Archives

  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br LNR based prognostic model

    2019-11-11


    LNR-based prognostic model for gastric cancer 87
    2.3. Statistical analysis
    Basic demographic data were summarized as n (%) for categorical variables and median with Nocodazole range of 95% con-fidence interval (CI) for continuous variables. Distribution of clinical variables was tabulated as n (%) and compared between the two groups using the Pearson chi-square test. Possible clinical and pathologic variables for OS after can-cer surgery were examined via univariate and multivariate logistic regressions. A prior statistical analysis plan was approved in univariate analysis, and variables with P values < 0.10 in univariate analysis were included for analysis in the multivariate model. A multivariate, propor-tional hazard Cox model with backward selection was per-formed to determine which factors were independently predictive of survival. A risk model was developed from a multivariate logistic regression model. The b-coefficients from the risk model were used to generate the points of the prognostic score for calculating survival time. Patients were further stratified into 3 prognostic groups according to the total score obtained from the prognostic score. Survival time was analyzed using the KaplaneMeier method. Log-rank tests were used to determine significant differences between the survival curves. The prognostic model was internally validated by using bootstrapping method (200 repetitions) to obtain a relatively unbiased estimate of the models’ performance. The monotonicity of each variable was assessed using the linear trend chi-square test. The linear trend test, homogeneity likelihood ratio, and c-index of each independent variable within the multivariate analysis and the full model were calculated to determine the model performance. All statistical analyses were per-formed using SPSS 17.0 software (SPSS Inc., Chicago, IL, USA) and R version 2.9.1 (The R Foundation for Statistical Computing, Vanderbilt University, Nashville, TN) using the Hmisc and Design libraries. All statistical assessments with P < 0.05 were considered significant.
    3. Results
    in the non-adjuvant chemotherapy group were generally older, poorer ECOG status, and higher ASA class.
    At the end of the study, 99 patients (73.3%) had died, and 95 (68.3%) had recurrent tumor. The median OS and DFS were 17.9 months (95% CI: 11.8e24.0) and 10.9 months (95% CI: 7.2e14.7), respectively. The results of univariate ana-lyses for clinical variables associated with OS and DFS are presented in Table 2. Five variables, namely, T-classifica-tion, LNR, CEA, ECOG PS, and adjuvant chemotherapy, showed a statistically significant effect on OS and DFS in univariate analysis. Age and CCI were two additional sig-nificant variables affecting OS in univariate analysis. The variables identified via univariate analysis to be signifi-cantly associated with OS were included in the multivariate analysis. T-classification, LNR, CEA, ECOG PS, and adjuvant chemotherapy were the only independent prognostic fac-tors for OS in multivariate analysis. The hazards ratios for each variable in multivariate analysis are shown in Fig. 1.
    Supplementary Table 1 shows the prognostic stratifica-tion performance among each independent variable in multivariate analysis and the entire model. The perfor-mance of the entire model is superior to each variable in terms of monotonicity, homogeneity, and discriminatory capability of prognostic prediction. The c-index of the model for OS and DFS was 0.79 (95% CI: 0.71e0.87) and 0.68 (95% CI: 0.59e0.77), respectively.
    The impact of type of adjuvant chemotherapy regimen on survival outcome was further analyzed in Supplementary Fig. 2. Among 86 patients age structure received adjuvant chemo-therapy, 35 patients (40.7%) received the XELOX regimen and 51 patients (59.3%) received 5FU or TS-1 or UFT regimen. In general, patients who received any regimen of chemotherapy had better overall survival outcomes in terms than those treated without adjuvant chemotherapy. There was no statistical difference in overall survival be-tween patients receiving XELOX or other chemotherapy regimens (P Z 0.13).