A randomized phase 3b study evaluating the safety and efficacy of risankizumab in adult patients with moderate-to-severe plaque psoriasis with non-pustular palmoplantar involvement.

BACKGROUND: In plaque psoriasis, palmoplantar areas are more difficult to treat. OBJECTIVE: Evaluate the safety and efficacy of risankizumab (RZB) versus placebo (PBO) for the treatment of palmoplantar psoriasis. METHODS: Patients were randomized to RZB or PBO for 16 weeks followed by RZB through week 52. The primary and secondary end points were achievement of palmoplantar Investigator's Global Assessment of "clear" or "almost clear" with ≥2-point reduction from baseline (ppIGA 0/1), achievement of ≥75%, ≥90%, and 100% improvement in Palmoplantar Psoriasis Area and Severity Index (PPASI 75, PPASI 90, PPASI 100) and achievement of static Physician Global Assessment of "clear" or "almost clear" with ≥2-point reduction from baseline (sPGA 0/1) at week 16. Safety was based on treatment-emergent adverse events. RESULTS: RZB demonstrated significant efficacy compared to PBO at week 16 in the patients achieving ppIGA 0/1 (33.3% vs 16.1% [P = .006]), PPASI 75 (42.5% vs 14.9% [P < .001]), PPASI 90 (27.6% vs 5.7% [P < .001]), sPGA 0/1 (32.2% vs 11.5% [P < .001]), and PPASI 100 (17.2% vs 1.1% [P < .001]). Results improved through week 52 with no new safety signals. LIMITATION: No biologic comparator. CONCLUSIONS: RZB demonstrated good tolerance and efficacy in palmoplantar psoriasis.

A class of computational methods to reduce selection bias when designing Phase 3 clinical trials.

When designing confirmatory Phase 3 studies, one usually evaluates one or more efficacious and safe treatment option(s) based on data from previous studies. However, several retrospective research articles reported the phenomenon of "diminished treatment effect in Phase 3" based on many case studies. Even under basic assumptions, it was shown that the commonly used estimator could substantially overestimate the efficacy of selected group(s). As alternatives, we propose a class of computational methods to reduce estimation bias and mean squared error with a broader scope of multiple treatment groups and flexibility to accommodate summary results by group as input. Based on simulation studies and a real data example, we provide practical implementation guidance for this class of methods under different scenarios. For more complicated problems, our framework can serve as a starting point with additional layers built in. Proposed methods can also be widely applied to other selection problems.

Efficacy of long-term risankizumab treatment for moderate-to-severe plaque psoriasis: Subgroup analyses by baseline characteristics and psoriatic disease manifestations through 256 weeks (LIMMitless trial).

BACKGROUND: Psoriasis is an inflammatory skin disease that impacts a heterogeneous group of patients and can have multiple clinical manifestations. Risankizumab is approved for the treatment of moderate-to-severe plaque psoriasis. OBJECTIVES: To evaluate the long-term efficacy of risankizumab according to baseline patient characteristics, and for the treatment of high-impact disease manifestations (nail, scalp and palmoplantar psoriasis), through 256 weeks of continuous treatment in the phase 3 LIMMitless study. METHODS: This subgroup analysis evaluated pooled data from patients with moderate-to-severe plaque psoriasis who were randomized to risankizumab 150 mg during two double-blind, phase 3, 52-week base studies (UltIMMa-1/2; NCT02684370/NCT02684357) and were enrolled in the phase 3 LIMMitless open-label extension study (NCT03047395). Subgroup assessments included the proportion of patients who achieved ≥90%/100% improvement in Psoriasis Area and Severity Index (PASI 90/100). Among patients with nail, scalp and/or palmoplantar psoriasis in addition to skin psoriasis, assessments included changes from baseline in and resolution of these three psoriatic manifestations. RESULTS: Overall, a numerically similar proportion of patients (N = 525) achieved PASI 90/100 through Week 256, regardless of their baseline age, sex, body mass index, weight, PASI or psoriatic arthritis status. Patients with nail, scalp and/or palmoplantar psoriasis experienced substantial improvements in manifestation-specific indices (mean improvement from baseline to Week 256 of >81%, >94% and >97%, respectively); in patients with all three manifestations (N = 121), 44.6% achieved complete clearance of these manifestations at Week 256. CONCLUSIONS: Risankizumab demonstrated generally consistent efficacy through 256 weeks across patient subgroups and showed durable long-term efficacy for psoriatic disease manifestations.

Differential effects of grazing intensity on carbon sequestration in arid versus humid grasslands across China.

Livestock grazing, as a primary utilization practice for grasslands, plays a crucial role in carbon cycling process and its budget. Whether the impacts of different grazing intensities on carbon sequestration vary with precipitation over a broad geographic scales across China's grasslands remains unclear. In the context of striving for carbon neutrality, we carried out a meta-analysis based on 156 peer-reviewed journal articles to synthesize the general impacts of different grazing intensities on carbon sequestration with different precipitations. Our results showed that light, moderate, and heavy grazing dramatically reduced the soil organic carbon stocks by 3.43 %, 13.68 %, and 16.77 % in arid grasslands, respectively (P < 0.05), while light and moderate grazing did not alter soil organic carbon stocks in humid grasslands (P > 0.05). Moreover, the change rates of soil organic carbon stocks were all tightly positively associated with those of soil water content under different grazing intensities (P < 0.05). Further analysis revealed strong positive relationships between mean annual precipitation with the change rates of above- and belowground biomasses, soil microbial biomass carbon, and soil organic carbon stocks under moderate grazing intensity (P < 0.05). These findings imply that carbon sequestration is relatively less tolerant to grazing disturbance in arid grasslands than humid grasslands, which may be primary due to the grazing-intensified water limitation for plant growth and soil microbial activities under low precipitation. Our study is of implication to predict carbon budget of China's grasslands and help adopt sustainable management to strive for carbon neutrality.

Efficacy and Safety of Risankizumab for the Treatment of Hidradenitis Suppurativa: A Phase 2, Randomized, Placebo-Controlled Trial.

INTRODUCTION: Hidradenitis suppurativa (HS) is a chronic, immune-mediated skin condition characterized by inflammatory lesions that can cause pain, impaired physical activity, and reduced quality of life. This study evaluated the efficacy and safety of risankizumab, a humanized immunoglobulin G1 monoclonal antibody that specifically inhibits interleukin 23 by binding to its p19 subunit, for the treatment of HS. METHODS: This phase II multicenter, randomized, placebo-controlled, double-blind study investigated the efficacy and safety of risankizumab in patients with moderate-to-severe HS. Patients were randomized 1:1:1 to receive subcutaneous risankizumab 180 mg; risankizumab 360 mg; or placebo at weeks 0, 1, 2, 4, and 12. Patients initially randomized to placebo received blinded risankizumab 360 mg at weeks 16, 17, and 18; patients initially randomized to risankizumab received blinded matching placebo at the same time points. From weeks 20-60, all patients received open-label risankizumab 360 mg every 8 weeks. The primary endpoint was the achievement of HS Clinical Response (HiSCR) at week 16. Safety was assessed by monitoring of treatment-emergent adverse events (TEAEs). RESULTS: A total of 243 patients were randomized (risankizumab 180 mg, n = 80; risankizumab 360 mg, n = 81; placebo, n = 82). HiSCR was achieved by 46.8% of patients with risankizumab 180 mg, 43.4% with risankizumab 360 mg, and 41.5% with placebo at week 16. The primary endpoint was not met, and the study was terminated early. Incidence of TEAEs, severe TEAEs, TEAEs considered possibly related to study drug, and TEAEs leading to discontinuation of study drug were generally low and comparable across treatment groups. CONCLUSION: Risankizumab does not appear to be an efficacious treatment for moderate-to-severe HS. Future studies to understand the complex molecular mechanisms underlying HS pathogenesis and develop improved therapies are warranted. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03926169.

Practitioners' perspectives on acupuncture treatment for postpartum depression: A qualitative study.

BACKGROUND: Acupuncture may become a treatment for postpartum depression (PPD). Currently, little is known about the use of acupuncture in the treatment of PPD from the point of view of practitioners. The aim of this study was to explore practitioners' perspectives on the treatment of PPD with acupuncture and provide suggestions for future improvement. METHODS: This study employed a qualitative descriptive method. Semistructured, open-ended interviews were conducted with 14 acupuncture practitioners from 7 hospitals via face-to-face or telephone interviews. The data were collected using interview outline from March to May 2022 and analysed using qualitative content analysis. RESULTS: In general, the use of acupuncture for treating PPD was positively regarded by practitioners. They claimed that acupuncture is both safe and helpful for breastfeeding women who are experiencing emotional discomfort and that it can alleviate a variety of somatic symptoms. The following three themes were extracted: (a) patient acceptance and compliance; (b) acupuncture as a treatment for PPD; and (c) the advantages and drawbacks of acupuncture treatment. CONCLUSION: Practitioners' optimistic outlooks demonstrated that acupuncture is a promising treatment option for PPD. However, the time cost was the most significant barrier to compliance. Future development will focus mostly on improving acupuncture equipment and the style of service.

Deep Neural Networks Guided Ensemble Learning for Point Estimation.

In modern statistics, interests shift from pursuing the uniformly minimum variance unbiased estimator to reducing mean squared error (MSE) or residual squared error. Shrinkage based estimation and regression methods offer better prediction accuracy and improved interpretation. However, the characterization of such optimal statistics in terms of minimizing MSE remains open and challenging in many problems, for example estimating treatment effect in adaptive clinical trials with pre-planned modifications to design aspects based on accumulated data. From an alternative perspective, we propose a deep neural network based automatic method to construct an improved estimator from existing ones. Theoretical properties are studied to provide guidance on applicability of our estimator to seek potential improvement. Simulation studies demonstrate that the proposed method has considerable finite-sample efficiency gain as compared with several common estimators. In the Adaptive COVID-19 Treatment Trial (ACTT) as an important application, our ensemble estimator essentially contributes to a more ethical and efficient adaptive clinical trial with fewer patients enrolled. The proposed framework can be generally applied to various statistical problems, and can be served as a reference measure to guide statistical research.

Finite-Sample Two-Group Composite Hypothesis Testing via Machine Learning.

In the problem of composite hypothesis testing, identifying the potential uniformly most powerful (UMP) unbiased test is of great interest. Beyond typical hypothesis settings with exponential family, it is usually challenging to prove the existence and further construct such UMP unbiased tests with finite sample size. For example in the COVID-19 pandemic with limited previous assumptions on the treatment for investigation and the standard of care, adaptive clinical trials are appealing due to ethical considerations, and the ability to accommodate uncertainty while conducting the trial. Although several methods have been proposed to control Type I error rates, how to find a more powerful hypothesis testing strategy is still an open question. Motivated by this problem, we propose an automatic framework of constructing test statistics and corresponding critical values via machine learning methods to enhance power in a finite sample. In this article, we particularly illustrate the performance using Deep Neural Networks (DNN) and discuss its advantages. Simulations and two case studies of adaptive designs demonstrate that our method is automatic, general and prespecified to construct statistics with satisfactory power in finite-sample. Supplemental materials are available online including R code and an R shiny app.

DL 101: Basic introduction to deep learning with its application in biomedical related fields.

Deep learning is a subfield of machine learning used to learn representations of data by successive layers. Remarkable achievements and breakthroughs have been made in image classification, speech recognition, et cetera, but the full capability of deep learning is still under exploration. As statistical researchers and practitioners, we are especially interested in leveraging and advancing deep learning techniques to address important and impactive problems in biomedical and other related fields. In this article, we provide a basic introduction to Feedforward Neural Networks (FNN) along with some intuitive explanations behind its strong functional representation. Guidance is provided on how to choose quite a few hyperparameters in neural networks for a specific problem. We further discuss several more advanced frameworks in deep learning. Some successful applications of deep learning in biomedical fields are also demonstrated. With this beginner's guide, we hope that interested readers can include deep learning in their toolbox to tackle future real-world questions and challenges.

Plant Community Traits Respond to Grazing Exclusion Duration in Alpine Meadow and Alpine Steppe on the Tibetan Plateau.

Grazing exclusion has been a primary ecological restoration practice since the implement of "Returning Grazing Land to Grassland" program in China. However, the debates on the effectiveness of grazing exclusion have kept for decades. To date, there has been still a poor understand of vegetation restoration with grazing exclusion duration in alpine meadows and alpine steppes, limiting the sustainable management of grasslands on the Tibetan Plateau. We collected data from previous studies and field surveys and conducted a meta-analysis to explore vegetation restoration with grazing exclusion durations in alpine meadows and alpine steppes. Our results showed that aboveground biomass significantly increased with short-term grazing exclusion (1-4 years) in alpine meadows, while medium-term grazing exclusion (5-8 years) in alpine steppes (P < 0.05). By contrast, belowground biomass significantly increased with medium-term grazing exclusion in alpine meadows, while short-term grazing exclusion in alpine steppes (P < 0.05). Long-term grazing exclusion significantly increased belowground biomass in both alpine meadows and alpine steppes. medium-tern, and long-term grazing exclusion (> 8 years) significantly increased species richness in alpine meadows (P < 0.05). Only long-term GE significantly increased Shannon-Wiener index in plant communities of alpine steppes. The efficiency of vegetation restoration in terms of productivity and diversity gradually decreased with increasing grazing exclusion duration. Precipitation significantly positively affected plant productivity restoration, suggesting that precipitation may be an important factor driving the differential responses of vegetation to grazing exclusion duration in alpine meadows and alpine steppes. Considering the effectiveness and efficiency of grazing exclusion for vegetation restoration, medium-term grazing exclusion are recommended for alpine meadows and alpine steppes.

Optimizing Graphical Procedures for Multiplicity Control in a Confirmatory Clinical Trial via Deep Learning.

In confirmatory clinical trials, it has been proposed to use a simple iterative graphical approach to construct and perform intersection hypotheses tests with a weighted Bonferroni-type procedure to control Type I errors in the strong sense. Given Phase II study results or other prior knowledge, it is usually of main interest to find the optimal graph that maximizes a certain objective function in a future Phase III study. In this article, we evaluate the performance of two existing derivative-free constrained methods, and further propose a deep learning enhanced optimization framework. Our method numerically approximates the objective function via feedforward neural networks (FNNs) and then performs optimization with available gradient information. It can be constrained so that some features of the testing procedure are held fixed while optimizing over other features. Simulation studies show that our FNN-based approach has a better balance between robustness and time efficiency than some existing derivative-free constrained optimization algorithms. Compared to the traditional stochastic search method, our optimizer has moderate multiplicity adjusted power gain when the number of hypotheses is relatively large. We further apply it to a case study to illustrate how to optimize a multiple testing procedure with respect to a specific study objective.

Analyzing longitudinal binary data in clinical studies.

In clinical studies, it is common to have binary outcomes collected over time as repeated measures. This manuscript reviews and evaluates two popular classes of statistical methods for analyzing binary response data with repeated measures: likelihood-based Generalized Linear Mixed Model (GLMM), and semiparametric Generalized Estimating Equation (GEE). Recommendations for choice of analysis model and points to consider for implementation in clinical studies in the presence of missing data are provided based on a comprehensive literature review, as well as, a simulation study evaluating the performance of both GLMM and GEE under scenarios representative of typical clinical trial settings. Under Missing at Random (MAR) assumption, GLMM is preferred over GEE, and the SAS PROC GLIMMIX marginal model is recommended for implementing GLMM in analyzing clinical trial data. When there is an underlying continuous variable used to define the binary response, and the missing proportion is high and/or unbalanced between treatment groups, a two-step approach combining Multiple Imputation (MI) and GEE (MI-GEE) is recommended.

Efficacy of Risankizumab versus Secukinumab in Patients with Moderate-to-Severe Psoriasis: Subgroup Analysis from the IMMerge Study.

INTRODUCTION: Patients with moderate-to-severe plaque psoriasis who experience poor clinical outcomes, including patients with obesity or prior treatment, need improved treatment options. Risankizumab specifically inhibits interleukin 23 and has demonstrated superior efficacy in active-comparator studies in patients with moderate-to-severe plaque psoriasis. We compared the efficacy of risankizumab with that of secukinumab across patient subgroups. METHODS: Subgroup analyses using data from the phase 3 IMMerge study (NCT03478787) were performed. Efficacy in adults with moderate-to-severe psoriasis treated with risankizumab 150 mg and secukinumab 300 mg was assessed as the proportion of patients who achieved ≥ 90% improvement in Psoriasis Area Severity Index (PASI 90) at week 52 across demographics and disease characteristics. Post hoc analyses evaluated the proportion of patients who achieved PASI 90 and the least-squares mean percent PASI improvement from baseline at week 52 by body weight and body mass index (BMI), PASI 90 by prior treatment, and clinical response [PASI 90, PASI 100, and/or static Physician's Global Assessment (sPGA) score of clear (0) or almost clear (1)] at week 16 and maintained particular response at week 52. Logistic regression analyses examined the effect of covariates (age, sex, BMI, baseline PASI, treatment) and potential interactions on PASI 90 at week 52. RESULTS: More patients who received risankizumab (n = 164) compared with secukinumab (n = 163) achieved PASI 90 at week 52, regardless of demographics and disease characteristics (BMI, prior treatment, disease duration, and maintenance of clinical response at week 52). Improvements in PASI were greater in patients taking risankizumab than those taking secukinumab, regardless of weight or BMI. Results from logistic regression analysis showed treatment type had a significant impact on PASI 90 (risankizumab versus secukinumab, p < 0.0001). CONCLUSION: Risankizumab showed consistently greater efficacy compared with secukinumab across different patient subgroups, and this was maintained through 52 weeks. TRIAL REGISTRATION: ClinicalTrials.gov identifier; NCT03478787.

Patients with moderate-to-severe plaque psoriasis are often unable to achieve treatment success with currently available biologic therapies when they have other conditions, such as obesity, or have previous biologic therapy exposure and/or failure. We studied patients in the IMMerge phase 3 clinical trial (NCT03478787) to assess the efficacy of risankizumab compared with secukinumab for the treatment of plaque psoriasis and to determine risankizumab's ability to remain effective after 52 weeks of administration. In our analysis, we looked across patient subgroups including patient body weight, body mass index, previous use of biologic therapies, length of time patients had been living with their disease, and the durability of risankizumab efficacy at 52 weeks. Results from our analysis showed that patients had greater success with risankizumab compared with secukinumab in treating their plaque psoriasis, despite their age, sex, race, and disease characteristics, and that risankizumab remained effective in treating plaque psoriasis at week 52. Previously reported safety results from the IMMerge clinical trial showed that there were no new concerns regarding side effects for either risankizumab or secukinumab. Overall, these results support the use of risankizumab to treat patients, including those who have other conditions or may not have had success with other therapies in treating their plaque psoriasis.

Efficacy, safety, usability, and acceptability of risankizumab 150 mg formulation administered by prefilled syringe or by an autoinjector for moderate to severe plaque psoriasis.

BACKGROUND: Risankizumab is approved for treatment of moderate to severe plaque psoriasis. Availability of a patient-controlled single self-injection of risankizumab may improve adherence and long-term management of psoriasis. OBJECTIVE: To investigate efficacy, safety, and usability of a new risankizumab 150 mg/mL formulation administered as a single subcutaneous injection via prefilled syringe (PFS) or autoinjector (AI). METHODS: Efficacy, safety, usability, and acceptability of risankizumab 150 mg/mL PFS or AI were investigated in adults with moderate to severe psoriasis in two phase 3 studies. Study 1 was a multicenter, randomized, double-blinded, placebo-controlled study that investigated 150 mg/mL risankizumab PFS; study 2 was a multicenter, single-arm, open-label study that investigated 150 mg/mL risankizumab AI. RESULTS: At week 16, risankizumab 150 mg/mL demonstrated efficacy vs. placebo (Psoriasis Area and Severity Index ≥90% improvement (PASI 90), 62.9% vs. 3.8%; static Physician Global Assessment (sPGA) 0/1, 78.1% vs. 9.6%; both p< .001) in study 1; in study 2, PASI 90 and sPGA 0/1 were 66.7%, and 81.5%, respectively. All patients successfully self-administered study treatments via PFS or AI. Acceptability of self-injection was high in both studies. Efficacy and safety of risankizumab 150 mg/mL were comparable with results from previous risankizumab phase 3 studies using the 90 mg/mL formulation. CONCLUSIONS: The efficacy, safety, and usability of 150 mg/mL risankizumab delivered as a single PFS or AI injection support use of this new formulation in patients with moderate to severe plaque psoriasis. CLINICAL TRIALS: NCT03875482 and NCT0387508.

Deep historical borrowing framework to prospectively and simultaneously synthesize control information in confirmatory clinical trials with multiple endpoints.

In current clinical trial development, historical information is receiving more attention as it provides utility beyond sample size calculation. Meta-analytic-predictive (MAP) priors and robust MAP priors have been proposed for prospectively borrowing historical data on a single endpoint. To simultaneously synthesize control information from multiple endpoints in confirmatory clinical trials, we propose to approximate posterior probabilities from a Bayesian hierarchical model and estimate critical values by deep learning to construct pre-specified strategies for hypothesis testing. This feature is important to ensure study integrity by establishing prospective decision functions before the trial conduct. Simulations are performed to show that our method properly controls family-wise error rate and preserves power as compared with a typical practice of choosing constant critical values given a subset of null space. Satisfactory performance under prior-data conflict is also demonstrated. We further illustrate our method using a case study in Immunology.

A practical response adaptive block randomization (RABR) design with analytic type I error protection.

Response adaptive randomization (RAR) is appealing from methodological, ethical, and pragmatic perspectives in the sense that subjects are more likely to be randomized to better performing treatment groups based on accumulating data. However, applications of RAR in confirmatory drug clinical trials with multiple active arms are limited largely due to its complexity, and lack of control of randomization ratios to different treatment groups. To address the aforementioned issues, we propose a Response Adaptive Block Randomization (RABR) design allowing arbitrarily prespecified randomization ratios for the control and high-performing groups to meet clinical trial objectives. We show the validity of the conventional unweighted test in RABR with a controlled type I error rate based on the weighted combination test for sample size adaptive design invoking no large sample approximation. The advantages of the proposed RABR in terms of robustly reaching target final sample size to meet regulatory requirements and increasing statistical power as compared with the popular Doubly Adaptive Biased Coin Design are demonstrated by statistical simulations and a practical clinical trial design example.

An automation-based adaptive seamless design for dose selection and confirmation with improved power and efficiency.

In a drug development program, the efficacy and safety of multiple doses can be evaluated in patients through a phase 2b dose ranging study. With a demonstrated dose response in the trial, promising doses are identified. Their effectiveness then is further investigated and confirmed in phase 3 studies. Although this two-step approach serves the purpose of the program, in general, it is inefficient because of its prolonged development duration and the exclusion of the phase 2b data in the final efficacy evaluation and confirmation which are only based on phase 3 data. To address the issue, we propose a new adaptive design, which seamlessly integrates the dose finding and confirmation steps under one pivotal study. Unlike existing adaptive seamless phase 2b/3 designs, the proposed design combines the response adaptive randomization, sample size modification, and multiple testing techniques to achieve better efficiency. The design can be easily implemented through an automated randomization process. At the end, a number of targeted doses are selected and their effectiveness is confirmed with guaranteed control of family-wise error rate.

Reprogrammable fluorescence logic sensing for biomolecules via RNA-like coenzyme A-based coordination polymer.

In this study, coenzyme A (CoA)-based coordination polymers (CPs) have been generated in situ by exploiting the reaction of thiols with metal ion (Au(III) or Ag(I)), which are dependent on both thiol-metal and aurophilic metal∙metal interaction. It is interesting to note that CPs-related biosensing capabilities toward some biomolecules including ascorbic acid (AA), cysteine (Cys) and glutathione (GSH) are also investigated via SYBR Green II (SGII)-derived fluorescence switchable mechanisms. The synthesized CPs display especial RNA-like structure and are capable of initiating the fluorescence of SGII. Conversely, AA, Cys or GSH can give rise to the structural destruction of RNA-like CPs, thus inhibiting the fluorescence signal, and quantitative detection of these biomolecules are achieved favorably with a detection limit of 7.2, 0.55 and 0.48 nM, respectively. Meanwhile, the fascinating fluorescence on-off property and simple synthetic process are employed to build a series of basic logic gates (YES, NOT, OR, AND, INHIBIT and NOR) and multiple configurable logic gates (OR-AND and OR-OR-INHIBIT) along with different logic inputs. In view of these, developing CoA-based CPs as a new material to execute logic operations provides a valuable platform to establish the next generation of advanced molecular devices for clinic diagnostic and biomedical research.

Modified Goldilocks Design with strict type I error control in confirmatory clinical trials.

Goldilocks Design (GD) utilizes predictive probability to adaptively select a trial's sample size based on accumulating data. In order to control type I error at a desired level for a subset of the null space, extensive simulations at the study design stage are required to choose critical values, which is a challenge for this type of Bayesian adaptive design to be used for confirmatory trials. In this article, we propose a Modified Goldilocks Design (MGD) where type I error is analytically controlled over the entire null space. We do so by applying the conditional invariance principle and a combination test approach on [Formula: see text]-values that are obtained from independent cohorts of subjects. Simulation studies show that despite analytic control of type I error rate, the proposed MGD has similar power when compared with the original GD. We further apply it to an example trial with time-to-event endpoint in oncology.

Reconsidering the efficiency of grazing exclusion using fences on the Tibetan Plateau.

Grazing exclusion using fences is a key policy being applied by the Chinese government to rehabilitate degraded grasslands on the Tibetan Plateau (TP) and elsewhere. However, there is a limited understanding of the effects of grazing exclusion on alpine ecosystem functions and services and its impacts on herders' livelihoods. Our meta-analyses and questionnaire-based surveys revealed that grazing exclusion with fences was effective in promoting aboveground vegetation growth for up to four years in degraded alpine meadows and for up to eight years in the alpine steppes of the TP. Longer-term fencing did not bring any ecological and economic benefits. We also found that fencing hindered wildlife movement, increased grazing pressure in unfenced areas, lowered the satisfaction of herders, and rendered substantial financial costs to both regional and national governments. We recommend that traditional free grazing should be encouraged if applicable, short-term fencing (for 4-8 years) should be adopted in severely degraded grasslands, and fencing should be avoided in key wildlife habitat areas, especially the protected large mammal species.