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Statistical controversies in cancer research: using standardized effect size graphs to enhance interpretability of cancer-related clinical trials with patient-reported outcomes.

ABSTRACT: Patient reported outcomes (PROs) are becoming increasingly important in cancer studies, particularly with the emphasis on patient centered outcome research. However, multiple PROs, using different scales, with different directions of favorability are often used within a trial, making interpretation difficult. To enhance interpretability, we propose the use of a standardized effect size graph, which shows all PROs from a study on the same figure, on the same scale. Plotting standardized effects with their 95% confidence intervals (CIs) on a single graph clearly showing the null value conveys a comprehensive picture of trial results. We demonstrate how to create such a graph using data from a randomized controlled trial that measured 12 PROs at two time points. The 24 effect sizes and CIs are shown on one graph and clearly indicate that the intervention is effective and sustained.

SUBMITTER: Bell ML

PROVIDER: S-EPMC5834129 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Statistical controversies in cancer research: using standardized effect size graphs to enhance interpretability of cancer-related clinical trials with patient-reported outcomes.

Bell M L ML Fiero M H MH Dhillon H M HM Bray V J VJ Vardy J L JL

Annals of oncology : official journal of the European Society for Medical Oncology 20170801 8

Patient reported outcomes (PROs) are becoming increasingly important in cancer studies, particularly with the emphasis on patient centered outcome research. However, multiple PROs, using different scales, with different directions of favorability are often used within a trial, making interpretation difficult. To enhance interpretability, we propose the use of a standardized effect size graph, which shows all PROs from a study on the same figure, on the same scale. Plotting standardized effects w ...[more]

PMID: 28327975

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Project description:BackgroundCausal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing.ResultsFirst, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causal graphs; previous work on graph randomization has studied undirected graphs and directed but unsigned graphs.ConclusionWe present algorithmic solutions to two statistical significance questions necessary to apply the causal graph methodology, a powerful tool for biological network analysis. The algorithms we present are both fast and provably correct. Our work may be of independent interest in non-biological contexts as well, as it generalizes mathematical results that have been studied extensively in other fields.

Dataset Information

Statistical controversies in cancer research: using standardized effect size graphs to enhance interpretability of cancer-related clinical trials with patient-reported outcomes.

Publications

Statistical controversies in cancer research: using standardized effect size graphs to enhance interpretability of cancer-related clinical trials with patient-reported outcomes.

Similar Datasets

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