Longitudinal Studies

Small-sample bias-corrected variance for regression modeling using proportional subdistribution hazards with clustered right censored data. (Zhou et al., 2012) Failure times within the same cluster are dependent. Four types of bias correction are included: the MD-type correction by Mancl and DeRouen (2001), the KC-type correction by Kauermann and Carroll (2001), the FG-type correction by Fay and Graubard (2001), and the MBN-type correction by Morel, Bokossa, and Neerchal (2003).

Faculty: Fan Li, PhD

Download: Cran R / crrcbcv package

Platform: R

Reference: doi.org (crrcbcv)

Analyzes longitudinal Electronic Health Record (EHR) data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes. The accompanying paper is Du et al (2024) <doi:10.48550/arXiv.2410.13113>.

Faculty: Bhramar Mukherjee, PhD

Download: Cran R / EHRmuse package

Platform: R

Reference: doi.org (EHRmuse)

R Shiny App for estimating intracluster correlation coecients to support the planning of longitudinal cluster randomized trials.

Faculty: Fan Li, PhD

Download: estimateICC package

Platform: R Shiny

Reference: doi.org (estimateICC)

Methods for Estimating Variance of GEE Logistic Regression Estimators under Various Working Correlation Structures.

Faculty: Lee Kennedy-Shaffer, PhD

Download: GitHub / gee-efficiency Package

Platform: R

Reference: doi.org (gee-efficiency)

SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments.

Faculty: Fan Li, PhD

Platform: SAS macro

Reference: doi.org (GEEMAEE)

The %ICC9 macro is a SAS version 9 macro that computes reliability coeÿcients (intraclass correlation coeÿcients) and their 95% confidence intervals. These quantities can be calculated after first adjusting for fixed effects.

Faculty: Donna Spiegelman, ScD

Download: %icc9 package

Platform: SAS

Reference: nih.gov (%icc9)

The %MEDIATE macro calculates the point and interval estimates, as well as a p-value, for the percent mediation of one effect by one or more intermediate variables. The macro is designed for treatment effects estimated as relative risks in Cox regression survival analysis using PROC PHREG and for treatment effects from generalized linear models using PROC GENMOD. When fitting log-binomial models with PROC GENMOD, an option is available to improve model convergence.

Faculty: Donna Spiegelman, ScD

Download: %mediates package

Platform: SAS

Reference: doi.org (%mediate)

Calculating the minimum number of participants (N) for a fixed number of measurements (r), given pre-specified power; the minimum number of repeated measurements (r) for fixed N, power, and pre-specified study length or time between visits; power for a given (N,r); and the optimal (N,r) subject to power or cost constraints. Compound symmetry, damped exponential, and random slopes covariances are supported.

Faculty: Donna Spiegelman, ScD

Download: OPTITXS.r package

Platform: Fortran

Reference: doi.org (OPTITXS.r)

The %RELRISK9 macro obtains relative risk estimates using PROC GENMOD with the binomial distribution and the log link. This is particularly useful when the odds ratio is not a good approximation to the rate ratio (e.g., because of high prevalence of the outcome or large relative risks).

Faculty: Donna Spiegelman, ScD

Download: %relrisk9 package

Platform: SAS

Reference: doi.org (%relrisk9)

Multivariate Adaptive Splines for Analysis of Longitudinal Data. The standalone program takes a data structure similar to that of "CTMBR", except that there is a time variable "t". We also have an R package.

Faculty: Heping Zhang, PhD

Download: MASAL package

Platform: Unix; R

Reference: doi.org (MASAL)

A modified version of alternating logistic regressions (ALR) with estimation based on orthogonalized residuals (ORTH) is implemented, which use paired estimating equations to jointly estimate parameters in marginal mean and within-association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). A finite-sample bias correction is provided to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and different bias-corrected variance estimators such as BC1, BC2, and BC3.

Faculty: Fan Li, PhD

Download: Cran R / ORTH.Ord package

Platform: R

Reference: doi.org (ORTH.Ord)

As immunological and clinical studies become more complex, there is an increasing need to analyze temporal immunophenotypes alongside demographic and clinical covariates, where each subject receives matrix-valued time series observations for potentially high-dimensional longitudinal features, as well as other static characterizations. Researchers aim to find the low-dimensional embedding of subjects using matrix-valued time series observations and investigate relationships between static clinical responses and the embedding. However, constructing these embeddings can be challenging due to high dimensionality, sparsity, and irregularity in sample collection over time. In addition, the incorporation of static auxiliary covariates is frequently desired during such a construction. To address these issues, we propose a smoothed probabilistic PARAFAC model with covariates (SPACO) that uses auxiliary covariates of interest. We provide extensive simulations to test different aspects of SPACO and demonstrate its application to an immunological dataset from patients with SARS-CoV-2 infection.

Faculty: Leying Guan, PhD

Download: GitHub / SPACO package

Platform: Python

Reference: doi.org (SPACO)

Stata module to compute bias-corrected (small-sample) standard errors for generalized estimating equations.

Faculty: Fan Li, PhD

Download: xtgeebcv package

Platform: stata module

Reference: doi.org (xtgeebcv)