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Table 2 Input and estimated parameters in Approach II

From: Impact of correlation of predictors on discrimination of risk models in development and external populations

Population

Input parameters for cases and controls

Estimated parameters for the population

ρ

Normal (μ, σ)

ρ

(μ, σ)

Unadjusted OR *

Adjusted OR **

SD of β 0+ ∑β i X i

AUC

A

Cases = 0.2

Ctrls = 0.2

μCases: (1, 2); Ctrls: (0, 0)

\( \sigma \)Cases: (2, 2); Ctrls: (2, 2)

0.25

μ: (0.2, 0.4); \( \sigma \): (2.04, 2.15)

1.28, 1.65

1.17, 1.60

1.13

0.770

B

Cases = 0.2

Ctrls = 0.4

,,

0.40

,,

,,

1.09, 1.60

1.09

0.765

C

Cases = 0.2

Ctrls = - 0.2

,,

-0.04

,,

,,

1.34, 1.68

1.25

0.785

D

Cases = 0.1

Ctrls = 0.1

,,

0.16

,,

,,

1.22, 1.62

1.17

0.777

E

Cases = - 0.1

Ctrls = - 0.1

,,

-0.02

,,

,,

1.35, 1.70

1.28

0.795

F

Cases = 0.2

Ctrls = 0.2

μCases: (1, 3); Ctrls: (0, 0)

SD Cases: (2, 2); Ctrls: (2, 2)

0.27

μ: (0.2, 0.6); \( \sigma \): (2.04, 2.33)

1.28, 2.12

1.11, 2.07

1.77

0.858

G

,,

μCases: (1, 3); Ctrls: (0, 2)

SD Cases: (2, 2); Ctrls: (2, 2)

0.23

μ: (0.2, 0.2); \( \sigma \): (2.04, 2.04)

1.28, 1.28

1.23, 1.23

0.67

0.676

H

,,

μCases: (1, 2); Ctrls: (0, 0)

SD Cases: (2, 3); Ctrls: (2,3)

0.24

μ: (0.2, 0.4); \( \sigma \): (2.04, 3.10)

1.28, 1.25

1.21, 1.22

0.80

0.705

I

,,

μCases: (1, 2); Ctrls: (0, 0)

SD Cases: (2, 1); Ctrls: (2, 1)

0.27

μ: (0.2, 0.4); \( \sigma \): (2.04, 1.28)

1.28, 7.39

1.05, 7.23

2.56

0.922

  1. In each population, a disease prevalence of 20% was used
  2. Population ‘A’ is considered as reference population and all other populations are compared w.r.t. ‘A’
  3. SD: Standard Deviation; OR: Odds Ratio; Ctrls: controls
  4. ρ: Pearson correlation between two continuous predictors
  5. A risk factor X ~ Normal (μ, σ) implies ‘X’ follows a normal distribution with mean μ and variance \( \sigma \) 2
  6. *when a risk factor is normally distributed in both cases and controls and sigma is the common variance of the risk factor in both cases and controls, then unadjusted OR = exp((μ Case –μ Control )/SD2) [19]
  7. **adjusted ORs estimated by fitting logistic model