Drawing causal inferences in epidemiological studies of early

life influences

Andy NessAlex GriffithsLaura HoweSam Leary

Note: for non-commercial purposes only

Structure of this talk

• Analytic studies, chance, bias and confounding

• Examples of chance, bias and confounding

• Approaches to chance, bias and confounding

Analytic studies, chance bias and confounding

Cohort and case-control studies

PAST PRESENT FUTURE

PROSPECTIVE COHORT STUDY

HISTORICAL COHORT STUDY

RETROSPECTIVE CASE CONTROL

STUDY

Cases and controls ascertainedExposure recalled

Disease recorded

Disease recorded

Bias

A trend in the collection, analysis, interpretation, publication or review of data leading to conclusions systematically different from the truth.

Missing data in early life studies

� May be years/decades between the early life exposure and the outcome of interest

� Loss to follow-up (usually) increases with time

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Num

ber of

que

stio

nnai

res

retu

rned

0 5 10 15

Age (years)

ALSPAC questionnaire

responses: birth to 15yrs

ConfoundingA confounder is a factor associated with the exposure which independently affects the risk of developing the disease.

Exposuree.g. Birthweight

Diseasee.g. Blood pressure

Counfoundere.g. Social class

Strength of the associationBiological credibilityConsistency with other studiesTime sequenceDose responseSpecificity

Causal Not causal

ChanceBiasConfounding

Is the association causal ?

Examples of chance, bias and confounding

Subgroup analyses Calcium intake during pregnancy and offspring blood

pressure at age 7, according to body mass index

* Intervention (2g calcium/day during pregnancy) vs placebo * Mean calcium intake during pregnancy approx 1g day

Findings from subgroup analysis may not be replicable

ALSPAC (observational), Leary et al. (N=6884)

-12

-10

-8

-6

-4

-2

0

2

4

6

≤14.8 14.8-15.8 15.8-17.0 >17.0

Body mass index age 7 years (kg/m2)

Ch

ang

e in

sys

toli

c b

loo

d p

ress

ure

(m

mH

g)

per

1g

in

crea

se i

n c

alci

um

*

Follow-up of calcium trial, Belizan et al. (N = 591)

-12

-10

-8

-6

-4

-2

0

2

4

6

≤14.4 14.4-15.7 15.7-17.5 >17.5

Body mass index age 7 years (kg/m2)

Dif

fere

nce

in

sys

toli

c b

loo

d p

ress

ure

(m

mH

g)

bet

wee

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rou

ps

*

Belizan et al. BMJ 1997;315:281-5 Leary et al. Archives of Disease in Childhood 2005;90:492-3

Birthweight and blood pressure

Rachel Huxley et al Lancet 2002

Advertisement in Boston Globe

0.1 0.5 0.75 1 1.25 1.5 1.75

Male health workers

Social insurance, men

Male chemical workers

Hyperlipidaemic men

Nursing home residents

Social insurance, women

Male physicians

Male smokers

(Ex)-smokers, asbestos workers

Trials

Cohorts

Skin cancer patients

USA

Finland

Switzerland

USA

USA

Finland

Cohorts combined

Trials combined

Finland

USA

USA

USA

Relative risk (95% CI)

Beta-carotene and Cardiovascular mortality

Egger et al BMJ;1998:316:61-66

Vitamin C and birth weight: Observational study and RCT

-150

-100

-50

0

50

100

150

Mathews et al1999

(Observational)

Poston et al2006

(RCT)

grams

“Well, so much for antioxidants.”

Approaches to chance, bias and confounding

Observational epidemiology?

Avoiding chance

• Design adequately powered studies• Do the correct analysis• Avoid statistical significance• Present p values and confidence intervals• Look at effect sizes and their clinical importance• Focus on pre-specified main effects• Report exploratory analyses as such• Replicate subgroup findings

The smaller the p-value, the stronger

the evidence against the null

hypothesis

P v

alu

e

.0001

.001

.01

.1

1

Weak evidence against the null hypothesis

Strong evidence against the null hypothesis

Increasing evidence against the null hypothesis withdecreasing P value

Interpretation of p values

Sterne JAC, Davey Smith G. British Medical Journal 2001;322:226-31Sifting the evidence: what’s wrong with significance tests?

Clinical significance?

Weight at birth and systolic blood pressure at age 3

1860 children (ALSPAC)

After adjustment for current size:Regression coefficient = -1.9 mmHg/kg

Confidence interval = -2.61, -1.21 mmHg/kgP < 0.0001

Interpretation:

Strong evidence against null hypothesis, blood pressure BUT differences small considering birthweight changes achievable

Avoiding bias (in cohorts)

• Reduce losses to follow up

• Report characteristics of those lost to follow up

• Compare complete case analysis versus analysis in subjects with missing data

• If missingness is related to values of observeddata, consider multiple imputation

• If missingness is related to values of unobserveddata, unbiased effect estimates are not possible

05

1015

2025

Wei

ght (

kg)

0 1 2 3 4 5Age (years)

Growth curves for weight, birth to 5 years

Unpublished data ALSPAC

Henderson et al, Thorax 2008;63:974-980

Avoiding confounding

• Follow up of trials • Temporal trends • Ecological explanatory power• Heterogeneity of confounding structure• Instrumental variable approaches• Specificity • Critical time periods• Sibling and twin studies in early origins

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

<1000

1000-1500

1501-2000

2001-2500

2501-3000

3001-3500

3501-4000

4001-4500

4501-5000>5000

NS

B ir th w e ig h t (g ram s)B ir th w e ig h t (g ram s)B ir th w e ig h t (g ram s)B ir th w e ig h t (g ram s)

1951

1967

2000

US Birthweight Distribution 1950-2000

Personal Communication Professor John Lynch

Owen et al. IJE (2005)

Mean Birthweight and systolic blood pressure (INTERSALT)

Breastfeeding and outcomes

ALSPAC Pelotas Belarus

SES gradient No SES gradient Trial

BP Inverse No association No effect

BMI Inverse No association No effect

IQ Higher IQ Higher IQ Higher IQ

Preliminary data Marie-Jo Brion et al

Instrumental variables

Need to find a variable which:

• is associated with the exposure of interest

• is not related to confounders

• has no direct effect on the outcome of interest (i.e. affects the outcome only indirectly through the exposure)

FTO acts via obesity

Maternal FTO and fat mass

6000

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8000

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Med

ian

tota

l fat

mas

s at

age

9 (

gram

s) TT AT AA

Offspring FTO genotype

TT AT TT AT AA AT AA

Maternal FTO genotype

Maternal smoking and offspring birth weight

• Maternal smoking associated with greater decrease in birth weight than paternal smoking

• Paternal smoking has little effect after adjusting for

maternal smoking

Davey Smith, BCPT 2008;102:245-256

Maternal smoking and offspring BP

Maternal and paternal smoking have similar

associations with offspring BP

Davey Smith, BCPT 2008;102:245-256

The New

Yorker,

November

2001

Any questions?