Fan Li, PhD

crrcbcv

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)

cvcrand

Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.

Faculty: Fan Li, PhD

Download: Cran R / cvcrand package

Platform: R

Reference: r-project.org (cvcrand)

CoxBcv

The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (2023, Biometrical Journal)

Faculty: Fan Li, PhD

Download: Cran R / CoxBcv package

Platform: R

Reference: doi.org (CoxBcv)

mediateP

Functions for calculating the point and interval estimates of the natural indirect effect (NIE), total effect (TE), and mediation proportion (MP), based on the product approach.

Faculty: Fan Li, PhD; Donna Spiegelman, ScD

Download: Cran R / mediateP package

Platform: R

Reference: doi.org (mediateP)

ORTH.Ord

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)

msqm

A R Package for Analysis of Marginal Structural Quantile Models. Contains inverse probability weighting, iterative conditional regression, and doubly robust estimation of marginal structural quantile model.

Faculty: Fan Li, PhD

Download: Cran R / msqm package

Platform: R

Reference: doi.org (msqm)

IC-OLS

Design stepped wedge cluster randomized trials analyzed through generalized estimating equations under a misspecified working independence correlation structure.

Faculty: Fan Li, PhD

Download: IC-OLS package

Platform: R Shiny

Reference: doi.org (IC-OLS)

SW-IC-binary-count

R Shiny App to estimate the information content of the stepped wedge designs with binary or count outcomes.

Faculty: Fan Li, PhD

Download: SW-IC-binary-count package

Platform: R Shiny

Reference: doi.org (SW-IC-binary-count)

estimateICC

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)

sample_size

R Shiny App for power calculation to detect treatment effect heterogeneity by a single binary effect modifier in a cluster randomized trial with binary outcomes.

Faculty: Fan Li, PhD

Download: sample_size package

Platform: R Shiny

Reference: doi.org (sample_size)

swcrtcalculator

R Shiny App and Stata module for finding the right power and sample size calculator for stepped wedge cluster randomized trials.

Faculty: Fan Li, PhD

Download: swcrtcalculator package

Platform: R Shiny

Reference: doi.org (swcrtcalculator)

xtgeebcv

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)

power_swgee

Stata module to compute power (under both a Z and t distribution) for cluster randomized stepped wedge designs.

Faculty: Fan Li, PhD

Download: power_swgee package

Platform: stata module

Reference: doi.org (power_swgee)

CRTFASTGEEPWR

Randomized trials based on marginal models and multilevel correlation structures

Faculty: Fan Li, PhD

Download: CRTFASTGEEPWR package

Platform: SAS macro

Reference: doi.org (CRTFASTGEEPWR)

GEEMAEE

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)

H2x2Factorial

Implements the sample size methods for hierarchical 2x2 factorial trials under two choices of effect estimands and a series of hypothesis tests proposed in "Sample size calculation in hierarchical 2x2 factorial trials with unequal cluster sizes" (under review), and provides the table and plot generators for the sample size estimations.

Faculty: Denise Esserman, PhD; Fan Li, PhD

Download: Cran R / H2x2Factorial Package

Platform: R

PSweight

Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.

Faculty: Fan Li, PhD; Guangyu Tong, PhD

Download: Cran R / PSweight package

Platform: R

Reference: The R Journal (PSweight)

swdpwr

To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed.

Faculty: Fan Li, PhD; Xin Zhou, PhD; Donna Spiegelman, ScD

Download: Cran R / swdpwr package

Platform: R; R Shiny

Reference: doi.org (swdpwr)