Characterizing Highly Benefited Patients in Randomized Clinical Trials

Charu Vivek, Rosenberg Paul B., Schneider Lon S., Drye Lea T., Rein Lisa, Shade David, Lyketsos Constantine G. and Frangakis Constantine E. ()
Additional contact information
Charu Vivek: Department of Biostatistics, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, USA
Rosenberg Paul B.: Department of Psychiatry, Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
Schneider Lon S.: Department of Psychiatry, University of Southern California, Los Angeles, CA, USA
Drye Lea T.: Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
Rein Lisa: Biostatistics Consulting Center, Medical College of Wisconsin, Milwaukee, WI, USA
Shade David: Department of Psychiatry, Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
Lyketsos Constantine G.: Department of Psychiatry, Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
Frangakis Constantine E.: Department of Biostatistics, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, USA

The International Journal of Biostatistics, 2017, vol. 13, issue 1, 11

Abstract: Physicians and patients may choose a certain treatment only if it is predicted to have a large effect for the profile of that patient. We consider randomized controlled trials in which the clinical goal is to identify as many patients as possible that can highly benefit from the treatment. This is challenging with large numbers of covariate profiles, first, because the theoretical, exact method is not feasible, and, second, because usual model-based methods typically give incorrect results. Better, more recent methods use a two-stage approach, where a first stage estimates a working model to produce a scalar predictor of the treatment effect for each covariate profile; and a second stage estimates empirically a high-benefit group based on the first-stage predictor. The problem with these methods is that each of the two stages is usually agnostic about the role of the other one in addressing the clinical goal. We propose a method that characterizes highly benefited patients by linking model estimation directly to the particular clinical goal. It is shown that the new method has the following two key properties in comparison with existing approaches: first, the meaning of the solution with regard to the clinical goal is the same, and second, the value of the solution is the best that can be achieved when using the working model as a predictor, even if that model is incorrect. In the Citalopram for Agitation in Alzheimer’s Disease (CitAD) randomized controlled trial, the new method identifies substantially larger groups of highly benefited patients, many of whom are missed by the standard method.

Keywords: heterogeneity in treatment effects; Alzheimer’s disease; high benefit; RCT (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2016-0045 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:13:y:2017:i:1:p:11:n:5

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2016-0045

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().