Academic journal article Iranian Journal of Public Health

A Comparison between Accelerated Failure-Time and Cox Proportional Hazard Models in Analyzing the Survival of Gastric Cancer Patients

Academic journal article Iranian Journal of Public Health

A Comparison between Accelerated Failure-Time and Cox Proportional Hazard Models in Analyzing the Survival of Gastric Cancer Patients

Article excerpt

(ProQuest: ... denotes formulae omitted.)


Gastric cancer is the one of the most prevalent reason of cancer-related death in the world. Now, gastric cancer contains 10% of cancers in the world and is one of the most common kinds of cancers (1). According to the statistics of Iran Cancer Institute, gastric cancer is the third most common cancer between Iranian women after breast cancer and the most common cancer be-tween Iranian men (2-6).

Gastric cancer is usually treated with surgery, ra-diotherapy, or chemotherapy. The elementary treatment of gastric cancer in initial stages is sur-gery; so it is considered as the prime treatment for cancer. Chemotherapy and radiotherapy will be used as supplementary treatments, if necessary. In advanced stages of the disease, surgical procedures, radiotherapy and chemotherapy are also used for the treatment but they do not usually achieve good outcomes. The odds of patients' complete recovery depend on the surgery but the time when the disease passes through the mucous membrane, it is possible lymph nodes Metastases and relapse in spite of the total surgery, which has been per-formed on the patient (7, 8).

One of the most important prognostic indicators which is considered after surgery and for patients with gastric cancer is an increase in patients' sur-vival rate especially the 5-year survival rate. Gas-tric cancer is difficult to treatment unless cancer is diagnosed at an elementary stage. Unfortunately, because early gastric cancer causes few symptoms, the cancer is usually advanced when the diagnosis is made. So conventional treatment such as sur-gery, chemotherapy and radiation therapy are not impressive in increasing the patients' survival rate (9, 10). For this reason, the 5-year survival rate for gastric cancer after surgery is reported to be less than 10% (11-15). The increase in these patients' survival rate after surgery involves identifying vari-ous factors, including individual, clinical, diagnos-tic and therapeutic.

There are various statistical methods to assess the effects of various factors on survival of cancer patients including parametric and Cox semi-parametric regression models. These models are divided into two basic categories: Proportional Hazard (PH) model and Accelerated Failure-time (AFT) model. In the proportional hazard regres-sion model, the effect of covariates is obtained on the hazard function. In this case, if baseline haz-ard is considered parametric, one of the Weibull, exponential and Gompertz models will be achieved. If the baseline hazard is considered non-parametric, the Cox proportional hazard model will be obtained. In the accelerated failure-time regression model, the effect of covariates on the logarithm of the survival time is assessed. The ob-tained models in this case include generalized gamma, Log-logistic, Log-normal, Weibull and exponential. Weibull and exponential are the only parametric regression models which have both a proportional hazards and an accelerated failure-time representation.

The proportional hazard model does not need to consider a specific probability distribution for the survival time; therefore, it is the most helpful model in analyzing survival data. But the effi-ciency of the model is severely dependent to pro-portional hazards assumption and, for this reason, The Cox model is often called proportional haz-ards model. In occasions where the proportional hazard model is not acceptable, estimates derived from Cox model will lead to an improper fitting of the model and incorrect inferences (16-22). Ac-celerated failure-time models are especially im-portant in such situations. These models-due to having a parametric distribution for the survival times-make statistical inference more accurate and lead to an proper fitting of the model(23).

Factors affecting the survival of cancer patients are often identified by Cox proportional hazard model (14, 24-30). Neither have these studies gen-erally tested proportional hazards assumption nor did they try to identify a proper model as an alter-native to proportional hazards model. …

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