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 points^b	15
Unclear	34
Candidate selection methods
Prespecification	30
Univariable analysis	63
Prespecification + univariable analysis	5
Unclear	3
Statistical model
Cox proportional hazard regression	90
Others^c	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

^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.