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A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted wireless powered communication network (WPCN) is proposed, where two energy-limited devices first harvest energy from a hybrid access point (HAP) and then use that energy to transmit information back. To fully eliminate the doubly-near-far effect in WPCNs, two STAR-RIS operating protocol-driven transmission strategies, namely energy splitting non-orthogonal multiple access (ES- NOMA) and time switching time division multiple access (TS- TDMA) are proposed. For each strategy, the corresponding optimization problem is formulated to maximize the minimum throughput by jointly optimizing time allocation, user transmit power, active HAP beamforming, and passive STAR-RIS beamforming. For ES-NOMA, the resulting intractable problem is solved via a two-layer algorithm, which exploits the one-dimensional search and block coordinate descent methods in an iterative manner. For TS-TDMA, the optimal active beamforming and passive beamforming are first determined according to the maximum-ratio transmission beamformer. Then, the optimal solution of the time allocation variables is obtained by solving a standard convex problem. Numerical results show that: 1) the STAR-RIS can achieve considerable performance improvements for both strategies compared to the conventional RIS; 2) TS- TDMA is preferred for single-antenna scenarios, whereas ES- NOMA is better suited for multi-antenna scenarios; and 3) the superiority of ES-NOMA over TS-TDMA is enhanced as the number of STAR-RIS elements increases.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is proposed. More particularly, an STAR-RIS is deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), where two practical STAR-RIS operating protocols, namely energy splitting (ES) and time switching (TS), are employed. Under the imperfect channel state information (CSI) condition, a multi-objective optimization problem (MOOP) framework, that simultaneously maximizes the minimum data rate and minimum harvested power, is employed to investigate the fundamental rate-energy trade-off between IUs and EUs. To obtain the optimal robust resource allocation strategy, the MOOP is first transformed into a single-objective optimization problem (SOOP) via the {\epsilon}-constraint method, which is then reformulated by approximating semi-infinite inequality constraints with the S-procedure. For ES, an alternating optimization (AO)-based algorithm is proposed to jointly design AP active beamforming and STAR-RIS passive beamforming, where a penalty method is leveraged in STAR-RIS beamforming design. Furthermore, the developed algorithm is extended to optimize the time allocation policy and beamforming vectors in a two-layer iterative manner for TS. Numerical results reveal that: 1) deploying STAR-RISs achieves a significant performance gain over conventional RISs, especially in terms of harvested power for EUs; 2) the ES protocol obtains a better user fairness performance when focusing only on IUs or EUs, while the TS protocol yields a better balance between IUs and EUs; 3) the imperfect CSI affects IUs more significantly than EUs, whereas TS can confer a more robust design to attenuate these effects.

A novel coexisting passive reconfigurable intelligent surface (RIS) and active decode-and-forward (DF) relay assisted non-orthogonal multiple access (NOMA) transmission framework is proposed. In particular, two communication protocols are conceived, namely Hybrid NOMA (H-NOMA) and Full NOMA (F-NOMA). Based on the proposed two protocols, both the sum rate maximization and max-min rate fairness problems are formulated for jointly optimizing the power allocation at the access point and relay as well as the passive beamforming design at the RIS. To tackle the non-convex problems, an alternating optimization (AO) based algorithm is first developed, where the transmit power and the RIS phase-shift are alternatingly optimized by leveraging the two-dimensional search and rank-relaxed difference-of-convex (DC) programming, respectively. Then, a two-layer penalty based joint optimization (JO) algorithm is developed to jointly optimize the resource allocation coefficients within each iteration. Finally, numerical results demonstrate that: i) the proposed coexisting RIS and relay assisted transmission framework is capable of achieving a significant user performance improvement than conventional schemes without RIS or relay; ii) compared with the AO algorithm, the JO algorithm requires less execution time at the cost of a slight performance loss; and iii) the H-NOMA and F-NOMA protocols are generally preferable for ensuring user rate fairness and enhancing user sum rate, respectively.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink (DL) active and uplink (UL) backscatter communication (BackCom) framework is proposed. More particularly, a full-duplex (FD) base station (BS) communicates with the DL users via the STAR-RIS's transmission link, while exciting and receiving the information from the UL BackCom devices with the aid of the STAR-RIS's reflection link. Non-orthogonal multiple access (NOMA) is exploited in both DL and UL communications for improving the spectrum efficiency. The system weighted sum rate maximization problem is formulated for jointly optimizing the FD BS active receive and transmit beamforming, the STAR- RIS passive beamforming, and the DL NOMA decoding orders, subject to the DL user's individual rate constraint. To tackle this challenging non-convex problem, we propose an alternating optimization (AO) based algorithm for the joint active and passive beamforming design with a given DL NOMA decoding order. To address the potential high computational complexity required for exhaustive searching all the NOMA decoding orders, an efficient NOMA user ordering scheme is further developed. Finally, numerical results demonstrate that: i) compared with the baseline schemes employing conventional RISs or space division multiple access, the proposed scheme achieves higher performance gains; and ii) higher UL rate gain is obtained at a cost of DL performance degradation, as a remedy, a more flexible performance tradeoff can be achieved by introducing the STAR-RIS.

A novel hybrid active-passive reconfigurable intelligent surface (RIS) transmitter enabled downlink multi-user communication system is investigated. Specifically, RISs are exploited to serve as transmitter antennas, where each element can flexibly switch between active and passive modes to deliver information to multiple users. The system energy efficiency (EE) maximization problem is formulated by jointly optimizing the RIS element scheduling and beamforming coefficients, as well as the power allocation coefficients, subject to the user's individual rate requirement and the maximum RIS amplification power constraint. Using the Dinkelbach relaxation, the original mixed-integer nonlinear programming problem is transformed into a nonfractional optimization problem with a two-layer structure, which is solved by the alternating optimization approach. In particular, an exhaustive search method is proposed to determine the optimal operating mode for each RIS element. Then, the RIS beamforming and power allocation coefficients are properly designed in an alternating manner. To overcome the potentially high complexity caused by exhaustive searching, we further develop a joint RIS element mode and beamforming optimization scheme by exploiting the Big-M formulation technique. Numerical results validate that: 1) The proposed hybrid RIS scheme yields higher EE than the baseline multi-antenna schemes employing fully active/passive RIS or conventional radio frequency chains; 2) Both proposed algorithms are effective in improving the system performance, especially the latter can achieve precise design of RIS elements with low complexity; and 3) For a fixed-size hybrid RIS, maximum EE can be reaped by setting only a minority of elements to operate in the active mode.

Network meta-analysis (NMA) is a useful tool to compare multiple interventions simultaneously in a single meta-analysis, it can be very helpful for medical decision making when the study aims to find the best therapy among several active candidates. However, the validity of its results is threatened by the publication bias issue. Existing methods to handle the publication bias issue in the standard pairwise meta-analysis are hard to extend to this area with the complicated data structure and the underlying assumptions for pooling the data. In this paper, we aimed to provide a flexible inverse probability weighting (IPW) framework along with several t-type selection functions to deal with the publication bias problem in the NMA context. To solve these proposed selection functions, we recommend making use of the additional information from the unpublished studies from multiple clinical trial registries. A comprehensive numerical study and a real example showed that our methodology can help obtain more accurate estimates and higher coverage probabilities, and improve other properties of an NMA (e.g., ranking the interventions).

The summary receiver operating characteristic (SROC) curve has been recommended as one important meta-analytical summary to represent the accuracy of a diagnostic test in the presence of heterogeneous cutoff values. However, selective publication of diagnostic studies for meta-analysis can induce publication bias (PB) on the estimate of the SROC curve. Several sensitivity analysis methods have been developed to quantify PB on the SROC curve, and all these methods utilize parametric selection functions to model the selective publication mechanism. The main contribution of this article is to propose a new sensitivity analysis approach that derives the worst-case bounds for the SROC curve by adopting nonparametric selection functions under minimal assumptions. The estimation procedures of the worst-case bounds use the Monte Carlo method to obtain the SROC curves along with the corresponding area under the curves in the worst case where the maximum possible PB under a range of marginal selection probabilities is considered. We apply the proposed method to a real-world meta-analysis to show that the worst-case bounds of the SROC curves can provide useful insights for discussing the robustness of meta-analytical findings on diagnostic test accuracy.

In estimating the average treatment effect in observational studies, the influence of confounders should be appropriately addressed. To this end, the propensity score is widely used. If the propensity scores are known for all the subjects, bias due to confounders can be adjusted by using the inverse probability weighting (IPW) by the propensity score. Since the propensity score is unknown in general, it is usually estimated by the parametric logistic regression model with unknown parameters estimated by solving the score equation under the strongly ignorable treatment assignment (SITA) assumption. Violation of the SITA assumption and/or misspecification of the propensity score model can cause serious bias in estimating the average treatment effect. To relax the SITA assumption, the IPW estimator based on the outcome-dependent propensity score has been successfully introduced. However, it still depends on the correctly specified parametric model and its identification. In this paper, we propose a simple sensitivity analysis method for unmeasured confounders. In the standard practice, the estimating equation is used to estimate the unknown parameters in the parametric propensity score model. Our idea is to make inference on the average causal effect by removing restrictive parametric model assumptions while still utilizing the estimating equation. Using estimating equations as constraints, which the true propensity scores asymptotically satisfy, we construct the worst-case bounds for the average treatment effect with linear programming. Different from the existing sensitivity analysis methods, we construct the worst-case bounds with minimal assumptions. We illustrate our proposal by simulation studies and a real-world example., Comment: 16 pages, 5 tables, 2 figures

In the analysis of prognosis studies with time-to-event outcomes, dichotomization of patients is often made. As the evaluations of prognostic capacity, the survivals of groups with high/low expression of the biomarker are often estimated by the Kaplan-Meier method, and the difference between groups is summarized via the hazard ratio (HR). The high/low expressions are usually determined by study-specific cutoff values, which brings heterogeneity over multiple prognosis studies and difficulty to synthesizing the results in a simple way. In meta-analysis of diagnostic studies with binary outcomes, the summary receiver operating characteristics (SROC) analysis provides a useful cutoff-free summary over studies. Recently, this methodology has been extended to the time-dependent SROC analysis for time-to-event outcomes in meta-analysis of prognosis studies. In this paper, we propose a sensitivity analysis method for evaluating the impact of publication bias on the time-dependent SROC analysis. Our proposal extends the recently introduced sensitivity analysis method for meta-analysis of diagnostic studies based on the bivariate normal model on sensitivity and specificity pairs. To model the selective publication process specific to prognosis studies, we introduce a trivariate model on the time-dependent sensitivity and specificity and the log-transformed HR. Based on the proved asymptotic property of the trivariate model, we introduce a likelihood based sensitivity analysis method based on the conditional likelihood constrained by the expected proportion of published studies. We illustrate the proposed sensitivity analysis method through the meta-analysis of Ki67 for breast cancer. Simulation studies are conducted to evaluate the performance of the proposed method., Comment: 22 pages, 6 tables, 2 figures

Publication bias is a major concern in conducting systematic reviews and meta-analyses. Various sensitivity analysis or bias-correction methods have been developed based on selection models and they have some advantages over the widely used bias-correction method of the trim-and-fill method. However, likelihood methods based on selection models may have difficulty in obtaining precise estimates and reasonable confidence intervals or require a complicated sensitivity analysis process. In this paper, we develop a simple publication bias adjustment method utilizing information on conducted but still unpublished trials from clinical trial registries. We introduce an estimating equation for parameter estimation in the selection function by regarding the publication bias issue as a missing data problem under missing not at random. With the estimated selection function, we introduce the inverse probability weighting (IPW) method to estimate the overall mean across studies. Furthermore, the IPW versions of heterogeneity measures such as the between-study variance and the I2 measure are proposed. We propose methods to construct asymptotic confidence intervals and suggest intervals based on parametric bootstrapping as an alternative. Through numerical experiments, we observed that the estimators successfully eliminate biases and the confidence intervals had empirical coverage probabilities close to the nominal level. On the other hand, the asymptotic confidence interval is much wider in some scenarios than the bootstrap confidence interval. Therefore, the latter is recommended for practical use., Comment: 50 pages, 3 figures