A comparison of the logistic regression and the Cox proportional hazard models in retrospective studies on the prognosis of patients with gastric cancer

J Surg Oncol. 1993 Jan;52(1):9-13. doi: 10.1002/jso.2930520104.

Abstract

To define the independent prognostic factors reducing survival time for gastric cancer, we compared the logistic regression and the Cox proportional hazard models applied to patients who underwent curative gastrectomy. All patients were evaluated after being followed for long fixed periods. Of 1,019, 269 (26.4%) died of tumor recurrence within a 5-year period and 36 (3.5%) died over 5 years after the original surgery. With regard to survival time, multivariate analyses using the Cox proportional hazard model in a stepwise manner adjusted for the sex, age, and 10 other factors, suggested that size of tumor (P < 0.01, relative risk [rr] = 1.0962), degree of gastric wall invasion (P < 0.01, rr < 1.3520), and status of lymph node metastasis (P < 0.01, rr = 1.6572) were the most independent prognostic factors. As well as, using the stepwise logistic regression model, size of tumor, (P < 0.01, odds ratio [or] = 1.115), degree of gastric wall invasion (P < 0.01, or = 1.428), and status of lymph node metastasis (P < 0.01, or = 2.182) were also the most independent risk factors for recurrence within 5 years after surgery. Although regression coefficients are not all the same, these three factors proved significant in both multivariate analyses. This equation for risk factors for prognosis is approached when searching for an appropriate method of retrospective studies using multivariate analyses.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Female
  • Follow-Up Studies
  • Gastrectomy
  • Humans
  • Logistic Models*
  • Male
  • Multivariate Analysis
  • Neoplasm Recurrence, Local / mortality*
  • Prognosis
  • Proportional Hazards Models*
  • Retrospective Studies
  • Risk Factors
  • Stomach Neoplasms / mortality*
  • Stomach Neoplasms / surgery
  • Survival Analysis
  • Time Factors