= Solution
\b[Use a <longitudinal study> with repeated measurements of the same individuals.] Recruit people across several starting ages and measure the same ear, with a standardized anatomical definition and instrument, at baseline and subsequent visits over several years. Repeat measurements at each visit to estimate <measurement error>; where practical, conceal previous measurements from the observer. Record sex, body size, birth cohort and observer, and document reasons for loss to follow-up.
A simple <random-intercept linear mixed model> for ear length $Y_{ij}$ at elapsed follow-up time $t_{ij}$ is
$$
Y_{ij}=\alpha_i+\beta t_{ij}+\gamma^Tz_{ij}+\varepsilon_{ij}.
$$
The individual intercept $\alpha_i$ absorbs persistent differences between people. Alternatively, differences $Y_{ij}-Y_{i0}$ remove that intercept directly. Estimate the within-person <regression coefficient> $\beta$ and its <confidence interval>, accounting for the <correlation> of repeated measurements. Compare it with the cross-sectional slope rather than treating the two slopes as automatically identical.
This <longitudinal study> directly checks whether an individual's ears tend to lengthen as time passes. Several entry-age cohorts help assess whether the change varies with starting age. Calendar-period changes and selective dropout can still affect interpretation, so repeated observation alone does not eliminate every possible source of <confounding> or <selection bias>.
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