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Journal articles on the topic 'Survival analysis studies'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Yang, Liu, and Kristiaan Pelckmans. "Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer." International Journal of Machine Learning and Computing 4, no. 6 (2014): 483–90. http://dx.doi.org/10.7763/ijmlc.2014.v6.459.

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2

Langova, Katerina. "SURVIVAL ANALYSIS FOR CLINICAL STUDIES." Biomedical Papers 152, no. 2 (December 1, 2008): 303–7. http://dx.doi.org/10.5507/bp.2008.048.

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3

Tachmazidou, Ioanna, Toby Andrew, Claudio J. Verzilli, Michael R. Johnson, and Maria De Iorio. "Bayesian survival analysis in genetic association studies." Bioinformatics 24, no. 18 (July 9, 2008): 2030–36. http://dx.doi.org/10.1093/bioinformatics/btn351.

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4

Chen, D. G., and Y. L. Lio. "Comparative Studies on Frailties in Survival Analysis." Communications in Statistics - Simulation and Computation 37, no. 8 (August 27, 2008): 1631–46. http://dx.doi.org/10.1080/03610910802061727.

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5

Cnaan, Avital, and Louise Ryan. "Survival analysis in natural history studies of disease." Statistics in Medicine 8, no. 10 (October 1989): 1255–68. http://dx.doi.org/10.1002/sim.4780081009.

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6

BULL, KATE, and DAVID J. SPIEGELHALTER. "TUTORIAL IN BIOSTATISTICS SURVIVAL ANALYSIS IN OBSERVATIONAL STUDIES." Statistics in Medicine 16, no. 9 (May 15, 1997): 1041–74. http://dx.doi.org/10.1002/(sici)1097-0258(19970515)16:9<1041::aid-sim506>3.0.co;2-f.

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7

Pollock, Kenneth H., Scott R. Winterstein, Christine M. Bunck, and Paul D. Curtis. "Survival Analysis in Telemetry Studies: The Staggered Entry Design." Journal of Wildlife Management 53, no. 1 (January 1989): 7. http://dx.doi.org/10.2307/3801296.

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8

Shih, Joanna H., and Nilanjan Chatterjee. "Analysis of Survival Data from Case-Control Family Studies." Biometrics 58, no. 3 (September 2002): 502–9. http://dx.doi.org/10.1111/j.0006-341x.2002.00502.x.

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9

Kosińska, Magdalena, and Anita Szwed. "Application of Survival Analysis in Studies of Human Ontogeny." Applied Mathematics 05, no. 11 (2014): 1697–704. http://dx.doi.org/10.4236/am.2014.511162.

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10

Khayyat, A., M. Asad, A. Aslam, R. Rabbani, S. Fabara, S. Chandramohan, N. Unachukwu, et al. "Neuroendocrine tumors and Survival- a meta-analysis." American Journal of Clinical Pathology 156, Supplement_1 (October 1, 2021): S57. http://dx.doi.org/10.1093/ajcp/aqab191.117.

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Abstract Introduction/Objective Neuroendocrine tumors (NET) are a rare group of epithelial neoplasm present in gastrointestinal tract (GI) (67.5%), bronchopulmonary tree (25.3-30%) and in 15% cases primary sites cannot be identified. Although endoscopic screening, improvement in pathological techniques and early detection have shown improvement in NET survival rates, the prognosis is very poor. In this study we aimed to evaluate the effect of Gastrointestinal pancreatic NETs (GEP NETs) grade on overall survival. Methods/Case Report We searched observational studies describing the overall survival or prognostic factors of primary GEP NETs from May 2011 -May 2021 following PRISMA guidelines. Studies describing the effect of primary grade 3 GEP NETs on overall survival were included. Meta-analysis was performed and pooled hazard ratio and their 95% confidence interval (95%CI) were obtained. The forest plots were created using random effects models and sensitivity analysis was performed to account for the heterogeneity. Results (if a Case Study enter NA) Seven studies with 7692 confirmed patients were included. In our meta-analysis grade 3 GEP NET were associated with higher odds of poor survival (pooled HR: 2.73; 95% CI: 1.36–5.47; p = 0.005), with 92% heterogeneity between studies (p < 0.0001). To account for heterogeneity, sensitivity analysis was performed by removing two outlying studies (Fathi et al. and Foubert et al.) on funnel plots. The results after sensitivity analysis did not change and still showed significant association of grade 3 with poor survival (pooled HR: 4.53; 95% CI: 3.54–5.78; p < 0.00001), with no heterogeneity between studies (p = 0.72; I2 = 0%). Conclusion Our meta-analysis found that grade 3 GEP NETs is associated with poor survival and additional future studies are needed to identify other risk factors associated with poor survival in GEP NETs to improve the mortality.

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11

Liu, Qin, and Ru-liang Zhang. "Survival Analysis of Intelligent Society." Proceedings 47, no. 1 (May 18, 2020): 42. http://dx.doi.org/10.3390/proceedings2020047042.

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As a breakthrough in the manner of human survival, the future intelligent society gives the development of our survival infinite opportunities and prospects. However, it also inevitably gives us a lot of new problems. Intelligent society makes individual survival viability face the danger of degradation. Human survival experiences and feelings may be confronted with many psychological troubles and other more serious problems. Therefore, this paper studies the positive and negative effects of the future intelligent society and puts forward reasonable countermeasures to eliminate the possible disorder and crime in the intelligent society from the perspective of morality and the rule of law.

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Liu, Qin, and Ru-liang Zhang. "Survival Analysis of Intelligent Society." Proceedings 47, no. 1 (May 18, 2020): 42. http://dx.doi.org/10.3390/proceedings47010042.

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As a breakthrough in the manner of human survival, the future intelligent society gives the development of our survival infinite opportunities and prospects. However, it also inevitably gives us a lot of new problems. Intelligent society makes individual survival viability face the danger of degradation. Human survival experiences and feelings may be confronted with many psychological troubles and other more serious problems. Therefore, this paper studies the positive and negative effects of the future intelligent society and puts forward reasonable countermeasures to eliminate the possible disorder and crime in the intelligent society from the perspective of morality and the rule of law.

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13

Hosseini, Behnaz. "Women’s survival through social media: A narrative analysis." Asian Journal of Women's Studies 25, no. 2 (April 3, 2019): 180–97. http://dx.doi.org/10.1080/12259276.2019.1610612.

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14

Meira-Machado, Luís, Carla Moreira, Gustavo Soutinho, and Marta Azevedo. "Analysis of Survival Data with Multiple Events." WSEAS TRANSACTIONS ON MATHEMATICS 21 (December 31, 2022): 854–63. http://dx.doi.org/10.37394/23206.2022.21.97.

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An important aim in biomedical studies is to study how an intermediate event and prognostic factors influence the course of disease of a patient. In most cases, the effect of the intermediate event is considered a timedependent covariate and studied using extensions of the Cox proportional hazards model. Additionally, many of these studies often involve several endpoints, making the traditional approaches much more complicated. In such cases, multi-state models provide a useful tool to describe the survival process. This article aims to illustrate how multi-state models can be used as an alternative to traditional approaches. It also aims to offer guidelines for the correct use of these approaches through the analysis of survival data of patients with breast cancer. Several analyses were performed, and methods to evaluate the effect of covariates on transition intensities and to test some usual assumptions are discussed. Tree-based survival models, like the Cox proportional hazards models, are popular methods for constructing a prediction model in the field of medical research. We also present the results obtained by applying some tree-based models to the breast cancer data while showing their interpretation and utility. An overview of available software and software developed by the authors is provided to aid researchers in choosing an appropriate software tool for their purposes.

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15

Fang, Hong-Bin, Tong Tong Wu, Aaron P. Rapoport, and Ming Tan. "Survival analysis with functional covariates for partial follow-up studies." Statistical Methods in Medical Research 25, no. 6 (July 11, 2016): 2405–19. http://dx.doi.org/10.1177/0962280214523586.

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Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received stem cell transplants and T cell infusions after the transplants. The absolute lymphocyte cell counts were collected serially during hospitalization. Those patients are still followed up if they are alive after hospitalization, while their absolute lymphocyte cell counts cannot be measured after that. Another complication is that absolute lymphocyte cell counts are sparsely and irregularly measured. The conventional method using Cox model with time-varying covariates is not applicable because of the different lengths of observation periods. Analysis based on each single observation obviously underutilizes available information and, more seriously, may yield misleading results. This so-called partial follow-up study design represents increasingly common predictive modeling problem where we have serial multiple biomarkers up to a certain time point, which is shorter than the total length of follow-up. We therefore propose a solution to the partial follow-up design. The new method combines functional principal components analysis and survival analysis with selection of those functional covariates. It also has the advantage of handling sparse and irregularly measured longitudinal observations of covariates and measurement errors. Our analysis based on functional principal components reveals that it is the patterns of the trajectories of absolute lymphocyte cell counts, instead of the actual counts, that affect patient’s disease-free survival time.

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Harkanen, T., M. A. Larmas, J. I. Virtanen, and E. Arjas. "Applying Modern Survival Analysis Methods to Longitudinal Dental Caries Studies." Journal of Dental Research 81, no. 2 (February 1, 2002): 144–48. http://dx.doi.org/10.1177/154405910208100212.

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Härkänen, T., M. A. Larmas, J. I. Virtanen, and E. Arjas. "Applying Modern Survival Analysis Methods to Longitudinal Dental Caries Studies." Journal of Dental Research 81, no. 2 (February 2002): 144–48. http://dx.doi.org/10.1177/0810144.

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18

Lao, Chang S. "Statistical considerations for survival analysis from medical device clinical studies." Journal of Biopharmaceutical Statistics 5, no. 2 (January 1, 1995): 159–70. http://dx.doi.org/10.1080/10543409508835105.

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19

Park, Donghyun. "Application of Survival Analysis to CTD Risk Assessment." Proceedings of the Human Factors Society Annual Meeting 36, no. 10 (October 1992): 783–87. http://dx.doi.org/10.1177/154193129203601031.

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This paper demonstrates the application of survival models to CTD (Cumulative Trauma Disorder) studies. Survival analysis techniques are usually applied to the analysis of prospective epidemiological studies examining related risk factors. However, survival models have never been used in CTD etiology, perhaps due to mathematical complexities imposed on survival analysis techniques and its interpretation. The other reason might be the fact that it has not been considered as serious as other diseases usually studied in epidemiology. However, the conditions and assumptions of survival models fit completely into CTD studies (the existence of concomitant variables, a heterogeneous study population, censored observations etc.). Thus it is inappropriate to analyze CTD problems (specifically etiology and prevention) using typical statistical technique (ANOVA or ordinary regression). In this study, 143 subjects participated from an automobile carpet manufacturing plant which was experiencing a high number of CTD cases (107 cases, 1988–1989) and two groups of potential risk factors were examined. They were mainly categorized into personal and job-related information. This information was collected through survey questionnaires and video-taping. As the first step of analysis, survival, hazard and probability density functions were estimated. The estimated survival function shows that CTD incidence rate remained relatively constant, fluctuating 5–15%, through the first 12 years. Also, univariate associations between survival time and individual risk factors were tested using log rank and Wilcoxen rank test. Finally, the CTD data was fitted to ‘Proportional Hazard Model' (the most generalized survival model with distribution-free baseline hazard function). This model explains 75% (R2=0.75) of CTD data with the following covariates; cycle time per part, number of meals a day, dominant hand, general attitude, hand posture, degree of physical fitness, hobby and job title. The feasibility of the Proportional Hazard Modeling was investigated by test and plot. The test was conducted under the global null hypothesis about the significance of the overall model and t vs. log(-log(S(t))) was plotted to check with the assumption of proportionality. Both results ascertain the feasibility of Proportional Hazard Modeling for CTD studies.

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20

Aisyi, Mururul, Ayu Hutami Syarif, Nur Asih Anggraeni, Adhitya Bayu Perdana, Hutomo Rezky, Agus Kosasih, and Achmad Basuki. "Survival Analysis in Pediatric Osteosarcoma." Indonesian Journal of Cancer 13, no. 2 (July 13, 2019): 21. http://dx.doi.org/10.33371/ijoc.v13i2.622.

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Background: Osteosarcoma is the most common bone malignancy in childhood and adolescence. Despite significant advances in diagnostic and therapeutic modalities, osteosarcoma has generally poor prognosis. Several studies highlighted the prognostic significance of demographic and clinical parameters in the pediatric population. Controversy exists about which obvious factors of mortality in some institutions. Meanwhile, little is known about the overall survival (OS) of pediatric osteosarcoma in Dharmais Cancer Hospital.The aim of this study is to estimate the OS in pediatric osteosarcoma.Methods: This retrospective cohort study enrolled 41 children with osteosarcoma during the period of January 2010-September 2017. Patients were selected using inclusion and exclusion criteria. Overall survival (OS) and patient variables were plotted using Kaplan-Meier.Results: Our result showed that all patients admitted to our hospital in advanced stages (IIB and III). Their median OS was 23 months (12.3-33.7 months) with the survival probability was 29.4%.Conclusions: Median OS of pediatric osteosarcoma in our institution was 23 months and the survival probability was 29.4%. It can be used as evidence showing lack of cancer awareness and early detection in our country.

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21

Lotan, Yair, Jeffrey A. Cadeddu, J. Jack Lee, Claus G. Roehrborn, and Scott M. Lippman. "Implications of the Prostate Cancer Prevention Trial: A Decision Analysis Model of Survival Outcomes." Journal of Clinical Oncology 23, no. 9 (March 20, 2005): 1911–20. http://dx.doi.org/10.1200/jco.2005.03.137.

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Purpose To assess the estimated effect of finasteride prevention of prostate cancer on overall survival. Methods Data for our decision tree model came from men in the two arms (finasteride or placebo) of the Prostate Cancer Prevention Trial (PCPT) and from clinically localized prostate cancer patients studied for long-term survival outcomes. Our model compared survival outcomes for men treated with finasteride or placebo. Prostate cancer rates were based on the 7-year period prevalence of prostate cancer detected in the PCPT; survival probabilities were abstracted from the long-term outcome studies. We assessed variability in the PCPT and long-term survival studies to test the variability of our model. Results Survival advantages for a finasteride-treated (v those not treated with finasteride) population include gains of 1.7 months in 15-year cause-specific survival (assuming finasteride-altered Gleason scores and prostate cancer prevalence rates in the PCPT), of up to 3 months for cancers treated conservatively or surgically (assuming finasteride does not alter Gleason scores), and of 0.35 months (assuming the rate of cancers detected by for-cause biopsies in the PCPT), which increased to 1.7 months when assuming a 30% rate of biopsy-detected cancer in the PCPT placebo group. Model-variability analyses support several survival benefits associated with finasteride (eg, the uniform benefits assuming finasteride does not alter Gleason scores) but question certain others (eg, in 15-year recurrence-free survivals assuming finasteride does alter Gleason scores). Conclusion Finasteride can impart survival benefits according to our model, especially when we assume that finasteride does not alter Gleason scores.

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22

Engler, David, and Yi Li. "Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies." Statistical Applications in Genetics and Molecular Biology 8, no. 1 (January 11, 2009): 1–22. http://dx.doi.org/10.2202/1544-6115.1423.

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23

Thenmozhi, Mani, Visalakshi Jeyaseelan, Lakshmanan Jeyaseelan, Rita Isaac, and Rupa Vedantam. "Survival analysis in longitudinal studies for recurrent events: Applications and challenges." Clinical Epidemiology and Global Health 7, no. 2 (June 2019): 253–60. http://dx.doi.org/10.1016/j.cegh.2019.01.013.

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24

Kansal, Kalyani, Manisha Mareddy, Kelly L. Sloane, Alexa A. Minc, Peter V. Rabins, John B. McGready, and Chiadi U. Onyike. "Survival in Frontotemporal Dementia Phenotypes: A Meta-Analysis." Dementia and Geriatric Cognitive Disorders 41, no. 1-2 (2016): 109–22. http://dx.doi.org/10.1159/000443205.

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Background: Survival in frontotemporal dementia (FTD) is not well understood. We conducted a mixed effects meta-analysis of survival in FTD to examine phenotype differences and contributory factors. Methods: The PubMed, Medline, EMBASE, CINAHL, PsycINFO and Cochrane databases were searched for studies describing survival or natural history of behavioral variant FTD (bvFTD), progressive non-fluent aphasia (PNFA), semantic dementia (SD), FTD with amyotrophic lateral sclerosis (FTD-ALS), progressive supranuclear palsy and corticobasal degeneration. There were no language restrictions. Results: We included 27 studies (2,462 subjects). Aggregate mean and median survival were derived for each phenotype and, for comparison, Alzheimer's disease (AD) (using data from the selected studies). Survival was shortest in FTD-ALS (2.5 years). Mean survival was longest in bvFTD and PNFA (8 years) and median survival in SD (12 years). AD was comparable in survival to all except FTD-ALS. Age and sex did not affect survival; the education effect was equivocal. Heterogeneity in FTD survival was largely, but not wholly, explained by phenotypes. Conclusions: Survival differs for FTD phenotypes but, except for FTD-ALS, compares well to AD survival. Elucidating the potential causes of within-phenotype heterogeneity in survival (such as complicating features and comorbidities) may open up opportunities for tailored interventions.

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Dodd, Lori E., Reed F. Johnson, Joseph E. Blaney, and Dean Follmann. "Matched Longitudinal Analysis of Biomarkers Associated with Survival." Clinical and Vaccine Immunology 21, no. 8 (June 18, 2014): 1145–52. http://dx.doi.org/10.1128/cvi.00252-14.

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ABSTRACTThe identification of host or pathogen factors linked to clinical outcome is a common goal in many animal studies of infectious diseases. When the disease is fatal, statistical analysis of such factors may be biased from missing observations due to deaths. For example, when observations of a subject are censored before completing the intended study period, the complete trajectory will not be observed. Even if the factor is not associated with outcome, comparisons of data from survivors with those from nonsurvivors may lead to the wrong conclusions regarding associations with survival. Comparisons between subjects must account for differing observation lengths for those who survive relative to those who do not. Analyzing data over an interval common to all subjects provides one solution but requires eliminating data, some of which may be informative about the differences between groups. Here, we present a novel approach, matched longitudinal analysis (MLA), for analyzing such data based on matching biomarker intervals for survivors and nonsurvivors. We describe the results from simulation studies and from a study of monkeypox virus infection in nonhuman primates. In our application, MLA identified low monocyte chemoattractant protein-1 (MCP-1) levels as having a statistically significant association with survival, whereas the alternative methods did not identify an association. The method has general application to longitudinal studies that seek to find associations of biomarker changes with survival.

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Streiner, David L. "Stayin' Alive: An Introduction to Survival Analysis." Canadian Journal of Psychiatry 40, no. 8 (October 1995): 439–44. http://dx.doi.org/10.1177/070674379504000804.

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In some studies, the outcome of interest is the time until some event occurs: readmission to hospital, the next manic episode, or even death. Survival analysis is a techique which can be used to analyze such data. It has added usefulness because it allows us to use data from subjects who drop out of sight over the course of the follow-up period as well as from those who do not experience the event by the time the study ends. This article introduces this technique and provides some guidelines for designing follow-up trials.

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Delisle, Megan, Bader Alshamsan, Kalki Nagaratnam, Denise Smith, Ying Wang, and Amirrtha Srikanthan. "Metastasectomy in Leiomyosarcoma: A Systematic Review and Pooled Survival Analysis." Cancers 14, no. 13 (June 21, 2022): 3055. http://dx.doi.org/10.3390/cancers14133055.

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This study assesses the survival in patients undergoing metastasectomy for leiomyosarcoma (LMS) and compares the outcomes by the site of metastasectomy. We conducted a systematic review and pooled survival analysis of patients undergoing metastasectomy for LMS. Survival was compared between sites of metastasectomy. We identified 23 studies including 573 patients undergoing metastasectomy for LMS. The pooled median survival was 59.6 months (95% CI 33.3 to 66.0). The pooled median survival was longest for lung metastasectomy (72.8 months 95% CI 63.0 to 82.5), followed by liver (34.8 months 95% CI 22.3 to 47.2), spine (14.1 months 95% CI 8.6 to 19.7), and brain (14 months 95% CI 6.7 to 21.3). Two studies compared the survival outcomes between patients who did, versus who did not undergo metastasectomy; both demonstrated a significantly improved survival with metastasectomy. We conclude that surgery is currently being utilized for LMS metastases to the lung, liver, spine, and brain with acceptable survival. Although low quality, comparative studies support a survival benefit with metastasectomy. In the absence of randomized studies, it is impossible to determine whether the survival benefit associated with metastasectomy is due to careful patient selection rather than a surgical advantage; limited data were included about patient selection.

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Asbaş, Caner, Zühal Şenyuva, and Şule Tuzlukaya. "New Organizations in Complex Networks: Survival and Success." Central European Management Journal 30, no. 1 (March 15, 2022): 11–39. http://dx.doi.org/10.7206/cemj.2658-0845.68.

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Purpose: The present study investigates the survival and success of new organizations in the light of complex network theory. Methodology: The empirical data was collected using the survey method from the technology park companies are analyzed with social network analysis. Two main methods were used in this study: descriptive statistics and social network analysis. Findings: The findings indicate that new nodes appearing because of splitting up of bigger nodes from present or other related networks have a higher degree of centrality. In practice, this means that companies founded by former members of large-scale companies from these networks are more successful due to the ease in providing the flow of resources and information through previous links. This suggests that the imprint effect can be observed in the appearance, lifecycle, and performance of new nodes in complex networks. Originality: The literature lacks studies on new organizations’ lifecycle in complex networks despite the existence of studies about new organizations in organizational networks. This study examines the appearance, success, and survival of new organizations in networks by complex network approaches such as dynamism, dissipative structures, and uncertainties.

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Han, Susu, Tao Huang, Xing Wu, Xiyu Wang, Wen Li, Shanshan Liu, Wei Yang, et al. "Prognostic value of ALDH1 and Nestin in advanced cancer: a systematic meta-analysis with trial sequential analysis." Therapeutic Advances in Medical Oncology 11 (January 2019): 175883591983083. http://dx.doi.org/10.1177/1758835919830831.

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Background: Novel prognostic markers and therapeutic targets for advanced cancer are urgently needed. This report with trial sequential analysis (TSA) was first conducted to provide robust estimates of the correlation between aldehyde dehydrogenase 1 (ALDH1) and Nestin and clinical outcomes of advanced cancer patients. Methods: Hazard ratios (HRs) with 95% confidence intervals (CIs) were summarized for overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), cancer-specific survival (CSS), relapse/recurrence-free survival (RFS), and metastasis-free survival (MFS) from multivariable analysis. TSA was performed to control for random errors. Results: A total of 20 studies with 2050 patients (ALDH1: 15 studies with 1557 patients and Nestin: 5 studies with 493 patients) were identified. ALDH1 (HR = 2.28, p < 0.001) and Nestin (HR = 2.39, p < 0.001) were associated with a worse OS, as confirmed by TSA. Nestin positivity was linked to a poor PFS (HR = 2.08, p < 0.001), but ALDH1 was not linked to DFS, RFS, MFS, or PFS, and TSA showed that more studies were needed. Subgroup analysis by tumor type indicated that ALDH1 positivity may be associated with shorter OS in breast, head and neck cancers, but there was no association with colorectal cancer. Subgroup analysis by study source showed that ALDH1 positivity was correlated with a worse OS for Japanese (HR = 1.94, p = 0.002) and European patients (HR = 4.15, p < 0.001), but there was no association for Chinese patients. Subgroup analysis by survival rate showed that ALDH1 positivity correlated with poor OS at ⩾ 5 years (HR = 2.33, p < 0.001) or 10 years (HR = 1.76, p = 0.038). Conclusions: ALDH1 may be more valuable as an effective therapeutic target than Nestin for improving the long-term survival rate of advanced cancer. Additional prospective clinical trials are needed across different cancer types.

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Fabrizi, F., P. Martin, V. Dixit, and P. Messa. "Meta-analysis of observational studies: hepatitis C and survival after renal transplant." Journal of Viral Hepatitis 21, no. 5 (July 30, 2013): 314–24. http://dx.doi.org/10.1111/jvh.12148.

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Fabrizi, F., V. Dixit, P. Martin, and P. Messa. "Hepatitis B and survival after renal transplant: meta-analysis of observational studies." Journal of Viral Hepatitis 21, no. 8 (October 31, 2013): 542–50. http://dx.doi.org/10.1111/jvh.12184.

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32

Kasza, Jessica, Darren Wraith, Karen Lamb, and Rory Wolfe. "Survival analysis of time-to-event data in respiratory health research studies." Respirology 19, no. 4 (April 1, 2014): 483–92. http://dx.doi.org/10.1111/resp.12281.

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33

Xu, Jinfeng. "High-Dimensional Cox Regression Analysis in Genetic Studies with Censored Survival Outcomes." Journal of Probability and Statistics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/478680.

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With the advancement of high-throughput technologies, nowadays high-dimensional genomic and proteomic data are easy to obtain and have become ever increasingly important in unveiling the complex etiology of many diseases. While relating a large number of factors to a survival outcome through the Cox relative risk model, various techniques have been proposed in the literature. We review some recently developed methods for such analysis. For high-dimensional variable selection in the Cox model with parametric relative risk, we consider the univariate shrinkage method (US) using the lasso penalty and the penalized partial likelihood method using the folded penalties (PPL). The penalization methods are not restricted to the finite-dimensional case. For the high-dimensional (p→∞,p≪n) or ultrahigh-dimensional case (n→∞,n≪p), both the sure independence screening (SIS) method and the extended Bayesian information criterion (EBIC) can be further incorporated into the penalization methods for variable selection. We also consider the penalization method for the Cox model with semiparametric relative risk, and the modified partial least squares method for the Cox model. The comparison of different methods is discussed and numerical examples are provided for the illustration. Finally, areas of further research are presented.

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Tsai, Kuenhi, Kenneth H. Pollock, and Cavell Brownie. "Effects of Violation of Assumptions for Survival Analysis Methods in Radiotelemetry Studies." Journal of Wildlife Management 63, no. 4 (October 1999): 1369. http://dx.doi.org/10.2307/3802856.

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35

Utley, M., S. Gallivan, A. Young, N. Cox, P. Davies, J. Dixey, P. Emery, et al. "Potential bias in Kaplan–Meier survival analysis applied to rheumatology drug studies." Rheumatology 39, no. 1 (January 2000): 1–2. http://dx.doi.org/10.1093/rheumatology/39.1.1.

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36

Keiding, Niels, Matthew W. Knuiman, Avital Cnaan, and Louise Ryan. "Letter to the editor survival analysis in natural history studies of disease." Statistics in Medicine 9, no. 10 (October 1990): 1221–22. http://dx.doi.org/10.1002/sim.4780091011.

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Dey, Tanujit, Anish Mukherjee, and Sounak Chakraborty. "A Practical Overview and Reporting Strategies for Statistical Analysis of Survival Studies." Chest 158, no. 1 (July 2020): S39—S48. http://dx.doi.org/10.1016/j.chest.2020.03.015.

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Furukawa, Kyoji, Dale L. Preston, Munechika Misumi, and Harry M. Cullings. "Handling incomplete smoking history data in survival analysis." Statistical Methods in Medical Research 26, no. 2 (October 26, 2014): 707–23. http://dx.doi.org/10.1177/0962280214556794.

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While data are unavoidably missing or incomplete in most observational studies, consequences of mishandling such incompleteness in analysis are often overlooked. When time-varying information is collected irregularly and infrequently over a long period, even precisely obtained data may implicitly involve substantial incompleteness. Motivated by an analysis to quantitatively evaluate the effects of smoking and radiation on lung cancer risks among Japanese atomic-bomb survivors, we provide a unique application of multiple imputation to incompletely observed smoking histories under the assumption of missing at random. Predicting missing values for the age of smoking initiation and, given initiation, smoking intensity and cessation age, analyses can be based on complete, though partially imputed, smoking histories. A simulation study shows that multiple imputation appropriately conditioned on the outcome and other relevant variables can produce consistent estimates when data are missing at random. Our approach is particularly appealing in large cohort studies where a considerable amount of time-varying information is incomplete under a mechanism depending in a complex manner on other variables. In application to the motivating example, this approach is expected to reduce estimation bias that might be unavoidable in naive analyses, while keeping efficiency by retaining known information.

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Fitt, B., G. Loy, E. Christopher, P. M. Brennan, and M. T. C. Poon. "P13.04 Survival analyses for determining clinical relevance of molecular markers in translational glioblastoma research - a systematic review." Neuro-Oncology 23, Supplement_2 (September 1, 2021): ii33. http://dx.doi.org/10.1093/neuonc/noab180.114.

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Abstract BACKGROUND Pre-clinical glioblastoma studies can assess the relevance of their findings to patient survival using integrated clinical and genomic data. Validity of univariable analyses requires an assumption that molecular markers are randomly distributed across patient characteristics to mitigate the confounding effects of clinical variables. Multivariable survival analyses adjusting for clinical variables do not change the association if this assumption holds. We aimed to assess this by summarising the types of survival analyses and their results in translational glioblastoma research. MATERIAL AND METHODS We systematically searched Medline and Embase Jan 2008 to Feb 2021 for glioblastoma cell line or animal studies validating their molecular markers in The Cancer Genome Atlas (TCGA) or the Chinese Glioma Genome Atlas (CGGA) using survival analyses. Studies that exclusively used genomic data without laboratory findings were excluded. Two reviewers independently assessed study eligibility and extracted data. Data items included patient inclusion criteria, characteristics of survival analyses, and whether molecular markers had statistically significant association with overall survival. RESULTS Of 1,047 potentially eligible studies, we included 59 pre-clinical glioblastoma studies that tested the association between their molecular markers and survival using TCGA or CGGA data. All studies used TCGA data and 2 also used CGGA data. Sixteen (27%) studies specified their patient inclusion criteria from TCGA for survival analysis. Eight studies exclusively investigated sets of molecular markers, leaving 51 studies reporting 126 molecular markers. Eighteen (31%) studies used multivariable survival analysis in addition to univariable analyses. All molecular markers underwent univariable analyses, of which 12 (10%) molecular markers had additional multivariable survival analyses. In the 13 multivariable analyses on 12 molecular markers, four (31%) markers were associated with survival in the univariable analyses but not in the multivariable analyses. CONCLUSION Most pre-clinical studies used univariable survival analyses alone in public genomic repositories to assess the relevance of their results to patient survival. Our findings demonstrated that multivariable analyses are needed to account for confounding effects of clinical variables. Using relevant components from reporting guidelines for observational studies can improve the transparency and quality of translational studies.

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Almathkour, Fatmah, M. E. Ghitany, Ramesh C. Gupta, and J. Mazucheli. "Analysis of Survival Data by a Weibull-generalized Sibuya Distribution." Austrian Journal of Statistics 51, no. 3 (August 22, 2022): 121–40. http://dx.doi.org/10.17713/ajs.v51i3.1273.

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In this paper, we consider a survival model of a series system with random sample size, Z. Such a situation arises in competing risk analysis where the number of causes of failure is random and only the minimum of the survival times due to various causes is observed. Considering the distribution of Z as generalized Sibuya and the baseline distribution as Weibull, a Weibull-generalized Sibuya distribution is derived. The structural properties of the proposed model are studied along with the maximum likelihood estimation of the parameters. Extensive simulation studies are carried out to study the performance of the estimators. For illustration, two real data sets are analyzed and it is shown that the proposed model fits better than some of the existing models.

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Islam, Kazi Anisha, Larry Ka-Yue Chow, Ngar Woon Kam, Ying Wang, Chi Leung Chiang, Horace Cheuk-Wai Choi, Yun-Fei Xia, Anne Wing-Mui Lee, Wai Tong Ng, and Wei Dai. "Prognostic Biomarkers for Survival in Nasopharyngeal Carcinoma: A Systematic Review of the Literature." Cancers 14, no. 9 (April 24, 2022): 2122. http://dx.doi.org/10.3390/cancers14092122.

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This systematic review aims to identify prognostic molecular biomarkers which demonstrate strong evidence and a low risk of bias in predicting the survival of nasopharyngeal carcinoma (NPC) patients. The literature was searched for on PubMed to identify original clinical studies and meta-analyses which reported associations between molecular biomarkers and survival, including ≥150 patients with a survival analysis, and the results were validated in at least one independent cohort, while meta-analyses must include ≥1000 patients with a survival analysis. Seventeen studies fulfilled these criteria—two studies on single nucleotide polymorphisms (SNPs), three studies on methylation biomarkers, two studies on microRNA biomarkers, one study on mutational signature, six studies on gene expression panels, and three meta-analyses on gene expressions. The comparison between the hazard ratios of high-risk and low-risk patients along with a multivariate analysis are used to indicate that these biomarkers have significant independent prognostic values for survival. The biomarkers also indicate a response to certain treatments and whether they could be used as therapeutic targets. This review highlights that patients’ genetics, epigenetics, and signatures of cancer and immune cells in the tumor microenvironment (TME) play a vital role in determining their survival.

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Lambert, Paul C. "Modeling of the Cure Fraction in Survival Studies." Stata Journal: Promoting communications on statistics and Stata 7, no. 3 (September 2007): 351–75. http://dx.doi.org/10.1177/1536867x0700700304.

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Cure models are a special type of survival analysis model where it is assumed that there are a proportion of subjects who will never experience the event and thus the survival curve will eventually reach a plateau. In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction is of interest to patients and a useful measure to monitor trends and differences in survival of curable disease. I will describe the strsmix and strsnmix commands, which fit the two main types of cure fraction model, namely, the mixture and nonmixture cure fraction models. These models allow incorporation of the expected background mortality rate and thus enable the modeling of relative survival when cure is a possibility. I give an example to illustrate the commands.

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Bai, Lu, and Daniel Gillen. "Survival analysis via cox proportional hazards additive models." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1650003. http://dx.doi.org/10.1142/s2425038416500036.

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The Cox proportional hazards model is commonly used to examine the covariate-adjusted association between a predictor of interest and the risk of mortality for censored survival data. However, it assumes a parametric relationship between covariates and mortality risk though a linear predictor. Generalized additive models (GAMs) provide a flexible extension of the usual linear model and are capable of capturing nonlinear effects of predictors while retaining additivity between the predictor effects. In this paper, we provide a review of GAMs and incorporate bivariate additive modeling into the Cox model for censored survival data with applications to estimating geolocation effects on survival in spatial epidemiologic studies.

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Klakattawi, Hadeel S. "Survival analysis of cancer patients using a new extended Weibull distribution." PLOS ONE 17, no. 2 (February 23, 2022): e0264229. http://dx.doi.org/10.1371/journal.pone.0264229.

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One of the most important applications of statistical analysis is in health research and applications. Cancer studies are mostly required special statistical considerations in order to find the appropriate model for fitting the survival data. Existing classical distributions rarely fit such data well and an increasing interest has been shown recently in developing more flexible distributions by introducing some additional parameters to the basic model. In this paper, a new five-parameters distribution referred as alpha power Kumaraswamy Weibull distribution is introduced and studied. Particularly, this distribution extends the Weibull distribution based on a novel technique that combines two well known generalisation methods, namely, alpha power and T-X transformations. Different characteristics of the proposed distribution, including moments, quantiles, Rényi entropy and order statistics are obtained. The method of maximum likelihood is applied in order to estimate the model parameters based on complete and censored data. The performance of these estimators are examined via conducting some simulation studies. The potential importance and applicability of the proposed distribution is illustrated empirically by means of six datasets that describe the survival of some cancer patients. The results of the analysis indicated to the promising performance of the alpha power Kumaraswamy Weibull distribution in practice comparing to some other competing distributions.

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Prajapati, Hasmukh J., and Hyun S. "Kevin" Kim. "Evidence-based treatment algorithm for BCLC C advanced hepatocellular carcinoma: A comprehensive review and meta-analysis." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e15649-e15649. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e15649.

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e15649 Background: Barcelona clinic liver cancer (BCLC) C advanced hepatocellular carcinoma (aHCC) has a poor prognosis. Different treatment methods have shown a survival benefit. The purpose of the study is to suggest the treatment algorithm based on a comprehensive review of the literature on aHCC treated with different methods. Methods: Studies were identified by searching Google Scholar using the following keywords: ‘‘advanced hepatocellular carcinoma’’ or ‘‘advanced HCC’’ or “BCLC C” in a time period from 2008 to 2017. Search identified more than 700 articles. Then, articles were searched manually for BCLC C HCC. Articles were excluded if they dealt with only liver metastases or portal vein thrombosis, or if they did not report median survival. A total of 3 randomized control studies (RCT) and 21 non-RCT studies met the inclusion criteria and were reviewed. Results:Overall median survivals (OS) according to different methods is shown in table 1. OSs of aHCC treated with TACE, Yittrium 90 transarterial radioembolization (Y90 TARE) and sorafenib were not significantly different (p>0.5). The pooled results of NRCT demonstrated that Child Pugh class A or without portal vein thrombosis (PVT) or ECOG PS 0 treated with TACE had best median survivals of 15 months(m), 17 m and 20 m respectively. Conclusions: No treatment method appears clearly better than any other. However, aHCC patients with Child Pugh class A or ECOG PS 0 or without PVT treated with TACE showed highest survival. Sorafenib/TACE or sorafenib/Y90 TARE combinations show promise as an effective and a tolerable treatment strategy for advanced HCC. Radiation therapy alone showed poor survival benefit. [Table: see text]

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Combescure, C., DS Courvoisier, G. Haller, and TV Perneger. "Meta-analysis of two-arm studies: Modeling the intervention effect from survival probabilities." Statistical Methods in Medical Research 25, no. 2 (December 24, 2012): 857–71. http://dx.doi.org/10.1177/0962280212469716.

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Duan, Wenhou, Suyun Li, Xin Meng, Yanxin Sun, and Chongqi Jia. "Smoking and survival of breast cancer patients: A meta-analysis of cohort studies." Breast 33 (June 2017): 117–24. http://dx.doi.org/10.1016/j.breast.2017.03.012.

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Ibrahim, Ezzeldin M., and Abdelaziz Al-Homaidh. "Physical activity and survival after breast cancer diagnosis: meta-analysis of published studies." Medical Oncology 28, no. 3 (April 22, 2010): 753–65. http://dx.doi.org/10.1007/s12032-010-9536-x.

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von Gadow, Klaus, Heyns Kotze, Thomas Seifert, Kai Staupendahl, and Juan G. Álvarez González. "Potential density and tree survival: an analysis based on South African spacing studies." Southern Forests: a Journal of Forest Science 77, no. 2 (December 3, 2014): 115–22. http://dx.doi.org/10.2989/20702620.2014.984151.

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Fabrizi, Fabrizio, Paul Martin, Vivek Dixit, Fasiha Kanwal, and Gareth Dulai. "HBsAg Seropositive Status and Survival After Renal Transplantation: Meta-Analysis of Observational Studies." American Journal of Transplantation 5, no. 12 (September 28, 2005): 2913–21. http://dx.doi.org/10.1111/j.1600-6143.2005.01113.x.

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