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Power Measurement, The p-values, Bias, Confidence Interval
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>> Power Measurement, The p-values, Bias, Confidence Interval
The confidence intervals which are calculated for measuring the treatment effect also show the range n which lays the true treatment effect.
We calculate the p-values in order to assess that whether the results of the trials have occurred simply because of chance (it is assumed that there is no real difference between the new and the old treatment and that the study is well conducted)
The confidence intervals are more preferred to the p-values as they are used for knowing the range of all the possible effect sizes which are compatible with the data.
The cut-off provided by the p-values is the point beyond which the findings can be termed a statistically significant.
The embracement of values having no difference between treatments by the confidence interval is an indication towards the fact that the treatments which are under investigation are not significantly different from the control.
These confidence intervals aid the interpretation of various clinical data by putting the upper and lower bounds of the likely sizes having any true effects. Before the confidence intervals are interpreted, it is necessary to assess the biases. Non significance does not imply any effect. Small studies have often reported non- significant even when there are important and real effects present which could have been detected by larger studies. Statistically significant does not always mean clinically important. The size of the effect determines its importance and not the statistical significance.
Measuring the size of the effect
The sizes of the effects can be measured by variety of ways. The two most widely used ways are relative risk reduction and absolute risk reduction. Relative measures are used to emphasize the potential benefits, while the absolute measures provide across- the board-summary. When subjected to correct interpretation, either of them can be correct.
These are used to provide different information which arises out of the hypotheses test. The hypothesis testing is used for producing the decision for an observed difference which can be statistically significant or non-significant. In contrast, the confidence intervals give the range of the observed effect size. The range’s construction is done in a way so that we get to know the likeliness of capturing the true, but unknown size of the effect. Therefore the confidence interval can be defined as – a range of values for given variables of interest which are constructed so that the range gets a specified probability for including the variable’s true value. The confidence level is the specified probability and the confidence limits are the end points of the confidence intervals.
By the p-values, it is possible to assess whether the findings are significantly different or not from a given reference value. The theory and calculations use in the two approaches of p-values and confidence intervals are the same.
The systematic errors which result form the way the study was designed, interpreted or executed is termed as bias. Some common flaws which can occur in the treatment trials can be failure in randomization which leads to creation of unbalanced groups, poor blinding which lead to unfair treatment along with biased assessments.
The starting point of both the p-values and confidence intervals is obtained from the results which are observed in the study. It is essential to check first whether it is an unbiased study. Confidence interval can then answer the question that what is the actual range of the real effects which can take place that are compatible with the data. The confidence interval is the range which will have the true value of the main measuring effect 95% of the times. Now we can do two things. First is that when the confidence interval embrace the value of having no effect, the findings can be termed as non significant and vice-versa. Hence, the confidence interval provides the information identical to p-value. But confidence intervals go beyond that and also tell us how small or large the real effects can be and also provide us the findings which are observed, by chance. The additional information helps us in interpreting both the borderline significance and the also the non significance.
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