Table 1.
Analytical strategies in growth research and their applications
| Strategy | Applications | |
|---|---|---|
| Size | Internal Z-scores | Standardize a measure for systematic differences between sexes (or systematic differences between any other sub-groups, such as ethnicities) |
| Standardize a measure for between-child differences in exact age at assessment (using the LMS method) | ||
| Transform a skewed measure so that it is normally distributed (using the LMS method) | ||
| External Z-scores | Compare the mean and distribution of a measure against that in some other sample (typically the reference sample of a growth chart) | |
| Standardize, to some extent, a measure for systematic differences between sexes (when a small sample size prohibits the use of internal Z-scores) | ||
| Standardize, to some extent, a measure for between-child differences in exact age at assessment (when a small sample size prohibits the use of internal Z-scores) | ||
| Transform a skewed measure so that it approximates a normal distribution (if the growth reference was constructed using LMS or some other technique that adjusts for skewness) | ||
| Indices | Standardize a measure for between-child differences in total body size (typically taken to be height) | |
| Conditional size measures | Standardize a measure for between-child differences in total body size (typically taken to be height) | |
| Growth | Conditional regression models | Quantify the association of size at one age with an outcome at a second age, conditional on size at the second age (combined with a life course plot to quantify the association of growth between the two ages with the outcome) |
| Quantify the association of growth between two ages with an outcome at the second age, conditional on size at the first age | ||
| Regression with conditional growth measures | Quantify the associations of growth during different consecutive age periods with some outcome, conditional on size at the first age | |
| Growth curves | Individual growth curves | Characterize a child's growth (by fitting a growth curve that summarizes his or her longitudinal data in a few biologically meaningful parameters and/ or derived traits) |
| Characterize average growth in a sample, after fitting multiple individual growth curves (by producing a mean-constant growth curve) | ||
| Characterize between-child and population variation in growth, after fitting multiple individual growth curves (by inspecting the pooled biologically meaningful parameters and/ or derived traits) | ||
| Relate growth to some distal outcome, other growth process, or survival process (using a two-step strategy) | ||
| Mixed effects growth curves | Simultaneously characterize the growth of every child in a sample and the average growth in that sample (by modeling and therefore quantifying within-child and between-child variation) | |
| Quantify systematic differences in growth due to independent variables, such as sex and ethnicity (by adding these variables into the model as fixed effects) | ||
| Relate growth to some distal outcome, other growth process, or survival process (using a one or two-step strategy) | ||
| Latent growth curves | *Same as for mixed effects growth curves* | |
| Patterns of growth | Growth mixture modeling | *Same as for mixed effects growth curves* |
| Identify distinct unobserved groups (i.e., latent classes) of individuals who share similar average growth curves | ||
| Characterize the determinants of latent class membership and investigate whether or not systematic differences in growth due to independent variables, such as sex and ethnicity, differ across the latent classes | ||
| Relate the latent classes to some distal outcome, other growth process, or survival process (using a one or two-step strategy) |
LMS, lambda-mu-sigma.