Package: adoptr 1.1.1.9000
adoptr: Adaptive Optimal Two-Stage Designs
Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) <doi:10.1002/sim.8291> and Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.
Authors:
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adoptr.pdf |adoptr.html✨
adoptr/json (API)
NEWS
# Install 'adoptr' in R: |
install.packages('adoptr', repos = c('https://optad.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/optad/adoptr/issues
Last updated 2 months agofrom:0029cd9017. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 02 2024 |
R-4.5-win | OK | Nov 02 2024 |
R-4.5-linux | OK | Nov 02 2024 |
R-4.4-win | OK | Nov 02 2024 |
R-4.4-mac | OK | Nov 02 2024 |
R-4.3-win | OK | Nov 02 2024 |
R-4.3-mac | OK | Nov 02 2024 |
Exports:ANOVAAverageN2Binomialboundsc2ChiSquaredcompositeconditionConditionalPowerConditionalSampleSizeContinuousPriorcumulative_distribution_functionevaluateexpectationexpectedExpectedNumberOfEventsExpectedSampleSizeget_initial_designget_lower_boundary_designget_tau_ANOVAget_tau_Pearson2xKget_tau_ZSquaredget_upper_boundary_designGroupSequentialDesignmake_fixedmake_tunableMaximumSampleSizeminimizenn1N1n2NestedModelsNormalOneStageDesignPearson2xKplotPointMassPriorposteriorPowerpredictive_cdfpredictive_pdfprobability_density_functionquantilesimulateStudentsubject_tosummarySurvivalSurvivalDesigntunable_parametersTwoStageDesignupdateZSquared
Composite Scores
Rendered fromcomposite-scores.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-06-19
Started: 2024-06-06
Conditional Scores and Constraints
Rendered fromconditional-scores.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-06-06
Started: 2024-06-06
Defining New Scores
Rendered fromdefining-new-scores.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-06-06
Started: 2024-06-06
Designs for non-normal Endpoints with approximately normal test statistics
Rendered fromother-endpoints.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-07-23
Started: 2024-07-23
Get started with adoptr
Rendered fromadoptr.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-07-16
Started: 2024-06-06
The adoptr Package: Adaptive Optimal Designs for Clinical Trials in R
Rendered fromadoptr_jss.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-06-19
Started: 2024-06-06
Working with priors
Rendered fromworking-with-priors.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-06-06
Started: 2024-06-06
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Adaptive Optimal Two-Stage Designs | adoptr-package adoptr |
Analysis of Variance | ANOVA ANOVA-class get_tau_ANOVA |
Regularization via L1 norm | AverageN2 AverageN2-class evaluate,AverageN2,TwoStageDesign-method |
Binomial data distribution | Binomial Binomial-class quantile,Binomial-method simulate,Binomial,numeric-method |
Get support of a prior or data distribution | bounds bounds,ContinuousPrior-method bounds,PointMassPrior-method |
Query critical values of a design | c2 c2,OneStageDesign,numeric-method c2,TwoStageDesign,numeric-method |
Chi-Squared data distribution | ChiSquared ChiSquared-class quantile,ChiSquared-method simulate,ChiSquared,numeric-method |
Score Composition | composite evaluate,CompositeScore,TwoStageDesign-method |
Condition a prior on an interval | condition condition,ContinuousPrior,numeric-method condition,PointMassPrior,numeric-method |
(Conditional) Power of a Design | ConditionalPower ConditionalPower-class evaluate,ConditionalPower,TwoStageDesign-method Power |
(Conditional) Sample Size of a Design | ConditionalSampleSize ConditionalSampleSize-class evaluate,ConditionalSampleSize,TwoStageDesign-method ExpectedNumberOfEvents ExpectedSampleSize |
Formulating Constraints | <=,ConditionalScore,ConditionalScore-method <=,ConditionalScore,numeric-method <=,numeric,ConditionalScore-method <=,numeric,UnconditionalScore-method <=,UnconditionalScore,numeric-method <=,UnconditionalScore,UnconditionalScore-method >=,ConditionalScore,ConditionalScore-method >=,ConditionalScore,numeric-method >=,numeric,ConditionalScore-method >=,numeric,UnconditionalScore-method >=,UnconditionalScore,numeric-method >=,UnconditionalScore,UnconditionalScore-method Constraints evaluate,Constraint,TwoStageDesign-method |
Continuous univariate prior distributions | ContinuousPrior ContinuousPrior-class |
Cumulative distribution function | cumulative_distribution_function cumulative_distribution_function,Binomial,numeric,numeric,numeric-method cumulative_distribution_function,ChiSquared,numeric,numeric,numeric-method cumulative_distribution_function,NestedModels,numeric,numeric,numeric-method cumulative_distribution_function,Normal,numeric,numeric,numeric-method cumulative_distribution_function,Student,numeric,numeric,numeric-method cumulative_distribution_function,Survival,numeric,numeric,numeric-method |
Data distributions | DataDistribution DataDistribution-class |
Expected value of a function | expectation expectation,ContinuousPrior,function-method expectation,PointMassPrior,function-method |
Initial design | get_initial_design |
Boundary designs | get_lower_boundary_design get_lower_boundary_design,GroupSequentialDesign-method get_lower_boundary_design,OneStageDesign-method get_lower_boundary_design,TwoStageDesign-method get_upper_boundary_design get_upper_boundary_design,GroupSequentialDesign-method get_upper_boundary_design,OneStageDesign-method get_upper_boundary_design,TwoStageDesign-method |
Group-sequential two-stage designs | GroupSequentialDesign GroupSequentialDesign,numeric-method GroupSequentialDesign-class TwoStageDesign,GroupSequentialDesign-method TwoStageDesign,GroupSequentialDesignSurvival-method |
Group-sequential two-stage designs for time-to-event-endpoints | GroupSequentialDesignSurvival-class |
Fix parameters during optimization | make_fixed make_fixed,TwoStageDesign-method make_tunable make_tunable,TwoStageDesign-method |
Maximum Sample Size of a Design | evaluate,MaximumSampleSize,TwoStageDesign-method MaximumSampleSize MaximumSampleSize-class |
Find optimal two-stage design by constraint minimization | minimize |
Query sample size of a design | n n,TwoStageDesign,numeric-method n1 n1,TwoStageDesign-method n2 n2,GroupSequentialDesign,numeric-method n2,OneStageDesign,numeric-method n2,TwoStageDesign,numeric-method |
Regularize n1 | evaluate,N1,TwoStageDesign-method N1 N1-class |
F-Distribution | NestedModels NestedModels-class quantile,NestedModels-method simulate,NestedModels,numeric-method |
Normal data distribution | Normal Normal-class quantile,Normal-method simulate,Normal,numeric-method |
One-stage designs | OneStageDesign OneStageDesign,numeric-method OneStageDesign-class plot,OneStageDesign-method TwoStageDesign,OneStageDesign-method TwoStageDesign,OneStageDesignSurvival-method |
One-stage designs for time-to-event endpoints | OneStageDesignSurvival-class |
Pearson's chi-squared test for contingency tables | get_tau_Pearson2xK Pearson2xK Pearson2xK-class |
Plot 'TwoStageDesign' with optional set of conditional scores | plot,TwoStageDesign-method |
Univariate discrete point mass priors | PointMassPrior PointMassPrior-class |
Compute posterior distribution | posterior posterior,DataDistribution,ContinuousPrior,numeric-method posterior,DataDistribution,PointMassPrior,numeric-method |
Predictive CDF | predictive_cdf predictive_cdf,DataDistribution,ContinuousPrior,numeric-method predictive_cdf,DataDistribution,PointMassPrior,numeric-method |
Predictive PDF | predictive_pdf predictive_pdf,DataDistribution,ContinuousPrior,numeric-method predictive_pdf,DataDistribution,PointMassPrior,numeric-method |
Printing an optimization result | print print.adoptrOptimizationResult |
Univariate prior on model parameter | Prior Prior-class |
Probability density function | probability_density_function probability_density_function,Binomial,numeric,numeric,numeric-method probability_density_function,ChiSquared,numeric,numeric,numeric-method probability_density_function,NestedModels,numeric,numeric,numeric-method probability_density_function,Normal,numeric,numeric,numeric-method probability_density_function,Student,numeric,numeric,numeric-method probability_density_function,Survival,numeric,numeric,numeric-method |
Scores | evaluate evaluate,IntegralScore,TwoStageDesign-method expected expected,ConditionalScore-method Scores |
Draw samples from a two-stage design | simulate,TwoStageDesign,numeric-method |
Student's t data distribution | quantile,Student-method simulate,Student,numeric-method Student Student-class |
Create a collection of constraints | ConstraintCollection evaluate,ConstraintsCollection,TwoStageDesign-method subject_to |
Log-rank test | quantile,Survival-method simulate,Survival,numeric-method Survival Survival-class |
SurvivalDesign | GroupSequentialDesign,GroupSequentialDesign-method OneStageDesign,OneStageDesign-method SurvivalDesign SurvivalDesign,GroupSequentialDesign-method SurvivalDesign,OneStageDesign-method SurvivalDesign,TwoStageDesign-method TwoStageDesign,TwoStageDesign-method |
Switch between numeric and S4 class representation of a design | tunable_parameters tunable_parameters,TwoStageDesign-method update,OneStageDesign-method update,TwoStageDesign-method |
Two-stage designs | summary,TwoStageDesign-method TwoStageDesign TwoStageDesign,numeric-method TwoStageDesign-class |
Two-stage design for time-to-event-endpoints | TwoStageDesignSurvival-class |
Distribution class of a squared normal distribution | get_tau_ZSquared ZSquared ZSquared-class |