Longitudinal studies: Cornerstone for causal modeling of dynamic relationships

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Longitudinal studies Cornerstone for causal
modeling of dynamic relationships
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Illustrative examples from the Cebu Longitudinal
Health and Nutrition Survey

• Prospective, community-based sample of 1983-4
  birth cohort, follows mothers and index infant
  from urbanrural areas of Metro Cebu, The
  Philippines
• Bi-monthly surveys birth-2yr, follow-up surveys
  in 1991, 1994, 1998, 2002, 2005
• Extensive individual, household and community data

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Types of longitudinal studies

• Same individuals over time
• Common age at enrolment (e.g. birth cohort)
• Life course studies, individual trajectories
• Challenging to separate age vs time effects
• Eg, diet changes over time because kids get older
  or because there is a secular trend in dietary
  behaviors
• Different ages at enrolment
• Panels/cross sectional time series Different
  individual over time, in common units (e.g.
  community, school, household)
• Allow study of trends over time, but not
  individual trajectories
• Mixed repeatedly study individuals, but with
  replacement
• Each poses different challenges for data
  collection and analysis

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Focus on cohort studies repeated measures of
the same individuals, over time allow for

• Identification of sequence of events, providing
  basis for causal inference
• Comparison of inter vs intra-individual variation
  in susceptibility, behavior, health
• Response to shock or intervention differs between
  individuals
• Individual growth rates vary with age

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Longitudinal Study Challenges

• Cost (time, )
• Attrition
• Bias associated with repeated contacts with
  individuals
• observer effects
• sampling bias amplified by repetition of surveys
• panel conditioning changes in response to
  participation

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Challenges of collecting longitudinal data

• Research priorities and funding opportunities
  change over time funding infrequently covers
  more than 5 years at a time.
• Example Cebu Longitudinal Health and Nutrition
  Survey

Survey year Focus Funder
1983-86 Infant feeding, growth, morbidity, mortality NICHD, Ford Foundation
1991 Growth, school enrollment, IQ World Bank Nestle Foundation
1994 Family planning and womens lives USAID Womens Studies Project
1998 Adolescent Health Mellon Foundation
2002 Effects of health on young adult human capital NIH-Fogarty ISHED
2005 Add biomarkers of CVD risk factors NIH-Fogarty ISHED Obesity roadmap funds
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Methodological challenges of collecting
longitudinal data

• Technology for data collection and storage
  changes over time
• Face to face vs. ACASI
• Measurement Issues
• Change in personnel collecting data
• interobserver reliability is harder to maintain
  and measure over time
• Change in how questions are asked
• e.g. Analysis reveals flawed question on round 1
  do we change the question on round 2?
• Change in how questions are answered
• different social climate or respondent knowledge
  gained over time (perhaps by study participation)
  may affect veracity
• Who responds? Child vs mother? At what age does
  a child become the respondent?
• Change in meaning of indicators over time
• E.g. wealth TV vs computer vs. car over time

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Dilemmas and choices.

• Expanding the survey may increase respondent
  burden and compromise participation rates
• But Failure to expand the survey represents
  missed opportunities
• Follow-up of all migrants is desirable
• But Follow-up is costly and not always feasible
• Changing how a question is asked eliminates
  comparability over time
• But keeping a flawed question is bad science

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Data collection challenges

• How often should participants be surveyed?
• Frequent measurement allows sequence of events to
  be identified
• Pregnancygtgtgtquit schoolgtgtgtmarriage
• Quit schoolgtgtgtmarrygtgtgtpregnancy
• Respondent burden, contamination of sample

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Analysis challenges

• Specialized techniques are needed to accommodate
  the strengths and weaknesses of longitudinal data
• Accounting for complexity
• Accounting for changing inputs across the
  lifecycle

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Analysis challenges

• Accounting for differences in susceptibility
• Example parental investment may change based on
  acquired characteristics of the child
• Example developmental origins of adult disease
  key premise is that prenatal factors alter
  response to subsequent exposures
• Intergenerational studies

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Challenges Selection bias related to attrition

• Loss to follow-up Death, Migration, Refusal
• May result in sample which is markedly different
  from baseline sample in measured and unmeasured
  attributes
• Biased estimates may be obtained if the
  relationships of interest are fundamentally
  different in those remaining vs. lost,
  particularly when differences relate to
  unmeasured characteristics

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Tools for handling selection bias

• Heckman-type models estimate likelihood of being
  in the sample simultaneously with outcome of
  interest
• Difficult to account for multiple reasons for
  attrition (with different potential for bias, e.g
  death vs migration)

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Challenges growth trajectories and functional
forms

• Ideallywe would like models to accommodate
• Non-linear growth trajectories
• Differences in shape of trajectories at different
  ages, and in the relationship of exposures to
  outcomes at different ages

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Latent growth curves A category of Structural
Equation Models

• Random intercepts and random slopes allow each
  case to have a different trajectory over time
• Random coefficients incorporated into SEMs by
  considering them as latent variables
• Capitalize on SEM strengths, including
• ML methods for missing data
• Estimation of different non linear forms of
  trajectories, including piecewise to identify
  different curve segments
• Measures of model fit and
• Inclusion of latent covariates and repeated
  covariates
• Latent variables derived from multiple measured
  variables
• Account for bi-directional relationships

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Data demands for econometric models

• Detailed, time-varying, high quality exogenous
  variables
• Often this means community level variables, so
  data collection cannot be limited to individual
  or household level information

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Whats on the frontier for new longitudinal
methods?

• ..new data, methodologies, and tools from both
  inside and outside the social sciences are
  demonstrating real promise in advancing these
  sciences from descriptive to predictive ones
• Longitudinal surveys is one of 6 listed
  frontiers
• Improved statistical methods is another (but this
  section is about using the internet to conduct
  surveys!!)

Butz WP, Torrey BB Some Frontiers in Social
Science. Science June 2006
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What is on the frontier??

• Addition of biomarkers
• Overcoming squeamishness of social scientists
• Lack of laboratory facilities
• What methodological improvements are needed?
• Innovative data collection and tracking
• Use of GPS and PDAs