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 e-mail me regarding new packages or major package updates.

Design and Monitoring

• Blockrand creates randomizations for block random clinical trials. It can also produce a PDF file of randomization cards.
• Experiment contains tools for clinical experiments, e.g., a randomization tool, and it provides a few special analysis options for clinical trials.
• 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.
• gsDesign derives group sequential designs and describes their properties.
• ldBand from Hmisc 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.
• ldbounds uses Lan-DeMets Method for group sequential trial; its functions calculate bounds and probabilities of a group sequential trial.
• PwrGSD is a set of tools to compute power in a group sequential design.
• seqmon 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 (2001).

Design and Analysis

• 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 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.
• Package DoseFinding 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 MCPMod methodology. Currently only normally distributed homoscedastic endpoints are supported.

Analysis for Specific Designs

• bifactorial makes global and multiple inferences for given bi- and trifactorial clinical trial designs using bootstrap methods and a classical approach.
• The package ClinicalRobustPriors 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.
• MChtest 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 dichotomous endpoint.

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 two-sample, K-sample (non-parametric 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.
• epicalc provides a set of functions for sample size calculations, such as n.for.2means, n.for.2p, n.for.equi.2p, n.for.noninferior.2p, n.for.cluster.2p, etc.
• ae.dotplot from HH shows a two-panel 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, 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 measures analysis.
• 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, two-sample tests, parametric accelerated failure models, Cox model. Delayed entry (truncation) allowed for all models; interval censoring for parametric models. Case-cohort designs.
• ssanv 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.

Meta-Analysis

• copas is a package for statistical methods to model and adjust for bias in meta-analysis
• meta is for fixed and random effects meta-analysis. It has Functions for tests of bias, forest and funnel plot.
• metafor 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.
• rmeta 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.