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1.
Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints. 相似文献
2.
Annette Kopp-Schneider Silvia Calderazzo Manuel Wiesenfarth 《Biometrical journal. Biometrische Zeitschrift》2020,62(2):361-374
In the era of precision medicine, novel designs are developed to deal with flexible clinical trials that incorporate many treatment strategies for multiple diseases in one trial setting. This situation often leads to small sample sizes in disease-treatment combinations and has fostered the discussion about the benefits of borrowing of external or historical information for decision-making in these trials. Several methods have been proposed that dynamically discount the amount of information borrowed from historical data based on the conformity between historical and current data. Specifically, Bayesian methods have been recommended and numerous investigations have been performed to characterize the properties of the various borrowing mechanisms with respect to the gain to be expected in the trials. However, there is common understanding that the risk of type I error inflation exists when information is borrowed and many simulation studies are carried out to quantify this effect. To add transparency to the debate, we show that if prior information is conditioned upon and a uniformly most powerful test exists, strict control of type I error implies that no power gain is possible under any mechanism of incorporation of prior information, including dynamic borrowing. The basis of the argument is to consider the test decision function as a function of the current data even when external information is included. We exemplify this finding in the case of a pediatric arm appended to an adult trial and dichotomous outcome for various methods of dynamic borrowing from adult information to the pediatric arm. In conclusion, if use of relevant external data is desired, the requirement of strict type I error control has to be replaced by more appropriate metrics. 相似文献
3.
Nadim Ballout Lola Etievant Vivian Viallon 《Biometrical journal. Biometrische Zeitschrift》2023,65(5):2200047
Cross-validation is the standard method for hyperparameter tuning, or calibration, of machine learning algorithms. The adaptive lasso is a popular class of penalized approaches based on weighted L1-norm penalties, with weights derived from an initial estimate of the model parameter. Although it violates the paramount principle of cross-validation, according to which no information from the hold-out test set should be used when constructing the model on the training set, a “naive” cross-validation scheme is often implemented for the calibration of the adaptive lasso. The unsuitability of this naive cross-validation scheme in this context has not been well documented in the literature. In this work, we recall why the naive scheme is theoretically unsuitable and how proper cross-validation should be implemented in this particular context. Using both synthetic and real-world examples and considering several versions of the adaptive lasso, we illustrate the flaws of the naive scheme in practice. In particular, we show that it can lead to the selection of adaptive lasso estimates that perform substantially worse than those selected via a proper scheme in terms of both support recovery and prediction error. In other words, our results show that the theoretical unsuitability of the naive scheme translates into suboptimality in practice, and call for abandoning it. 相似文献
4.
A method is proposed that aims at identifying clusters of individuals that show similar patterns when observed repeatedly. We consider linear‐mixed models that are widely used for the modeling of longitudinal data. In contrast to the classical assumption of a normal distribution for the random effects a finite mixture of normal distributions is assumed. Typically, the number of mixture components is unknown and has to be chosen, ideally by data driven tools. For this purpose, an EM algorithm‐based approach is considered that uses a penalized normal mixture as random effects distribution. The penalty term shrinks the pairwise distances of cluster centers based on the group lasso and the fused lasso method. The effect is that individuals with similar time trends are merged into the same cluster. The strength of regularization is determined by one penalization parameter. For finding the optimal penalization parameter a new model choice criterion is proposed. 相似文献
5.
Hoi Min Ng Binyan Jiang Kin Yau Wong 《Biometrical journal. Biometrische Zeitschrift》2023,65(1):2100139
Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right-censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study. 相似文献
6.
Many approaches for variable selection with multiply imputed data in the development of a prognostic model have been proposed. However, no method prevails as uniformly best. We conducted a simulation study with a binary outcome and a logistic regression model to compare two classes of variable selection methods in the presence of MI data: (I) Model selection on bootstrap data, using backward elimination based on AIC or lasso, and fit the final model based on the most frequently (e.g. ) selected variables over all MI and bootstrap data sets; (II) Model selection on original MI data, using lasso. The final model is obtained by (i) averaging estimates of variables that were selected in any MI data set or (ii) in 50% of the MI data; (iii) performing lasso on the stacked MI data, and (iv) as in (iii) but using individual weights as determined by the fraction of missingness. In all lasso models, we used both the optimal penalty and the 1‐se rule. We considered recalibrating models to correct for overshrinkage due to the suboptimal penalty by refitting the linear predictor or all individual variables. We applied the methods on a real dataset of 951 adult patients with tuberculous meningitis to predict mortality within nine months. Overall, applying lasso selection with the 1‐se penalty shows the best performance, both in approach I and II. Stacking MI data is an attractive approach because it does not require choosing a selection threshold when combining results from separate MI data sets 相似文献
7.
Stella Erdmann Dominic Edelmann Meinhard Kieser 《Biometrical journal. Biometrische Zeitschrift》2023,65(6):2200023
The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants’ fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design—the prediction design—may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/ ) facilitates the application of the proposed methods, while a real-world application example illustrates them. 相似文献
8.
Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are often available from historical studies. Motivated by an application to Helicobacter pylori infection, we show how to borrow strength from such data to improve efficiency of inference in the clinical trial. Under an exchangeability assumption about the potential outcome mean, we show that the semiparametric efficiency bound for estimating the average treatment effect can be reduced by incorporating both the clinical trial data and external controls. We then derive a doubly robust and locally efficient estimator. The improvement in efficiency is prominent especially when the external control data set has a large sample size and small variability. Our method allows for a relaxed overlap assumption, and we illustrate with the case where the clinical trial only contains a treated group. We also develop doubly robust and locally efficient approaches that extrapolate the causal effect in the clinical trial to the external population and the overall population. Our results also offer a meaningful implication for trial design and data collection. We evaluate the finite-sample performance of the proposed estimators via simulation. In the Helicobacter pylori infection application, our approach shows that the combination treatment has potential efficacy advantages over the triple therapy. 相似文献
9.
10.
Chuoxin Ma Chunyu Wang Jianxin Pan 《Biometrical journal. Biometrische Zeitschrift》2023,65(2):2100334
In cardiovascular disease studies, a large number of risk factors are measured but it often remains unknown whether all of them are relevant variables and whether the impact of these variables is changing with time or remains constant. In addition, more than one kind of cardiovascular disease events can be observed in the same patient and events of different types are possibly correlated. It is expected that different kinds of events are associated with different covariates and the forms of covariate effects also vary between event types. To tackle these problems, we proposed a multistate modeling framework for the joint analysis of multitype recurrent events and terminal event. Model structure selection is performed to identify covariates with time-varying coefficients, time-independent coefficients, and null effects. This helps in understanding the disease process as it can detect relevant covariates and identify the temporal dynamics of the covariate effects. It also provides a more parsimonious model to achieve better risk prediction. The performance of the proposed model and selection method is evaluated in numerical studies and illustrated on a real dataset from the Atherosclerosis Risk in Communities study. 相似文献
11.
Jinmei Chen;Lixin Li;Yuhao Feng;Shein-Chung Chow;Ming Tan;Jianhong Pan;Pingyan Chen;Ying Wu; 《Biometrical journal. Biometrische Zeitschrift》2024,66(8):e70003
External data (e.g., real-world data (RWD) and historical data) have become more readily available. This has led to rapidly increasing interest in exploring and evaluating ways of utilizing external data to facilitate traditional clinical trials (TCT), especially for rare diseases with high unmet medical needs where a TCT would be impractical and/or unethical. In this article, we focus on hybrid studies that incorporate external data into randomized clinical trials to augment the control arm and explore a complex innovative design. A sequential adaptive design conducts multiple interim assessments to improve the accuracy of estimates of agreement between external data and current data. At each interim assessment, we apply the inverse probability weighted power prior (IPW-PP) method to adaptively borrow information from external data to account for confounding and heterogeneity. The randomization ratio is dynamically adjusted during the interim assessment based on accumulatively augmented information to reduce the sample size of the current trial. Additionally, the proposed design can be extended to allow interim analyses for early efficacy/futility stopping, that is, early assessment of trial success or failure based on accumulated data, potentially reducing ineffective treatment exposure and unnecessary time and resources. The performance of the proposed method and design is evaluated via extensive simulation studies. The sequential adaptive design and IPW-PP approach having desirable properties are implemented. 相似文献
12.
13.
Harini Narayanan Lars Behle Martin F. Luna Michael Sokolov Gonzalo Guillén-Gosálbez Massimo Morbidelli Alessandro Butté 《Biotechnology and bioengineering》2020,117(9):2703-2714
In a decade when Industry 4.0 and quality by design are major technology drivers of biopharma, automated and adaptive process monitoring and control are inevitable requirements and model-based solutions are key enablers in fulfilling these goals. Despite strong advancement in process digitalization, in most cases, the generated datasets are not sufficient for relying on purely data-driven methods, whereas the underlying complex bioprocesses are still not completely understood. In this regard, hybrid models are emerging as a timely pragmatic solution to synergistically combine available process data and mechanistic understanding. In this study, we show a novel application of the hybrid-EKF framework, that is, hybrid models coupled with an extended Kalman filter for real-time monitoring, control, and automated decision-making in mammalian cell culture processing. We show that, in the considered application, the predictive monitoring accuracy of such a framework improves by at least 35% when developed with hybrid models with respect to industrial benchmark tools based on PLS models. In addition, we also highlight the advantages of this approach in industrial applications related to conditional process feeding and process monitoring. With regard to the latter, for an industrial use case, we demonstrate that the application of hybrid-EKF as a soft sensor for titer shows a 50% improvement in prediction accuracy compared with state-of-the-art soft sensor tools. 相似文献
14.
Xi Tian Wen-Hao Xu Aihetaimujiang Anwaier Hong-Kai Wang Fang-Ning Wan Da-Long Cao Wen-Jie Luo Guo-Hai Shi Yuan-Yuan Qu Hai-Liang Zhang Ding-Wei Ye 《Journal of cellular and molecular medicine》2021,25(8):3898-3911
This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large-scale multiomics analysis and real-world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis-related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI’s GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki-67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients’ prognosis using proteomic data, gene expression data and real-world data and this prognostic model showed stronger predictive value than other biomarkers (Ki-67, Beta-catenin, etc) in multi-cohorts. 相似文献
15.
Yi Long Zhijiang Du Chaofeng Chen Weidong Wang Long He Xiwang Mao Guoqiang Xu Guangyu Zhao Xiaoqi Li Wei Dong 《仿生工程学报（英文版）》2017,14(2)
An electrically actuated lower extremity exoskeleton is developed,in which only the knee joint is actuated actively while other joints linked by elastic elements are actuated passively.This paper describes the critical design criteria and presents the process of design and calculation of the actuation system.A flexible physical Human-Robot-Interaction (pHRI) measurement device is designed and applied to detect the human movement,which comprises two force sensors and two gasbags attached to the inner surface of the connection cuff.An online adaptive pHRI minimization control strategy is proposed and implemented to drive the robotic exoskeleton system to follow the motion trajectory of human limb.The measured pHRI information is fused by the Variance Weighted Average (VWA) method.The Mean Square Values (MSV) of pHRI and control torque are utilized to evaluate the performance of the exoskeleton.To improve the comfort level and reduce energy consumption,the gravity compensation is taken into consideration when the control law is designed.Finally,practical experiments are performed on healthy users.Experimental results show that the proposed system can assist people to walk and the outlined control strategy is valid and effective. 相似文献
16.
Johanna Mielke Heinz Schmidli Byron Jones 《Biometrical journal. Biometrische Zeitschrift》2018,60(3):564-582
For the approval of biosimilars, it is, in most cases, necessary to conduct large Phase III clinical trials in patients to convince the regulatory authorities that the product is comparable in terms of efficacy and safety to the originator product. As the originator product has already been studied in several trials beforehand, it seems natural to include this historical information into the showing of equivalent efficacy. Since all studies for the regulatory approval of biosimilars are confirmatory studies, it is required that the statistical approach has reasonable frequentist properties, most importantly, that the Type I error rate is controlled—at least in all scenarios that are realistic in practice. However, it is well known that the incorporation of historical information can lead to an inflation of the Type I error rate in the case of a conflict between the distribution of the historical data and the distribution of the trial data. We illustrate this issue and confirm, using the Bayesian robustified meta‐analytic‐predictive (MAP) approach as an example, that simultaneously controlling the Type I error rate over the complete parameter space and gaining power in comparison to a standard frequentist approach that only considers the data in the new study, is not possible. We propose a hybrid Bayesian‐frequentist approach for binary endpoints that controls the Type I error rate in the neighborhood of the center of the prior distribution, while improving the power. We study the properties of this approach in an extensive simulation study and provide a real‐world example. 相似文献
17.
生物自然居群间的基因流不但可以阻止遗传分化以维持物种的完整性,而且也能积极响应生物多样化的进程。理解与基因流相关的适应性进化及其内在机理将有助于我们更好地认识生物物种形成和多样化的原始动力以及真正原因。该文通过对植物种内和种间居群基因流动态进行讨论,阐述了近年来有关植物基因流动态的一些重要理论观念和研究进展,以期为相关领域动态及趋势研究提供参考。 相似文献
18.
Stability and motor adaptation in human arm movements 总被引：3，自引：0，他引：3
Burdet E Tee KP Mareels I Milner TE Chew CM Franklin DW Osu R Kawato M 《Biological cybernetics》2006,94(1):20-32
In control, stability captures the reproducibility of motions and the robustness to environmental and internal perturbations.
This paper examines how stability can be evaluated in human movements, and possible mechanisms by which humans ensure stability.
First, a measure of stability is introduced, which is simple to apply to human movements and corresponds to Lyapunov exponents.
Its application to real data shows that it is able to distinguish effectively between stable and unstable dynamics. A computational
model is then used to investigate stability in human arm movements, which takes into account motor output variability and
computes the force to perform a task according to an inverse dynamics model. Simulation results suggest that even a large
time delay does not affect movement stability as long as the reflex feedback is small relative to muscle elasticity. Simulations
are also used to demonstrate that existing learning schemes, using a monotonic antisymmetric update law, cannot compensate
for unstable dynamics. An impedance compensation algorithm is introduced to learn unstable dynamics, which produces similar
adaptation responses to those found in experiments. 相似文献
19.
条背萤幼虫水生适应性形态与游泳行为研究 总被引：2，自引：2，他引：2
研究了条背萤Luciolasubstriata幼虫的形态特征及其对游泳行为的适应。形态及扫描电镜观察发现,条背萤幼虫存在二态现象。1～2龄幼虫虫体扁平,多毛。有7对呼吸鳃,分别位于腹部第1～7节。3～6龄幼虫虫体扁平呈船形,无呼吸鳃,靠气管呼吸。二者均具有扁平桨状的足、燕尾状尾节及位于尾节末端的圆柱形粘附器官。条背萤幼虫游动时身体腹面朝上,呈仰泳姿态,足向后划水。3～6龄幼虫仰泳时足共有8种摆动姿势。幼虫仰泳时足摆动1个周期所需时间为(0.611±0.16)s。腹部末端可上下左右摆动,当幼虫向前游动时,尾部上下摆动1个周期所需时间为(1.795±0.44)s。幼虫的游泳速度为(0.85±0.16)mh。仰泳中的幼虫改变方向时,头部和尾部同时向身体的一侧弯曲,当头部与尾部呈近90°时,幼虫用力将尾部伸直,此时水产生一个反作用力继续推动幼虫转向,幼虫转向的范围为0～90°。条背萤2种类型幼虫呼吸系统的不同决定着幼虫外部形态的差异及游泳行为的不同,而导致这种呼吸系统、形态及运动行为不同的原因很可能是条背萤对环境的适应性进化。 相似文献
20.
Recent guidance from the Food and Drug Administration for the evaluation of new therapies in the treatment of type 2 diabetes (T2DM) calls for a program-wide meta-analysis of cardiovascular (CV) outcomes. In this context, we develop a new Bayesian meta-analysis approach using survival regression models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We propose a Bayesian sample size determination methodology for meta-analysis clinical trial design with a focus on controlling the type I error and power. We also propose the partial borrowing power prior to incorporate the historical survival meta data into the statistical design. Various properties of the proposed methodology are examined and an efficient Markov chain Monte Carlo sampling algorithm is developed to sample from the posterior distributions. In addition, we develop a simulation-based algorithm for computing various quantities, such as the power and the type I error in the Bayesian meta-analysis trial design. The proposed methodology is applied to the design of a phase 2/3 development program including a noninferiority clinical trial for CV risk assessment in T2DM studies. 相似文献