Flexible and Efficient Evaluation of Principal Surrogates/Treatment Effect Modifiers

Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2018), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, kxy074 . The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results.

pssmooth is an R package implementing estimation and testing procedures described in Inference on treatment effect modification by biomarker response in a three-phase sampling design by Michal Juraska, Ying Huang, and Peter B. Gilbert.


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Reference manual

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1.0.2 by Michal Juraska, 25 days ago


Report a bug at https://github.com/mjuraska/pssmooth/issues

Browse source code at https://github.com/cran/pssmooth

Authors: Michal Juraska [aut, cre]

Documentation:   PDF Manual  

GPL-2 license

Imports graphics, stats, osDesign, np, chngpt, MASS

Suggests knitr, rmarkdown

See at CRAN