Abstract
Longitudinal studies often feature incomplete response and covariate data. It is well known that biases can arise from naive analyses of available data, but the precise impact of incomplete data depends on the frequency of missing data and the strength of the association between the response variables and covariates and the missing-data indicators. Various factors may influence the availability of response and covariate data at scheduled assessment times, and at any given assessment time the response may be missing, covariate data may be missing, or both response and covariate data may be missing. Here we show that it is important to take the association between the missing data indicators for these two processes into account through joint models. Inverse probability-weighted generalized estimating equations offer an appealing approach for doing this. Here we develop these equations for a particular model generating intermittently missing-at-random data. Empirical studies demonstrate that the consistent estimators arising from the proposed methods have very small empirical biases in moderate samples. Supplemental materials are available online.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 336-353 |
| Number of pages | 18 |
| Journal | Journal of the American Statistical Association |
| Volume | 105 |
| Issue number | 489 |
| DOIs | |
| State | Published - Mar 1 2010 |
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Keywords
- Generalized estimating equation
- Inverse probability weight
- Joint model
- Longitudinal data
- Missing covariate
- Missing response
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Weighted generalized estimating functions for longitudinal response and covariate data that are missing at random. / Chen, Baojiang; Yi, Grace Y.; Cook, Richard J.
In: Journal of the American Statistical Association, Vol. 105, No. 489, 01.03.2010, p. 336-353.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Weighted generalized estimating functions for longitudinal response and covariate data that are missing at random
AU - Chen, Baojiang
AU - Yi, Grace Y.
AU - Cook, Richard J.
PY - 2010/3/1
Y1 - 2010/3/1
N2 - Longitudinal studies often feature incomplete response and covariate data. It is well known that biases can arise from naive analyses of available data, but the precise impact of incomplete data depends on the frequency of missing data and the strength of the association between the response variables and covariates and the missing-data indicators. Various factors may influence the availability of response and covariate data at scheduled assessment times, and at any given assessment time the response may be missing, covariate data may be missing, or both response and covariate data may be missing. Here we show that it is important to take the association between the missing data indicators for these two processes into account through joint models. Inverse probability-weighted generalized estimating equations offer an appealing approach for doing this. Here we develop these equations for a particular model generating intermittently missing-at-random data. Empirical studies demonstrate that the consistent estimators arising from the proposed methods have very small empirical biases in moderate samples. Supplemental materials are available online.
AB - Longitudinal studies often feature incomplete response and covariate data. It is well known that biases can arise from naive analyses of available data, but the precise impact of incomplete data depends on the frequency of missing data and the strength of the association between the response variables and covariates and the missing-data indicators. Various factors may influence the availability of response and covariate data at scheduled assessment times, and at any given assessment time the response may be missing, covariate data may be missing, or both response and covariate data may be missing. Here we show that it is important to take the association between the missing data indicators for these two processes into account through joint models. Inverse probability-weighted generalized estimating equations offer an appealing approach for doing this. Here we develop these equations for a particular model generating intermittently missing-at-random data. Empirical studies demonstrate that the consistent estimators arising from the proposed methods have very small empirical biases in moderate samples. Supplemental materials are available online.
KW - Generalized estimating equation
KW - Inverse probability weight
KW - Joint model
KW - Longitudinal data
KW - Missing covariate
KW - Missing response
UR - http://www.scopus.com/inward/record.url?scp=77952557371&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952557371&partnerID=8YFLogxK
U2 - 10.1198/jasa.2010.tm08551
DO - 10.1198/jasa.2010.tm08551
M3 - Article
AN - SCOPUS:77952557371
VL - 105
SP - 336
EP - 353
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
IS - 489
ER -