Longitudinal studies: Cornerstone for causal modeling of dynamic relationships.
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Transcript of 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 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
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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 replacementEach 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 BankNestle Foundation
1994 Family planning and women’s lives
USAID: Women’s 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 ISHEDObesity 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• Pregnancy>>>quit school>>>marriage• Quit school>>>marry>>>pregnancy
• 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• 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
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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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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
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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