Lee Kennedy-Shaffer, PhD

Methods and analysis for the impact of rule changes on MLB and its players, using quasi-experimental methods and panel data.

Faculty: Lee Kennedy-Shaffer, PhD

Download: GitHub / Baseball-QEs Package

Platform: R, R Shiny

Reference: doi.org (Baseball-QEs)

Developing functions to implement the generalized Difference-in-Differences (DID) estimator approach to analysis of stepped wedge cluster-randomized trials and observational/quasi-experimental panel data.

Faculty: Lee Kennedy-Shaffer, PhD

Download: GitHub / GenDID Package

Platform: R

Reference: doi.org (GenDID)

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)

Randomized controlled trials are crucial for the evaluation of interventions such as vaccinations, but the design and analysis of these studies during infectious disease outbreaks is complicated by statistical, ethical, and logistical factors. Attempts to resolve these complexities have led to the proposal of a variety of trial designs, including individual randomization and several types of cluster randomization designs: parallel-arm, ring vaccination, and stepped wedge designs. Because of the strong time trends present in infectious disease incidence, however, methods generally used to analyze stepped wedge trials might not perform well in these settings. Using simulated outbreaks, we evaluated various designs and analysis methods, including recently proposed methods for analyzing stepped wedge trials, to determine the statistical properties of these methods. While new methods for analyzing stepped wedge trials can provide some improvement over previous methods, we find that they still lag behind parallel-arm cluster-randomized trials and individually randomized trials in achieving adequate power to detect intervention effects. We also find that these methods are highly sensitive to the weighting of effect estimates across time periods. Despite the value of new methods, stepped wedge trials still have statistical disadvantages compared with other trial designs in epidemic settings.

Faculty: Lee Kennedy-Shaffer, PhD

Download: GitHub / SW-CRT-outbreak Package

Platform: R

Reference: doi.org (SW-CRT-outbreak)

Sample Size Calculations for Stratified IRTs and CRTs. The ui.R and server.R files create a Shiny app that can be used to find the sample size required for a target stratified IRT or CRT via a user-friendly interface. Sensitivity plots and data to the proportion of individuals in a given stratum are also available here.

Faculty: Lee Kennedy-Shaffer, PhD

Download: GitHub / strat-crt-ss Package

Platform: R, R Shiny

Reference: doi.org (strat-crt-ss)

SW-CRT Analysis Methods.R implements the analysis methods for stepped wedge cluster randomized trials detailed in Kennedy-Shaffer et al. 2020. These include the novel methods from that paper (SC, CO, COSC, and ENS) as well as the versions of existing methods described in that article (MEM from Hussey & Hughes 2007, CPI from Hooper et al. 2016, the permutation test versions of these from Wang and De Gruttola 2017 and Ji et al. 2017, and NPWP from Thompson et al. 2018).

Faculty: Lee Kennedy-Shaffer, PhD

Download: GitHub / SW-CRT-analysis Package

Platform: R

Reference: doi.org (SW-CRT-analysis)

Methods to infer epidemiologic incidence curves from viral load (PCR Ct value) data, sampled cross-sectionally or in repeated cross-sections.

Faculty: Lee Kennedy-Shaffer, PhD

Download: GitHub / virosolver package

Platform: R

Reference: doi.org (virosolver)