Advantages | Limitations | |
---|---|---|
Method | - Simple implementation | - Collinearity in longitudinal phthalate measures can cause instable effect estimates and inflated variance estimates |
Multiple logistic regression model | - Jointly account for longitudinal phthalate measures in one model | - Requires time points to be uniform |
- Only the subjects with complete data are used | ||
- Difficult interpretation | ||
Method | - Simple implementation | - No straightforward way to combine results from multiple regression models to assess aggregate effect of phthalate levels on preterm birth |
Parallel cross-sectional logistic regression models | - Subjects with incomplete data can be retained | - Control for family-wise error rate using Bonferroni correction may be too conservative |
- Simple interpretation | ||
Method | - Simple implementation | - Difficult to handle time-varying covariates |
Model using mean exposure across visits as a summary | - Simple way to account for and summarize longitudinal phthalate measures | - Limited if data are unbalanced and/or not missing at random |
- Straightforward interpretation | - Trends of phthalate measures relevant to the outcome may be missed | |
- Improved power when exposure has poor stability over time and exposure levels themselves are most relevant to the outcome | ||
Method | - Simple implementation | - May be inappropriate when maximum concentrations are indicative of recent rather than acute exposure |
Model Using maximum exposure value across visits as summary | - Straightforward interpretation | - Deposition of time-varying covariates is questionable |
- Powerful when the association is not driven by the longitudinal trend and/or average level but rather an acute instance of phthalate exposure | ||
Method | - Flexible modeling of exposure pattern over time in Stage 1 | |
Two stage mixed effects model | - Examines effect of characteristics carried from Stage 1 in Stage 2 | - Uncertainty from Stage 1 is not incorporated in Stage 2 which may lead to biased results |
- Naturally accounts for between subject heterogeneity | - May not be useful when phthalate levels are unstable over time | |
Method | - Accounts for longitudinal nature of exposure | - Not temporally logical |
Generalized additive mixed model to contrast exposure trajectories | - Trends of exposure can be depicted parametrically or non-parametrically for each group | - Risk cannot be estimated |
Method | - Allows risk estimation based on cluster identity | - Requires dataset to be balanced and complete |
Gaussian mixture model by clustering the exposure values | - Characteristics of each cluster well-depicted by a multivariate Gaussian distribution | - Requires longitudinal phthalate measures to follow a multivariate Gaussian distribution |
- Direct interpretation | - Subtle characteristics cannot be captured by the first two moments | |
- Computationally expensive | ||
Method | - Accounts for longitudinal nature of the exposure and time-varying covariates | - May be underpowered if trends of phthalate levels are unimportant |
Functional clustering model | - Allows risk estimation based on cluster identity | - Trends are unreliable if the data are sparse with (few time points for each subject) |
- Does not require exposure to be balance and complete | ||
- Direct interpretation | ||
Method | - Accounts for longitudinal nature of the exposure and time-varying covariates | - Difficult interpretation |
Functional logistic regression model | - Does not require exposure to be balanced and complete | - Trends are unreliable if the data are sparse (few time points for each subject) |
- Longitudinal information is entirely retained in FPC scores | - Choice of number of principal and number of basis function via BIC is ad-hoc |