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
views
ExperimentalDesign,
Survival,
Pharmacokinetics
for more details on these topics. Please feel free
to email me regarding new packages or major package updates.
Design and Monitoring

TrialSize
This package has more than 80 functions from the book
Sample Size Calculations in Clinical Research
(Chow & Wang & Shao, 2007, 2nd ed., Chapman &Hall/CRC).

asd
This Package runs simulations for adaptive seamless designs using early outcomes for treatment selection.

bcrm
This package implements a wide variety of one and twoparameter Bayesian CRM designs. The program can run interactively, allowing the user to enter outcomes after each cohort has been recruited, or via simulation to assess operating characteristics.

blockrand
creates randomizations for block random clinical
trials. It can also produce a PDF file of randomization cards.

clusterPower
Calculate power for cluster randomized trials (CRTs) that compare two means, two proportions, or two counts using closedform solutions. In addition, calculate power for cluster randomized crossover trials using Monte Carlo methods. For more information, see Reich et al. (2012)
doi:10.1371/journal.pone.0035564
.

conf.design
This small package contains a series of simple tools for constructing and manipulating confounded and fractional factorial designs.

CRTSize
This package contains basic tools for the purpose of sample size estimation in cluster (group) randomized trials. The package contains traditional powerbased methods, empirical smoothing (Rotondi and Donner, 2009), and updated metaanalysis techniques (Rotondi and Donner, 2011).

dfcrm
This package provides functions to run the CRM and
TITECRM in phase I trials and calibration tools for trial planning purposes.

ewoc
An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998)
doi:10.1002/(SICI)10970258(19980530)17:10%3C1103::AIDSIM793%3E3.0.CO;29
. It calculates the next dose as a clinical trial proceeds as well as performs simulations to obtain operating characteristics.

experiment
contains tools for clinical experiments, e.g., a
randomization tool, and it provides a few special analysis options for clinical
trials.

FrF2
This package creates regular and nonregular Fractional Factorial designs. Furthermore, analysis tools for Fractional Factorial designs with 2level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the builtin function alias). The package is currently subject to intensive development. While much of the intended functionality is already available, some changes and improvements are still to be expected.

GroupSeq
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.

gsbDesign
Group Sequential Operating Characteristics for Clinical, Bayesian twoarm Trials with known Sigma and Normal Endpoints.

gsDesign
derives group sequential designs and describes their
properties.

ldBand
from
Hmisc
computes and plots group
sequential stopping boundaries from the LanDeMets method with a variety of
aspending functions using the ld98 program from the Department of
Biostatistics, University of Wisconsin written by DM Reboussin, DL DeMets, KM
Kim, and KKG Lan.

ldbounds
uses LanDeMets Method for group sequential trial; its
functions calculate bounds and probabilities of a group sequential trial.

Mediana
Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survivaltype and counttype endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.

PIPS
generates predicted interval plots, simulates
and plots confidence intervals of an effect estimate given observed data
and a hypothesis about the distribution of future data.

PowerTOST
contains functions to calculate power and sample size for various study designs used for bioequivalence studies. See function known.designs() for study designs covered. Moreover the package contains functions for power and sample size based on 'expected' power in case of uncertain (estimated) variability. Added are functions for the power and sample size for the ratio of two means with normally distributed data on the original scale (based on Fieller's confidence ('fiducial') interval).

pwr
has power analysis functions along the lines of Cohen (1988).

PwrGSD
is a set of tools to compute power in a group sequential
design.

qtlDesign
provides tools for the design of QTL experiments.

samplesize
computes sample size for Student's ttest with equal and nonequal variances and for the WilcoxonMannWhitney test for categorical data with and without ties.
Design and Analysis

Package
AGSDest
This package provides tools and functions for parameter estimation in adaptive group sequential trials.

Package
clinfun
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 twostage boundary, Optimal and Minimax 2stage Phase II
designs given by Richard Simon, the exact 1stage 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.

Package
CRM
Continual Reassessment Method (CRM) simulator for Phase I Clinical Trials.

Package
dfpk
Statistical methods involving PK measures are provided, in the dose allocation process during a Phase I clinical trials. These methods enter pharmacokinetics (PK) in the dose finding designs in different ways, including covariates models, dependent variable or hierarchical models. This package provides functions to generate data from several scenarios and functions to run simulations which their objective is to determine the maximum tolerated dose (MTD).

Package
dfped
A unified method for designing and analysing dosefinding trials in paediatrics, while bridging information from adults, is proposed in the dfped package. The dose range can be calculated under three extrapolation methods: linear, allometry and maturation adjustment, using pharmacokinetic (PK) data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dosetoxicity and doseefficacy relationships can be obtained using early phase adult toxicity and efficacy data at several dose levels through dfped package. Priors are used into the dose finding process through a Bayesian model selection or adaptive priors, to facilitate adjusting the amount of prior information to differences between adults and children. This calibrates the model to adjust for misspecification if the adult and paediatric data are very different. User can use his/her own Bayesian model written in Stan code through the dfped package. A template of this model is proposed in the examples of the corresponding R functions in the package. Finally, in this package you can find a simulation function for one trial or for more than one trial.

Package
DoseFinding
provides functions for the design and analysis
of dosefinding experiments (for example pharmaceutical Phase II clinical trials).
It provides functions for: multiple contrast tests, fitting nonlinear doseresponse models,
calculating optimal designs and an implementation of the
MCPMod
methodology.
Currently only normally distributed homoscedastic endpoints are supported.

MCPMod
This package implements a methodology for the design and analysis of doseresponse studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738748). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCPMod methodology.

Package
TEQR
The target equivalence range (TEQR) design is a frequentist implementation of the modified toxicity probability interval (mTPI) design and a competitor to the standard 3+3 design (3+3). The 3+3 is the work horse design in Phase I. It is good at determining if a safe dose exits, but provides poor accuracy and precision in estimating the level of toxicity at the maximum tolerated dose (MTD). The TEQR is better than the 3+3 when compared on: 1) the number of times the dose at or nearest the target toxicity level was selected as the MTD, 2) the number of subjects assigned to doses levels, at or nearest the MTD, and 3) the overall trial DLT rate. TEQR more accurately and more precisely estimates the rate of toxicity at the MTD because a larger number of subjects are studied at the MTD dose. The TEQR on average uses fewer subjects and provide reasonably comparable results to the continual reassessment method (CRM) in the number of times the dose at or nearest the target toxicity level was selected as the MTD and the number of subjects assigned doses, at, or nearest the target and in overall DLT rate.

Package
ThreeArmedTrials
Design and analyze threearm noninferiority or superiority trials which follow a goldstandard design, i.e. trials with an experimental treatment, an active, and a placebo control.
Analysis for Specific Designs

adaptTest
The functions defined in this program serve for implementing adaptive twostage tests. Currently, four tests are included: Bauer and Koehne (1994), Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional error function. Userdefined tests can also be implemented. Reference: Vandemeulebroecke, An investigation of twostage tests, Statistica Sinica 2006.

clinsig
This function calculates both parametric and nonparametric versions of the JacobsonTruax estimates of clinical significance.

nppbib
implements a nonparametric statistical test for rank or score data from partiallybalanced incomplete blockdesign experiments.

speff2trial, the package performs estimation and testing of the
treatment effect in a 2group randomized clinical trial with a quantitative or
dichotomous endpoint.

ThreeGroups
This package implements the Maximum Likelihood estimator for threegroup designs proposed by Gerber, Green, Kaplan, and Kern (2010).
Analysis in General

Base R, especially the stats package, has a lot of functionality useful
for design and analysis of clinical trials. For example,
chisq.test,
prop.test,
binom.test,
t.test,
wilcox.test,
kruskal.test,
mcnemar.test,
cor.test,
power.t.test,
power.prop.test,
power.anova.test,
lm,
glm,
nls,
anova
(and its
lm
and
glm
methods) among many others.

asypow
has a set of routines for calculating power and related
quantities utilizing asymptotic likelihood ratio methods.

binomSamSize
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
sampling.

coin
offers conditional inference procedures for the general
independence problem including twosample, Ksample (nonparametric ANOVA),
correlation, censored, ordered and multivariate problems.

epibasix
has functions such as
diffdetect,
n4means
for continuous outcome and
n4props
and
functions for matched pairs analysis in randomized trials.

ae.dotplot
from
HH
shows a twopanel display
of the most frequently occurring adverse events
in the active arm of a clinical study.

The
Hmisc
package contains around 200 miscellaneous functions useful for such things as data analysis,
highlevel 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
measures analysis.

InformativeCensoring
Multiple Imputation for Informative Censoring. This package implements two methods. Gamma Imputation from Jackson et al. (2014)
doi:10.1002/sim.6274
and Risk Score Imputation from Hsu et al. (2009)
doi:10.1002/sim.3480
.

multcomp
covers simultaneous tests and confidence intervals for
general linear hypotheses in parametric models, including linear, generalized
linear, linear mixed effects, and survival models.

survival
contains descriptive statistics, twosample tests,
parametric accelerated failure models, Cox model. Delayed entry (truncation)
allowed for all models; interval censoring for parametric models. Casecohort
designs.

ssanv
is a set of functions to calculate sample size for
twosample difference in means tests. Does adjustments for either nonadherence
or variability that comes from using data to estimate parameters.
MetaAnalysis

metasens
is a package for statistical methods to model and adjust
for bias in metaanalysis

meta
is for fixed and random effects metaanalysis. It has
Functions for tests of bias, forest and funnel plot.

metafor
consists of a collection of functions for conducting
metaanalyses. Fixed and randomeffects models (with and without
moderators) can be fitted via the general linear (mixedeffects) model. For 2x2
table data, the MantelHaenszel and Peto's method are also implemented.

metaLik
Likelihood inference in metaanalysis and metaregression models.

rmeta
has functions for simple fixed and random effects
metaanalysis for twosample comparisons and cumulative metaanalyses. Draws
standard summary plots, funnel plots, and computes summaries and tests for
association and heterogeneity.