SEARCH

SEARCH BY CITATION

Keywords:

  • prostate-specific antigen;
  • doubling time;
  • random coefficient model;
  • prostate carcinoma

Abstract

BACKGROUND

Prostate-specific antigen (PSA) doubling time (PSADT) has emerged as an important surrogate marker of disease progression and survival in men with prostate carcinoma. The literature is replete with different methods for calculating PSADT. The objective of the current study was to identify the method that best described PSA growth over time and predicted disease-specific survival in men with androgen-independent prostate carcinoma.

METHODS

PSADT was calculated for 122 patients with androgen-independent prostate carcinoma using 2 commonly used methods: best-line fit (BLF) and first and last observations (FLO). Then, PSADT was calculated by using both a random coefficient linear (RCL) model and a random coefficient quadratic (RCQ) model. Statistical analysis was used to compare the ability of the methods to fit the patients' PSA profiles and to predict disease-specific survival.

RESULTS

The RCQ model provided the best fit of the patients' PSA profiles, as determined according to the significance of the added parameters for the RCQ equation (P ≤ 0.002). The PSADT estimates from the FLO method, the RCL model, and the RCQ model were highly significant predictors (P < 0.001) of disease-specific survival, whereas estimates from the BLF method were not found to be significant predictors (P = 0.66). PSADT estimates from the RCQ and RCL models provided an improved correlation of disease-specific survival (both R2 = 0.55) compared to the FLO (R2 = 0.11) and BFL (R2 = 0.003) methods.

CONCLUSIONS

Random coefficient methods provided a more reliable fit of PSA profiles than other models and were superior to other available models for predicting disease-specific survival in patients with androgen-independent prostate carcinoma. The authors concluded that consideration should be given to applying the RCL or RCQ models in future assessments of PSADT as a predictive parameter. Cancer 2006. © 2006 American Cancer Society.