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A 'Missing Not at Random' (MNAR) and 'Missing at Random' (MAR) Growth Model Comparison with a Buprenorphine/Naloxone Clinical Trial.

Addiction 2014 (in press). [doi: 10.1111/add.12714]

Sterling McPherson, PhD (Washington State University, PN Node), Celestina Barbosa-Leiker, PhD (Washington State University, PN Node), Mary Rose Mamey, MA (Washington State University, PN Node), Michael McDonell, PhD (University of Washington, PN Node), Craig K. Enders, PhD (Arizona State University), John M. Roll, PhD (Washington State University, PN Node).

This secondary analysis of data from National Drug Abuse Treatment Clinical Trials Network protocol CTN-0003 ("Suboxone (Buprenorphine/Naloxone) Taper: A Comparison of Taper Schedules") compared three missing data strategies: 1) Latent growth model that assumes the data are missing at random (MAR), 2) Diggle-Kenward missing not at random (MNAR) model where dropout is a function of previous/concurrent urinalysis (UA) submissions, and 3) Wu-Carroll MNAR model where dropout is a function of the growth factors. CTN-0003 examined a 7-day versus 28-day taper for buprenorphine/naloxone to see which taper schedule reduced the likelihood of submitting an opioid-positive UA during treatment.

The MAR model showed a significant effect (B=-0.45, p <0.05) of trial arm on the opioid-positive UA slope (i.e., 28-day taper participants were less likely to submit a positive UA over time) with a small effect size (d=0.20). The MNAR Diggle-Kenward model demonstrated a significant (B=-0.64, p<0.01) effect of trial arm on the slope with a large effect size (d=0.82). The MNAR Wu-Carroll model evidenced a significant (B=-0.41, p<0.05) effect of trial arm on the UA slope that was relatively small (d=0.31).

Conclusions: This performance comparison of three missing data strategies (latent growth model, Diggle-Kenward selection model, Wu-Carrol selection model) on sample data indicates a need for increased use of sensitivity analyses in clinical trial research. Given the potential sensitivity of the trial arm effect to missing data assumptions, it is critical for researchers to consider whether the assumptions associated with each model are defensible. (Article (Peer-Reviewed), PDF, English, 2014)

Keywords: Buprenorphine/Naloxone | CTN platform/ancillary study | Missing data | Statistical analysis | Statistical models | Taper schedules | Addiction (journal)

Document No: 1096, PMID: 25170740, PMCID: PMC42709222.

Submitted by CTN Dissemination Librarians, 9/3/2014.

AUTHORS SEARCH LINK
Barbosa-Leiker, Celestina
Enders, Craig K.
Mamey, Mary Rose
McDonell, Michael
McPherson, Sterling mail
Roll, John M.

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Supported by a grant from the National Institute on Drug Abuse to the University of Washington Alcohol and Drug Abuse Institute.
The materials on this site have neither been created nor reviewed by NIDA.
Updated 1/2016 -- http://ctndisseminationlibrary.org/display/1096.htm
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