#datascienceanalytics

[michael2007]

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[statswork]

Multiplicity Problem in Clinical Trials and Some Statistical Approaches (on Wattpad) https://my.w.tt/oaeqbLJih2 In most of the clinical trial problem, researchers often face multiple testing problems that have an impact on type I and type II error rates, results in invalid inference. Thus, the multiplicity issue should be considered at the beginning stage i.e. starting from the design, Data Analysis and Interpretation of the study. What is multiplicity? Inflation of type I error rate from a Multiple Testing problem is commonly referred as multiplicity. Type I error is simply the error rate when rejecting the null hypothesis when it is true and it is referred as significance level of the trial. If there exists the problem of multiplicity, then it should be adjusted in the testing problem. Why do we need to consider multiple testing adjustments? Suppose a set of hypotheses are tested within the same study simultaneously, then the probability of rejecting at least one null hypothesis (i.e) type I error rate is increased, thereby results in a high risk of finding false positive.