Modelling survival in acute severe illness: Cox versus accelerated failure time models

Moran, John L.; Bersten, Andrew D.; Solomon, P. J.; Edibam, Cyrus; Hunt, Tamara

doi:10.1111/j.1365-2753.2007.00806.x

Cited by 8 publications

(7 citation statements)

References 60 publications

(70 reference statements)

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“…The strength of our study was using an AFT model in a sample of women selected from an ongoing populationbased cohort with an average follow-up of 12.5 years and adjusting the results for the main known confounders. AFT regression gives more valid results and minor bias than PH cox models due to a specific statistical distribution for the survival time (41)(42)(43). The ANM was determined retrospectively so that the women were asked to remember the time of their last menstrual cycle within three years since their last interview.…”

Section: Strengths and Limitationmentioning

confidence: 99%

Anthropometric Indices and Age at Natural Menopause: A 15-Year Follow-up Population-Based Study

Farahmand

Tehrani

Rahmati

et al. 2021

Int J Endocrinol Metab

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Background: Following menopause, the risk of many diseases is increased, and this situation may be due to changes in anthropometric indices (AI), while the association between adiposity and age at natural menopause (ANM) is not clear yet. Objectives: This longitudinal study was conducted to investigate the ability of AI in predicting ANM. Methods: For this purpose, a total of 3,876 women aged > 20 years old from participants of the Tehran lipid and glucose study (TLGS) met our eligibility criteria. The association between ANM and various AIs was assessed using the Accelerated Failure Time (AFT) model, and time ratio (TR) with 95% confidence intervals were reported in this longitudinal study. Results: The median [interquartile range (IQR) 25 - 75] of the survival time was 12.5 (7.9 - 15.8) years. At the end of the follow-up, 1,479 (38.2%) of the participants reached menopause. The median time to natural menopause was decreased by about 2% with one standard deviation (SD) increase of both a body shape index (ABSI) (time ratio (TR): 0.98, 95% CI: 0.97, 0.99) and lipid accumulation product (LAP) (TR: 0.98, 95% CI: 0.98, 0.99) z-scores; and this time was increased by about 1% (TR: 1.01, 95% CI: 1.00, 1.02) with one SD increase in body mass index (BMI) z-score. Conclusions: The ABSI, LAP, and BMI were the most useful AIs for identification of the time to menopause onset, and ABSI and LAP were inversely associated with the ANM. Also, the BMI was directly associated with the ANM.

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Section: Strengths and Limitationmentioning

confidence: 99%

Anthropometric Indices and Age at Natural Menopause: A 15-Year Follow-up Population-Based Study

Farahmand

Tehrani

Rahmati

et al. 2021

Int J Endocrinol Metab

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“…Survival analysis investigates prognostic factors of survival in patients using methods such as the Kaplan-Meier method and the Cox proportional hazards model (8). The Cox model is the most common statistical model in analyzing survival data (9).…”

Section: Introductionmentioning

confidence: 99%

Identifying Survival Predictive Factors in Patients with Breast Cancer: A 16-Year Cohort Study Using Cox Maximum Penalized Likelihood Method

Alvandi

Rasekhi

Ariana

2019

Iran Red Crescent Med J

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Background: Cancer is the second leading cause of death globally, and it was responsible for almost 9.6 million deaths in 2018. Breast cancer (BC) is the most common cancer among women with almost two million new cases worldwide in 2018. Thus, it is necessary to study new methods to estimate the survival predictive factors in BC patients. Objectives: This cohort study aimed to fit a Cox model to BC data using partial likelihood (PL) and new maximum penalized likelihood (MPL) methods in order to determine the predictive factors of survival time and compare the accuracy of these two methods. Methods: This prospective cohort study used the data of 356 women with BC registered at the Cancer Research Center of Shahid Beheshti University of Medical Sciences in Tehran, Iran. The patients were identified from 1999 to 2015. The Cox model by new MPL and PL methods was used with variables such as the stage of cancer, tumor grade, estrogen receptor, and several other variables for univariate and multiple analyses. Results: The mean age ± standard deviation (SD) of patients at diagnosis was about 48 ± 11.27 years ranging from 24 to 84 years. Using the new MPL method, in addition to lymphovascular invasion and recurrence variables, estrogen receptor (P = 0.045) also had a statistically significant relationship with survival. The standard errors of most variables were smaller when using the MLP method than the PL method. The overall one-year, two-year, five-year, and 10-year survival rates based on the baseline hazard estimate were 96%, 92%, 70%, and 51%, respectively. Conclusions: In the analysis of BC data, new MPL method can help identify the factors that affect the survival of patients more accurately than usual methods do. This method decreases the standard error of most variables and can be applied for identifying predictive factors more accurately than previous methods.

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“…To proceed, the paper extends upon a method advanced by Zhou , which thus far has received little attention in the medical literature (but see ). The method presented relies on a simple transformation of a random variable generated according to a truncated piecewise exponential distribution, where the bounds of truncation allow the user to specify the minimum and maximum number of measurements that are of interest for a particular application, and the piecewise nature of the distribution allows covariates to vary as step functions over the time scale (for a different perspective that uses the piecewise exponential distribution to directly model time‐dependent effects in proportional hazards situations, see ).…”

Section: Introductionmentioning

confidence: 99%

Data generation for the Cox proportional hazards model with time-dependent covariates: a method for medical researchers

Hendry

2013

Statist. Med.

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The proliferation of longitudinal studies has increased the importance of statistical methods for time-to-event data that can incorporate time-dependent covariates. The Cox proportional hazards model is one such method that is widely used. As more extensions of the Cox model with time-dependent covariates are developed, simulations studies will grow in importance as well. An essential starting point for simulation studies of time-to-event models is the ability to produce simulated survival times from a known data generating process. This paper develops a method for the generation of survival times that follow a Cox proportional hazards model with time-dependent covariates. The method presented relies on a simple transformation of random variables generated according to a truncated piecewise exponential distribution, and allows practitioners great flexibility and control over both the number of time-dependent covariates and the number of time periods in the duration of follow-up measurement. Within this framework, an additional argument is suggested that allows researchers to generate time-to-event data in which covariates change at integer-valued steps of the time scale. The purpose of this approach is to produce data for simulation experiments that mimic the types of data structures applied researchers encounter when using longitudinal biomedical data. Validity is assessed in a set of simulation experiments and results indicate that the proposed procedure performs well in producing data that conform to the assumptions of the Cox proportional hazards model.

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Modelling survival in acute severe illness: Cox versus accelerated failure time models

Abstract: Time dependence of predictors of survival in ALI/ARDS exists and must be appropriately modelled. The Cox model with time-varying covariates remains a flexible model in survival analysis of patients with acute severe illness.

Cited by 8 publications

References 60 publications

Anthropometric Indices and Age at Natural Menopause: A 15-Year Follow-up Population-Based Study

Anthropometric Indices and Age at Natural Menopause: A 15-Year Follow-up Population-Based Study

Identifying Survival Predictive Factors in Patients with Breast Cancer: A 16-Year Cohort Study Using Cox Maximum Penalized Likelihood Method

Data generation for the Cox proportional hazards model with time-dependent covariates: a method for medical researchers

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