Longitudinal studies:
Cornerstone for causal
modeling of dynamic
relationships
Illustrative examples from the Cebu
Longitudinal Health and Nutrition Survey
• Prospective, communitybased 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
• 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:
• 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
• 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
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 women’s lives USAID: Women’s Studies
Project
1998
Adolescent Health
2002
Effects of health on young adult NIH-Fogarty ISHED
human capital
2005
Add biomarkers of CVD risk
factors
Mellon Foundation
NIH-Fogarty ISHED
Obesity roadmap funds
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….
• 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
• 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
• Specialized techniques are needed to
accommodate the strengths and
weaknesses of longitudinal data
• Accounting for complexity
• Accounting for changing inputs across
the lifecycle
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
• 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
• 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
• 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
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?
• ..”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??
• 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