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 (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 . 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: Flexible and Efficient Evaluation of Principal Surrogates/Treatment Effect Modifiers

pssmooth is an R package implementing estimation and testing procedures described in Juraska M., Huang Y., and Gilbert P. B. (2018), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, kxy074, https://doi.org/10.1093/biostatistics/kxy074.