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

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illustrative examples from the cebu longitudinal health and nutrition survey
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 urban&rural 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
types of longitudinal studies
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

focus on cohort studies repeated measures of the same individuals over time allow for
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
longitudinal study challenges
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
challenges of collecting longitudinal data
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

methodological challenges of collecting longitudinal data
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
dilemmas and choices
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
data collection challenges
Data collection challenges
  • How often should participants be surveyed?
  • Frequent measurement allows sequence of events to be identified
    • Pregnancy>>>quit school>>>marriage
    • Quit school>>>marry>>>pregnancy
  • Respondent burden, “contamination” of sample
analysis challenges
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
analysis challenges1
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
challenges selection bias related to attrition
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
tools for handling selection bias
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)
challenges growth trajectories and functional forms
Challenges: growth trajectories and functional forms
  • Ideally…we 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
latent growth curves a category of structural equation models
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
data demands for econometric models
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
what s on the frontier for new longitudinal methods
What’s 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

what is on the frontier
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