IU Indianapolis ScholarWorks :: Browsing by Subject "Survival analysis"

Browsing by Subject "Survival analysis"

Now showing 1 - 10 of 24

Adjusting Mortality for Loss to Follow-Up: Analysis of Five ART Programmes in Sub-Saharan Africa
(Public Library of Science, 2010-11-30) Brinkhof, Martin W. G.; Spycher, Ben D.; Yiannoutsos, Constantin; Weigel, Ralf; Wood, Robin; Messou, Eugène; Boulle, Andrew; Egger, Matthias; Sterne, Jonathan A. C.; Biostatistics, School of Public Health
Evaluation of antiretroviral treatment (ART) programmes in sub-Saharan Africa is difficult because many patients are lost to follow-up. Outcomes in these patients are generally unknown but studies tracing patients have shown mortality to be high. We adjusted programme-level mortality in the first year of antiretroviral treatment (ART) for excess mortality in patients lost to follow-up. Methods and Findings Treatment-naïve patients starting combination ART in five programmes in Côte d'Ivoire, Kenya, Malawi and South Africa were eligible. Patients whose last visit was at least nine months before the closure of the database were considered lost to follow-up. We filled missing survival times in these patients by multiple imputation, using estimates of mortality from studies that traced patients lost to follow-up. Data were analyzed using Weibull models, adjusting for age, sex, ART regimen, CD4 cell count, clinical stage and treatment programme. A total of 15,915 HIV-infected patients (median CD4 cell count 110 cells/µL, median age 35 years, 68% female) were included; 1,001 (6.3%) were known to have died and 1,285 (14.3%) were lost to follow-up in the first year of ART. Crude estimates of mortality at one year ranged from 5.7% (95% CI 4.9–6.5%) to 10.9% (9.6–12.4%) across the five programmes. Estimated mortality hazard ratios comparing patients lost to follow-up with those remaining in care ranged from 6 to 23. Adjusted estimates based on these hazard ratios ranged from 10.2% (8.9–11.6%) to 16.9% (15.0–19.1%), with relative increases in mortality ranging from 27% to 73% across programmes. Conclusions Naïve survival analysis ignoring excess mortality in patients lost to follow-up may greatly underestimate overall mortality, and bias ART programme evaluations. Adjusted mortality estimates can be obtained based on excess mortality rates in patients lost to follow-up.
Alzheimer's disease genetic risk variants beyond APOE ε4 predict mortality
(Elsevier, 2017-08-24) Mez, Jesse; Marden, Jessica R.; Mukherjee, Shubhabrata; Walter, Stefan; Gibbons, Laura E.; Gross, Alden L.; Zahodne, Laura B.; Gilsanz, Paola; Brewster, Paul; Nho, Kwangsik; Crane, Paul K.; Larson, Eric B.; Glymour, M. Maria; Radiology and Imaging Sciences, School of Medicine
• A genetic risk score from 21 non-APOE late-onset Alzheimer's disease risk variants predicts mortality. • The genetic risk score likely confers risk for mortality through its effect on dementia incidence. • Late-onset Alzheimer's disease risk loci effect estimates from genome-wide association unlikely suffer from selection bias.
Applications of Time to Event Analysis in Clinical Data
(2021-12) Xu, Chenjia; Gao, Sujuan; Liu, Hao; Zang, Yong; Zhang, Jianjun; Zhao, Yi
Survival analysis has broad applications in diverse research areas. In this dissertation, we consider an innovative application of survival analysis approach to phase I dose-finding design and the modeling of multivariate survival data. In the first part of the dissertation, we apply time to event analysis in an innovative dose-finding design. To account for the unique feature of a new class of oncology drugs, T-cell engagers, we propose a phase I dose-finding method incorporating systematic intra-subject dose escalation. We utilize survival analysis approach to analyze intra-subject dose-escalation data and to identify the maximum tolerated dose. We evaluate the operating characteristics of the proposed design through simulation studies and compare it to existing methodologies. The second part of the dissertation focuses on multivariate survival data with semi-competing risks. Time-to-event data from the same subject are often correlated. In addition, semi-competing risks are sometimes present with correlated events when a terminal event can censor other non-terminal events but not vice versa. We use a semiparametric frailty model to account for the dependence between correlated survival events and semi-competing risks and adopt penalized partial likelihood (PPL) approach for parameter estimation. In addition, we investigate methods for variable selection in semi-parametric frailty models and propose a double penalized partial likelihood (DPPL) procedure for variable selection of fixed effects in frailty models. We consider two penalty functions, least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (SCAD) penalty. The proposed methods are evaluated in simulation studies and illustrated using data from Indianapolis-Ibadan Dementia Project.
Clinicopathological and Prognostic Characteristics in Dedifferentiated/Poorly Differentiated Chordomas: A Pooled Analysis of Individual Patient Data From 58 Studies and Comparison With Conventional Chordomas
(Frontiers Media, 2021-08-13) Liu, Fu-Sheng; Zheng, Bo-Wen; Zhang, Tao-Lan; Li, Jing; Lv, Guo-Hua; Yan, Yi-Guo; Huang, Wei; Zou, Ming-Xiang; Radiation Oncology, School of Medicine
Background: Currently, the clinicopathological and prognostic characteristics of dedifferentiated chordoma (DC) and poorly differentiated chordoma (PDC) remain poorly understood. In this study, we sought to characterize clinicopathological parameters in a large PDC/DC cohort and determine their correlations with progression-free survival (PFS) and overall survival (OS) of patients. We also attempted to compare clinical features between PDC/DC and conventional chordoma (CC). Methods: Literature searches (from inception to June 01, 2020) using Medline, Embase, Google Scholar and Wanfang databases were conducted to identify eligible studies according to predefined criteria. The local database at our center was also retrospectively reviewed to include CC patients for comparative analysis. Results: Fifty-eight studies from the literature and 90 CC patients from our local institute were identified; in total, 54 PDC patients and 96 DC patients were analyzed. Overall, PDC or DC had distinct characteristics from CC, while PDC and DC shared similar clinical features. Adjuvant radiotherapy and chemotherapy were associated with both PFS and OS in PDC patients in the univariate and/or multivariate analyses. In the DC cohort, tumor resection type, adjuvant chemotherapy and tumor dedifferentiation components significantly affected PFS, whereas none of them were predictive of outcome in the multivariate analysis. By analyzing OS, we found that surgery, resection type and the time to dedifferentiation predicted the survival of DC patients; however, only surgery remained significant after adjusting for other covariables. Conclusions: These data may offer useful information to better understand the clinical characteristics of PDC/DC and may be helpful in improving the outcome prediction of patients.
Clinicopathological and Prognostic Characteristics in Spinal Chondroblastomas: A Pooled Analysis of Individual Patient Data From a Single Institute and 27 Studies
(Sage, 2023) Zheng, Bo-Wen; Huang, Wei; Liu, Fu-Sheng; Zhang, Tao-Lan; Wang, Xiao-Bin; Li, Jing; Lv, Guo-Hua; Yan, Yi-Guo; Zou, Ming-Xiang; Radiation Oncology, School of Medicine
Study design: Retrospective pooled analysis of individual patient data. Objectives: Spinal chondroblastoma (CB) is a very rare pathology and its clinicopathological and prognostic features remain unclear. Here, we sought to characterize the clinicopathological data of a large spinal CB cohort and determine factors affecting the local recurrence-free survival (LRFS) and overall survival (OS) of patients. Methods: Electronic searches using Medline, Embase, Google Scholar and Wanfang databases were performed to identify eligible studies per predefined criteria. A retrospective review was also conducted to include additional patients at our center. Results: Twenty-seven studies from the literature and 8 patients from our local institute were identified, yielding a total of 61 patients for analysis. Overall, there were no differences in clinicopathological characteristics between the local and literature cohorts, except for absence or presence of spinal canal invasion by tumor on imagings and chicken-wire calcification in tumor tissues. Univariate Kaplan-Meier analysis revealed that previous treatment, preoperative or postoperative neurological deficits, type of tumor resection, secondary aneurysmal bone cyst (ABC), chicken-wire calcification and radiotherapy correlated closely with LRFS, though only type of tumor resection, chicken-wire calcification and radiotherapy were predictive of outcome based on multivariate Cox analysis. Analyzing OS, we found that a history of preoperative treatment, concurrent ABC, chicken-wire calcification, type of tumor resection and adjuvant radiotherapy had a significant association with survival, whereas only type of tumor resection remained statistically significant after adjusting for other covariables. Conclusion: These data may be helpful in prognostic risk stratification and individualized therapy decision making for patients.
Cox-sMBPLS: An Algorithm for Disease Survival Prediction and Multi-Omics Module Discovery Incorporating Cis-Regulatory Quantitative Effects
(Frontiers Media, 2021-08-02) Vahabi, Nasim; McDonough, Caitrin W.; Desai, Ankit A.; Cavallari, Larisa H.; Duarte, Julio D.; Michailidis, George; Medicine, School of Medicine
Background: The development of high-throughput techniques has enabled profiling a large number of biomolecules across a number of molecular compartments. The challenge then becomes to integrate such multimodal Omics data to gain insights into biological processes and disease onset and progression mechanisms. Further, given the high dimensionality of such data, incorporating prior biological information on interactions between molecular compartments when developing statistical models for data integration is beneficial, especially in settings involving a small number of samples. Results: We develop a supervised model for time to event data (e.g., death, biochemical recurrence) that simultaneously accounts for redundant information within Omics profiles and leverages prior biological associations between them through a multi-block PLS framework. The interactions between data from different molecular compartments (e.g., epigenome, transcriptome, methylome, etc.) were captured by using cis-regulatory quantitative effects in the proposed model. The model, coined Cox-sMBPLS, exhibits superior prediction performance and improved feature selection based on both simulation studies and analysis of data from heart failure patients. Conclusion: The proposed supervised Cox-sMBPLS model can effectively incorporate prior biological information in the survival prediction system, leading to improved prediction performance and feature selection. It also enables the identification of multi-Omics modules of biomolecules that impact the patients' survival probability and also provides insights into potential relevant risk factors that merit further investigation.
Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations
(BMC, 2020) Huang, Zhi; Johnson, Travis S.; Han, Zhi; Helm, Bryan; Cao, Sha; Zhang, Chi; Salama, Paul; Rizkalla, Maher; Yu, Christina Y.; Cheng, Jun; Xiang, Shunian; Zhan, Xiaohui; Zhang, Jie; Huang, Kun; Medicine, School of Medicine
Background: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. Methods: In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. Results: All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. Conclusions: Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.
Exercise Induced Changes of Vital Signs in Adults with Sickle Cell Disease
(Wiley, 2021) Johnson, Solomon; Gordeuk, Victor R.; Machado, Roberto; Gibbs, J. Simon R.; Hildesheim, Mariana; Little, Jane A.; Kato, Gregory J.; Gladwin, Mark T.; Nouraie, Mehdi; Medicine, School of Medicine
The six-minute walk test (6MWT) has been used in patients with sickle cell disease (SCD), in conjunction with tricuspid regurgitant velocity (TRV) and plasma N-terminal pro-brain natriuretic peptide (NT-pro BNP), to assess risk of having pulmonary hypertension. Exercise-induced vital sign changes (VSCs) are predictors of clinical outcomes in other diseases. In this study, we assess the predictors and prognostic value of 6MWT VSC in adult SCD patients. Data from a multinational study of SCD patients (Treatment of Pulmonary Hypertension with Sildenafil: walk-PHaSST) were used to calculate the 6MWT VSC. Predictors of VSC were identified by a multivariable analysis, and a survival analysis was conducted by the Cox proportional hazard method. An increase in heart rate was observed in 90% of the 630 SCD adults, 77% of patients had an increase in systolic blood pressure (SBP), and 50% of patients had a decrease in oxygen saturation. TRV (odds ratio [OR] = 1.82, p = .020), absolute reticulocyte count (OR = 1.03, p < .001), and hemoglobin (OR = 0.99, p = .035) predicted oxygen desaturation ≥ 3% during the 6MWT. In the adjusted analysis, SBP increase during the 6MWT was associated with improved survival (hazards ratio = 0.3, 95% confidence interval: 0.1-0.8). Increases in heart rate and blood pressure, as well as oxygen desaturation, are common in adults with SCD during the 6MWT. VSC is associated with markers of anemia and TRV and can be used for risk stratification. Any increase in SBP during the 6MWT was associated with improved survival and may be indicative of a patient's ability to increase stroke volume.
A Gamma-frailty proportional hazards model for bivariate interval-censored data
(Elsevier, 2018-12) Gamage, Prabhashi W. Withana; McMahan, Christopher S.; Wang, Lianming; Tu, Wanzhu; Biostatistics, School of Public Health
Correlated survival data naturally arise from many clinical and epidemiological studies. For the analysis of such data, the Gamma-frailty proportional hazards (PH) model is a popular choice because the regression parameters have marginal interpretations and the statistical association between the failure times can be explicitly quantified via Kendall’s tau. Despite their popularity, Gamma-frailty PH models for correlated interval-censored data have not received as much attention as analogous models for right-censored data. A Gamma-frailty PH model for bivariate interval-censored data is presented and an easy to implement expectation–maximization (EM) algorithm for model fitting is developed. The proposed model adopts a monotone spline representation for the purposes of approximating the unknown conditional cumulative baseline hazard functions, significantly reducing the number of unknown parameters while retaining modeling flexibility. The EM algorithm was derived from a data augmentation procedure involving latent Poisson random variables. Extensive numerical studies illustrate that the proposed method can provide reliable estimation and valid inference, and is moreover robust to the misspecification of the frailty distribution. To further illustrate its use, the proposed method is used to analyze data from an epidemiological study of sexually transmitted infections.
Genetic Polymorphisms in ADRB2 and ADRB1 Are Associated with Differential Survival in Heart Failure Patients Taking β-Blocker
(Springer Nature, 2022) Guerra, Leonardo A.; Lteif, Christelle; Arwood, Meghan J.; McDonough, Caitrin W.; Dumeny, Leanne; Desai, Ankit A.; Cavallari, Larisa H.; Duarte, Julio D.; Medicine, School of Medicine
Single nucleotide polymorphisms (SNPs) have been associated with differential beta-blocker (BB) effects on heart rate, blood pressure, and left ventricular ejection fraction in various patient populations. This study aimed to determine if SNPs previously associated with BB response are also associated with differential survival in heart failure (HF) patients receiving BBs. HF patient data were derived from electronic health records and the Social Security Death Index. Associations and interactions between BB dose, SNP genotype, and the outcome of death were assessed using a Cox proportional-hazard model adjusting for covariates known to be associated with differential survival in HF patients. Two SNPs, ADRB1 Arg389Gly and ADRB2 Glu27Gln, displayed significant interactions (Pint = 0.043 and Pint = 0.017, respectively) with BB dose and their association with mortality. Our study suggests that ADRB2 27Glu and ADRB1 389Arg may confer a larger survival benefit with higher BB doses in patients with HF.

Browsing by Subject "Survival analysis"

Results Per Page

Sort Options