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ORIGINAL ARTICLE
Prognostic factors of first-ever stroke patients in suburban Malaysia by comparing regression models
1
Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Terengganu, Terengganu, MALAYSIA
2
Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, MALAYSIA
3
Faculty of Medicine, Medical Campus, Universiti Sultan Zainal Abidin, Jalan Sultan Mahmud, Kuala Terengganu, Terengganu, MALAYSIA
4
Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500 Selangor Darul Ehsan, MALAYSIA
5
Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, 600077, INDIA
6
Faculty of Health and Life Sciences, INTI International University, Nilai, Negeri Sembilan, MALAYSIA
7
Faculty of Dentistry, AIMST University, Bedong, Kedah, MALAYSIA
8
Centre for Global Health Research, Saveetha Medical College and Hospital, Saveetha University, Chennai, Tamil Nadu, INDIA
Online publication date: 2023-09-24
Publication date: 2023-11-01
Electron J Gen Med 2023;20(6):em545
KEYWORDS
Cox proportional hazards regressionmultinomial logistic regressionfirst-ever strokeprognosticfactorshealth risk
ABSTRACT
Introduction:
The aim of this study was to compare regression models based on the parameter estimates of prognostic factors of mortality in first-ever stroke patients.
Methods:
A retrospective study among 432 first-ever stroke patients admitted to Hospital Universiti Sains Malaysia, Kelantan, Malaysia, was carried out. Patient’s medical records were extracted using a standardized data collection sheet. The statistical analyses used for modelling the prognostic factors of mortality were Cox proportional hazards regression, multinomial logistic regression, and multiple logistic regression.
Results:
A total of 101 (23.4%) events of death were identified and 331 patients (76.6%) were alive. Despite using three different statistical analyses, the results were very similar in terms of five major aspects of parameter estimates, namely direction, estimation, precision, significance, and magnitude of risk assessment. It was reported slightly better in Cox proportional hazards regression model, especially in terms of the precision of the results.
Conclusions:
Given that this study had compared the findings from three different types of advanced statistical methods, this research has clearly yielded that with data of high quality, the selection of appropriate statistical method should not be a worrisome problem for researchers who may not be of expertise in the field of medical statistics.
The aim of this study was to compare regression models based on the parameter estimates of prognostic factors of mortality in first-ever stroke patients.
Methods:
A retrospective study among 432 first-ever stroke patients admitted to Hospital Universiti Sains Malaysia, Kelantan, Malaysia, was carried out. Patient’s medical records were extracted using a standardized data collection sheet. The statistical analyses used for modelling the prognostic factors of mortality were Cox proportional hazards regression, multinomial logistic regression, and multiple logistic regression.
Results:
A total of 101 (23.4%) events of death were identified and 331 patients (76.6%) were alive. Despite using three different statistical analyses, the results were very similar in terms of five major aspects of parameter estimates, namely direction, estimation, precision, significance, and magnitude of risk assessment. It was reported slightly better in Cox proportional hazards regression model, especially in terms of the precision of the results.
Conclusions:
Given that this study had compared the findings from three different types of advanced statistical methods, this research has clearly yielded that with data of high quality, the selection of appropriate statistical method should not be a worrisome problem for researchers who may not be of expertise in the field of medical statistics.
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