If we observe repeated binary outcomes over time then there may be dependence in outcomes\u00a0and a test for dependence may be sought for such data. However, tests for dependence\u00a0in models for repeated measures remain a challenge where covariates are associated\u00a0with previous outcomes and both covariates and previous outcomes are included simultaneously\u00a0in a model. This paper displays the nature of such problems (i.e. dependence among\u00a0outcomes may depend on the association between covariates and previous outcomes) inherent\u00a0in models for repeated binary outcomes that can distort the estimates and may produce\u00a0misleading results. In the context of application of regressive models, this paper discusses\u00a0conditions for which the regressive models can be safely employed. All these are shown\u00a0on the basis of simple relationships between the conditional, marginal and joint probability\u00a0mass functions for the bivariate binary outcomes which can be extended to the multivariate\u00a0data stemmed from repeated measures. Some test procedures are suggested and applications\u00a0are demonstrated using both simulations and real life data. Both the applications\u00a0clearly indicate the utility of the proposed tests.<\/p>\n