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Logistic regression in meta-analysis using aggregate data
Journal of Applied Statistics (2000)
  • Bei-Hung Chang, Boston University
  • Stuart Lipsitz
  • Christine Waternaux, New York State Psychiatric Institute
We derived two methods to estimate the logistic regression coefficients in a meta-analysis when only the 'aggregate' data (mean values) from each study are available. The estimators we proposed are the discriminant function estimator and the reverse Taylor series approximation. These two methods of estimation gave similar estimators using an example of individual data. However, when aggregate data were used, the discriminant function estimators were quite different from the other two estimators. A simulation study was then performed to evaluate the performance of these two estimators as well as the estimator obtained from the model that simply uses the aggregate data in a logistic regression model. The simulation study showed that all three estimators are biased. The bias increases as the variance of the covariate increases. The distribution type of the covariates also affects the bias. In general, the estimator from the logistic regression using the aggregate data has less bias and better coverage probabilities than the other two estimators. We concluded that analysts should be cautious in using aggregate data to estimate the parameters of the logistic regression model for the underlying individual data.
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Citation Information
Bei-Hung Chang, Stuart Lipsitz, and Christine Waternaux. Logistic regression in meta-analysis using aggregate data. Journal Of Applied Statistics Vol. 27 , Iss. 4, 2000. p. 411-424.