Testing the Equality of a High Dimensional Set of Densities
c(
person("Marta", "Cousido Rocha", email = "martacousido@uvigo.es", role = c("aut", "cre")),
person("José Carlos", "Soage González", email = "jsoage@uvigo.es", role = "ctr"),
person("Jacobo", "de Uña Álvarez", email = "jacobo@uvigo.es", role = c("aut", "ths")),
person("Jeffrey", "D. Hart", email = "hart@stat.tamu.edu", role = "aut"),
person("Ivan", "Kojadinovic", email = "ivan.kojadinovic@univ-pau.fr", role = "cph"),
person("A.", "Patton", email = "andrew.patton@duke.edu", role = "cph"),
person("C.", "Parmeter", email = "c.parmeter@miami.edu", role = "cph"),
person("J.", "Racine", email = "racinej@mcmaster.ca", role = "cph"))
The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.
Marta Cousido Rocha [aut, cre],
José Carlos Soage González [ctr],
Jacobo de Uña Álvarez [aut, ths],
Jeffrey D. Hart [aut],
Ivan Kojadinovic [cph],
A. Patton [cph],
C. Parmeter [cph],
J. Racine [cph]

Normalize expression data using equivalently expressed genes
Li-Xuan Qin <qinl@mskcc.org>, Jaya M. Satagopan
<satagopj@mskcc.org>
Normalize oligonucleotide gene expression data using
equivalently expressed genes in experiments comparing two
groups of samples (for example, case-control samples), as
outlined in L-X Qin and JM Satagopan (2009).

R (>= 2.8.0), lme4, mvtnorm, Matrix, lattice

Li-Xuan Qin <qinl@mskcc.org>, Jaya M. Satagopan
<satagopj@mskcc.org>, Brian Denton <dentonb@mskcc.org>

Modeling, Confidence Intervals and Equivalence of Survival
Curves
We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points.
For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula.
The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration.
All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data <arXiv:2009.06699>.