158s Abstracts

F77 IMPACT OF TRIALS WITH NULL NUMBER OF

EVENTS IN META-ANALYSIS

Michel Cucherat, Alain Leizorovicz and Jean-Pierre Bolssel Service de Pharmacologic Clinique

H6pital Cardiologique Lyon, France

Iutroduction: In a meta-analysis, the existence of one or more trials with a null numbers of events in one groups poses a problem of calculation and estimation of the common odds- ratio. This situation arises due to trials with insufficient size and rare events. A null denominator lead to a division by zero. The problem of calculation can be however solved by the use of a pseudo-count method. A small constant such as 0.5 or 0.25 is added to both the group size and the number of events. Nevertheless the problem of estimation remains, and the value of the odds ratio depends directly on the trial size and the value of the constant.

Methods: We have explored this problem by Monte-Carlo simulation. Sets of fictitious trials were generated with a known events rate in the control group. The size of the trials was adjusted in order to vary the frequency of trials with a null number of events. Several meta- analysis methods, based on a tied multiplicative model, were used to pool these trials. The estimated common effect treatment was compared with the real parameter. For each situation, 1000 replications were averaged.

Results: It appears that the estimated common treatment effect began to be biased when the sum of the weights of the trials with null number of events increased. The relative error is greater than 20% when the contribution to the me&analysis of the trials with a null number of events was more than 50%, and was about 70% when this kind of trials contributes for more than 90%.

Conclusion: The results of a meta-analysis with a high proportion of trials with a null number of events must be interpreted with caution and new methods must be developed to prevent this bias and to be able to use the information supplied by these trials.

F78 PSEUDO-LIKELIHOOD METHODS FOR GENERAL MISSING DATA

MECHANISMS

Bart Michiek and Geert Molenberghs Limburgs Universitair Centrum

Diepenbeek, Belgium

In a longitudinal data setting with dropouts, it is often difficult to construct a full likelihood model. It might be easier to specify several conditional distributions instead. In many cases, conditional distributions reflect more closely questions of scientific interest. Different techniques to analyze data based on conditional models are explored, such as pseudo-likelihood (Arnold, Castillo, and Sarabia. 1992). In addition to constructing point estimates, valid precision estimates can be obtained using a sandwich type estimator, as in general&d estimating equations (Liang and Zeger, 1986). Another approach is provided by the Markov Chain Monte Carlo method, with the Gibbs sampler as a particularly valuable tool.

These techniques will be applied when data are missing (completely) at random, but also to informative missingness. Different methods will be compared based on data from a clinical study.

Amoki, B.C., Castillo, E. And Sarabia, J.M.(1992) Conditionally Specified Distributions, Lecture Notes in Statistics 73, New York: Springer Verlag. Liang. K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika 73, 13-22