Ten patients with advanced diabetic nephropathy (kidney...

Ten patients with advanced diabetic nephropathy (kidney complications of diabetes) were treated with captopril over an 8-week period [9]. Urinary protein was measured before and after drug therapy, with results listed in Table 8.16 in both the raw and In scale. What is your best estimate of the percent change in urinary protein based on the data in Table $8.16 ?$

Ten patients with advanced diabetic nephropathy (kidney complications of diabetes) were treated with captopril over an 8-week period [9]. Urinary protein was measured before and after drug therapy, with results listed in Table 8.16 in both the raw and In scale.
What is your best estimate of the percent change in urinary protein based on the data in Table $8.16 ?$

Key Concepts

Percent Change

Percent change is used to express the difference between an initial value and a new value as a percentage of the initial value. It is calculated by taking the difference between the new and original values, dividing by the original value, and then multiplying by 100. This metric is widely used in various fields to compare changes over time relative to a starting point.

Log Transformation

Log transformation involves converting data by taking the natural logarithm (or another logarithm) of each data point. This process is especially useful when data span several orders of magnitude or exhibit skewness, as it helps stabilize variance and normalize distributions. In the context of comparing pre- and post-treatment values, differences on the logarithmic scale can be interpreted as multiplicative changes once back-transformed.

Back Transformation from the Log Scale

When data have been log-transformed, the differences computed on the logarithmic scale must be exponentiated in order to return to the original scale of measurement. This back transformation allows one to interpret the differences as ratios or percentage changes, providing a clear understanding of multiplicative effects that occurred between the baseline and follow-up measurements.

Paired Data Comparison

Paired data analysis involves comparing measurements taken from the same subjects at two different time points, such as before and after an intervention. This method allows for a more precise estimation of change by controlling for inter-individual variability, and is fundamental in studies designed to evaluate the effect of a treatment or intervention by assessing within-subject differences.

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