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Estimates of absolute treatment benefit for individual patients required careful modeling of statistical interactions.

ABSTRACT: We aimed to compare modeling approaches to estimate the individual survival benefit of treatment with either coronary artery bypass graft surgery (CABG) or percutaneous coronary intervention (PCI) for patients with complex coronary artery disease.We estimated survival with Cox regression models that included the treatment variable (CABG/PCI) interacting with either an internally developed overall prognostic index (PI) or with individual prognostic factors. We analyzed data of patients who were randomized in the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery trial (1,800 patients, 178 deaths).A negligible interaction with the PI (P = 0.51) led to 4-year survival estimates in favor of CABG for all patients. In contrast, individual interactions indicated substantial relative treatment effect heterogeneity (overall interaction P = 0.004), and estimates of 4-year survival were numerically in favor of CABG for 1,275 of 1,800 patients (71%; 519 with 95% confidence). To test the more complex model with individual interactions, we first used penalized regression, resulting in smaller but largely consistent individual estimates of the survival difference between CABG and PCI. Second, strong treatment interactions were confirmed at external validation in 2,891 patients from a multinational registry.Modeling strategies that omit interactions may result in misleading estimates of absolute treatment benefit for individual patients with the potential hazard of suboptimal decision making.

SUBMITTER: van Klaveren D

PROVIDER: S-EPMC5569577 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Estimates of absolute treatment benefit for individual patients required careful modeling of statistical interactions.

van Klaveren David D Vergouwe Yvonne Y Farooq Vasim V Serruys Patrick W PW Steyerberg Ewout W EW

Journal of clinical epidemiology 20150227 11

<h4>Objectives</h4>We aimed to compare modeling approaches to estimate the individual survival benefit of treatment with either coronary artery bypass graft surgery (CABG) or percutaneous coronary intervention (PCI) for patients with complex coronary artery disease.<h4>Study design and setting</h4>We estimated survival with Cox regression models that included the treatment variable (CABG/PCI) interacting with either an internally developed overall prognostic index (PI) or with individual prognos ...[more]

PMID: 25814403

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Project description:Applications in structural biology and medicinal chemistry require protein-ligand scoring functions for two distinct tasks: (i) ranking different poses of a small molecule in a protein binding site and (ii) ranking different small molecules by their complementarity to a protein site. Using probability theory, we developed two atomic distance-dependent statistical scoring functions: PoseScore was optimized for recognizing native binding geometries of ligands from other poses and RankScore was optimized for distinguishing ligands from nonbinding molecules. Both scores are based on a set of 8,885 crystallographic structures of protein-ligand complexes but differ in the values of three key parameters. Factors influencing the accuracy of scoring were investigated, including the maximal atomic distance and non-native ligand geometries used for scoring, as well as the use of protein models instead of crystallographic structures for training and testing the scoring function. For the test set of 19 targets, RankScore improved the ligand enrichment (logAUC) and early enrichment (EF(1)) scores computed by DOCK 3.6 for 13 and 14 targets, respectively. In addition, RankScore performed better at rescoring than each of seven other scoring functions tested. Accepting both the crystal structure and decoy geometries with all-atom root-mean-square errors of up to 2 Å from the crystal structure as correct binding poses, PoseScore gave the best score to a correct binding pose among 100 decoys for 88% of all cases in a benchmark set containing 100 protein-ligand complexes. PoseScore accuracy is comparable to that of DrugScore(CSD) and ITScore/SE and superior to 12 other tested scoring functions. Therefore, RankScore can facilitate ligand discovery, by ranking complexes of the target with different small molecules; PoseScore can be used for protein-ligand complex structure prediction, by ranking different conformations of a given protein-ligand pair. The statistical potentials are available through the Integrative Modeling Platform (IMP) software package (http://salilab.org/imp) and the LigScore Web server (http://salilab.org/ligscore/).

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Estimates of absolute treatment benefit for individual patients required careful modeling of statistical interactions.

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Estimates of absolute treatment benefit for individual patients required careful modeling of statistical interactions.

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