Competing risks analysis of patients with Brain Stroke: a comparison of two different approaches
Abstract
Objectives: Cumulative incidence function (CIF) measures the survival time of a particular hazard in the presence of others, while cause-specific (CS) one ignores the competing risks. The present study aimed to fit CIF and CS function for brain stroke (BS) patients and compare the results by the cause of death.
Materials and method: In the study, 332 patients with the definitive diagnosis of BS were followed up for 10 years, and their mortality status due to BS or other causes was evaluated. In addition, significance tests and parameters were estimated by using STATA 14 software by considering the CS and CIF-based regression model.
Results: Based on the results of CIF and CS analyses concerning the variables with similar significance, the hazard ratio of age at diagnosis (68-59 years (91%,2.61), ≥76 years (2.14,3.03) during diagnosis enhanced in the death for other causes, while an increase was observed in this ratio for sex (38%,2.35%), as well as the history of heart disease (44%,47%) and blood pressure (57%,64%) regarding BS-caused death, respectively. Regarding the significant variables, the correlation strength of CIF model was more in the BS-caused death by considering p-value, while CS one had stronger correlation in the death due to other causes.
Conclusion: The estimation of CIF analysis, along with CS one for the competing risks, is suggested to provide more precise information about patients’ status in order to support adopted clinical decisions when aiming at assessing health related to a specific cause economically and determining the probability of occurring an intended event among other causes.
1. Boehme AK, Esenwa C, Elkind MS.
Stroke risk factors, genetics, and prevention.
Circulation research. 2017;120(3):472-95.
2. Ovbiagele B, Goldstein LB, Higashida
RT, Howard VJ, Johnston SC, Khavjou OA,
et al. Forecasting the future of stroke in the
United States: a policy statement from the
American Heart Association and American
Stroke Association. Stroke. 2013;44(8):2361-
75.
3. Popa-Wagner A, Petcu E, Capitanescu
B, Hermann D, Radu E, Gresita A. AGEING
AS A RISK FACTOR FOR CEREBRAL
Competing Risks and Analysis of Patients with Brain Stroke ...
Norouzi S et al. ISCHEMIA. UNDERLYING MECHANISMS
AND THERAPY IN ANIMAL MODELS
AND IN THE CLINIC. Mechanisms of Ageing
and Development. 2020:111312.
4. Kleinbaum DG, Klein M. Survival
analysis: Springer; 2010.
5. Klein JP, Bajorunaite R. Inference
for competing risks. Handbook of statistics.
2003;23:291-311.
6. Kaplan EL, Meier P. Nonparametric
estimation from incomplete observations.
Journal of the American statistical association.
1958;53(282):457-81.
7. Fox J. Cause-specific hazard
proportional-hazards regression for survival
data. An R and S-PLUS companion to applied
regression. 2002;2002.
8. Ray S, Dacosta-Byfield S, Ganguli A,
Bonthapally V, Teitelbaum A. Comparative
analysis of survival, treatment, cost and
resource use among patients newly diagnosed
with brain metastasis by initial primary cancer.
Journal of neuro-oncology. 2013;114(1):117-
25.
9. Fine JP, Gray RJ. A proportional
hazards model for the subdistribution of a
competing risk. Journal of the American
statistical association. 1999;94(446):496-509.
10. Asghari Jafarabadi M, Mohammadi
SM, Hajizadeh E, Fatemi SR. An evulation of
5-year survival of metastatic colon and rectal
cancer patients using cumulative incidence
models. Koomesh journal. 2013;14(2):207-14.
11. Zhang MJ, Fine J. Summarizing
differences in cumulative incidence functions.
Statistics in Medicine. 2008;27(24):4939-49.
12. Dignam J, Bryant J, Wieand HS.
Analysis of Cause-Specific Events in
Competing Risks Survival Data. Handbook of
Statistics. 2003;23:313-29.
13. Thorvaldsen P, Asplund K, Kuulasmaa
K, Rajakangas A-M, Schroll M. Stroke
incidence, case fatality, and mortality
in the WHO MONICA project. Stroke.
1995;26(3):361-7.
14. Koton S, Tanne D, Green MS, Bornstein
NM. Mortality and predictors of death 1 month
and 3 years after first-ever ischemic stroke:
data from the first national acute stroke Israeli
survey (NASIS 2004). Neuroepidemiology.
2010;34(2):90-6.
15. Rutten-Jacobs LC, Arntz RM, Maaijwee
NA, Schoonderwaldt HC, Dorresteijn LD,
van Dijk EJ, et al. Long-term mortality after
stroke among adults aged 18 to 50 years. Jama.
2013;309(11):1136-44.
16. Kimura K, Minematsu K, Kazui S,
Yamaguchi T. Mortality and cause of death
after hospital discharge in 10,981 patients with
ischemic stroke and transient ischemic attack.
Cerebrovascular diseases. 2005;19(3):171-8.
17. Mogensen UB, Olsen TS, Andersen
KK, Gerds TA. Cause-specific mortality after
stroke: relation to age, sex, stroke severity,
and risk factors in a 10-year follow-up study.
Journal of stroke and cerebrovascular diseases.
2013;22(7):e59-e65.
18. Ekker MS, Verhoeven JI, Vaartjes
I, Jolink WMT, Klijn CJM, de Leeuw F-E.
Association of stroke among adults aged 18
to 49 years with long-term mortality. Jama.
2019;321(21):2113-23.
19. Lim HJ, Zhang X, Dyck R, Osgood
N. Methods of competing risks analysis of
end-stage renal disease and mortality among
people with diabetes. BMC medical research
methodology. 2010;10(1):97.
20. Hyun J, Lim J, editors. Comparison of
Three Different Approaches for Competing
Risks Analysis of Patients with Diabetes.
Canada-Korea Conference on Science and
Technology; 2011.
21. Grambauer N, Schumacher M,
Beyersmann J. Proportional subdistribution
hazards modeling offers a summary analysis,
even if misspecified. Statistics in medicine.
2010;29(7‐8):875-84.
22. Andersen PK, Geskus RB, de Witte T,
Putter H. Competing risks in epidemiology:
possibilities and pitfalls. International journal
of epidemiology. 2012;41(3):861-70.
23. Tai B-C, Grundy RG, Machin D. On
the importance of accounting for competing
risks in pediatric cancer trials designed to
delay or avoid radiotherapy: I. Basic concepts
and first analyses. International Journal of
Radiation Oncology* Biology* Physics.
2010;76(5):1493-9.
24. Wolbers M, Koller MT, Witteman
JC, Steyerberg EW. Prognostic models with
competing risks: methods and application to coronary risk prediction. Epidemiology.
2009:555-61.
25. Tai BC, Machin D, White I, Gebski
V. Competing risks analysis of patients
with osteosarcoma: a comparison of four
different approaches. Statistics in medicine.
2001;20(5):661-84.
26. Huang X, Zhang N. Regression
survival analysis with an assumed copula for
dependent censoring: a sensitivity analysis
approach. Biometrics. 2008;64(4):1090-9.
27. Chin C-C, Wang J-Y, Yeh C-Y, Kuo Y-H,
Huang W-S, Yeh C-H. Metastatic lymph node
ratio is a more precise predictor of prognosis
than number of lymph node metastases in
stage III colon cancer. International journal of
colorectal disease. 2009;24(11):1297-302.
Files | ||
Issue | Vol 8 No 3 (2022) | |
Section | Original Article(s) | |
DOI | https://doi.org/10.18502/jbe.v8i3.12286 | |
Keywords | ||
competing risks, cause-specific, cumulative incidence function, brain stroke |
Rights and permissions | |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |