Abstract
We propose a consistent and locally efficient method of estimating the model parameters of a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where, in many cases, we have limited resources to design experiments and gather data in long-term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial to have an estimator that is robust to such violations; then we could make better use of current data harvested using various valuable resources. Our method generalizes the framework presented in Garcia & Ma (2016) which also deals with a logistic mixed effect model but only considers a random intercept. A simulation study reveals that our proposed estimator remains consistent even when the independence and normality assumptions are violated. This contrasts favourably with the traditional maximum likelihood estimator which is likely to be inconsistent when there is dependence between the covariates and random effects. Application of this work to a study of Huntington's disease reveals that disease diagnosis can be enhanced using assessments of cognitive performance. The Canadian Journal of Statistics 47: 140–156; 2019
| Original language | English (US) |
|---|---|
| Pages (from-to) | 140-156 |
| Number of pages | 17 |
| Journal | Canadian Journal of Statistics |
| Volume | 47 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 2019 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty
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A consistent estimator for logistic mixed effect models. / Wei, Yizheng; Ma, Yanyuan; Garcia, Tanya P.; Sinha, Samiran.
In: Canadian Journal of Statistics, Vol. 47, No. 2, 06.2019, p. 140-156.Research output: Contribution to journal › Article
TY - JOUR
T1 - A consistent estimator for logistic mixed effect models
AU - Wei, Yizheng
AU - Ma, Yanyuan
AU - Garcia, Tanya P.
AU - Sinha, Samiran
PY - 2019/6
Y1 - 2019/6
N2 - We propose a consistent and locally efficient method of estimating the model parameters of a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where, in many cases, we have limited resources to design experiments and gather data in long-term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial to have an estimator that is robust to such violations; then we could make better use of current data harvested using various valuable resources. Our method generalizes the framework presented in Garcia & Ma (2016) which also deals with a logistic mixed effect model but only considers a random intercept. A simulation study reveals that our proposed estimator remains consistent even when the independence and normality assumptions are violated. This contrasts favourably with the traditional maximum likelihood estimator which is likely to be inconsistent when there is dependence between the covariates and random effects. Application of this work to a study of Huntington's disease reveals that disease diagnosis can be enhanced using assessments of cognitive performance. The Canadian Journal of Statistics 47: 140–156; 2019
AB - We propose a consistent and locally efficient method of estimating the model parameters of a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where, in many cases, we have limited resources to design experiments and gather data in long-term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial to have an estimator that is robust to such violations; then we could make better use of current data harvested using various valuable resources. Our method generalizes the framework presented in Garcia & Ma (2016) which also deals with a logistic mixed effect model but only considers a random intercept. A simulation study reveals that our proposed estimator remains consistent even when the independence and normality assumptions are violated. This contrasts favourably with the traditional maximum likelihood estimator which is likely to be inconsistent when there is dependence between the covariates and random effects. Application of this work to a study of Huntington's disease reveals that disease diagnosis can be enhanced using assessments of cognitive performance. The Canadian Journal of Statistics 47: 140–156; 2019
UR - http://www.scopus.com/inward/record.url?scp=85060580356&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060580356&partnerID=8YFLogxK
U2 - 10.1002/cjs.11482
DO - 10.1002/cjs.11482
M3 - Article
C2 - 31274953
AN - SCOPUS:85060580356
VL - 47
SP - 140
EP - 156
JO - Canadian Journal of Statistics
JF - Canadian Journal of Statistics
SN - 0319-5724
IS - 2
ER -