CRAN Task View: Clinical Trial Design, Monitoring, and Analysis
|Contact:||Ed.Zhang.jr at gmail.com|
This task view gathers information on specific R packages for design,
monitoring and analysis of data from clinical trials. It focuses on including
packages for clinical trial design and monitoring in general plus data analysis
packages for a specific type of design. Also, it gives a brief introduction to
important packages for analyzing clinical trial data. Please refer to task
for more details on these topics. Please feel free
to e-mail me regarding new packages or major package updates.
Design and Monitoring
creates randomizations for block random clinical
trials. It can also produce a PDF file of randomization cards.
contains tools for clinical experiments, e.g., a
randomization tool, and it provides a few special analysis options for clinical
performs computations related to group sequential
designs via the alpha spending approach, i.e., interim analyses need not be
equally spaced, and their number need not be specified in advance.
derives group sequential designs and describes their
computes and plots group
sequential stopping boundaries from the Lan-DeMets method with a variety of
a-spending functions using the ld98 program from the Department of
Biostatistics, University of Wisconsin written by DM Reboussin, DL DeMets, KM
Kim, and KKG Lan.
uses Lan-DeMets Method for group sequential trial; its
functions calculate bounds and probabilities of a group sequential trial.
is a set of tools to compute power in a group sequential
is computes the probability of crossing sequential
efficacy and futility boundaries in a clinical trial. It implements the
Armitage-McPherson and Rowe Algorithm using the method described in Schoenfeld
Design and Analysis
has functions for both design and analysis of
clinical trials. For phase II trials, it has functions to calculate sample size,
effect size, and power based on Fisher's exact test, the operating
characteristics of a two-stage boundary, Optimal and Minimax 2-stage Phase II
designs given by Richard Simon, the exact 1-stage Phase II design and can
compute a stopping rule and its operating characteristics for toxicity
monitoring based repeated significance testing. For phase III trials, it can
calculate sample size for group sequential designs.
provides functions for the design and analysis
of dose-finding experiments (for example pharmaceutical Phase II clinical trials).
It provides functions for: multiple contrast tests, fitting non-linear dose-response models,
calculating optimal designs and an implementation of the
Currently only normally distributed homoscedastic endpoints are supported.
Analysis for Specific Designs
makes global and multiple inferences for given bi-
and trifactorial clinical trial designs using bootstrap methods and a classical
can be employed for computing
distributions (prior, likelihood, and posterior) and moments of robust models:
Cauchy/Binomial, Cauchy/Normal and Berger/Normal. Furthermore, the assessment of
the hyperparameters and the posterior analysis can be processed.
performs Monte Carlo hypothesis tests. It allows a couple
of different sequential stopping boundaries (a truncated sequential probability
ratio test boundary and a boundary proposed by Besag and Clifford (1991). It
gives valid p-values and confidence intervals on p-values.
speff2trial, the package performs estimation and testing of the
treatment effect in a 2-group randomized clinical trial with a quantitative or
Analysis in General
Base R, especially the stats package, has a lot of functionality useful
for design and analysis of clinical trials. For example,
methods) among many others.
has a set of routines for calculating power and related
quantities utilizing asymptotic likelihood ratio methods.
is a suite of functions for computing confidence
intervals and necessary sample sizes for the success probability parameter
Bernoulli distribution under simple random sampling or under pooled
offers conditional inference procedures for the general
independence problem including two-sample, K-sample (non-parametric ANOVA),
correlation, censored, ordered and multivariate problems.
has functions such as
for continuous outcome and
functions for matched pairs analysis in randomized trials.
provides a set of functions for sample size calculations,
shows a two-panel display
of the most frequently occurring adverse events
in the active arm of a clinical study.
package contains around 200 miscellaneous functions useful for such things as data analysis,
high-level graphics, utility operations, functions for computing sample size and power, translating
SAS datasets into S, imputing missing values, advanced table making, variable clustering, character
string manipulation, conversion of S objects to LaTeX code, recoding variables, and bootstrap repeated
covers simultaneous tests and confidence intervals for
general linear hypotheses in parametric models, including linear, generalized
linear, linear mixed effects, and survival models.
contains descriptive statistics, two-sample tests,
parametric accelerated failure models, Cox model. Delayed entry (truncation)
allowed for all models; interval censoring for parametric models. Case-cohort
is a set of functions to calculate sample size for
two-sample difference in means tests. Does adjustments for either nonadherence
or variability that comes from using data to estimate parameters.
is a package for statistical methods to model and adjust
for bias in meta-analysis
is for fixed and random effects meta-analysis. It has
Functions for tests of bias, forest and funnel plot.
consists of a collection of functions for conducting
meta-analyses. Fixed- and random-effects models (with and without
moderators) can be fitted via the general linear (mixed-effects) model. For 2x2
table data, the Mantel-Haenszel and Peto's method are also implemented.
has functions for simple fixed and random effects
meta-analysis for two-sample comparisons and cumulative meta-analyses. Draws
standard summary plots, funnel plots, and computes summaries and tests for
association and heterogeneity.