We propose an improved estimation method for the destructive cure rate model by introducing a generic maximum likelihood algorithm, the sequential quadratic Hamiltonian (SQH) scheme, which employs a gradient-free optimization approach. The SQH algorithm is applied to the destructive cure model with exponentially weighted Poisson competing risks, and its performance is evaluated through a comprehensive simulation study. Specifically, we compare the model-fitting accuracy of SQH with the recently developed conjugate gradient line search (CGLS) algorithm. Given that the CGLS method has been shown to outperform the widely used expectation-maximization algorithm and other optimization routines available in R (e.g. optim, nlm, and Rcgmin), our focus is on assessing whether the SQH algorithm can offer further improvements. Simulation results show that the SQH algorithm yields parameter estimates with consistently lower bias and root mean square error, resulting in more accurate and precise cure rate estimation. Furthermore, due to its gradient-free nature, the SQH algorithm requires less CPU time than CGLS. These advantages position the SQH algorithm as a preferred estimation method over CGLS for the destructive cure rate model. To demonstrate its practical utility, we apply the SQH algorithm to a well-known melanoma dataset and present the analysis results.
Using historical controls for clinical trial data analysis may increase statistical power and reduce required sample sizes when studying rare diseases. Most existing literature on borrowing information from historical controls with survival outcomes studied right-censored data. However, events of interest are often confirmed during routine follow-up visits or when patients display symptoms, leading to the recording of events after their occurrence. Treating these recorded events as an exact time rather than as interval-censored data may introduce bias into the assessment of treatment effect. When combining data sources, differential patterns of assessment timing may impact the comparability of the data and contribute to bias unless properly accounted for. Additionally, historical controls are often obtained by matching with clinical trial data, or which existing commensurate prior models have not been explored. To address these issues, we propose commensurate prior models with random effects for matched interval-censored survival data. These models effectively borrow information based on the degree of comparability between historical controls and clinical trial data. Simulation studies demonstrate the model's ability to control Type I error well, where the two data sources exhibit exchangeability or non-exchangeability. We apply our proposed methods by reanalyzing data from BMT CTN 1101 and BMT CTN 0901, incorporating corresponding historical datasets.
In this paper, we explore unsupervised clustering algorithms on real-world single-cell RNA-sequencing datasets. While single-cell RNA sequencing technologies have revolutionized the ability to profile gene expression at the resolution of individual cells, providing unprecedented insights into cellular heterogeneity and revealing previously undetectable rare cell subpopulations, the high dimensionality, sparsity, and noise inherent to scRNA-seq data pose significant challenges for traditional clustering methods in accurately delineating distinct cell types, states, and trajectories from the transcriptomic profiles. Conventional algorithms often struggle to capture the complex structure and geometry of single-cell gene expression landscapes and can be sensitive to the curse of dimensionality or fail to account for non-linear manifold structures underlying the data. In this study, we propose random sampling-based consensus clustering framework with the manifold-based spectral clustering to predict cluster numbers. Furthermore, the geodesic distance measure and two weighting measures are integrated into the generally used similarity kernel to enhance clustering performance on scRNA-seq data. We found that, when applied to scRNA-seq datasets with different types of variations, the geodesic distance-based similarity kernel with a discrete weighting measure performs best on datasets having discrete variations.
A Bayesian approach for variable selection is developed for use in models with a misclassified binary predictor variable. We define the main outcome model containing the latent predictor, the measurement model associated with the prevalence of the predictor, and the sensitivity and specificity models of the fallible classifier conditioned on the true value of the predictor. We use binary indicator variables to execute the Gibbs sampler-based variable selection process, and we identify the highest posterior probability model given the data. We demonstrate the performance of the procedure in several simulation studies, and we utilize the selection method to optimize model performance in two datasets.
In this article, we propose a marginal structural ordinal logistic regression model (MS-OLRM) to assess treatment effects on ordinal outcomes. Many statistical methods have been developed to estimate average treatment effect (ATE) when the outcome is continuous or binary. The methodology for assessing the effect of treatment for an ordinal outcome is less studied. To address this, we propose utilizing a superiority score as a measure of treatment effect, assessing whether the outcome under treatment is stochastically larger than the outcome under control. Our approach involves employing MS-OLRM in conjunction with Inverse Probability of Treatment Weighting (IPTW) to estimate the superiority score under treatment compared to the control. This methodology adjusts for confounding factors between treatment and outcome by utilizing IPTW, ensuring that all covariates are balanced among different treatment groups in the weighted sample. To assess the performance of the proposed method, we conduct extensive simulation studies. Finally, we apply the developed method to assess the treatment effects of medications and behavioral therapies on patients' recovery from alcohol use disorders using the Kentucky Medicaid 2012-2019 database.
