Abstract
Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 979-995 |
| Number of pages | 17 |
| Journal | Journal of Applied Statistics |
| Volume | 43 |
| Issue number | 6 |
| DOIs | |
| State | Published - Apr 25 2016 |
| Externally published | Yes |
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Keywords
- GEE
- GLIMMIX
- intraclass correlation
- marginal models
- NLMIXED
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Power analysis for clustered non-continuous responses in multicenter trials. / Chen, T.; Knox, K.; Arora, J.; Tang, W.; Kowalski, J.; Tu, X. M.
In: Journal of Applied Statistics, Vol. 43, No. 6, 25.04.2016, p. 979-995.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Power analysis for clustered non-continuous responses in multicenter trials
AU - Chen, T.
AU - Knox, K.
AU - Arora, J.
AU - Tang, W.
AU - Kowalski, J.
AU - Tu, X. M.
PY - 2016/4/25
Y1 - 2016/4/25
N2 - Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.
AB - Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.
KW - GEE
KW - GLIMMIX
KW - intraclass correlation
KW - marginal models
KW - NLMIXED
UR - http://www.scopus.com/inward/record.url?scp=84958929418&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958929418&partnerID=8YFLogxK
U2 - 10.1080/02664763.2015.1089218
DO - 10.1080/02664763.2015.1089218
M3 - Article
AN - SCOPUS:84958929418
VL - 43
SP - 979
EP - 995
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
SN - 0266-4763
IS - 6
ER -