| Item Type | Journal Article |
|---|---|
| Author | Kenneth Lange |
| Author | Xun-Jian Li |
| Author | Hua Zhou |
| Date | 2025-08-04 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://www.tandfonline.com/doi/full/10.1080/00031305.2025.2526535 |
| Accessed | 8/10/2025, 4:22:59 PM |
| Pages | 1-11 |
| Publication | The American Statistician |
| DOI | 10.1080/00031305.2025.2526535 |
| Journal Abbr | The American Statistician |
| ISSN | 0003-1305, 1537-2731 |
| Date Added | 8/10/2025, 4:22:59 PM |
| Modified | 8/10/2025, 4:23:46 PM |
| Item Type | Journal Article |
|---|---|
| Author | Feng Gu |
| Author | Jiaqing Chen |
| Author | Jinjing Wang |
| Author | Yibo Long |
| Author | Xiaofan Wang |
| Author | Yangxin Huang |
| Abstract | ABSTRACT In the realm of clinical medical research, semi‐competing risks data are usually observed in practice, yet there are few studies on the joint models of longitudinal and semi‐competing risks data. In this paper, a joint model for longitudinal and semi‐competing risks data is proposed. Based on the expectile regression, a linear mixed‐effects longitudinal sub‐model is formulated, and a Cox proportional hazards survival sub‐model is considered under the framework of semi‐competing risks. The two sub‐models are linked by a shared longitudinal trajectory function. To accommodate the time‐varying relationship between the longitudinal response variable and covariates, as well as to introduce flexibility to the structural linkage between longitudinal and survival processes, we incorporate the time‐varying coefficients into the joint model in the form of nonparametric functions. The simultaneous Bayesian inference method is utilized to estimate the model parameters, which not only overcomes the convergence problem, but also improves the accuracy of the parameter estimation while effectively reducing the computational burden. The simulation studies are conducted to assess the performance of the proposed joint model and methodology. Finally, we analyze a dataset from the Multicenter AIDS Cohort Study to illustrate the real application of the proposed model and method. In both simulation studies and empirical analyses, joint modeling methods demonstrate performance that meets expected effects. |
| Date | 08/2025 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.70219 |
| Accessed | 8/10/2025, 4:35:09 PM |
| Volume | 44 |
| Pages | e70219 |
| Publication | Statistics in Medicine |
| DOI | 10.1002/sim.70219 |
| Issue | 18-19 |
| Journal Abbr | Statistics in Medicine |
| ISSN | 0277-6715, 1097-0258 |
| Date Added | 8/10/2025, 4:35:09 PM |
| Modified | 8/10/2025, 4:36:21 PM |
| Item Type | Journal Article |
|---|---|
| Author | Junhui Mi |
| Author | Rahul D. Tendulkar |
| Author | Sarah M. C. Sittenfeld |
| Author | Sujata Patil |
| Author | Emily C. Zabor |
| Abstract | ABSTRACT Methods to handle missing data have been extensively explored in the context of estimation and descriptive studies, with multiple imputation being the most widely used method in clinical research. However, in the context of clinical risk prediction models, where the goal is often to achieve high prediction accuracy and to make predictions for future patients, there are different considerations regarding the handling of missing covariate data. As a result, deterministic imputation is better suited to the setting of clinical risk prediction models, since the outcome is not included in the imputation model and the imputation method can be easily applied to future patients. In this paper, we provide a tutorial demonstrating how to conduct bootstrapping followed by deterministic imputation of missing covariate data to construct and internally validate the performance of a clinical risk prediction model in the presence of missing data. Simulation study results are provided to help guide when imputation may be appropriate in real‐world applications. |
| Date | 08/2025 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.70203 |
| Accessed | 8/10/2025, 4:31:30 PM |
| Volume | 44 |
| Pages | e70203 |
| Publication | Statistics in Medicine |
| DOI | 10.1002/sim.70203 |
| Issue | 18-19 |
| Journal Abbr | Statistics in Medicine |
| ISSN | 0277-6715, 1097-0258 |
| Date Added | 8/10/2025, 4:31:30 PM |
| Modified | 8/10/2025, 4:32:40 PM |
| Item Type | Journal Article |
|---|---|
| Author | Sinclair Awounvo |
| Author | Meinhard Kieser |
| Author | Manuel Feißt |
| Date | 8/2025 |
| Language | en |
| Short Title | Combining multiple imputation with internal model validation in clinical prediction modeling |
| Library Catalog | DOI.org (Crossref) |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S0895435625002495 |
| Accessed | 8/10/2025, 4:51:27 PM |
| Pages | 111916 |
| Publication | Journal of Clinical Epidemiology |
| DOI | 10.1016/j.jclinepi.2025.111916 |
| Journal Abbr | Journal of Clinical Epidemiology |
| ISSN | 08954356 |
| Date Added | 8/10/2025, 4:51:27 PM |
| Modified | 8/10/2025, 4:52:00 PM |
| Item Type | Journal Article |
|---|---|
| Author | Md. Belal Hossain |
| Author | Mohsen Sadatsafavi |
| Author | James C. Johnston |
| Author | Hubert Wong |
| Author | Victoria J. Cook |
| Author | Mohammad Ehsanul Karim |
| Date | 2025-08-04 |
| Language | en |
| Short Title | LASSO-Based Survival Prediction Modeling with Multiply Imputed Data |
| Library Catalog | DOI.org (Crossref) |
| URL | https://www.tandfonline.com/doi/full/10.1080/00031305.2025.2526545 |
| Accessed | 8/10/2025, 4:29:44 PM |
| Pages | 1-12 |
| Publication | The American Statistician |
| DOI | 10.1080/00031305.2025.2526545 |
| Journal Abbr | The American Statistician |
| ISSN | 0003-1305, 1537-2731 |
| Date Added | 8/10/2025, 4:29:44 PM |
| Modified | 8/10/2025, 4:30:16 PM |
| Item Type | Journal Article |
|---|---|
| Author | Zhou Tang |
| Author | Ted Westling |
| Date | 2025-10-13 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://www.tandfonline.com/doi/full/10.1080/10618600.2025.2547064 |
| Accessed | 10/15/2025, 11:24:15 AM |
| Pages | 1-12 |
| Publication | Journal of Computational and Graphical Statistics |
| DOI | 10.1080/10618600.2025.2547064 |
| Journal Abbr | Journal of Computational and Graphical Statistics |
| ISSN | 1061-8600, 1537-2715 |
| Date Added | 10/15/2025, 11:24:15 AM |
| Modified | 10/15/2025, 11:25:24 AM |
| Item Type | Journal Article |
|---|---|
| Author | Emily Shives |
| Author | Yared Gurmu |
| Author | Wonyul Lee |
| Author | Emily Morris |
| Author | Yan Wang |
| Abstract | ABSTRACT Many rare disease clinical trials are underpowered to detect a moderate treatment effect of an investigational product due to the limited number of participants available for the trials. In addition, given the complex, multisystemic nature of many rare diseases, it is challenging to confidently prespecify a single primary efficacy endpoint that is applicable to all trial participants with a heterogeneous clinical manifestation of their disease. Traditional trial designs and analysis methods often used in more common diseases to analyze the same endpoint(s) for all patients may be inefficient or impractical for a rare disease with heterogeneous clinical manifestations. To address these issues, we propose a novel trial design and analytic approach that allows for an evaluation of stratum‐specific efficacy endpoints in a broader population of participants. We develop several nonparametric global test methods that can accommodate the novel design and provide global evaluation of treatment effects. Using a case example in patients with Fabry disease, our simulation studies illustrate that the novel design evaluated using the global test methods may be more sensitive to detect a treatment effect compared to the traditional design that uses the same endpoint(s) for all patients. |
| Date | 08/2025 |
| Language | en |
| Library Catalog | DOI.org (Crossref) |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.70206 |
| Accessed | 8/10/2025, 4:37:45 PM |
| Volume | 44 |
| Pages | e70206 |
| Publication | Statistics in Medicine |
| DOI | 10.1002/sim.70206 |
| Issue | 18-19 |
| Journal Abbr | Statistics in Medicine |
| ISSN | 0277-6715, 1097-0258 |
| Date Added | 8/10/2025, 4:37:45 PM |
| Modified | 8/10/2025, 4:38:13 PM |
| Item Type | Journal Article |
|---|---|
| Author | Jeffrey D. Blume |
| Author | Lucy D’Agostino McGowan |
| Author | William D. Dupont |
| Author | Robert A. Greevy |
| Editor | Neil R. Smalheiser |
| Date | 2018-3-22 |
| Language | en |
| Short Title | Second-generation p-values |
| Library Catalog | DOI.org (Crossref) |
| URL | https://dx.plos.org/10.1371/journal.pone.0188299 |
| Accessed | 9/4/2025, 8:51:13 AM |
| Volume | 13 |
| Pages | e0188299 |
| Publication | PLOS ONE |
| DOI | 10.1371/journal.pone.0188299 |
| Issue | 3 |
| Journal Abbr | PLoS ONE |
| ISSN | 1932-6203 |
| Date Added | 9/4/2025, 8:51:13 AM |
| Modified | 9/4/2025, 8:51:41 AM |