1
|
Ollier E. Fast selection of nonlinear mixed effect models using penalized likelihood. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2021.107373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
2
|
Ollier E, Blanchard P, Le Teuff G, Michiels S. Penalized Poisson model for network meta-analysis of individual patient time-to-event data. Stat Med 2021; 41:340-355. [PMID: 34710951 DOI: 10.1002/sim.9240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 10/14/2021] [Accepted: 10/15/2021] [Indexed: 12/15/2022]
Abstract
Network meta-analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modeling of covariates or covariate-treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate-by-treatment interactions or nonproportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time-to-event data. A major difficulty is to jointly account for between-trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time-to-event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma.
Collapse
Affiliation(s)
- Edouard Ollier
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Université Paris-Saclay, Villejuif, France.,Oncostat U1018, Inserm, Université Paris-Saclay, Labeled Ligue Contre le Cancer, Villejuif, France.,SAINBIOSE U1059, Equipe DVH, Université Jean Monnet, Saint-Etienne, France
| | - Pierre Blanchard
- Oncostat U1018, Inserm, Université Paris-Saclay, Labeled Ligue Contre le Cancer, Villejuif, France.,Département de Radiothérapie, Gustave Roussy, Université Paris-Saclay, Villejuif, France
| | - Gwénaël Le Teuff
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Université Paris-Saclay, Villejuif, France.,Oncostat U1018, Inserm, Université Paris-Saclay, Labeled Ligue Contre le Cancer, Villejuif, France
| | - Stefan Michiels
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Université Paris-Saclay, Villejuif, France.,Oncostat U1018, Inserm, Université Paris-Saclay, Labeled Ligue Contre le Cancer, Villejuif, France
| |
Collapse
|
3
|
Tang L, Song PXK. Poststratification fusion learning in longitudinal data analysis. Biometrics 2020; 77:914-928. [PMID: 32683671 DOI: 10.1111/biom.13333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 05/12/2020] [Accepted: 07/08/2020] [Indexed: 11/28/2022]
Abstract
Stratification is a very commonly used approach in biomedical studies to handle sample heterogeneity arising from, for examples, clinical units, patient subgroups, or missing-data. A key rationale behind such approach is to overcome potential sampling biases in statistical inference. Two issues of such stratification-based strategy are (i) whether individual strata are sufficiently distinctive to warrant stratification, and (ii) sample size attrition resulted from the stratification may potentially lead to loss of statistical power. To address these issues, we propose a penalized generalized estimating equations approach to reducing the complexity of parametric model structures due to excessive stratification. Specifically, we develop a data-driven fusion learning approach for longitudinal data that improves estimation efficiency by integrating information across similar strata, yet still allows necessary separation for stratum-specific conclusions. The proposed method is evaluated by simulation studies and applied to a motivating example of psychiatric study to demonstrate its usefulness in real world settings.
Collapse
Affiliation(s)
- Lu Tang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Peter X-K Song
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| |
Collapse
|
4
|
|