Spatial correlated vortex arrays.

Spatial correlated vortex arrays may form in the same beam when a random source contains multiple helical phase structures. We introduced two types of partially coherent sources with Cartesian and polar symmetric helical phase structure and reveal the characteristics of their radiated fields, respectively. It is demonstrated that far fields generated by these families of sources carry interesting features through the joint regulation of coherence and topological charge, being lattice-like vortex patterns with adjustable dimension and shape.

Is Intention to Treat Still the Gold Standard or Should Health Technology Assessment Agencies Embrace a Broader Estimands Framework?: Insights and Perspectives From the National Institute for Health and Care Excellence and Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen on the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use E9 (R1) Addendum.

OBJECTIVES: The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E9 (R1) addendum will have an important impact on the design and analysis of randomized controlled clinical trials, which represent crucial sources of evidence in health technology assessments, and on the intention-to-treat (ITT) principle in particular. This article brings together a task force of health economists and statisticians in academic institutes and the pharmaceutical industry, to examine the implications of the addendum from the perspective of the National Institute for Health and Care Excellence (NICE) and the Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG) and to address the question of whether the ITT principle should be considered the gold standard for estimating treatment effects. METHODS: We review the ITT principle, as introduced in the ICH E9 guideline. We then present an overview of the ICH E9 (R1) addendum and its estimand framework, highlighting its premise and the proposed strategies for handling intercurrent events, and examine some cases among submissions to IQWiG and NICE. RESULTS: IQWiG and NICE appear to have diverging perspectives around the relevance of the ITT principle and, in particular, the acceptance of hypothetical strategies for estimating treatment effects, as suggested by examples where the sponsor proposed an alternative approach to the ITT principle when accounting for treatment switching for interventional oncology trials. CONCLUSIONS: The ICH E9 (R1) addendum supports the use of methods that depart from the ITT principle. The relevance of estimands using these methods depends on the perspectives and objectives of payers. It is challenging to design a study that meets all stakeholders' research questions. Different estimands may serve to answer different relevant questions or decision problems.

Adaptive designs for best treatment identification with top-two Thompson sampling and acceleration.

We consider outcome adaptive phase II or phase II/III trials to identify the best treatment for further development. Different from many other multi-arm multi-stage designs, we borrow approaches for the best arm identification in multi-armed bandit (MAB) approaches developed for machine learning and adapt them for clinical trial purposes. The best arm identification in MAB focuses on the error rate of identification at the end of the trial, but we are also interested in the cumulative benefit of trial patients, for example, the frequency of patients treated with the best treatment. In particular, we consider Top-Two Thompson Sampling (TTTS) and propose an acceleration approach for better performance in drug development scenarios in which the sample size is much smaller than that considered in machine learning applications. We also propose a variant of TTTS (TTTS2) which is simpler, easier for implementation, and has comparable performance in small sample settings. An extensive simulation study was conducted to evaluate the performance of the proposed approach in multiple typical scenarios in drug development.

Propensity score matching and stratification using multiparty data without pooling.

Matching and stratification based on confounding factors or propensity scores (PS) are powerful approaches for reducing confounding bias in indirect treatment comparisons. However, implementing these approaches requires pooled individual patient data (IPD). The research presented here was motivated by an indirect comparison between a single-armed trial in acute myeloid leukemia (AML), and two external AML registries with current treatments for a control. For confidentiality reasons, IPD cannot be pooled. Common approaches to adjusting confounding bias, such as PS matching or stratification, cannot be applied as 1) a model for PS, for example, a logistic model, cannot be fitted without pooling covariate data; 2) pooling response data may be necessary for some statistical inference (e.g., estimating the SE of mean difference of matched pairs) after PS matching. We propose a set of approaches that do not require pooling IPD, using a combination of methods including a linear discriminant for matching and stratification, and secure multiparty computation for estimation of within-pair sample variance and for calculations involving multiple control sources. The approaches only need to share aggregated data offline, rather than real-time secure data transfer, as required by typical secure multiparty computation for model fitting. For survival analysis, we propose an approach using restricted mean survival time. A simulation study was conducted to evaluate this approach in several scenarios, in particular, with a mixture of continuous and binary covariates. The results confirmed the robustness and efficiency of the proposed approach. A real data example is also provided for illustration.

Random sources with rectangular coherence.

A convenient method for modeling partially coherent sources with rectangular coherence is introduced by structuring the degree of coherence as two separable arbitrary functions with arbitrary dependence of variables. The included examples have demonstrated new opportunities of modeling random sources for beam shaping applications by coherence modulation. The first example discusses a class of rectangular sinc-correlated models generating radiating fields with self-focusing features. As a second example, we introduce a new type of partially coherent vortex beams, which has a unique feature of self-rotation around the optical axis upon propagation.

Improving the performance of Bayesian logistic regression model with overdose control in oncology dose-finding studies.

An accurately identified maximum tolerated dose (MTD) serves as the cornerstone of successful subsequent phases in oncology drug development. Bayesian logistic regression model (BLRM) is a popular and versatile model-based dose-finding design. However, BLRM with original overdose control strategy has been reported to be safe but "excessively conservative." In this article, we investigate the reason for conservativeness and point out that a major reason could be the lack of appropriate underdose control. We propose designs that balance overdose and underdose control to improve the performance over the original BLRM. Simulation results reveal that the new designs have better accuracy and treat more patients at MTD.

Respiratory Muscle Training Reduces Respiratory Complications and Improves Swallowing Function After Stroke: A Systematic Review and Meta-Analysis.

OBJECTIVE: To investigate whether respiratory muscle training is capable of reducing the occurrence of respiratory complications and improving dysphagia (swallowing or cough function) after stroke. DATA SOURCES: Cochrane Library, Excerpta Medical Database (EMBASE), PUBMED, and Web of Science were searched for studies published in English; the China Biology Medicine (CBM), China Science and Technology Journal Database (VIP), China National Knowledge Infrastructure (CNKI), and Wanfang Database were searched for studies published in Chinese up to August 10, 2021. STUDY SELECTION: Eleven randomized control trials (RCTs) (N=523) met the inclusion criteria were included in this systematic review. DATA EXTRACTION: Data and information were extracted by two reviewers independently and disagreements was resolved by consensus with a third coauthor. Primary outcome was the occurrence of respiratory complications, secondary outcomes would be represented by swallowing and cough function. The quality of each included RCT were assessed by Cochrane risk-of-bias criteria and the GRADE evidence profile was provided to present information about the body of evidence and judgments about the certainty of underlying evidence for each outcome. DATA SYNTHESIS: Respiratory muscle training reduced the risk of respiratory complications (relative risk, 0.51; 95% confidence interval [CI], 0.28-0.93; I2=0%; P=.03; absolute risk difference, 0.068; number need to treat, 14.71) compared with no or sham respiratory intervention. It also decreased the liquid-type Penetration-Aspiration Scale scores by 0.81 (95% CI, -1.19 to -0.43; I2=39%; P<.0001). There was no significant association between respiratory muscle training and Functional Oral Intake Scale (FOIS) scores, cough function: increased FOIS scores by 0.47 (95% CI, -0.45 to 1.39; I2=55%; P=.32), decreased peak expiratory cough flow of voluntary cough by 18.70 L per minute (95% CI, -59.74 to 22.33; I2=19%; P=.37) and increased peak expiratory cough flow of reflex cough by 0.05 L per minute (95% CI, -40.78 to 40.87; I2=0%; P>.99). CONCLUSION: This meta-analysis provided evidence that respiratory muscle training is effective in reducing the risk of respiratory complications and improving dysphagia by reducing penetration or aspiration during swallowing liquid bolus after stroke. However, there was no sufficient evidence to determine that respiratory muscle training improves cough function. Additional multicenter studies using larger patient cohorts are required to validate and support these findings. Furthermore, long-term follow-up studies should be performed to measure outcomes, while avoiding bias due to confounding factors such as heterogeneity of the etiologies of dysphagia.

Study on Omentin-1 and miR-502-3p in osteoporotic fracture.

OBJECTIVES: To explore the expression and correlation of Omentin-1 and miR-502-3p in serum of patients with osteoporotic fracture (OPF). METHODS: Sixty OPF patients diagnosed and treated in our hospital from June 2018 to December 2019 were included in group A. Fifty-six osteoporosis patients without fractures were included in group B. Omentin-1 and miR-502-3p levels were detected by enzyme-linked immunosorbent assay (ELISA) and real-time quantitative PCR (qRT-PCR). Their predictive value for diagnostic efficiency was assessed by ROC curve. Spearman's rank correlation test was used for correlation analysis. The risk factors related to the prognosis of OPF were analyzed by Logistic univariate and multivariate analysis. RESULTS: The expression of Omentin-1 and miR-502-3p in group A was markedly lower than in group B (P<0.001). Spearman correlation analysis showed that in OPF, there was a negative correlation between serum Omentin-1 and TNF-α (r=0.8579, P<0.001), a negative correlation between serum miR-502-3p and TNF-α (r= 0.8653, P<0.001), and a positive correlation between serum Omentin-1 and miR-502-3p (r= 0.8764, P<0.001). CONCLUSIONS: Omentin-1 and miR-502-3p were down-regulated in serum of patients with OPF, both of which could be used as potential biomarkers for the diagnosis and disease evaluation of OPF.

Response-adaptive trial designs with accelerated Thompson sampling.

In a clinical trial, sometimes it is desirable to allocate as many patients as possible to the best treatment, in particular, when a trial for a rare disease may contain a considerable portion of the whole target population. The Gittins index rule is a powerful tool for sequentially allocating patients to the best treatment based on the responses of patients already treated. However, its application in clinical trials is limited due to technical complexity and lack of randomness. Thompson sampling is an appealing approach, since it makes a compromise between optimal treatment allocation and randomness with some desirable optimal properties in the machine learning context. However, in clinical trial settings, multiple simulation studies have shown disappointing results with Thompson samplers. We consider how to improve short-run performance of Thompson sampling and propose a novel acceleration approach. This approach can also be applied to situations when patients can only be allocated by batch and is very easy to implement without using complex algorithms. A simulation study showed that this approach could improve the performance of Thompson sampling in terms of average total response rate. An application to a redesign of a preference trial to maximize patient's satisfaction is also presented.

To use or not to use propensity score matching?

Propensity score matching (PSM) has been widely used to reduce confounding biases in observational studies. Its properties for statistical inference have also been investigated and well documented. However, some recent publications showed concern of using PSM, especially on increasing postmatching covariate imbalance, leading to discussion on whether PSM should be used or not. We review empirical and theoretical evidence for and against its use in practice and revisit the property of equal percent bias reduction and adapt it to more practical situations, showing that PSM has some additional desirable properties. With a small simulation, we explore the impact of caliper width on biases due to mismatching in matched samples and due to the difference between matched and target populations and show some issue of PSM may be due to inadequate caliper selection. In summary, we argue that the right question should be when and how to use PSM rather than to use or not to use it and give suggestions accordingly.

Effects of transcranial direct current stimulation with virtual reality on upper limb function in patients with ischemic stroke: a randomized controlled trial.

BACKGROUND: Non-invasive brain stimulation techniques have been shown in several studies to improve the motor recovery of the affected upper-limbs in stroke patients. This study aims to investigate whether or not cathodal transcranial direct current stimulation (c-tDCS), combined with virtual reality (VR), is superior to VR alone in reducing motor impairment and improving upper limb function and quality of life in stroke patients. METHODS: Forty patients who suffered ischemic stroke between 2 weeks to 12 months were recruited for this single-blind randomized control trial. The patients were randomly assigned either to an experimental group who receiving c-tDCS and VR, or a control group receiving sham stimulation and VR. The cathodal electrode was positioned over the primary motor cortex (M1) of the unaffected hemisphere. The treatment session consisted of 20 min of daily therapy, for 10 sessions over a 2-week period. The outcome measures were the Fugl-Meyer Upper Extremity (FM-UE), the Action Research Arm Test (ARAT) and the Barthel Index (BI). RESULTS: The two groups were comparable in demographic characteristic and motor impairment. After 2 weeks of intervention, both groups demonstrated significant improvement in FM-UE, ARAT and BI scores (P<0.05).The experiment group demonstrated more improvement in FM-UE than the control group (10.1 vs. 6.4, p = 0.003) and, ARAT (7.0 vs 3.6, p = 0.026) and BI (12.8 vs 8.5, p = 0.043). CONCLUSIONS: The findings from our study support that c-tDCS, along with VR, can facilitate a stronger beneficial effect on upper limb motor impairment, function and quality of life than VR alone in patients with ischemic stroke. TRIAL REGISTRATION: The study was registered in the Chinese Clinical Trial Registry (ChiCTR1800019386) in November 8, 2018-Retrospectively registered.

Covariate adjustment for randomized controlled trials revisited.

Covariate adjustment for the estimation of treatment effect for randomized controlled trials (RCT) is a simple approach with a long history, hence, its pros and cons have been well-investigated and published in the literature. It is worthwhile to revisit this topic since recently there has been significant investigation and development on model assumptions, robustness to model mis-specification, in particular, regarding the Neyman-Rubin model and the average treatment effect estimand. This paper discusses key results of the investigation and development and their practical implication on pharmaceutical statistics. Accordingly, we recommend that appropriate covariate adjustment should be more widely used for RCTs for both hypothesis testing and estimation.

Polyacrylic acid-coated nanoparticles loaded with recombinant tissue plasminogen activator for the treatment of mice with ischemic stroke.

Nanoparticle-based thrombolysis is a potential new treatment for stroke. The aim of this study was to investigate the efficacy of targeted thrombolysis using recombinant tissue plasminogen activator (rtPA). The rtPA was covalently bound to magnetic nanoparticles (MNP) and maintained at the target site using an external magnet. Polyacrylic acid (PAA)-coated MNP were synthesized and rtPA was then bound to the resultant PAA-MNP via carbodiimide-mediated amide bonds. For the in vitro tests, blood clots were formed in plastic centrifuge tubes with anti-coagulated plasma, thrombin and calcium chloride. For the in vivo tests, mice with ferric chloride-induced distal middle cerebral artery occlusion were treated with phosphate-buffered saline (PBS), MNP, rtPA, or MNP-rtPA (n = 6 mice per group). The binding efficacy was 80.7 ± 1.5 µg rtPA bound to 1 mg PAA-MNP. In the in vitro tests, the mean lysis percentage dramatically increased from 1.28% in the MNP group without rotation to 77.40% in the rtPA + MNP group with rotating magnetic field. The lysis efficiency of MNP-rtPA was 27.3 ± 1.3%, and it increased to 42.8 ± 2.8% with magnetic field rotation. The mean sizes of the infarct areas of the PBS, MNP, rtPA, and MNP-rtPA mouse groups were 20.09 ± 6.07, 18.28 ± 2.69, 8.65 ± 3.63 and 4.40 ± 2.46 mm3, respectively. Thus, targeted MNP-rtPA accelerated thrombolysis and reduced the infarct area in a mouse model of cerebral embolism. This approach may serve as a feasible and effective treatment for embolic cerebral ischemia.

A simple, doubly robust, efficient estimator for survival functions using pseudo observations.

Survival functions are often estimated by nonparametric estimators such as the Kaplan-Meier estimator. For valid estimation, proper adjustment for confounding factors is needed when treatment assignment may depend on confounding factors. Inverse probability weighting is a commonly used approach, especially when there is a large number of potential confounders to adjust for. Direct adjustment may also be used if the relationship between the time-to-event and all confounders can be modeled. However, either approach requires a correctly specified model for the relationship between confounders and treatment allocation or between confounders and the time-to-event. We propose a pseudo-observation-based doubly robust estimator, which is valid when either the treatment allocation model or the time-to-event model is correctly specified and is generally more efficient than the inverse probability weighting approach. The approach can be easily implemented using standard software. A simulation study was conducted to evaluate this approach under a number of scenarios, and the results are presented and discussed. The results confirm robustness and efficiency of the proposed approach. A real data example is also provided for illustration.

Solid cancer incidence among Chinese medical diagnostic x-ray workers, 1950-1995: Estimation of radiation-related risks.

The objective of this study was to estimate solid cancer risk attributable to long-term, fractionated occupational exposure to low doses of ionizing radiation. Based on cancer incidence for the period 1950-1995 in a cohort of 27,011 Chinese medical diagnostic X-ray workers and a comparison cohort of 25,782 Chinese physicians who did not use X-ray equipment in their work, we used Poisson regression to fit excess relative risk (ERR) and excess absolute risk (EAR) dose-response models for incidence of all solid cancers combined. Radiation dose reconstruction was based on a previously published method that relied on simulating measurements for multiple X-ray machines, workplaces and working conditions, information about protective measures, including use of lead aprons, and work histories. The resulting model was used to estimate calendar year-specific badge dose calibrated as personal dose equivalent (Sv). To obtain calendar year-specific colon doses (Gy), we applied a standard organ conversion factor. A total of 1,643 cases of solid cancer were identified in 1.45 million person-years of follow-up. In both ERR and EAR models, a statistically significant radiation dose-response relationship was observed for solid cancers as a group. Averaged over both sexes, and using colon dose as the dose metric, the estimated ERR/Gy was 0.87 (95% CI: 0.48, 1.45), and the EAR was 22 per 10(4)PY-Gy (95% CI: 14, 32) at age 50. We obtained estimates of the ERR and EAR of solid cancers per unit dose that are compatible with those derived from other populations chronically exposed to low dose-rate occupational or environmental radiation.

mTOR signaling inhibition modulates macrophage/microglia-mediated neuroinflammation and secondary injury via regulatory T cells after focal ischemia.

Signaling by the mammalian target of rapamycin (mTOR) plays an important role in the modulation of both innate and adaptive immune responses. However, the role and underlying mechanism of mTOR signaling in poststroke neuroinflammation are largely unexplored. In this study, we injected rapamycin, a mTOR inhibitor, by the intracerebroventricular route 6 h after focal ischemic stroke in rats. We found that rapamycin significantly reduced lesion volume and improved behavioral deficits. Notably, infiltration of γδ T cells and granulocytes, which are detrimental to the ischemic brain, was profoundly reduced after rapamycin treatment, as was the production of proinflammatory cytokines and chemokines by macrophages and microglia. Rapamycin treatment prevented brain macrophage polarization toward the M1 type. In addition, we also found that rapamycin significantly enhanced anti-inflammation activity of regulatory T cells (Tregs), which decreased production of proinflammatory cytokines and chemokines by macrophages and microglia. Depletion of Tregs partially elevated macrophage/microglia-induced neuroinflammation after stroke. Our data suggest that rapamycin can attenuate secondary injury and motor deficits after focal ischemia by enhancing the anti-inflammation activity of Tregs to restrain poststroke neuroinflammation.

Modelling PK/QT relationships from Phase I dose-escalation trials for drug combinations and developing quantitative risk assessments of clinically relevant QT prolongations.

In current industry practice, it is difficult to assess QT effects at potential therapeutic doses based on Phase I dose-escalation trials in oncology due to data scarcity, particularly in combinations trials. In this paper, we propose to use dose-concentration and concentration-QT models jointly to model the exposures and effects of multiple drugs in combination. The fitted models then can be used to make early predictions for QT prolongation to aid choosing recommended dose combinations for further investigation. The models consider potential correlation between concentrations of test drugs and potential drug-drug interactions at PK and QT levels. In addition, this approach allows for the assessment of the probability of QT prolongation exceeding given thresholds of clinical significance. The performance of this approach was examined via simulation under practical scenarios for dose-escalation trials for a combination of two drugs. The simulation results show that invaluable information of QT effects at therapeutic dose combinations can be gained by the proposed approaches. Early detection of dose combinations with substantial QT prolongation is evaluated effectively through the CIs of the predicted peak QT prolongation at each dose combination. Furthermore, the probability of QT prolongation exceeding a certain threshold is also computed to support early detection of safety signals while accounting for uncertainty associated with data from Phase I studies. While the prediction of QT effects is sensitive to the dose escalation process, the sensitivity and limited sample size should be considered when providing support to the decision-making process for further developing certain dose combinations. Copyright © 2016 John Wiley & Sons, Ltd.