Abstract
Aims: To compare three missing data strategies: (i) the latent growth model that assumes the data are missing at random (MAR) model; (ii) the Diggle-Kenward missing not at random (MNAR) model, where dropout is a function of previous/concurrent urinalysis (UA) submissions; and (iii) the Wu-Carroll MNAR model where dropout is a function of the growth factors. Design: Secondary data analysis of a National Drug Abuse Treatment Clinical Trials Network trial that examined a 7-day versus 28-day taper (i.e. stepwise decrease in buprenorphine/naloxone) on the likelihood of submitting an opioid-positive UA during treatment. Setting: 11 out-patient treatment settings in 10 US cities. Participants: A total of 516 opioid-dependent participants. Measurements: Opioid UAs provided across the 4-week treatment period. Findings: 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 showed 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 51-58 |
| Number of pages | 8 |
| Journal | Addiction |
| Volume | 110 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2015 |
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Keywords
- Latent growth modeling
- Longitudinal missing data
- Missing not at random models
- Randomized clinical trials
- Sensitivity analysis
- Substance use disorder treatment
ASJC Scopus subject areas
- Medicine (miscellaneous)
- Psychiatry and Mental health
Cite this
A 'missing not at random' (MNAR) and 'missing at random' (MAR) growth model comparison with a buprenorphine/naloxone clinical trial. / Mcpherson, Sterling; Barbosa-Leiker, Celestina; Mamey, Mary Rose; Mcdonell, Michael; Enders, Craig K.; Roll, John.
In: Addiction, Vol. 110, No. 1, 01.01.2015, p. 51-58.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A 'missing not at random' (MNAR) and 'missing at random' (MAR) growth model comparison with a buprenorphine/naloxone clinical trial
AU - Mcpherson, Sterling
AU - Barbosa-Leiker, Celestina
AU - Mamey, Mary Rose
AU - Mcdonell, Michael
AU - Enders, Craig K.
AU - Roll, John
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Aims: To compare three missing data strategies: (i) the latent growth model that assumes the data are missing at random (MAR) model; (ii) the Diggle-Kenward missing not at random (MNAR) model, where dropout is a function of previous/concurrent urinalysis (UA) submissions; and (iii) the Wu-Carroll MNAR model where dropout is a function of the growth factors. Design: Secondary data analysis of a National Drug Abuse Treatment Clinical Trials Network trial that examined a 7-day versus 28-day taper (i.e. stepwise decrease in buprenorphine/naloxone) on the likelihood of submitting an opioid-positive UA during treatment. Setting: 11 out-patient treatment settings in 10 US cities. Participants: A total of 516 opioid-dependent participants. Measurements: Opioid UAs provided across the 4-week treatment period. Findings: 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 showed 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.
AB - Aims: To compare three missing data strategies: (i) the latent growth model that assumes the data are missing at random (MAR) model; (ii) the Diggle-Kenward missing not at random (MNAR) model, where dropout is a function of previous/concurrent urinalysis (UA) submissions; and (iii) the Wu-Carroll MNAR model where dropout is a function of the growth factors. Design: Secondary data analysis of a National Drug Abuse Treatment Clinical Trials Network trial that examined a 7-day versus 28-day taper (i.e. stepwise decrease in buprenorphine/naloxone) on the likelihood of submitting an opioid-positive UA during treatment. Setting: 11 out-patient treatment settings in 10 US cities. Participants: A total of 516 opioid-dependent participants. Measurements: Opioid UAs provided across the 4-week treatment period. Findings: 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 showed 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.
KW - Latent growth modeling
KW - Longitudinal missing data
KW - Missing not at random models
KW - Randomized clinical trials
KW - Sensitivity analysis
KW - Substance use disorder treatment
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U2 - 10.1111/add.12714
DO - 10.1111/add.12714
M3 - Article
VL - 110
SP - 51
EP - 58
JO - Addiction
JF - Addiction
SN - 0965-2140
IS - 1
ER -