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Table 2 Summary of input parameters and assumptions in the Monte Carlo simulation of the SCDS results adjusted for outcome misclassification, selection bias and confounding

From: Estimating uncertainty in observational studies of associations between continuous variables: example of methylmercury and neuropsychological testing in children

Input Parameters

Distribution

Source (reference)

Outcome misclassification (information bias)

Observed exposure: mercury concentration in maternal hair (mg/g)

Meanx = 6.9, SDx = 4.5

Myers et al., 2003 (7)

Observed outcome: Score on Boston naming test

Meany = 26.5, SDy = 4.8

Myers et al., 2003 (7)

Observed b1

N (-0.012, 0.046)

Myers et al., 2003 (7)

Observed b0

= 26.5 - 6.9 × Observed b1

Derived using standard linear regression formula (b0 = -b1 )

P1: proportion of exposed with a1 (negative) adjustment

U (0.1,0.3)

Hypothetical (no data available)

P2: proportion of exposed with a2 (positive) adjustment

U (0.1,0.3)

 

a1: relative adjustment in outcome for proportion p1 of subjects

U (0.0,1.95)

Hypothetical (no data available), limits chosen as to allow BNT score vary between 0 and 60

a2: relative adjustment in outcome for proportion p2 of subjects

U (0.0,1.95)

 

Selection bias

Observed exposure: mercury concentration in cord blood (mg/L)

See above

10,000 vectors (Meany, Sdy, b0, b1) adjusted for information bias

Output of Information Bias module

Number of subjects included in the analysis

643

Myers et al. 2003 (7)

Number of eligible subjects

1480

Calculated as 740 × 2 (740 are ~50% of eligible population (7)

Number of subjects excluded from the analysis

837

Calculated as 1480 - 643

Relative difference between mean exposure of subjects not included and mean exposure of included subjects

U (-5%,5%)

Hypothetical (no data available)

Relative difference between mean outcome of subjects not included and mean outcome of included subjects

U (-10%,10%)

Hypothetical (no data available)

Slope multiplier (to get to slope of non-included subjects)

U (0,2)

Hypothetical (no data available)

Confounding

10,000 vectors (Meanx, SDx, Meany, SDy, b0, b1) adjusted for information and selection bias

Output of Selection Bias Module

Pearson correlation between confounder (WAIS) and exposure

U (-0.5, 0.5)

Hypothetical (no data available)

Pearson correlation between confounder (WAIS) and outcome

U (0.2, 0.8)

Hypothetical (no data available)

  1. N = normal distribution, U = uniform distribution