Literature DB >> 26177009

Implications of Heterogeneity of Treatment Effect for Reporting and Analysis of Randomized Trials in Critical Care.

Theodore J Iwashyna1,2,3, James F Burke4, Jeremy B Sussman1,3, Hallie C Prescott1, Rodney A Hayward1,3, Derek C Angus5.   

Abstract

Randomized clinical trials (RCTs) are conducted to guide clinicians' selection of therapies for individual patients. Currently, RCTs in critical care often report an overall mean effect and selected individual subgroups. Yet work in other fields suggests that such reporting practices can be improved. Specifically, this Critical Care Perspective reviews recent work on so-called "heterogeneity of treatment effect" (HTE) by baseline risk and extends that work to examine its applicability to trials of acute respiratory failure and severe sepsis. Because patients in RCTs in critical care medicine-and patients in intensive care units-have wide variability in their risk of death, these patients will have wide variability in the absolute benefit that they can derive from a given therapy. If the side effects of the therapy are not perfectly collinear with the treatment benefits, this will result in HTE, where different patients experience quite different expected benefits of a therapy. We use simulations of RCTs to demonstrate that such HTE could result in apparent paradoxes, including: (1) positive trials of therapies that are beneficial overall but consistently harm or have little benefit to low-risk patients who met enrollment criteria, and (2) overall negative trials of therapies that still consistently benefit high-risk patients. We further show that these results persist even in the presence of causes of death unmodified by the treatment under study. These results have implications for reporting and analyzing RCT data, both to better understand how our therapies work and to improve the bedside applicability of RCTs. We suggest a plan for measurement in future RCTs in the critically ill.

Entities:  

Keywords:  acute respiratory failure; heterogeneity of treatment effect; precision medicine; randomized clinical trials; sepsis

Mesh:

Year:  2015        PMID: 26177009      PMCID: PMC4642199          DOI: 10.1164/rccm.201411-2125CP

Source DB:  PubMed          Journal:  Am J Respir Crit Care Med        ISSN: 1073-449X            Impact factor:   21.405


  25 in total

1.  Prediction of benefit from carotid endarterectomy in individual patients: a risk-modelling study. European Carotid Surgery Trialists' Collaborative Group.

Authors:  P M Rothwell; C P Warlow
Journal:  Lancet       Date:  1999-06-19       Impact factor: 79.321

2.  External validity of randomised controlled trials: "to whom do the results of this trial apply?".

Authors:  Peter M Rothwell
Journal:  Lancet       Date:  2005 Jan 1-7       Impact factor: 79.321

3.  Treating individuals 2. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation.

Authors:  Peter M Rothwell
Journal:  Lancet       Date:  2005 Jan 8-14       Impact factor: 79.321

4.  Reporting clinical trial results to inform providers, payers, and consumers.

Authors:  Rodney A Hayward; David M Kent; Sandeep Vijan; Timothy P Hofer
Journal:  Health Aff (Millwood)       Date:  2005 Nov-Dec       Impact factor: 6.301

5.  Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification.

Authors:  David M Kent; Rodney A Hayward
Journal:  JAMA       Date:  2007-09-12       Impact factor: 56.272

6.  Comparison of medical admissions to intensive care units in the United States and United Kingdom.

Authors:  Hannah Wunsch; Derek C Angus; David A Harrison; Walter T Linde-Zwirble; Kathryn M Rowan
Journal:  Am J Respir Crit Care Med       Date:  2011-03-25       Impact factor: 21.405

7.  Treating individuals 3: from subgroups to individuals: general principles and the example of carotid endarterectomy.

Authors:  Peter M Rothwell; Ziyah Mehta; Sally C Howard; Sergei A Gutnikov; Charles P Warlow
Journal:  Lancet       Date:  2005 Jan 15-21       Impact factor: 79.321

8.  Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure.

Authors:  Marta L Render; H Myra Kim; James Deddens; Siva Sivaganesin; Deborah E Welsh; Karen Bickel; Ron Freyberg; Stephen Timmons; Joseph Johnston; Alfred F Connors; Douglas Wagner; Timothy P Hofer
Journal:  Crit Care Med       Date:  2005-05       Impact factor: 7.598

9.  The impact of high-risk patients on the results of clinical trials.

Authors:  J P Ioannidis; J Lau
Journal:  J Clin Epidemiol       Date:  1997-10       Impact factor: 6.437

10.  Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis.

Authors:  Rodney A Hayward; David M Kent; Sandeep Vijan; Timothy P Hofer
Journal:  BMC Med Res Methodol       Date:  2006-04-13       Impact factor: 4.615

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  87 in total

1.  Reply: Risk-based Heterogeneity of Treatment Effect in Trials and Implications for Surveillance of Clinical Effectiveness Using Regression Discontinuity Designs.

Authors:  Theodore J Iwashyna; James F Burke; Jeremy B Sussman; Hallie C Prescott; Rodney A Hayward; Derek C Angus
Journal:  Am J Respir Crit Care Med       Date:  2015-12-01       Impact factor: 21.405

2.  Predicting in-hospital mortality for initial survivors of acute respiratory compromise (ARC) events: Development and validation of the ARC Score.

Authors:  Ari Moskowitz; Lars W Andersen; Mathias Karlsson; Anne V Grossestreuer; Maureen Chase; Michael N Cocchi; Katherine Berg; Michael W Donnino
Journal:  Resuscitation       Date:  2017-03-04       Impact factor: 5.262

3.  Application of a Framework to Assess the Usefulness of Alternative Sepsis Criteria.

Authors:  Christopher W Seymour; Craig M Coopersmith; Clifford S Deutschman; Foster Gesten; Michael Klompas; Mitchell Levy; Gregory S Martin; Tiffany M Osborn; Chanu Rhee; David K Warren; R Scott Watson; Derek C Angus
Journal:  Crit Care Med       Date:  2016-03       Impact factor: 7.598

4.  Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R.

Authors:  Zhongheng Zhang; Fionn Murtagh; Sven Van Poucke; Su Lin; Peng Lan
Journal:  Ann Transl Med       Date:  2017-02

5.  Heterogeneity of Treatment Effect by Baseline Risk in a Trial of Balanced Crystalloids versus Saline.

Authors:  Andrew C McKown; Luis E Huerta; Todd W Rice; Matthew W Semler
Journal:  Am J Respir Crit Care Med       Date:  2018-09-15       Impact factor: 21.405

6.  Do trials that report a neutral or negative treatment effect improve the care of critically ill patients? No.

Authors:  Jean-Louis Vincent; John J Marini; Antonio Pesenti
Journal:  Intensive Care Med       Date:  2018-06-11       Impact factor: 17.440

7.  Corrigendum for Intensive Care Society State of the Art 2016 Abstracts.

Authors: 
Journal:  J Intensive Care Soc       Date:  2018-07-18

Review 8.  The intensive care medicine research agenda on septic shock.

Authors:  Anders Perner; Anthony C Gordon; Derek C Angus; Francois Lamontagne; Flavia Machado; James A Russell; Jean-Francois Timsit; John C Marshall; John Myburgh; Manu Shankar-Hari; Mervyn Singer
Journal:  Intensive Care Med       Date:  2017-05-12       Impact factor: 17.440

Review 9.  Toward Smarter Lumping and Smarter Splitting: Rethinking Strategies for Sepsis and Acute Respiratory Distress Syndrome Clinical Trial Design.

Authors:  Hallie C Prescott; Carolyn S Calfee; B Taylor Thompson; Derek C Angus; Vincent X Liu
Journal:  Am J Respir Crit Care Med       Date:  2016-07-15       Impact factor: 21.405

10.  Predicting Major Adverse Kidney Events among Critically Ill Adults Using the Electronic Health Record.

Authors:  Andrew C McKown; Li Wang; Jonathan P Wanderer; Jesse Ehrenfeld; Todd W Rice; Gordon R Bernard; Matthew W Semler
Journal:  J Med Syst       Date:  2017-08-31       Impact factor: 4.460

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