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Rigorous & Consistent Evaluation of Biomarker/Subgroup Identification

Rigorous & Consistent Evaluation of Biomarker/Subgroup Identification. Biomarker/Subgroup Analysis/Identification Sub-team QSPI Multiplicity Working Group October 1, 2013. Objectives. Short term: Test the platform Compare an initial set of methods Collaborate on a publication Longer term:

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Rigorous & Consistent Evaluation of Biomarker/Subgroup Identification

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  1. Rigorous & Consistent Evaluation of Biomarker/Subgroup Identification Biomarker/Subgroup Analysis/Identification Sub-team QSPI Multiplicity Working Group October 1, 2013

  2. Objectives • Short term: • Test the platform • Compare an initial set of methods • Collaborate on a publication • Longer term: • Build knowledge base of good methods

  3. Predictive Biomarker Project Three components: Data generation (consistency) Analysis methods (openness) Performance measures (consistency)

  4. Available Files What is provided: • notes.docx • pbpsfolder • pbpe folder • Read “notes.docx” • Just “how”, not “why” • Run examples in “pbps_calls.R” • Use help files if needed • Run examples in “pbpe_calls.R” • Use help files if needed

  5. “pbps” Options Dimensions: • n_datasets • n_subj (overall number) • n_pred Random number seeds for reproducibility: • pred_seed • rando_seed • Not needed of rando_method is “ordered” or “alternating” • predmarkers_seed • resp_seed

  6. “pbps” Options (cont.) • pred_method (“genetic”) • Leave blank for uncorrelated X’s • gen_n_genes • gen_n_blocks_gene • gen_n_snps_block • rando_method (“random”, “alternating”, “ordered”)

  7. “pbps” Options (cont.) • n_predmarkers • gen_maf • predmarkers_method • “unrestricted”, “one per gene”, “one per block”, “one per gene (unique)”, “one per block (unique)” • predmarkers_etype • A vector, e.g. “c(dominant, recessive, dominant)” • predmarkers_esize (also a vector) • resp_trt0_mneg (mean response) • resp_trt1_mneg (mean treatment effect) • resp_sd • subgrp_def(optional)

  8. “pbps” Options (cont.) • out_dataset_format • “concatenated”, “separate” • out_dataset • out_info • out_truth(used as a prefix) Not planning to use initially: • Custom X’s using pred_file • Custom randomization scheme using rando_file • Custom list of predictive markers: predmarkers_method is • “user-specified” (also need predmarkers_list) • Or “partitioned”

  9. “pbps” Output • Datasets • CSV file • id, trt, y, X's • Info file • TXT files listing selected options • New info: correlations, predictive markers, seeds • “Truth” files: CSV files, one column per dataset • Marker “truth” file (0/1 for each marker) • Effect “truth” file (expected TE for each subject) • Subject “truth” file (0/1 for each subject)

  10. Analysis Any analysis method, as long as it: • Uses standard datasets as input • Produces these standard results • Marker “result” file • Same format as marker “truth” file • Subject “result” file • Same format as subject “truth” file • Estimate “result” file (optional) • One row only, one number per dataset

  11. Next Steps • Test programs • Another discussion on performance measures • Improve programs as necessary • Scenarios of interest • e.g. therapeutic area, phase, dimensions • Initial list of BSID methods to be evaluated • Collaborate on method evaluation • Collaborate on a publication

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