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31.
Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long‐range connections. This notion has prompted the quickly developing family of connectivity‐based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in‐vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies. Hum Brain Mapp 36:4771–4792, 2015. © 2015 Wiley Periodicals, Inc. 相似文献
32.
针对混合励磁同步电动机在高速区的弱磁运行特点,首先推导出其弱磁调速过程中保持平稳运行的电流变化关系,然后在此基础上提出了一种采用模糊控制与粒子群优化算法分阶段电流调节的效率最优控制方法。该控制方法的基本思想是,当电机处于弱磁调速过渡阶段时,通过模糊控制器对电流进行初步调节,使其迅速起动或进行状态调整,获得较高的动态性能;当电机进入稳态运行阶段后,以铜耗最小化为目标,采用粒子群优化算法对电流进行进一步调整,使其实现效率最优化控制。最后,通过仿真与实验结果分析验证了上述控制方法的有效性。 相似文献
33.
34.
Two problems that arise in making causal inferences for nonmortality outcomes such as bronchopulmonary dysplasia (BPD) are unmeasured confounding and censoring by death, ie, the outcome is observed only when subjects survive. In randomized experiments with noncompliance and no censoring by death, instrumental variable (IV) methods can be used to control for the unmeasured confounding. But, when there is censoring by death, the average causal treatment effect cannot be identified under usual assumptions but can be studied for a specific subpopulation by using sensitivity analysis with additional assumptions. However, evaluating the local average treatment effect (LATE) in observational studies with censoring by death problems while controlling for unmeasured confounding is not well studied. We develop a novel sensitivity analysis method based on IV models for studying the LATE. Specifically, we present the identification results under an additional assumption and propose a three-step procedure for the LATE estimation. Also, we propose an improved two-step procedure by simultaneously estimating the instrument propensity score (ie, the probability of instrument given covariates) and the parameters induced by the assumption. We show with simulation studies that the two-step procedure can be more robust and efficient than the three-step procedure. Finally, we apply our sensitivity analysis methods to a study on the effect of delivery at high-level neonatal intensive care units on the risk of BPD. 相似文献
35.
Ellen C. Caniglia James M. Robins Lauren E. Cain Caroline Sabin Roger Logan Sophie Abgrall Michael J. Mugavero Sonia Hernández-Díaz Laurence Meyer Remonie Seng Daniel R. Drozd George R. Seage III Fabrice Bonnet Fabien Le Marec Richard D. Moore Peter Reiss Ard van Sighem William C. Mathews Inma Jarrín Belén Alejos Steven G. Deeks Roberto Muga Stephen L. Boswell Elena Ferrer Joseph J. Eron John Gill Antonio Pacheco Beatriz Grinsztejn Sonia Napravnik Sophie Jose Andrew Phillips Amy Justice Janet Tate Heiner C. Bucher Matthias Egger Hansjakob Furrer Jose M. Miro Jordi Casabona Kholoud Porter Giota Touloumi Heidi Crane Dominique Costagliola Michael Saag Miguel A. Hernán 《Statistics in medicine》2019,38(13):2428-2446
Decisions about when to start or switch a therapy often depend on the frequency with which individuals are monitored or tested. For example, the optimal time to switch antiretroviral therapy depends on the frequency with which HIV-positive individuals have HIV RNA measured. This paper describes an approach to use observational data for the comparison of joint monitoring and treatment strategies and applies the method to a clinically relevant question in HIV research: when can monitoring frequency be decreased and when should individuals switch from a first-line treatment regimen to a new regimen? We outline the target trial that would compare the dynamic strategies of interest and then describe how to emulate it using data from HIV-positive individuals included in the HIV-CAUSAL Collaboration and the Centers for AIDS Research Network of Integrated Clinical Systems. When, as in our example, few individuals follow the dynamic strategies of interest over long periods of follow-up, we describe how to leverage an additional assumption: no direct effect of monitoring on the outcome of interest. We compare our results with and without the “no direct effect” assumption. We found little differences on survival and AIDS-free survival between strategies where monitoring frequency was decreased at a CD4 threshold of 350 cells/μl compared with 500 cells/μl and where treatment was switched at an HIV-RNA threshold of 1000 copies/ml compared with 200 copies/ml. The “no direct effect” assumption resulted in efficiency improvements for the risk difference estimates ranging from an 7- to 53-fold increase in the effective sample size. 相似文献
36.
Estelina S. M. Capistrano Erica E. M. Moodie Alexandra M. Schmidt 《Statistics in medicine》2019,38(13):2447-2466
We develop a Bayesian approach to estimate the average treatment effect on the treated in the presence of confounding. The approach builds on developments proposed by Saarela et al in the context of marginal structural models, using importance sampling weights to adjust for confounding and estimate a causal effect. The Bayesian bootstrap is adopted to approximate posterior distributions of interest and avoid the issue of feedback that arises in Bayesian causal estimation relying on a joint likelihood. We present results from simulation studies to estimate the average treatment effect on the treated, evaluating the impact of sample size and the strength of confounding on estimation. We illustrate our approach using the classic Right Heart Catheterization data set and find a negative causal effect of the exposure on 30-day survival, in accordance with previous analyses of these data. We also apply our approach to the data set of the National Center for Health Statistics Birth Data and obtain a negative effect of maternal smoking during pregnancy on birth weight. 相似文献
37.
Alioune Sall Karine Aubé Xavier Trudel Chantal Brisson Denis Talbot 《Statistics in medicine》2019,38(17):3168-3183
Marginal structural models (MSMs) allow estimating the causal effect of a time-varying exposure on an outcome in the presence of time-dependent confounding. The parameters of MSMs can be estimated utilizing an inverse probability of treatment weight estimator under certain assumptions. One of these assumptions is that the proposed causal model relating the outcome to exposure history is correctly specified. However, in practice, the true model is unknown. We propose a test that employs the observed data to attempt validating the assumption that the model is correctly specified. The performance of the proposed test is investigated with a simulation study. We illustrate our approach by estimating the effect of repeated exposure to psychosocial stressors at work on ambulatory blood pressure in a large cohort of white-collar workers in Québec City, Canada. Code examples in SAS and R are provided to facilitate the implementation of the test. 相似文献
38.
Baldur P. Magnusson Heinz Schmidli Nicolas Rouyrre Daniel O. Scharfstein 《Statistics in medicine》2019,38(23):4761-4771
The treatment effect in subgroups of patients is often of interest in randomized controlled clinical trials, as this may provide useful information on how to treat which patients best. When a specific subgroup is characterized by the absence of certain events that happen postrandomization, a naive analysis on the subset of patients without these events may be misleading. The principal stratification framework allows one to define an appropriate causal estimand in such settings. Statistical inference for the principal stratum estimand hinges on scientifically justified assumptions, which can be included with Bayesian methods through prior distributions. Our motivating example is a large randomized placebo-controlled trial of siponimod in patients with secondary progressive multiple sclerosis. The primary objective of this trial was to demonstrate the efficacy of siponimod relative to placebo in delaying disability progression for the whole study population. However, the treatment effect in the subgroup of patients who would not relapse during the trial is relevant from both a scientific and patient perspective. Assessing this subgroup treatment effect is challenging as there is strong evidence that siponimod reduces relapses. We describe in detail the scientific question of interest, the principal stratum estimand, the corresponding analysis method for binary endpoints, and sensitivity analyses. Although our work is motivated by a randomized clinical trial, the approach has broader appeal and could be adapted for observational studies. 相似文献
39.
Jennifer A. Hutcheon Susan Moskosky Cande V. Ananth Olga Basso Peter A. Briss Cynthia D. Ferré Brittni N. Frederiksen Sam Harper Sonia Hernández‐Díaz Ashley H. Hirai Russell S. Kirby Mark A. Klebanoff Laura Lindberg Sunni L. Mumford Heidi D. Nelson Robert W. Platt Lauren M. Rossen Alison M. Stuebe Marie E. Thoma Catherine J. Vladutiu Katherine A. Ahrens 《Paediatric and perinatal epidemiology》2019,33(1):O15-O24
40.
Lu Mao 《Statistics in medicine》2019,38(19):3628-3641
Rodent survival-sacrifice experiments are routinely conducted to assess the tumor-inducing potential of a certain exposure or drug. Because most tumors under study are impalpable, animals are examined at death for evidence of tumor formation. In some studies, the cause of death is ascertained by a pathologist to account for possible correlation between tumor development and death. Existing methods for survival-sacrifice data with cause-of-death information have been restricted to multi-group testing or one-sample estimation of tumor onset distribution and thus do not provide a natural way to quantify treatment effect or dose-response relationship. In this paper, we propose semiparametric regression methods under the popular proportional hazards model for both tumor onset and tumor-caused death. For inference, we develop a maximum pseudo-likelihood estimation procedure using a modified iterative convex minorant algorithm, which is guaranteed to converge to the unique maximizer of the objective function. Simulation studies under different tumor rates show that the new methods provide valid inference on the covariate-outcome relationship and outperform alternative approaches. A real study investigating the effects of benzidine dihydrochloride on liver tumor in mice is analyzed as an illustration. 相似文献