Section 1
For input, that require logarithmic transformation (RR, OR, HR, IRR, ... etc.)
Explanations & examples: Here it's possible to calculate the weighted estimate (weighted average) between two or more estimates that require log transformation, in other words estimates such as odds ratios (OR), risk ratios (RR), incidence rate ratios (IRR) ... etc., and also to compare two estimates with each other to see if they could be equal. If the estimates are not already log transformed before they are entered into the table, then choose the input option "No" under "Are the input values already log transformed". If the inputs are already log transformed, choose "Yes". Nonlog transformed values must be entered as the estimates and their 95 % confidence intervals. Log transformed values must be entered as the log of the values and the standard error of the log transformed values. Two of the estimates entered can be compared to see if they are equal by calculating their ratio and the difference of their log transformation. If the number 1 is included in the 95 % confidence interval of the ratio, then the null hypothesis H0 can not be rejected, namely that the ratio is 1, and there is no significant difference between the two estimates. If 1 is included in the 95 % confidence interval of the ratio then 0 is included in the 95 % confidence interval of the difference between the log transformed estimates. If, however, 1 is not in the 95 % confidence interval of the ratio, then H0 is rejected and the estimates are significantly different from each other on a five percent significance level. It is only recommended to use and interpret the weighted average of the estimates entered when they are not significantly different from each other. If they are not significantly different, then the weighted estimate will be the common value of all the estimates combined into one. In the calculation of the weighted estimate the estimates involved will contribute with their "weights" meaning that "heavier" estimates (with more persons in the study) will contribute more, so that the weighted estimate will be closer to the "heavier" of the involved estimates. For more info and the formulas used in the calculations, please see the page medical statistics formulas. Example:In a study^{(a)} the OR value 1.29 [1.05 : 1.57] was determined in a logistic regression of the connection between poor eyesight and experiencing a fall among elderly. In another, similar study, the OR value was determined to be 1.61 [1.32 : 1.96]. We want to compare the two OR values to see if they can be assumed equal and, if so, calculate the weighted estimate between them. First of all, we see that neither of the two OR values is included in the 95 % confidence interval of the other one. If this had been the case, we could already then establish, that the OR values were not significantly different. And if there had been no overlap at all between the confidence intervals, this would have established that the OR values were significantly different. In this case, however, with some overlap between the intervals, it's impossible to conclude anything from the intervals alone.Entering the OR values and their confidence intervals into the table, we get a ratio between them of 0.8012 with a 95 % confidence interval of [0.6044 : 1.0623]. Since 1 is included in the 95 % CI of the ratio, it cannot be rejected that the ratio could be 1, and the OR values are therefore not significantly different from each other on a five percent significance level. We could also have done the test instead and looked at the confidence interval. The zvalue in the test is 1.5401 and the corresponding pvalue 0.1235 which is more than 0.05, so we don't reject the null hypothesis. Since the OR values are not significantly different from each other, we can combine them into one by calculating their weighted estimate, which is 1.4439 with 95 % CI [1.2541 : 1.6626]. The weighted estimate, in this case, is the effect that visual impairment has on the odds of falling, having adjusted for the different studies. The interpretation of the weighted OR of 1.4439 is that if you take two elderly people, who are equal regarding everything else (i.e. also from the same study), where one has visual impairment and the other hasn't, then the visually impaired will have 1.4436 times higher odds of experiencing a fall than the nonimpaired. The weighted estimate is significant since 1 is not included in its 95 % confidence interval. So visual impairment does have a significant effect on the odds of falling, having adjusted for different studies. ^{(a)} Yip J et al.: Visual acuity, selfreported vision and falls in the EPICNorfolk Eye study. British Journal of Opthamology 2014;98:377382 

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