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thank you for the introduction uh indeed but before iamp;#39;m going to talk about that iamp;#39;ll give you an introduction to measurement error and misclassification so measurement error or misclassification which is concerning binary variables may bias the estimates of parameters even when the error is entirely random or independent people often think that itamp;#39;s always guaranteed to be attenuation towards a null but this is only the case when the misclassification is non-differential when the exposure has no more than two categories so no multinomial or ordinal and all covariates are measured without error and even then itamp;#39;s quite difficult to quantify the by the bias uh when a coverage is measured without with error especially when thereamp;#39;s multiple covariates and in the extreme case with extreme misclassification the sign of an observed association can be reversed now if you go to ipd meta-analysis we must consider that there may be different measurements u
