Lipids Standardization and DAGs Enrique Schisterman, PhD Epidemiology Branch – DESPR – NICHD 1

Lipids Standardization and DAGsEnrique Schisterman, PhDEpidemiology Branch – DESPR – NICHD

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Background PCBs are lipophilic xenobiotics

Literature on exposure to PCBs is conflicting

Poses challenges for interpretation of potential health risks

Differences in laboratory practices may account for some of the equivocal findings

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Background Limited understanding of the true relations between:

Serum and adipose tissue concentrations of PCBs

Serum PCBs and serum lipids

Which in turn makes model specification difficult

3

Background Investigators typically make assumptions about the

relation between serum lipids and serum PCBs expressing PCB measurements as:

Wet-weight (PCB per unit serum)

Lipid-standardization value (PCB concentration per unit lipids)

Adjusted model (Lipids are separate term)

Two-stage analysis (Lipids are regressed on PCBs with residuals entered as individual risk factors)

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Lipid Standardization The various strategies for handling serum lipids imply

different causal pathways

What is considered best practice in the lab may have an unintended impact on data analysis

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Objectives Describe 4 proposed approaches for handling serum

lipids

Show the impact of different common statistical modeling approaches on risk estimates

Evaluate the bias under a range of plausible causal systems

Determine which model best reflects underlying causal assumptions

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Proposed Approach 1 Unadjusted: Wet-weight values

PCBs per unit serum

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Proposed Approach 2 Lipid Standardization

PCB concentration per unit of lipids

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Proposed Approach 3 Adjusted

Lipids is a separate term

Lipids is a predictor/potential confounder

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Proposed Approach 4 Two-stage model

Lipids are regressed on PCBs with residuals entered as individual risk factors

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Proposed Approaches Unadjusted: Wet-weight values

Lipid Standardized

Adjusted

Two-Stage

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Simulation Study To evaluate the impact of these approaches for handling

serum lipids in models on risk estimates, Schisterman et al. simulated data from a log normal distribution to determine bias

They varied: True underlying causal relations Statistical model used for risk estimates Relation between PCBs and serum lipids Measurement error in serum lipids

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DAG A: Simple cause & effect PCBS causes Y, Lipids unrelated

PCBS Y

Lipids

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PCBS Y

Lipids

Model logit(Y)=… % BiasUnadjusted PCBS -0.8

Standardized PCBS / Lipids -75.9

Adjusted PCBS + Lipids -0.7

Two-stage PCBS + Residuals -0.714


PCBS Y

Lipids





DAG A: Extensions

PCBS Y

Lipids

PCBS Y

Lipids

1

PCBS Y

Lipids

PCBS Y

LipidsA

2 3

Model A 1 2 3

Unadjusted -0.8 1.2 0.4 -0.4Standardized -75.9 -51.3 -79.8 -85.0Adjusted -0.7 1.8 0.8 -0.1Two-stage -0.7 1.8 0.5 -0.3

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DAG B: Confounding U causes PCBS and Lipids, both cause Y

PCBS Y

LipidsU

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DAG B: Confounding U causes PCBS and Lipids, both cause Y

PCBS Y

LipidsU

Model logit(Y)=… % BiasUnadjusted PCBS 24.0


Adjusted PCBS + Lipids 0.1

Two-stage PCBS + Residuals 27.218

DAG B: Extensions

Model B 1 2

Unadjusted 24.0 -15.4 -11.2Standardized -128.8 -351.3 -128.3Adjusted 0.1 -99.4 -25.4Two-stage 27.2 1.1 -8.7

PCBS Y

Lipids

PCBS Y

Lipids

1

PCBS Y

Lipids

2

A

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DAG B: Extensions

PCBS Y

Lipids

PCBS Y

Lipids

1

PCBS Y

Lipids

2

Model B 1 2

Unadjusted 24.0 -15.4 -11.2Standardized -128.8 -351.3 -128.3Adjusted 0.1 -99.4 -25.4Two-stage 27.2 1.1 -8.7

A

Overadjustment

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DAG C: PCBS per Lipids as Ascending Proxy PCB in adipose tissue causes PCB in serum per lipids, and

causes Y PCBS/Lipids Y

PCBA

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DAG C: PCBS per Lipids as Ascending Proxy PCB in adipose tissue causes PCB in serum per lipids, and

causes Y PCBS/Lipids Y

PCBA





Summary of Bias by DAG

DAG Type UnadjustedStandardiz

edAdjusted Two-stage

A

Cause & Effect

-0.8 -75.9 -0.7 -0.7A1 1.2 -51.3 1.8 1.8A2 0.4 -79.8 0.8 0.5A3 -0.4 -85 -0.1 -0.3

BConfoundin

g24 -129 0.1 27.2

B1 Intermediate

-15.4 -351 -99.4 1.1B2 -11.2 -128 -25.4 -8.7C PCBs/Lipids -86.3 -1 -1 -85.9

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Summary Evaluated 4 statistical models commonly used to assess

the effects of PCBs (or other lipophilic environmental contaminants) on human health

Each model showed minimal bias for at least the causal truth for which it was ideally suited Bias ranged from -351% to 24% Standardized model produced large biases for most of the

evaluated DAGs The adjusted model produced small biases even for the DAG

for which standardization is optimal

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Limitations Only considered DAGs with 2 to 4 factors

Including additional factors necessarily makes evaluation more complex and the trade-off between efficiency and robustness more important

Though in their simulation the adjusted model produced consistently unbiased estimates, there are situations where adjustment should be avoided Collider Common cause

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Conclusion Simulations demonstrate that statistical models that fail

to uphold the underlying causal assumptions lead to biased results This bias can have negative implications on the interpretation

of effects of exposures on human health end points

Investigators must consider biology, biologic medium, laboratory measurement, and other underlying modeling assumptions when devising a statistical model

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References Schisterman EF, Whitcomb BW, Buck Louis GM, Louis TA.

Lipid Adjustment in the Analysis of Environmental Contaminants and Human Health Risks. Environmental Health Perspectives 2005;113(7):853-7.

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Documents

Lipids Standardization and DAGs Enrique Schisterman, PhD Epidemiology Branch – DESPR – NICHD 1