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Diagnostic plots in Cox's regression model.

C H Chen1, P C Wang

  • 1Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, Republic of China.

Biometrics
|September 1, 1991
PubMed
Summary
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This study introduces two diagnostic plots for validating Cox proportional hazards models. These plots help assess covariate effects, detect nonlinearity, and identify influential data points in survival analysis.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Cox proportional hazards models are widely used for survival data analysis.
  • Model validation is crucial for reliable interpretation of results.
  • Existing diagnostic methods may not fully address specific model fitting issues.

Purpose of the Study:

  • To present novel diagnostic plots for Cox proportional hazards model validation.
  • To introduce the added variable plot for assessing covariate effects.
  • To introduce the constructed variable plot for detecting covariate nonlinearity.

Main Methods:

  • Development of the added variable plot to evaluate the impact of including a new covariate.
  • Application of the constructed variable plot to identify nonlinear relationships of covariates.

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  • Utilizing both plots to detect influential observations affecting model fit.

    • Main Results:

    • The added variable plot effectively assesses the contribution of covariates.
    • The constructed variable plot successfully identifies covariate nonlinearity.
    • Both diagnostic tools aid in identifying influential data points.

    Conclusions:

    • The presented diagnostic plots enhance the validation of Cox proportional hazards models.
    • These methods improve the assessment of covariate effects and model assumptions.
    • The utility of these plots is demonstrated using real-world cancer data examples.