Abstract
Background: The primary analysis in a longitudinal randomized controlled trial is sometimes a comparison of arms at a single time point. While a two-sample t-test is often used, missing data are common in longitudinal studies and decreases power by reducing sample size. Mixed models for repeated measures (MMRM) can test treatment effects at specific time points, have been shown to give unbiased estimates in certain missing data contexts, and may be more powerful than a two sample t-test. Methods: We conducted a simulation study to compare the performance of a complete-case t-test to a MMRM in terms of power and bias under different missing data mechanisms. Impact of within- and between-person variance, dropout mechanism, and variance-covariance structure were all considered. Results: While both complete-case t-test and MMRM provided unbiased estimation of treatment differences when data were missing completely at random, MMRM yielded an absolute power gain of up to 12 %. The MMRM provided up to 25 % absolute increased power over the t-test when data were missing at random, as well as unbiased estimation. Conclusions: Investigators interested in single time point comparisons should use a MMRM with a contrast to gain power and unbiased estimation of treatment effects instead of a complete-case two sample t-test.
| Original language | English (US) |
|---|---|
| Article number | 43 |
| Journal | BMC Medical Research Methodology |
| Volume | 16 |
| Issue number | 1 |
| DOIs | |
| State | Published - Apr 12 2016 |
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Keywords
- Complete-case
- Longitudinal
- Mean response profile
- Missing data
- Mixed model
- Power
- Repeated measures
- T-test
ASJC Scopus subject areas
- Health Informatics
- Epidemiology
Cite this
Single time point comparisons in longitudinal randomized controlled trials : Power and bias in the presence of missing data. / Ashbeck, Erin L.; Bell, Melanie L.
In: BMC Medical Research Methodology, Vol. 16, No. 1, 43, 12.04.2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Single time point comparisons in longitudinal randomized controlled trials
T2 - Power and bias in the presence of missing data
AU - Ashbeck, Erin L.
AU - Bell, Melanie L
PY - 2016/4/12
Y1 - 2016/4/12
N2 - Background: The primary analysis in a longitudinal randomized controlled trial is sometimes a comparison of arms at a single time point. While a two-sample t-test is often used, missing data are common in longitudinal studies and decreases power by reducing sample size. Mixed models for repeated measures (MMRM) can test treatment effects at specific time points, have been shown to give unbiased estimates in certain missing data contexts, and may be more powerful than a two sample t-test. Methods: We conducted a simulation study to compare the performance of a complete-case t-test to a MMRM in terms of power and bias under different missing data mechanisms. Impact of within- and between-person variance, dropout mechanism, and variance-covariance structure were all considered. Results: While both complete-case t-test and MMRM provided unbiased estimation of treatment differences when data were missing completely at random, MMRM yielded an absolute power gain of up to 12 %. The MMRM provided up to 25 % absolute increased power over the t-test when data were missing at random, as well as unbiased estimation. Conclusions: Investigators interested in single time point comparisons should use a MMRM with a contrast to gain power and unbiased estimation of treatment effects instead of a complete-case two sample t-test.
AB - Background: The primary analysis in a longitudinal randomized controlled trial is sometimes a comparison of arms at a single time point. While a two-sample t-test is often used, missing data are common in longitudinal studies and decreases power by reducing sample size. Mixed models for repeated measures (MMRM) can test treatment effects at specific time points, have been shown to give unbiased estimates in certain missing data contexts, and may be more powerful than a two sample t-test. Methods: We conducted a simulation study to compare the performance of a complete-case t-test to a MMRM in terms of power and bias under different missing data mechanisms. Impact of within- and between-person variance, dropout mechanism, and variance-covariance structure were all considered. Results: While both complete-case t-test and MMRM provided unbiased estimation of treatment differences when data were missing completely at random, MMRM yielded an absolute power gain of up to 12 %. The MMRM provided up to 25 % absolute increased power over the t-test when data were missing at random, as well as unbiased estimation. Conclusions: Investigators interested in single time point comparisons should use a MMRM with a contrast to gain power and unbiased estimation of treatment effects instead of a complete-case two sample t-test.
KW - Complete-case
KW - Longitudinal
KW - Mean response profile
KW - Missing data
KW - Mixed model
KW - Power
KW - Repeated measures
KW - T-test
UR - http://www.scopus.com/inward/record.url?scp=84963804585&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963804585&partnerID=8YFLogxK
U2 - 10.1186/s12874-016-0144-0
DO - 10.1186/s12874-016-0144-0
M3 - Article
C2 - 27068578
AN - SCOPUS:84963804585
VL - 16
JO - BMC Medical Research Methodology
JF - BMC Medical Research Methodology
SN - 1471-2288
IS - 1
M1 - 43
ER -