Getting started

1. Summary:  A brief illustration of the most commonly used RCTdesign functions (seqDesign(), seqOC(), seqPlotBoundary(), seqPlotASN(), seqPlotPower()) when the primary statistical endpoint is the mean of a continuous random variable. 
   

Commonly used probability models in RCT

1. Summary:  An overview of the specification of commonly used probability models (mean, geometric mean, proportions, hazards) when designing  RCT with seqDesign(). 
   

Boundary scales in RCTdesign

1. Summary:  A description of the various scales that can be used to display stopping boundaries in RCTdesign. Choices include the scale of testimated treatment effect, the Z statistic, the fixed sample P value, the error spending function, conditional power, Bayesian predictive power, and Bayesian posterior probabilities. 
   

Design families.

1. Summary:  An overview of the specification of various group sequential design families implemented in RCTdesign. Choices include the Kittelson & Emerson (1999) unified family (including as special cases O’Brien-Fleming (1979); Pocock (1977); Whitehead & Stratton (1983) triangular designs; Wang & Tsiatis (1987); Emerson & Fleming (1989) symmetric designs; Pampallona & Tsiatis (1994)) and several Lan & DeMets (1983) error spending families (including Kim & DeMets (1987) power family; Hwang, Shih & DeCani (1990); Anderson & Clark (2006) logistic family). 
   

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Evaluation of designs: Producing and understanding tabular output

1. Summary:  An overview of the various tables that are used to evaluate and compare clinical trial designs.  Choices include tables of the stopping boundaries, accrual patterns (in time to event probability models), power, ASN, stopping probabilities, and inference.
   

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Evaluation of designs: Producing and understanding plots

1. Summary:  An overview of the various plots that are used to evaluate and compare clinical trial designs.  Choices include plots of the stopping boundaries, accrual patterns (in time to event probability models), power, ASN, stopping probabilities, and inference.
   

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