IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v357y2024ics0277953624006543.html
   My bibliography  Save this article

Dynamic prediction of lung cancer suicide risk based on meteorological factors and clinical characteristics：A landmarking analysis approach

Author

Listed:
  • Zhou, Yuying
  • Lao, Jiahui
  • Cao, Yiting
  • Wang, Qianqian
  • Wang, Qin
  • Tang, Fang

Abstract

Suicide is a severe public health issue globally. Accurately identifying high-risk lung cancer patients for suicidal behavior and taking timely intervention measures has become a focus of current research. This study intended to construct dynamic prediction models for identifying suicide risk among lung cancer patients. Patients were sourced from the Surveillance, Epidemiology, and End Results database, while meteorological data was acquired from the Centers for Disease Control and Prevention. This cohort comprised 455, 708 eligible lung cancer patients from January 1979 to December 2011. A Cox proportional hazard regression model based on landmarking approach was employed to explore the impact of meteorological factors and clinical characteristics on suicide among lung cancer patients, and to build dynamic prediction models for the suicide risk of these patients. Additionally, subgroup analyses were conducted by age and sex. The model's performance was evaluated using the C-index, Brier score, area under curve (AUC) and calibration plot. During the study period, there were 666 deaths by suicide among lung cancer patients. Multivariable Cox results from the dynamic prediction model indicated that age, marital status, race, sex, primary site, stage, monthly average daily sunlight, and monthly average temperature were significant predictors of suicide. The dynamic prediction model demonstrated well consistency and discrimination capabilities. Subgroup analyses revealed that the association of monthly average daily sunlight and monthly average temperature with suicide remained significant among female and younger lung cancer patients. The dynamic prediction model can effectively incorporate covariates with time-varying to predict lung cancer patients' suicide death. The results of this study have significant implications for assessing lung cancer individuals' suicide risk.

Suggested Citation

  • Zhou, Yuying & Lao, Jiahui & Cao, Yiting & Wang, Qianqian & Wang, Qin & Tang, Fang, 2024. "Dynamic prediction of lung cancer suicide risk based on meteorological factors and clinical characteristics：A landmarking analysis approach," Social Science & Medicine, Elsevier, vol. 357(C).
    • Handle: RePEc:eee:socmed:v:357:y:2024:i:c:s0277953624006543
    DOI: 10.1016/j.socscimed.2024.117201
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953624006543
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socscimed.2024.117201?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Seo-Eun Cho & Zong Woo Geem & Kyoung-Sae Na, 2021. "Development of a Suicide Prediction Model for the Elderly Using Health Screening Data," IJERPH, MDPI, vol. 18(19), pages 1-10, September.
    2. Nicholas G. Zaorsky & Ying Zhang & Leonard Tuanquin & Shirley M. Bluethmann & Henry S. Park & Vernon M. Chinchilli, 2019. "Suicide among cancer patients," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    3. Hans C. Van Houwelingen, 2007. "Dynamic Prediction by Landmarking in Event History Analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 70-85, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gustavo Soutinho & Luís Meira-Machado, 2023. "Nonparametric estimation of the distribution of gap times for recurrent events," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 103-128, March.
    2. Yu Zheng & Tianxi Cai, 2017. "Augmented estimation for t‐year survival with censored regression models," Biometrics, The International Biometric Society, vol. 73(4), pages 1169-1178, December.
    3. Bin Qiu & Wei Guo & Fan Zhang & Fang Lv & Ying Ji & Yue Peng & Xiaoxi Chen & Hua Bao & Yang Xu & Yang Shao & Fengwei Tan & Qi Xue & Shugeng Gao & Jie He, 2021. "Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Kulinskaya, Elena & Gitsels, Lisanne Andra & Bakbergenuly, Ilyas & Wright, Nigel R., 2021. "Dynamic hazards modelling for predictive longevity risk assessment," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 222-231.
    5. Marlena Maziarz & Patrick Heagerty & Tianxi Cai & Yingye Zheng, 2017. "On longitudinal prediction with time-to-event outcome: Comparison of modeling options," Biometrics, The International Biometric Society, vol. 73(1), pages 83-93, March.
    6. Kyle Mani & Daxuan Deng & Christine Lin & Ming Wang & Melinda L. Hsu & Nicholas G. Zaorsky, 2024. "Causes of death among people living with metastatic cancer," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. Jing Zhang & Jing Ning & Ruosha Li, 2023. "Evaluating Dynamic Discrimination Performance of Risk Prediction Models for Survival Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 353-371, July.
    8. Dimitris Rizopoulos & Laura A. Hatfield & Bradley P. Carlin & Johanna J. M. Takkenberg, 2014. "Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1385-1397, December.
    9. Qing Liu & Gong Tang & Joseph P. Costantino & Chung‐Chou H. Chang, 2020. "Landmark proportional subdistribution hazards models for dynamic prediction of cumulative incidence functions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1145-1162, November.
    10. Zijing Yang & Chengfeng Zhang & Yawen Hou & Zheng Chen, 2023. "Analysis of dynamic restricted mean survival time based on pseudo‐observations," Biometrics, The International Biometric Society, vol. 79(4), pages 3690-3700, December.
    11. M. A. Nicolaie & J. C. van Houwelingen & T. M. de Witte & H. Putter, 2013. "Dynamic Pseudo-Observations: A Robust Approach to Dynamic Prediction in Competing Risks," Biometrics, The International Biometric Society, vol. 69(4), pages 1043-1052, December.
    12. Hein Putter & Hans C. Houwelingen, 2017. "Understanding Landmarking and Its Relation with Time-Dependent Cox Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 489-503, December.
    13. Liang Li & Sheng Luo & Bo Hu & Tom Greene, 2017. "Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 357-378, December.
    14. Cefis, Elena & Coad, Alex & Lucini-Paioni, Alessandro, 2023. "Landmarks as lighthouses: firms' innovation and modes of exit during the business cycle," Research Policy, Elsevier, vol. 52(8).
    15. Daniel Commenges & Benoit Liquet & Cécile Proust-Lima, 2012. "Choice of Prognostic Estimators in Joint Models by Estimating Differences of Expected Conditional Kullback–Leibler Risks," Biometrics, The International Biometric Society, vol. 68(2), pages 380-387, June.
    16. Sean M. Devlin & Mithat Gönen & Glenn Heller, 2020. "Measuring the temporal prognostic utility of a baseline risk score," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 856-871, October.
    17. Medina-Olivares, Victor & Calabrese, Raffaella & Crook, Jonathan & Lindgren, Finn, 2023. "Joint models for longitudinal and discrete survival data in credit scoring," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1457-1473.
    18. Gustavo Soutinho & Luís Meira-Machado, 2022. "Methods for checking the Markov condition in multi-state survival data," Computational Statistics, Springer, vol. 37(2), pages 751-780, April.
    19. Qi Gong & Douglas E. Schaubel, 2013. "Partly Conditional Estimation of the Effect of a Time-Dependent Factor in the Presence of Dependent Censoring," Biometrics, The International Biometric Society, vol. 69(2), pages 338-347, June.
    20. Jiehuan Sun & Jose D. Herazo‐Maya & Philip L. Molyneaux & Toby M. Maher & Naftali Kaminski & Hongyu Zhao, 2019. "Regularized Latent Class Model for Joint Analysis of High‐Dimensional Longitudinal Biomarkers and a Time‐to‐Event Outcome," Biometrics, The International Biometric Society, vol. 75(1), pages 69-77, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:357:y:2024:i:c:s0277953624006543. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.