femoral head. Bone remodeling and bone structural genes have the potential to contribute to the progression of LCPD when there is disequilibrium between bone resorption and bone formation. A case-control study was performed to search for associations of several common polymorphisms in the genes Receptor Activator for Nuclear Factor κappa B (RANK), Receptor Activator for Nuclear Factor κappa B Ligand (RANKL), osteoprotegerin (OPG), interleukin (IL)-6, and type 1 collagen (COL1A1) with LCPD susceptibility in Mexican children. A total of 23 children with LCPD and 46 healthy controls were genotyped for seven polymorphisms (rs3018362, rs12585014, rs2073618, rs1800795, rs1800796, rs1800012, and rs2586498) in the RANK, RANKL, OPG, IL-6, and COL1A1 genes by real-time polymerase chain reaction with TaqMan probes. The variant allele (C) of IL-6 rs1800795 was associated with increased risk of LCPD (odds ratio [OR]: 3.8, 95% confidence interval [CI]: [1.08-13.54], p = 0.033), adjusting data by body mass index (BMI) and coagulation factor V (FV), the association with increased risk remained (OR: 4.9, 95% CI: [1.14-21.04], p = 0.025). The OPG polymorphism rs2073618, specifically GC-GG carriers, was associated with a more than fourfold increased risk of developing LCPD (OR: 4.34, 95% CI: [1.04-18.12], p = 0.033) when data were adjusted by BMI-FV. There was no significant association between RANK rs3018362, RANKL rs12585014, IL-6 rs1800796, COL1A1 rs1800012, and rs2586498 polymorphisms and LCPD in a sample of Mexican children. The rs1800975 and rs2037618 polymorphisms in the IL-6 and OPG genes, respectively, are informative markers of increased risk of LCPD in Mexican children.

Total glucosides of white paeony capsule alleviate articular cartilage degeneration and aberrant subchondral bone remodeling in knee osteoarthritis.

Knee osteoarthritis (KOA) is a prevalent degenerative joint disease that is primarily managed by improving the destroyed cartilage and reversing subchondral bone remodeling. Total glucosides of white paeony (TGP) capsule primarily contains extracts from the white peony root and has been shown to have various pharmacological effects, but its role in KOA still requires comprehensive evaluation. In this study, we aimed to investigate the protective effect of TGP on knee cartilage and subchondral bone, as well as elucidate the underlying molecular mechanisms. The effect of TGP on KOA progression was evaluated in the destabilization of the medial meniscus (DMM)-induced KOA model of mouse and interleukin (IL)-1ß-induced KOA model of primary mouse chondrocytes. In vivo and in vitro experiments demonstrated that TGP had a protective effect on the cartilage. Treatment with TGP could induce the synthesis of critical elements in the cartilage extracellular matrix and downregulate the synthesis of degrading enzymes in the extracellular matrix. Regarding the underlying mechanisms, TGP inhibited the phosphorylation and nuclear translocation of p65 by regulating the nuclear factor-kappa B (NF-κB) signaling pathway. In addition, TGP could reduce the secretion of IL-1ß, IL-6, and tumor necrosis factor-α (TNF-α). Moreover, it has a sustained effect on coupled subchondral bone remodeling through regulation of the OPG/RANKL/RANK pathway. In conclusion, TGP may protect articular cartilage by downregulating the NF-κB signaling pathway and may support coupled subchondral bone remodeling by regulating OPG/RANKL/RANK signaling pathway in the DMM-induced KOA model of mouse, suggesting a new therapeutic potential for KOA treatment.

Accelerated multi-b-value multi-shot diffusion-weighted imaging based on EPI with keyhole and a low-rank tensor constraint.

PURPOSE: Multi-Shot (MS) Echo-Planar Imaging (EPI) may improve the in-plane resolution of multi-b-value DWI, yet it also considerably increases the scan time. Here we explored the combination of EPI with Keyhole (EPIK) and a calibrationless reconstruction algorithm for acceleration of multi-b-value MS-DWI. METHODS: We firstly analyzed the impact of nonuniform phase accrual in EPIK on the reconstructed image. Based on insights gained from the analysis, we developed a calibrationless reconstruction algorithm based on a Space-Contrast-Coil Locally Low-Rank Tensor (SCC-LLRT) constraint for reconstruction of EPIK-acquired data. We compared the algorithm with a modified SPatial-Angular Locally Low-Rank (SPA-LLR) algorithm through simulations, phantoms, and in vivo study. We then compared EPIK with uniformly undersampled EPI for accelerating multi-b-value DWI in 6 healthy subjects. RESULTS: Through theoretical derivations, we found that the reconstruction of EPIK with a SENSE-encoding-based algorithm, such as SPA-LLR, may cause additional aliasing artifacts due to the frequency-dependent distortion of the coil sensitivity. Results from simulations, phantoms, and in vivo study verified the theoretical finding by showing that the calibrationless SCC-LLRT algorithm reduced aliasing artifacts compared with SPA-LLR. Finally, EPIK with SCC-LLRT substantially reduced the ghosting artifacts compared with uniform undersampled multi-b-value DWI, decreasing the fitting errors in ADC (0.05 ± 0.01 vs 0.10 ± 0.01, P < 0.001) and IVIM mapping (0.026 ± 0.004 vs 0.06 ± 0.006, P < 0.001). CONCLUSION: The SCC-LLRT algorithm reduced the aliasing artifacts of EPIK by using a calibrationless modeling of the multi-coil data. The dense sampling of k-space center offers EPIK a potential to improve image quality for acceleration of multi-b-value MS-DWI.

A novel green synthesis of MnO2-coal composite for rapid removal of silver and lead from wastewater.

The presence of Ag(I) and Pb(II) ions in wastewater poses a significant threat to human health in contemporary times. This study aims to explore the development of a novel and economical adsorbent by grafting MnO2 particles onto low-rank coal, providing an innovative solution for the remediation of water contaminated with silver and lead. The synthesized nanocomposites, referred to as MnO2-coal, underwent thorough characterization using FTIR, XRD, BET, SEM, and TEM to highlight the feasibility of in-situ surface modification of coal with MnO2 nanoparticles. The adsorption of Ag(I) and Pb(II) from their respective aqueous solution onto MnO2-coal was systematically investigated, with optimization of key parameters such as pH, temperature, initial concentration, contact time, ionic strength, and competing ions. Remarkably adsorption equilibrium was achieved within a 10 min, resulting in impressive removal rates of 80-90% for both Ag(I) and Pb(II) at pH 6. The experimental data were evaluated using Langmuir, Freundlich, and Temkin isotherm models. The Langmuir isotherm model proved to be more accurate in representing the adsorption of Ag(I) and Pb(II) ions onto MnO2-coal, exhibiting high regression coefficients (R2 = 0.99) and maximum adsorption capacities of 93.57 and 61.98 mg/g, along with partition coefficients of 4.53 and 71.92 L/g for Ag(I) and Pb(II), respectively, at 293 K. Kinetic assessments employing PFO, PSO, Elovich, and IPD models indicated that the PFO and PSO models were most suitable for adsorption mechanism of Pb(II) and Ag(I) on MnO2-coal composites, respectively. Moreover, thermodynamic evaluation revealed the spontaneous and endothermic adsorption process for Ag(I), while exothermic behavior for adsorption of Pb(II). Importantly, this approach not only demonstrates cost-effectiveness but also environmental friendliness in treating heavy metal-contamination in water. The research suggests the potential of MnO2-coal composites as efficient and sustainable adsorbents for water purification applications.

Selection of the optimal personalized treatment from multiple treatments with right-censored multivariate outcome measures.

We propose a novel personalized concept for the optimal treatment selection for a situation where the response is a multivariate vector that could contain right-censored variables such as survival time. The proposed method can be applied with any number of treatments and outcome variables, under a broad set of models. Following a working semiparametric Single Index Model that relates covariates and responses, we first define a patient-specific composite score, constructed from individual covariates. We then estimate conditional means of each response, given the patient score, correspond to each treatment, using a nonparametric smooth estimator. Next, a rank aggregation technique is applied to estimate an ordering of treatments based on ranked lists of treatment performance measures given by conditional means. We handle the right-censored data by incorporating the inverse probability of censoring weighting to the corresponding estimators. An empirical study illustrates the performance of the proposed method in finite sample problems. To show the applicability of the proposed procedure for real data, we also present a data analysis using HIV clinical trial data, that contained a right-censored survival event as one of the endpoints.