scholarly journals Precision medicine: Statistical methods for estimating adaptive treatment strategies

2020 ◽  
Vol 55 (10) ◽  
pp. 1890-1896
Author(s):  
Erica E. M. Moodie ◽  
Elizabeth F. Krakow
2013 ◽  
Vol 33 (5) ◽  
pp. 760-771 ◽  
Author(s):  
Zhiguo Li ◽  
Marcia Valenstein ◽  
Paul Pfeiffer ◽  
Dara Ganoczy

Biometrics ◽  
2020 ◽  
Author(s):  
Noémi Kreif ◽  
Oleg Sofrygin ◽  
Julie A. Schmittdiel ◽  
Alyce S. Adams ◽  
Richard W. Grant ◽  
...  

Author(s):  
Romain S. Neugebauer ◽  
Julie A. Schmittdiel ◽  
Patrick J. O’Connor ◽  
Mark J. van der Laan

Author(s):  
Alena I. Oetting ◽  
Janet A Levy

The past two decades have brought new pharmacotherapies as well as behavioral therapies to the field of drug-addiction treatment (Carroll & Onken, 2005; Carroll, 2005; Ling & Smith, 2002; Fiellin, Kleber, Trumble-Hejduk, McLellan, & Kosten, 2004). Despite this progress, the treatment of addiction in clinical practice often remains a matter of trial and error. Some reasons for this difficulty are as follows. First, to date, no one treatment has been found that works well for most patients; that is, patients are heterogeneous in response to any specific treatment. Second, as many authors have pointed out (McLellan, 2002; McLellan, Lewis, O’Brien, & Kleber, 2000), addiction is often a chronic condition, with symptoms waxing and waning over time. Third, relapse is common. Therefore, the clinician is faced with, first, finding a sequence of treatments that works initially to stabilize the patient and, next, deciding which types of treatments will prevent relapse in the longer term. To inform this sequential clinical decision making, adaptive treatment strategies, that is, treatment strategies shaped by individual patient characteristics or patient responses to prior treatments, have been proposed (Greenhouse, Stangl, Kupfer, & Prien, 1991; Murphy, 2003, 2005; Murphy, Lynch, Oslin, McKay, & Tenhave, 2006; Murphy, Oslin, Rush, & Zhu, 2007; Lavori & Dawson, 2000; Lavori, Dawson, & Rush, 2000; Dawson & Lavori, 2003). Here is an example of an adaptive treatment strategy for prescription opioid dependence, modeled with modifications after a trial currently in progress within the Clinical Trials Network of the National Institute on Drug Abuse (Weiss, Sharpe, & Ling, 2010). . . . Example . . . . . . First, provide all patients with a 4-week course of buprenorphine/naloxone (Bup/Nx) plus medical management (MM) plus individual drug counseling (IDC) (Fiellin, Pantalon, Schottenfeld, Gordon, & O’Connor, 1999), culminating in a taper of the Bup/Nx. If at any time during these 4 weeks the patient meets the criterion for nonresponse, a second, longer treatment with Bup/Nx (12 weeks) is provided, accompanied by MM and cognitive behavior therapy (CBT). However, if the patient remains abstinent from opioid use during those 4 weeks, that is, responds to initial treatment, provide 12 additional weeks of relapse prevention therapy (RPT). . . .


Biostatistics ◽  
2020 ◽  
Author(s):  
Shouao Wang ◽  
Erica Em Moodie ◽  
David A Stephens ◽  
Jagtar S Nijjar

Summary Most estimation algorithms for adaptive treatment strategies assume that treatment rules at each decision point are independent from one another in the sense that they do not possess any common parameters. This is often unrealistic, as the same decisions may be made repeatedly over time. Sharing treatment-decision parameters across decision points offers several advantages, including estimation of fewer parameters and the clinical ease of a single, time-invariant decision to implement. We propose a new computational approach to estimation of shared-parameter G-estimation, which is efficient and shares the double robustness of the “unshared” sequential G-estimation. We use this approach to analyze data from the Scottish Early Rheumatoid Arthritis (SERA) Inception Cohort.


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