TY  - JOUR
T1  - Survival analysis of patients with chronic diseases: a statistical approach to mortality risk factors
A1  - Vladimiro Ibañez Quispe
A1  - Juan Reinaldo Paredes Quispe
A1  - Remo Choquejahua Acero
A1  - Teresa Paola Alvarez Rozas
A1  - Edgar Eloy Carpio Vargas
A1  - Godofredo Quispe Mamani
A1  - Percy Huata Panc
JF  - Journal of Advanced Pharmacy Education and Research
JO  - J Adv Pharm Educ Res
SN  - 2249-3379
Y1  - 2026
VL  - 16
IS  - 1
DO  - 10.51847/hVZCNSpDD9
SP  - 211
EP  - 221
N2  - Chronic diseases are leading causes of death worldwide, but mortality risk varies substantially across patients with similar diagnoses. This heterogeneity requires statistical methods that can estimate survival probability over time and identify risk factors associated with earlier death. Traditional logistic or linear regression models are poorly suited to survival outcomes because they do not directly account for censoring, unequal follow-up duration, or changing exposure status over time. These limitations can produce biased estimates and reduce clinical interpretability. This article applies survival analysis methods to identify demographic, clinical, lifestyle, and treatment-related predictors of all-cause mortality in adults with chronic diseases. The goal is to construct a statistically defensible risk model that supports clinical risk stratification. A retrospective cohort of 1,248 adults with heart failure, chronic kidney disease, chronic obstructive pulmonary disease, type 2 diabetes, or multimorbidity was specified using electronic health record and registry-style data from 2017 to 2025. Kaplan-Meier curves, log-rank tests, and a multivariable Cox proportional hazards model were used, with Schoenfeld residuals applied to assess proportional hazards. In the modeled cohort, 238 deaths occurred over a median follow-up of 4.2 years, providing sufficient events for multivariable modeling. Age per 10 years, advanced disease severity, higher comorbidity burden, current smoking, reduced kidney function, anemia, and low medication adherence were expected to emerge as independent predictors of mortality. Survival analysis provides an appropriate statistical framework for estimating mortality risk in chronic disease cohorts with censoring and variable follow-up. Properly specified models can identify high-risk patients, quantify hazard ratios, and support individualized monitoring and intervention strategies‎.
UR  - https://japer.in/article/survival-analysis-of-patients-with-chronic-diseases-a-statistical-approach-to-mortality-risk-factor-sxir2mzo6m8cccp
ER  -