By J. Rick Turner
This short discusses key statistical innovations that facilitate the inferential research of information accrued from a gaggle of people engaging in a pharmaceutical scientific trial, the estimation in their scientific importance within the normal inhabitants of people prone to be prescribed the drug if licensed, and the similar decision-making that happens at either the general public overall healthiness point (by regulatory enterprises while determining even if to approve a brand new drug for advertising) and the person sufferer point (by physicians and their sufferers whilst figuring out even if the sufferer may be prescribed a drug that's at the market). those strategies contain drug safeguard and efficacy, statistical importance, medical importance, and benefit-risk balance.
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Additional resources for Key Statistical Concepts in Clinical Trials for Pharma
Just as we saw in the previous chapter when evaluating degrees of risk, the use of confidence intervals is a meaningful strategy in clinical research. 00 mmHg (the decimal places have been added since the confidence intervals about to be presented are calculated to two decimal places). It is now appropriate to place a confidence interval around this value, which, as noted earlier, is now referred to as the treatment effect point estimate. In this situation, the two-sided 95% CI is commonly used to determine a range of values that we are 95% confident will cover the true but unknown population treatment effect.
Clinically significant, to decrease blood pressure by 5 mmHg? The answer may be yes. ) At the other end of the range of values, the same question can be asked for a decrease in blood pressure of 1 mmHg. Even though theoretically any decrease is to be welcomed, from a practical pharmacotherapy perspective, a drug that may lower MAP by 1 mmHg may not be considered a good candidate for marketing approval since there are likely to be other drugs already on the market that lower it to a greater degree.
Certainly, an analysis is conducted, but the procedure is also critically dependent on choosing the appropriate analytical strategy, interpreting the numerical results of the analysis in the context of the research question being asked, and consistently presenting the results with scientific and clinical decorum. Therefore, as noted by Kay , ‘‘to ensure that a meta-analysis is scientifically valid, it is necessary to plan and conduct the analysis in an appropriate way. ’’ Even though conducting a metaanalysis does not require a new trial to be conducted, it is still a research method in its own right.
Key Statistical Concepts in Clinical Trials for Pharma by J. Rick Turner