ClinicalTrials.md

name	topic	maintainer	email	version	source
ClinicalTrials	Clinical Trial Design, Monitoring, and Analysis	Ed Zhang, W. G. Zhang, R. G. Zhang	ClinicalTrials.TaskView@yahoo.com	2021-12-29	https://github.com/cran-task-views/ClinicalTrials/

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 r view("ExperimentalDesign"), r view("Survival"), r view("Pharmacokinetics"), r view("Meta-analysis") for more details on these topics.

Contributions are always welcome and encouraged, either via e-mail to the maintainer or by submitting an issue or pull request in the GitHub repository linked above.

Design and Monitoring

• r pkg("TrialSize", priority = "core") 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).
• r pkg("asd", priority = "core") This Package runs simulations for adaptive seamless designs using early outcomes for treatment selection.
• r pkg("bcrm", priority = "core") This package implements a wide variety of one and two-parameter 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.
• r pkg("blockrand", priority = "core") creates randomizations for block random clinical trials. It can also produce a PDF file of randomization cards.
• r pkg("clusterPower") Calculate power for cluster randomized trials (CRTs) that compare two means, two proportions, or two counts using closed-form 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 .
• r pkg("conf.design") This small package contains a series of simple tools for constructing and manipulating confounded and fractional factorial designs.
• r pkg("crmPack") Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules.
• r pkg("cosa") Implements bound constrained optimal sample allocation (BCOSA) framework described in Bulus & Dong (2019) for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes. Separate tools for statistical power and minimum detectable effect size computations are provided.
• r pkg("dfcrm", priority = "core") This package provides functions to run the CRM and TITE-CRM in phase I trials and calibration tools for trial planning purposes.
• r pkg("DTAT") Dose Titration Algorithm Tuning (DTAT) is a methodologic framework allowing dose individualization to be conceived as a continuous learning process that begins in early-phase clinical trials and continues throughout drug development, on into clinical practice. This package includes code that researchers may use to reproduce or extend key results of the DTAT research programme, plus tools for trialists to design and simulate a '3+3/PC' dose-finding study.
• r pkg("ewoc") An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9 . It calculates the next dose as a clinical trial proceeds as well as performs simulations to obtain operating characteristics.
• r pkg("experiment", priority = "core") contains tools for clinical experiments, e.g., a randomization tool, and it provides a few special analysis options for clinical trials.
• r pkg("FrF2") This package creates regular and non-regular Fractional Factorial designs. Furthermore, analysis tools for Fractional Factorial designs with 2-level 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 built-in 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.
• ldBand from r pkg("Hmisc", priority = "core") 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.
• r pkg("ldbounds", priority = "core") uses Lan-DeMets Method for group sequential trial; its functions calculate bounds and probabilities of a group sequential trial.
• r pkg("longpower", priority = "core")Compute power and sample size for linear models of longitudinal data. The package is described in Iddi and Donohue (2022) doi:10.32614/RJ-2022-022.
• r pkg("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, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.
• r pkg("PowerTOST", priority = "core") 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).
• r pkg("MinEDfind") The nonparametric two-stage Bayesian adaptive design is a novel phase II clinical trial design for finding the minimum effective dose (MinED). This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials.
• r pkg("presize") Bland (2009) recommended to base study sizes on the width of the confidence interval rather the power of a statistical test. The goal of 'presize' is to provide functions for such precision based sample size calculations. For a given sample size, the functions will return the precision (width of the confidence interval), and vice versa.
• r pkg("PowerUpR") Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments with continuous outcomes. Some of the functions can assist with planning two- and three-level cluster-randomized trials (CRTs) sensitive to multilevel moderation and mediation (2-1-1, 2-2-1, and 3-2-1).
• r pkg("pwr", priority = "core") has power analysis functions along the lines of Cohen (1988).
• r pkg("randomizeR") This tool enables the user to choose a randomization procedure based on sound scientific criteria. It comprises the generation of randomization sequences as well the assessment of randomization procedures based on carefully selected criteria. Furthermore, 'randomizeR' provides a function for the comparison of randomization procedures.
• r pkg("replicateBE") Performs comparative bioavailability calculations for Average Bioequivalence with Expanding Limits (ABEL). Implemented are 'Method A' and 'Method B' and the detection of outliers. If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to control the consumer risk. Average Bioequivalence - optionally with a tighter (narrow therapeutic index drugs) or wider acceptance range (Gulf Cooperation Council, South Africa: Cmax) - is implemented as well.
• r pkg("rpact") Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016). This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.
• r pkg("samplesize") computes sample size for Student's t-test with equal and nonequal variances and for the Wilcoxon-Mann-Whitney test for categorical data with and without ties.
• r pkg("simglm") Simulates regression models, including both simple regression and generalized linear mixed models with up to three level of nesting. Power simulations that are flexible allowing the specification of missing data, unbalanced designs, and different random error distributions are built into the package.
• r pkg("UnifiedDoseFinding") In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of a continuous response. This package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess design's operating characteristics under various scenarios.

Design and Analysis

• Package r pkg("AGSDest") This package provides tools and functions for parameter estimation in adaptive group sequential trials.
• Package r pkg("clinfun", priority = "core") 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 r pkg("CRM") Continual Reassessment Method (CRM) simulator for Phase I Clinical Trials.
• Package r pkg("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 r pkg("dfped") A unified method for designing and analysing dose-finding 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 dose-toxicity and dose-efficacy 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 r pkg("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 r pkg("MCPMod") methodology. Currently only normally distributed homoscedastic endpoints are supported.
• r pkg("MCPMod") This package implements a methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748). 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 r pkg("TEQR", priority = "core") 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 r pkg("ThreeArmedTrials") Design and analyze three-arm non-inferiority or superiority trials which follow a gold-standard design, i.e. trials with an experimental treatment, an active, and a placebo control.

Analysis for Specific Designs

• r pkg("adaptTest", priority = "core") The functions defined in this program serve for implementing adaptive two-stage tests. Currently, four tests are included: Bauer and Koehne (1994), Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional error function. User-defined tests can also be implemented. Reference: Vandemeulebroecke, An investigation of two-stage tests, Statistica Sinica 2006.
• r pkg("adaptr") simulates adaptive (multi-arm, multi-stage) clinical trials using adaptive stopping, adaptive arm dropping, and/or adaptive randomisation.
• r pkg("clinsig") This function calculates both parametric and non-parametric versions of the Jacobson-Truax estimates of clinical significance.
• r pkg("clinicalsignificance") The goal of this package is to provide all necessary tools for analyses of clinical significance in clinical intervention studies. In contrast to statistical significance, which assesses if it is probable that there is a treatment effect, clinical significance can be used to determine if a treatment effect is of practical use or meaningful for patients.
• r pkg("nppbib") implements a nonparametric statistical test for rank or score data from partially-balanced incomplete block-design experiments.
• r pkg("speff2trial", priority = "core"), the package performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative or dichotomous endpoint.
• r pkg("ThreeGroups") This package implements the Maximum Likelihood estimator for three-group 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.
• r pkg("accrualPlot") Tracking accrual in clinical trials is important for trial success. 'accrualPlot' provides functions to aid the tracking of accrual and predict when a trial will reach it's intended sample size.
• r pkg("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.
• r pkg("coin") offers conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems.
• r pkg("ctrdata") is a system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from four public registers
• r pkg("epibasix") has functions such as diffdetect, n4means for continuous outcome and n4props and functions for matched pairs analysis in randomized trials.
• ae.dotplot from r pkg("HH") shows a two-panel display of the most frequently occurring adverse events in the active arm of a clinical study.
• The r pkg("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.
• r pkg("mmrm") Implements mixed models for repeated measures (MMRM), a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond.
• r pkg("multcomp") covers simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models.
• r pkg("rbmi") Implements standard and reference based multiple imputation allowing for the imputation of longitudinal datasets using predefined strategies. The package is described in Gower-Page et al (2022) <doi: 10.21105/joss.04251>.
• r pkg("survival", priority = "core") 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.
• r pkg("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

• r pkg("metasens") is a package for statistical methods to model and adjust for bias in meta-analysis
• r pkg("meta") is for fixed and random effects meta-analysis. It has Functions for tests of bias, forest and funnel plot.
• r pkg("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.
• r pkg("metaLik") Likelihood inference in meta-analysis and meta-regression models.
• r pkg("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.

Links

• Regulatory Compliance and Validation Issues (A Guidance Document for the Use of R in Regulated Clinical Trial Environments)