The following options are available for the 4PL model in QuBAS:
Transformation of the dose
The estimation of the relative potency in the linear model assumes the dose will be analysed on a log scale. If the data for dose levels are not already presented on the log scale, the following log scale transformations are available:
Transformation of the response
A log transformation of the response can often result in more stable variance across the range of dose levels in the assay. The response can be transformed as follows:
It is also possible to apply a linear transformation to the response. This allows an offset to be added to the recorded dose, and a constant multiplier to be applied.
Weighting of the response
Another approach to achieving stable response variance across the range of dose levels in the assay is to apply weights to the responses which vary across the dose groups. When the variance at a mean response value of Y is A * YB, the appropriate weighting is:
Weight = 
QuBAS allows the user to select the values of A and B.
For example, if the variance is proportional to the mean response value, then A = 1 and B = 1 and the weighting is 1/response.
Outlier identification and removal
QuBAS allows for examination of the data for outliers, and the option to exclude the outliers. There are two options for the identification
Grubb’s test : significance level fixed at p = 0.05
Ref: Grubbs, Frank E. (1950). "Sample criteria for testing outlying observations". Annals of Mathematical Statistics. 21 (1): 27–58.
Studentised residuals : outlier if > 3
Allen J. Pope (1976), "The statistics of residuals and the detection of outliers", U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Ocean Survey, Geodetic Research and Development Laboratory, 136 pages
Method for ratios
There are 2 generally used methods for calculating confidence limits for ratio values.
Delta Method: based on a truncated Taylor series expansion (generally applicable to any function of random variables).
Fieller Method: developed specifically for ratios of random variables.
Generally the methods give very similar results, but Fieller's can sometimes create nonsensical intervals (see https://en.wikipedia.org/wiki/Fieller%27s_theorem) .
Variance estimate
There are several possible approaches to the estimation of the variance, σ2, which is needed for the calculation of confidence intervals. The variance can be based on:
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Residuals: the distances of each response from the fitted model
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Lack of fit: the distances of the mean response per dose group from the fitted model
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Pure error: the distances of each response from the mean in its dose group
Note that for the estimation of the variance of the RP and parameters, the correct selection is ‘Residuals’. The other options are provided for comparability with other bioassay software.
All p values and confidence intervals are calculated using the same selected variance method.
Orientation
This option allows the user to decide the sign of the B parameter, according to whether they prefer to have the A and D parameters on the left and right of the graph respectively (default setting), or vice versa.
