Franti?ek Barto?Quentin F. GronauBram TimmersWillem M. Otte...
19页
查看更多>>摘要：We outline a Bayesian model‐averaged (BMA) meta‐analysis for standardized mean differences in order to quantify evidence for both treatment effectiveness δ and across‐study heterogeneity τ. We construct four competing models by orthogonally combining two present‐absent assumptions, one for the treatment effect and one for across‐study heterogeneity. To inform the choice of prior distributions for the model parameters, we used 50% of the Cochrane Database of Systematic Reviews to specify rival prior distributions for δ and τ. The relative predictive performance of the competing models and rival prior distributions was assessed using the remaining 50% of the Cochrane Database. On average, ?1r—the model that assumes the presence of a treatment effect as well as across‐study heterogeneity—outpredicted the other models, but not by a large margin. Within ?1r, predictive adequacy was relatively constant across the rival prior distributions. We propose specific empirical prior distributions, both for the field in general and for each of 46 specific medical subdisciplines. An example from oral health demonstrates how the proposed prior distributions can be used to conduct a BMA meta‐analysis in the open‐source software R and JASP. The preregistered analysis plan is available at https://osf.io/zs3df/.
Minchun ZhouWarren D. TaylorHakmook KangBrian D. Boyd...
15页
查看更多>>摘要：Abstract Conventional regions of interest (ROIs)—level resting state fMRI (functional magnetic resonance imaging) response analyses do not rigorously model the underlying spatial correlation within each ROI. This can result in misleading inference. Moreover, they tend to estimate the temporal covariance matrix with the assumption of stationary time series, which may not always be valid. To overcome these limitations, we propose a double‐wavelet approach that simplifies temporal and spatial covariance structure because wavelet coefficients are approximately uncorrelated under mild regularity conditions. This property allows us to analyze much larger dimensions of spatial and temporal resting‐state fMRI data with reasonable computational burden. Another advantage of our double‐wavelet approach is that it does not require the stationarity assumption. Simulation studies show that our method reduced false positive and false negative rates by properly taking into account spatial and temporal correlations in data. We also demonstrate advantages of our method by using resting‐state fMRI data to study the difference in resting‐state functional connectivity between healthy subjects and patients with major depressive disorder.
查看更多>>摘要：Multiple imputation (MI) provides us with efficient estimators in model‐based methods for handling missing data under the true model. It is also well‐understood that design‐based estimators are robust methods that do not require accurately modeling the missing data; however, they can be inefficient. In any applied setting, it is difficult to know whether a missing data model may be good enough to win the bias‐efficiency trade‐off. Raking of weights is one approach that relies on constructing an auxiliary variable from data observed on the full cohort, which is then used to adjust the weights for the usual Horvitz‐Thompson estimator. Computing the optimally efficient raking estimator requires evaluating the expectation of the efficient score given the full cohort data, which is generally infeasible. We demonstrate MI as a practical method to compute a raking estimator that will be optimal. We compare this estimator to common parametric and semi‐parametric estimators, including standard MI. We show that while estimators, such as the semi‐parametric maximum likelihood and MI estimator, obtain optimal performance under the true model, the proposed raking estimator utilizing MI maintains a better robustness‐efficiency trade‐off even under mild model misspecification. We also show that the standard raking estimator, without MI, is often competitive with the optimal raking estimator. We demonstrate these properties through several numerical examples and provide a theoretical discussion of conditions for asymptotically superior relative efficiency of the proposed raking estimator.
查看更多>>摘要：Post‐GWAS analysis, in many cases, focuses on fine‐mapping targeted genetic regions discovered at GWAS‐stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for complex traits and disease outcomes using next‐generation sequencing (NGS) technologies. Large‐scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two‐phase sampling design and analysis is a cost‐reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, for example, smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual‐dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared with alternative designs while preserving type 1 error control. These results are especially evident under the more practical scenario where design values need to be defined a priori and are subject to misspecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at github.com/egosv/TwoPhase_postGWAS.
查看更多>>摘要：Variable screening plays an important role in ultra‐high‐dimensional data analysis. Most of the previous analyses have focused on individual predictor screening using marginal correlation or other rank‐based techniques. When predictors can be naturally grouped, the structure information should be incorporated while applying variable screening. This study presents a group screening procedure that is based on maximum Lq‐likelihood estimation, which is being increasingly used for robust estimation. The proposed method is robust against data contamination, including a heavy‐tailed distribution of the response and a mixture of observations from different distributions. The sure screening property is rigorously established. Simulations demonstrate the competitive performance of the proposed method, especially in terms of its robustness against data contamination. Two real data analyses are presented to further illustrate its performance.
查看更多>>摘要：Abstract This article proposes a new robust smooth‐threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. A novel working correlation matrix is proposed to capture correlations within the same subject. The proposed procedure works well when the number of covariates pn increases as the number of subjects n increases. The proposed estimates are competitive with the estimates obtained with the true correlation structure, especially when the data are contaminated. Moreover, the proposed method is robust against outliers in the response variables and/or covariates. Furthermore, the oracle properties for robust smooth‐threshold estimating equations under “large n, diverging pn” are established under some regularity conditions. Extensive simulation studies and a yeast cell cycle data are used to evaluate the performance of the proposed method, and results show that the proposed method is competitive with existing robust variable selection procedures.
查看更多>>摘要：Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM‐FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar‐on‐function regression method to include voxel‐based whole‐brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM‐FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
查看更多>>摘要：Abstract Before a new screening test can be used in routine screening, its performance needs to be compared to the standard screening test. This comparison is generally done in population screening trials with a screen‐positive design where participants undergo one or both screening tests after which disease verification takes place for those positive on at least one screening test. We consider the randomized paired screen‐positive design of Alonzo and Kittelson where participants are randomized to receive one of the two screening tests and only participants with a positive screening test subsequently receive the other screening test followed by disease verification. The tests are usually offered in an unblinded fashion in which case the screening uptake may differ between arms, in particular when one test is more burdensome than the other. When uptake is associated with disease, the estimator for the relative sensitivity derived by Alonzo and Kittelson may be biased and the type I error of the associated statistical test is no longer guaranteed to be controlled. We present methods for comparing sensitivities of screening tests in randomized paired screen‐positive trials that are robust to differential screening uptake. In a simulation study, we show that our methods adequately control the type I error when screening uptake is associated with disease. We apply the developed methods to data from the IMPROVE trial, a nonblinded cervical cancer screening trial comparing the accuracy of HPV testing on self‐collected versus provider‐collected samples. In this trial, screening uptake was higher among participants randomized to self‐collection.
查看更多>>摘要：Abstract Time‐dependent receiver operating characteristic curves are often used to evaluate the classification performance of continuous measures when considering time‐to‐event data. When one is interested in evaluating the predictive performance of multiple covariates, it is common to use the Cox proportional hazards model to obtain risk scores; however, previous work has shown that when the model is mis‐specified, the estimand corresponding to the partial likelihood estimator depends on the censoring distribution. In this manuscript, we show that when the risk score model is mis‐specified, the AUC will also depend on the censoring distribution, leading to either over‐ or under‐estimation of the risk score's predictive performance. We propose the use of censoring‐robust estimators to remove the dependence on the censoring distribution and provide empirical results supporting the use of censoring‐robust risk scores.
Xiaofei ChenDaniel F. HeitjanGerald GreilHaekyung Jeon‐Slaughter...
18页
查看更多>>摘要：Hypoplastic left heart syndrome is a congenital anomaly that is uniformly fatal in infancy without immediate treatment. The standard treatment consists of an initial Norwood procedure (stage 1) followed some months later by stage 2 palliation (S2P). The ideal timing of the S2P is uncertain. The Single Ventricle Reconstruction Trial (SVRT) randomized the procedure used in the initial Norwood operation, leaving the timing of the S2P to the discretion of the surgical team. To estimate the causal effect of the timing of S2P, we propose to impute the potential post‐S2P survival outcomes using statistical models under the Rubin Causal Model framework. With this approach, it is straightforward to estimate the causal effect of S2P timing on post‐S2P survival by directly comparing the imputed potential outcomes. Specifically, we consider a lognormal model and a restricted cubic spline model, evaluating their performance in Monte Carlo studies. When applied to the SVRT data, the models give somewhat different imputed values, but both support the conclusion that the optimal time for the S2P is at 6?months after the Norwood procedure.
NSTL
Wiley