Biometrics最新文献

When estimating heterogeneous treatment effects, missing outcome data can complicate treatment effect estimation, causing certain subgroups of the population to be poorly represented. In this work, we discuss this commonly overlooked problem and consider the impact that missing at random outcome data has on causal machine learning estimators for the conditional average treatment effect (CATE). We propose 2 de-biased machine learning estimators for the CATE, the mDR-learner, and mEP-learner, which address the issue of under-representation by integrating inverse probability of censoring weights into the DR-learner and EP-learner, respectively. We show that under reasonable conditions, these estimators are oracle efficient and illustrate their favorable performance through simulated data settings, comparing them to existing CATE estimators, including comparison to estimators that use common missing data techniques. We present an example of their application using the GBSG2 trial, exploring treatment effect heterogeneity when comparing hormonal therapies to non-hormonal therapies among breast cancer patients post surgery, and offer guidance on the decisions a practitioner must make when implementing these estimators.

Recurrent episode data frequently arise in chronic disease studies when an event of interest occurs repeatedly and each occurrence lasts for a random period of time. Understanding the heterogeneity in recurrent episode lengths can help guide dynamic and customized disease management. However, there has been relative sparse attention to methods tailored to this end. Existing approaches either do not confer direct interpretation on episode lengths or involve restrictive or unrealistic distributional assumptions, such as exchangeability of within-individual episode lengths. In this work, we propose a modeling strategy that overcomes these limitations through adopting quantile regression and sensibly incorporating time-dependent covariates. Treating recurrent episodes as clustered data, we develop an estimation procedure that properly handles the special complications, including dependent censoring, dependent truncation, and informative cluster size. Our estimation procedure is computationally simple and yields estimators with desirable asymptotic properties. Our numerical studies demonstrate the advantages of the proposed method over naive adaptations of existing approaches.

Generalized estimating equations (GEEs) are a popular statistical method for longitudinal data analysis, requiring specification of the first 2 marginal moments of the response along with a working correlation matrix to capture temporal correlations within a cluster. When it comes to prediction at future/new time points using GEEs, a standard approach adopted by practitioners and software is to base it simply on the marginal mean model. In this article, we propose an alternative approach to prediction for independent cluster GEEs. By viewing the GEE as solving an iterative working linear model, we borrow ideas from universal kriging to construct an adjusted predictor that exploits working cross-correlations between the current and new observations within the same cluster. We establish theoretical conditions for the adjusted GEE predictor to outperform the standard GEE predictor. Simulations and an application to longitudinal data on the growth of sitka spruces demonstrate that, even when we misspecify the working correlation structure, adjusted GEE predictors can achieve better performance relative to standard GEE predictors, the so-called "oracle" GEE predictor using all time points, and potentially even cluster-specific predictions from a generalized linear mixed model.

Diffusion tensor imaging (DTI) is a frequently used imaging modality to investigate white matter fiber connections of human brain. DTI provides an important tool for characterizing human brain structural organization. Common goals in DTI analysis include dimension reduction, denoising, and extraction of underlying structure networks. Blind source separation methods are often used to achieve these goals for other imaging modalities. However, there has been very limited work for multi-subject DTI data. Due to the special characteristics of the 3D diffusion tensor measured in DTI, existing methods such as standard independent component analysis (ICA) cannot be directly applied. We propose a Group Distributional ICA (G-DICA) method to fill this gap. G-DICA represents a fundamentally new blind source separation method that separates the parameters in the distribution function of the observed imaging data as a mixture of independent source signals. Decomposing multi-subject DTI using G-DICA uncovers structural networks corresponding to several major white matter fiber bundles in the brain. Through simulation studies and real data applications, the proposed G-DICA method demonstrates superior performance and improved reproducibility compared to the existing method.

Estimating species abundance under imperfect detection is a key challenge in biodiversity conservation. The N-mixture model, widely recognized for its ability to distinguish between abundance and individual detection probability without marking individuals, is constrained by its stringent closure assumption, which leads to biased estimates when violated in real-world settings. To address this limitation, we propose an extended framework based on a development of the mixed Gamma-Poisson model, incorporating a community parameter that represents the proportion of individuals consistently present throughout the survey period. This flexible framework generalizes both the zero-inflated type occupancy model and the standard N-mixture model as special cases, corresponding to community parameter values of 0 and 1, respectively. The model's effectiveness is validated through simulations and applications to real-world datasets, specifically with 5 species from the North American Breeding Bird Survey and 46 species from the Swiss Breeding Bird Survey, demonstrating its improved accuracy and adaptability in settings where strict closure may not hold.

Two-phase sampling designs are frequently applied in epidemiological studies and large-scale health surveys. In such designs, certain variables are collected exclusively within a second-phase random subsample of the initial first-phase sample, often due to factors such as high costs, response burden, or constraints on data collection or assessment. Consequently, second-phase sample estimators can be inefficient due to the diminished sample size. Model-assisted calibration methods have been used to improve the efficiency of second-phase estimators in regression analysis. However, limited literature provides valid finite population inferences of the calibration estimators that use appropriate calibration auxiliary variables while simultaneously accounting for the complex sample designs in the first- and second-phase samples. Moreover, no literature considers the "pooled design" where some covariates are measured exclusively in certain repeated survey cycles. This paper proposes calibrating the sample weights for the second-phase sample to the weighted first-phase sample based on score functions of the regression model that uses predictions of the second-phase variable for the first-phase sample. We establish the consistency of estimation using calibrated weights and provide variance estimation for the regression coefficients under the two-phase design or the pooled design nested within complex survey designs. Empirical evidence highlights the efficiency and robustness of the proposed calibration compared to existing calibration and imputation methods. Data examples from the National Health and Nutrition Examination Survey are provided.

In the realm of contemporary data analysis, the use of massive datasets has taken on heightened significance, albeit often entailing considerable demands on computational time and memory. While a multitude of existing works offer optimal subsampling methods for conducting analyses on subsamples with minimized efficiency loss, they notably lack tools for judiciously selecting the subsample size. To bridge this gap, our work introduces tools designed for choosing the subsample size. We focus on three settings: the Cox regression model for survival data with rare events, and logistic regression for both balanced and imbalanced datasets. Additionally, we present a new optimal subsampling procedure tailored to logistic regression with imbalanced data. The efficacy of these tools and procedures is demonstrated through an extensive simulation study and meticulous analyses of two sizable datasets: survival analysis of UK Biobank colorectal cancer data with about 350 million rows and logistic regression of linked birth and infant death data with about 28 million observations.

Many countries have established population-based biobanks, which are being used increasingly in epidemiological and clinical research. These biobanks offer opportunities for large-scale studies addressing questions beyond the scope of traditional clinical trials or cohort studies. However, using biobank data poses new challenges. Typically, biobank data are collected from a study cohort recruited over a defined calendar period, with subjects entering the study at various ages falling between $c_L$ and $c_U$. This work focuses on biobank data with individuals reporting disease-onset age upon recruitment, termed prevalent data, along with individuals initially recruited as healthy, and their disease onset observed during the follow-up period. We propose a novel cumulative incidence function (CIF) estimator that efficiently incorporates prevalent cases, in contrast to existing methods, providing two advantages: (1) increased efficiency and (2) CIF estimation for ages before the lower limit, $c_L$.

Clustering is widely used in biomedical research for meaningful subgroup identification. However, most existing clustering algorithms do not account for the statistical uncertainty of the resulting clusters and consequently may generate spurious clusters due to natural sampling variation. To address this problem, the Statistical Significance of Clustering (SigClust) method was developed to evaluate the significance of clusters in high-dimensional data. While SigClust has been successful in assessing clustering significance for continuous data, it is not specifically designed for discrete data, such as count data in genomics. Moreover, SigClust and its variations can suffer from reduced statistical power when applied to non-Gaussian high-dimensional data. To overcome these limitations, we propose SigClust-DEV, a method designed to evaluate the significance of clusters in count data. Through extensive simulations, we compare SigClust-DEV against other existing SigClust approaches across various count distributions and demonstrate its superior performance. Furthermore, we apply our proposed SigClust-DEV to Hydra single-cell RNA sequencing (scRNA) data and electronic health records (EHRs) of cancer patients to identify meaningful latent cell types and patient subgroups, respectively.