Statistics in Medicine最新文献

Hazard models are the most commonly used tool to analyze time-to-event data. If more than one time scale is relevant for the event under study, models are required that can incorporate the dependence of a hazard along two (or more) time scales. Such models should be flexible to capture the joint influence of several time scales, and nonparametric smoothing techniques are obvious candidates. $P$ -splines offer a flexible way to specify such hazard surfaces, and estimation is achieved by maximizing a penalized Poisson likelihood. Standard observation schemes, such as right-censoring and left-truncation, can be accommodated in a straightforward manner. Proportional hazards regression with a baseline hazard varying over two time scales is presented. Efficient computation is possible by generalized linear array model (GLAM) algorithms or by exploiting a sparse mixed model formulation. A companion R-package is provided.

In medical research, individual-level patient data provide invaluable information, but the patients' right to confidentiality remains of utmost priority. This poses a huge challenge when estimating statistical models such as a linear mixed model, which is an extension of linear regression models that can account for potential heterogeneity whenever data come from different data providers. Federated learning tackles this hurdle by estimating parameters without retrieving individual-level data. Instead, iterative communication of parameter estimate updates between the data providers and analysts is required. In this article, we propose an alternative framework to federated learning for fitting linear mixed models. Specifically, our approach only requires the mean, covariance, and sample size of multiple covariates from different data providers once. Using the principle of statistical sufficiency within the likelihood framework as theoretical support, this proposed strategy achieves estimates identical to those derived from actual individual-level data. We demonstrate this approach through real data on 15 068 patient records from 70 clinics at the Children's Hospital of Pennsylvania. Assuming that each clinic only shares summary statistics once, we model the COVID-19 polymerase chain reaction test cycle threshold as a function of patient information. Simplicity, communication efficiency, generalisability, and wider scope of implementation in any statistical software distinguish our approach from existing strategies in the literature.

Partially linear models provide a valuable tool for modeling failure time data with nonlinear covariate effects. Their applicability and importance in survival analysis have been widely acknowledged. To date, numerous inference methods for such models have been developed under traditional right censoring. However, the existing studies seldom target interval-censored data, which provide more coarse information and frequently occur in many scientific studies involving periodical follow-up. In this work, we propose a flexible class of partially linear transformation models to examine parametric and nonparametric covariate effects for interval-censored outcomes. We consider the sieve maximum likelihood estimation approach that approximates the cumulative baseline hazard function and nonparametric covariate effect with the monotone splines and $B$ -splines, respectively. We develop an easy-to-implement expectation-maximization algorithm coupled with three-stage data augmentation to facilitate maximization. We establish the consistency of the proposed estimators and the asymptotic distribution of parametric components based on the empirical process techniques. Numerical results from extensive simulation studies indicate that our proposed method performs satisfactorily in finite samples. An application to a study on hypobaric decompression sickness suggests that the variable TR360 exhibits a significant dynamic and nonlinear effect on the risk of developing hypobaric decompression sickness.

Multicancer detection (MCD) tests use blood specimens to detect preclinical cancers. A major concern is overdiagnosis, the detection of preclinical cancer on screening that would not have developed into symptomatic cancer in the absence of screening. Because overdiagnosis can lead to unnecessary and harmful treatments, its quantification is important. A key metric is the screen overdiagnosis fraction (SOF), the probability of overdiagnosis at screen detection. Estimating SOF is notoriously difficult because overdiagnosis is not observed. This estimation is more challenging with MCD tests because short-term results are needed as the technology is rapidly changing. To estimate average SOF for a program of yearly MCD tests, I introduce a novel method that requires at least two yearly MCD tests given to persons having a wide range of ages and applies only to cancers for which there is no conventional screening. The method assumes an exponential distribution for the sojourn time in an operational screen-detectable preclinical cancer (OPC) state, defined as once screen-detectable (positive screen and work-up), always screen-detectable. Because this assumption appears in only one term in the SOF formula, the results are robust to violations of the assumption. An SOF plot graphs average SOF versus mean sojourn time. With lung cancer screening data and synthetic data, SOF plots distinguished small from moderate levels of SOF. With its unique set of assumptions, the SOF plot would complement other modeling approaches for estimating SOF once sufficient short-term observational data on MCD tests become available.

Clinical decisions are often guided by clinical prediction models or diagnostic tests. Decision curve analysis (DCA) combines classical assessment of predictive performance with the consequences of using these strategies for clinical decision-making. In DCA, the best decision strategy is the one that maximizes the net benefit: the net number of true positives (or negatives) provided by a given strategy. Here, we employ Bayesian approaches to DCA, addressing four fundamental concerns when evaluating clinical decision strategies: (i) which strategies are clinically useful, (ii) what is the best available decision strategy, (iii) which of two competing strategies is better, and (iv) what is the expected net benefit loss associated with the current level of uncertainty. While often consistent with frequentist point estimates, fully Bayesian DCA allows for an intuitive probabilistic interpretation framework and the incorporation of prior evidence. We evaluate the methods using simulation and provide a comprehensive case study. Software implementation is available in the bayesDCA R package. Ultimately, the Bayesian DCA workflow may help clinicians and health policymakers adopt better-informed decisions.

In epidemiological studies, evaluating the health impacts stemming from multiple exposures is one of the important goals. To analyze the effects of multiple exposures on discrete or time-to-event health outcomes, researchers often employ generalized linear models, Cox proportional hazards models, and machine learning methods. However, observational studies are prone to unmeasured confounding factors, which can introduce the potential for substantial bias in the multiple exposure effects. To address this issue, we propose a novel outcome model-based sensitivity analysis method for non-Gaussian and time-to-event outcomes with multiple exposures. All the proposed sensitivity analysis problems are formulated as linear programming problems with quadratic and linear constraints, which can be solved efficiently. Analytic solutions are provided for some optimization problems, and a numerical study is performed to examine how the proposed sensitivity analysis behaves in finite samples. We illustrate the proposed method using two real data examples.

Missing data arise in most applied settings and are ubiquitous in electronic health records (EHR). When data are missing not at random (MNAR) with respect to measured covariates, sensitivity analyses are often considered. These solutions, however, are often unsatisfying in that they are not guaranteed to yield actionable conclusions. Motivated by an EHR-based study of long-term outcomes following bariatric surgery, we consider the use of double sampling as a means to mitigate MNAR outcome data when the statistical goals are estimation and inference regarding causal effects. We describe assumptions that are sufficient for the identification of the joint distribution of confounders, treatment, and outcome under this design. Additionally, we derive efficient and robust estimators of the average causal treatment effect under a nonparametric model and under a model assuming outcomes were, in fact, initially missing at random (MAR). We compare these in simulations to an approach that adaptively estimates based on evidence of violation of the MAR assumption. Finally, we also show that the proposed double sampling design can be extended to handle arbitrary coarsening mechanisms, and derive nonparametric efficient estimators of any smooth full data functional.

Identifying interventions that are optimally tailored to each individual is of significant interest in various fields, in particular precision medicine. Dynamic treatment regimes (DTRs) employ sequences of decision rules that utilize individual patient information to recommend treatments. However, the assumption that an individual's treatment does not impact the outcomes of others, known as the no interference assumption, is often challenged in practical settings. For example, in infectious disease studies, the vaccine status of individuals in close proximity can influence the likelihood of infection. Imposing this assumption when it, in fact, does not hold, may lead to biased results and impact the validity of the resulting DTR optimization. We extend the estimation method of dynamic weighted ordinary least squares (dWOLS), a doubly robust and easily implemented approach for estimating optimal DTRs, to incorporate the presence of interference within dyads (i.e., pairs of individuals). We formalize an appropriate outcome model and describe the estimation of an optimal decision rule in the dyadic-network context. Through comprehensive simulations and analysis of the Population Assessment of Tobacco and Health (PATH) data, we demonstrate the improved performance of the proposed joint optimization strategy compared to the current state-of-the-art conditional optimization methods in estimating the optimal treatment assignments when within-dyad interference exists.

To complement the conventional area under the ROC curve (AUC) which cannot fully describe the diagnostic accuracy of some non-standard biomarkers, we introduce a transformed ROC curve and its associated transformed AUC (TAUC) in this article, and show that TAUC can relate the original improper biomarker to a proper biomarker after a non-monotone transformation. We then provide nonparametric estimation of the non-monotone transformation and TAUC, and establish their consistency and asymptotic normality. We conduct extensive simulation studies to assess the performance of the proposed TAUC method and compare with the traditional methods. Case studies on real biomedical data are provided to illustrate the proposed TAUC method. We are able to identify more important biomarkers that tend to escape the traditional screening method.

As a crucial tool in neuroscience, mediation analysis has been developed and widely adopted to elucidate the role of intermediary variables derived from neuroimaging data. Typically, structural equation models (SEMs) are employed to investigate the influences of exposures on outcomes, with model coefficients being interpreted as causal effects. While existing SEMs have proven to be effective tools for mediation analysis involving various neuroimaging-related mediators, limited research has explored scenarios where these mediators are derived from the shape space. In addition, the linear relationship assumption adopted in existing SEMs may lead to substantial efficiency losses and decreased predictive accuracy in real-world applications. To address these challenges, we introduce a novel framework for shape mediation analysis, designed to explore the causal relationships between genetic exposures and clinical outcomes, whether mediated or unmediated by shape-related factors while accounting for potential confounding variables. Within our framework, we apply the square-root velocity function to extract elastic shape representations, which reside within the linear Hilbert space of square-integrable functions. Subsequently, we introduce a two-layer shape regression model to characterize the relationships among neurocognitive outcomes, elastic shape mediators, genetic exposures, and clinical confounders. Both estimation and inference procedures are established for unknown parameters along with the corresponding causal estimands. The asymptotic properties of estimated quantities are investigated as well. Both simulated studies and real-data analyses demonstrate the superior performance of our proposed method in terms of estimation accuracy and robustness when compared to existing approaches for estimating causal estimands.