Applied Epidemiologic Analysis

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Applied Epidemiologic Analysis Fall 2002 Applied Epidemiologic Analysis Patricia Cohen, Ph.D. Henian Chen, M.D., Ph. D. Teaching Assistants Julie Kranick Sylvia Taylor Chelsea Morroni Judith Weissman

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Applied Epidemiologic Analysis. Patricia Cohen, Ph.D. Henian Chen, M.D., Ph. D. Teaching Assistants Julie KranickSylvia Taylor Chelsea MorroniJudith Weissman. Lecture 5. Examination of conditional (interactive) effects and non-linear effects in multiple regression. Goals: - PowerPoint PPT Presentation

Transcript of Applied Epidemiologic Analysis

Page 1: Applied Epidemiologic Analysis

Applied Epidemiologic AnalysisFall 2002

Applied Epidemiologic Analysis

Patricia Cohen, Ph.D.

Henian Chen, M.D., Ph. D.

Teaching Assistants

Julie Kranick Sylvia TaylorChelsea Morroni Judith Weissman

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Applied Epidemiologic AnalysisFall 2002

Lecture 5

Examination of conditional (interactive) effectsand non-linear effects in multiple regression

Goals:• To examine studies in which interactive effects were examined among categorical or scaled exposures or other predictors of scaled disease indicators•To understand how some non-linear relationshps may be handled by re-scaling while others are intrinsically non-linear

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Objectives

• To appreciate two reasons for conducting analyses of interactive and non-linear relationships: – as assumption checks– as primary study questions

• To understand the methods and statistical indices used to investigate interactive and non-linear relationships

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Note re: strata and conditional effects

When analyses of relationships between risk and outcome are reported within strata, it may be possible to see conditional effects directly.

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Effects expressed as means of exposed and not exposed

• But, watch out for effects of differences in exposure or outcome rates on the magnitude of some effect indices.

.10 .30

.15 .32

Male Female

Young

Middle

.18Older

Not exposed Not exposedExposed Exposed

.40.15

.11

.16.31

.45

.48

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First Study : Role of genotype in the cycle of violence in maltreated children

Reference:

Caspi A, McClay J, Moffit TE, Mill J, Martin J, Craig IW, Taylor A, Poulton R, Science, 297, No 2, 851-854, 2002.

Primary goal: Determination of a possible gene – environment interaction

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The problem: Role of genotype study

Do maltreated children show a differential tendency to later antisocial behavior as a function of identifiable genetic differences?

Is the effect of child maltreatment conditional on genotype?

Is the effect of genotype modified by environmental conditions (child maltreatment)?

or, put another way

or, put another way

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Population studied, study design, and sample size : Role of genotype study

442 non-Maori New Zealand males who were part of a cohort studied from birth to age 26

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Measurement issues: Role of genotype study

Details of genotyping of DNA are provided in Supplemental material available on the Science website.

The measure of childhood maltreatment included:• observations of negative affect or behavior toward

child at age 3• harsh discipline at ages 7 or 9 by parent report• 2 or more changes in primary caretaker by age 10• retrospective adult report of physical or sexual child

abuse.

Cumulative index counted the number of these measures that were positive

• 64% = 0• 28% = 1 (called probable)• and 8% = 2 or more.

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Measurement issues: Role of genotype study

Genotype measure:• Fewer or more replicates of MAOA allele

Antisocial behavor: • Conduct disorder in adolescence (age 11,13,15, or 18)• Court record of conviction for a violent offence• Self reported age 26 tendency toward aggressive acts• Informant report at age 26 of symptoms of antisocial

personality disorder

Stratifiers: none

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The effect being estimated: The conditional effect of maltreatment depending on a polymorphism in the MAOA encoding gene

Basic analysis to answer study questions:Multiple linear regression analyses:

• Predictors: MAOIA, Maltreatment history, MAOIAxMaltreatment product

• Dependent variables: Indicators of antisocial behavior

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Selection and inclusion of confounders in the analysis

Examined IQ and family social class as potential confounders.

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What would the model be?

IQ and family social class as potential confounders.

MAO-A

IQ

Family socialclass

Child maltreatment

Aggression

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Table S2. Results of final regression analyses testing G ラ E interaction effects onantisocial outcomes. The table presents final models with main effects and interactions entered simultaneously. Childhood maltreatment was handled as a single quantitative variable in the regression analyses, ranging from no maltreatment to severe maltreatment.

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Composite indexof antisocial behavior (z scores)

Low MAOA activity

High MAOA activity

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Conclusion: Role of genotype study

• A functional polymorphism in the MAOA gene moderates the impact of early childhood maltreatment on the development of antisocial behavior in males.

• “Men having the combination of low-activity MAOA genotype and maltreatment were 12% of the male birth cohort by accounted for 44% of the cohort’s violent convictions, yielding an attributable risk fraction (11%) comparable to that of the major risk factors associated with cardiovascular disease. Moreover, 85% of cohort males having (this) genotype who were severely maltreated developed some form of antisocial behavior.”

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Second Study : Poverty, family process, and the mental health of immigrant children in Canada

Reference:

Beiser M, Hou F, Hyman I, & Tousignant M. American Journal of Public Health, 92, No 2, 220-227, 2002.

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The problem: Child mental health

The apparently good mental health of immigrant children in combination with the poverty that often characterizes immigrant families presents a paradox.

Do family variables account for this protective effect?

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Population studied, study design, and sample size : Child mental health

National cross-sectional survey of Canadian children ages 4-11, including:

– 684 immigrant children – 2573 offspring of immigrant parents– 10,092 offspring of non-immigrant parents.

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Measurement issues: Child mental health

Stratifiers: • Because

– the large samples being employed made every test of interaction significant as virtually a fore-gone conclusion

– the relationships with and among covariates were also different for the different groups

• Therefore, the 3 groups of children were treated as separate strata and their estimates from the regression equations compared in the text.

Used measures with previously reported reliabilities and validities.

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The effect being estimated: a: Differential effects of poverty on mental health indicators in native born children of non-immigrant parents, children of immigrant parents, and immigrant children.

b: Whether these differences are attributable to differences in family functioning.

Basic analysis to answer study questions:Multiple linear regression analyses of each group:• Dependent variables:

– Emotional problems– Behavioral problems in offspring

• Basic IV (“exposure”):– poverty

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Selection and inclusion of predictors in the analysis

• Control variables (Set 1) included – age– sex– length of stay in Canada– race/ethnicity– single parent

• Potential mediators (variables standing between or explaining the relationship between the exposure variable and the disease) – ineffective parenting– parental depression– family dysfunction.

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Applied Epidemiologic AnalysisFall 2002Note: constant units across samples

A model of these results

PovertyEmotional problems

Single parent

Ineffectiveparenting

Familydysfunction

Parental depression

3.12

2.52

1.56

52.2%

3.20

.07

15.6% .02

.10

.00

.21

.62

.52

.02

.12

.14

.17

1.42

Immigrant childrenChildren of nonimmigrant parents

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Models shown in paper

• The models shown in the paper use standardized coefficients to represent causal effects.

• Is this a problem?

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Conclusions: Child mental health

Family factors played a relatively weak role with foreign-born children.But for majority-culture children, material deprivation is a less significant threat to mental health than the familial concomitants of poverty.

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Third Study : Analysis of 13 P-DNA postlabeling studies on occupational cohorts exposed to air pollution

Reference:

Peluso M, Ceppi A, Munnia A, Runtoni R, Parodi S. American Journal of Epidemiology, 153, No 6, 546-558.

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The problem: DNA adducts

Guidelines for exposure to airborne pollutants resulting from combustion of organic matter (as in industry, residential heating, and motor vehicles) are based on quantitative studies of air pollution and DNA adducts in the exposed. An accumulated literature needs to be combined to give the best estimates of the nature of these relationships.

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Population studied, study design, and sample size : DNA adducts

A meta-analysis based on 36 sets of exposed-referent pairs of observations in 13 studies.

What is a meta-analysis?

How can we generate dose-response curves from a meta-analysis?

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Measurement issues: DNA adducts

Used exposed worker:referent ratios rather than absolute DNA adduct levels, since large inter-laboratory differences may be present in terms of number of adducts.

Measures of pollution using a specific protocol produced relatively comparable across-studies measure of polycyclic aromatic hydrocarbons (PAHs).

Response:

Dose:

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Selection and inclusion of confounders in the analysis

Discussed but not included in dose-response analyses (e.g. smoking, qualitatively different sources of pollutants)

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The effect being estimated: The dose-response nature of the relationship between measures of air pollution and DNA adducts

Basic analysis to answer study questions:• Multi-level linear regression analyses (taking into account

the cluster-effect of the study from which the subject data came):

• Dependent variables: – DNA adducts in white blood cells (ratio exposed/control)

• Basic IV (“exposure”): – PAH indicator B(a)P

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FIGURE 4. Dose-response relation between frequency ratios and external benzo[a]pyrene (B(a)P) concentrations in work environments in ameta-analysis of occupational exposure to air pollution (13–25), as predicted from equation 6. The inset shows an extrapolated dose-responsecurve at low exposure doses, assuming a linear dose-response relation, for B(a)P levels between 0 and 4.5 ng/m3, the lowest value in the database.FRi, frequency ratio for the ith study.

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Conclusions: DNA adducts

“levels of DNA adducts in exposed workers, with respect to referents (controls), were associated with B(a)P levels,..and that the relation of the dose-response curve was sublinear in heavily exposed industrial workers.”