Review Article
Prognostic Models for Predicting Overall Survival in Patients with Primary Gastric Cancer: A Systematic Review
Table 1
Characteristics of 101 model developments.
| | Model developments (n = 101) |
| Study characteristics | Publication year | | Before 2000 | 3 | 2001–2010 | 7 | 2011–2018 | 91 | Study location | | East Asia (China/Japan/Korea) | 76 | Non-Asian | 25 | Data source | | Clinical data/retrospective cohort | 91 | Prospective cohort | 7 | Randomized controlled trial | 3 | Patient characteristics | Male% (4/101 missing) | 67.6 (30.9, 80.3)a | Age (5/101 missing) | | Median (min, max) of mean | 60.0 (51.0, 70.0)a | Tumor TNM stage | | All | 46 | I–III | 36 | IV | 17 | No information | 2 | Gastrectomy | | No restriction | 28 | Only patients with gastrectomy | 71 | Only patients without gastrectomy | 2 | Model development | Sample size (training set) (14/101 missing) | 360 (29, 15320)a | Number of events | 193 (14, 9560)a | Event per variable (18/101 missing) | 25.1 (0.2, 1481.3)a | Length of follow-up (month) (53/101 missing) | 44.0 (6.7, 111.6)a | Start of outcome follow-up | | From diagnosis | 3 | From surgery | 49 | From other time pointsb | 15 | Unclear | 34 | Candidate selection methods | | Prespecification | 30 | Univariable analysis | 63 | Prespecification + univariable analysis | 5 | Unclear | 3 | Statistical model | | Cox proportional hazard regression | 90 | Othersc | 11 | Final predictor selection | | Full model | 10 | Stepwise (including forward and backward) | 68 | Unclear | 23 | Statistical assumptions ever checked | 9 | Number of final predictors | 5 (2, 53)a | Formats of presentations | | Score | 35 | Nomogram | 47 | Equation | 9 | Others (decision tree and neural network) | 4 | No | 6 | Predictive performance | | Discrimination | | AUC/c statistic | 67 | Others | 1 | No | 33 | Calibration | | Calibration plot | 45 | Hosmer–Lemeshow test | 3 | No | 55 | Model validation | | Internal | 30 | External | 21 | No | 54 |
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aMedian (min, max). bInitiation of chemotherapy (n = 10), metastasis (n = 3), and randomization (n = 2). cCART, Cox Lasso, discrimination analysis, Weibull model, neural network, and logistic model. AUC: area under curve.
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