Update chapters/21-sensitivity.qmd

Co-authored-by: Malcolm Barrett <[email protected]>

chapters/21-sensitivity.qmd

@@ -860,7 +860,7 @@ The relationship between an unmeasured confounder and the exposure can be charac
 
 These characterizations allow researchers to specify the unmeasured confounder-exposure relationship in sensitivity analyses, accommodating different types of confounders and levels of knowledge about their distribution.
 
-Our unmeasured confounder here, 'Historic high temperature', is continuous. For this example, let's assume it is normally distributed. We say to assume "unit variance" (a variance of 1), because it makes it easier to talk about the impact of the confounder in standard-deviation terms. Let's assume that on days with extra magic morning hours the historic high temperature is normally distributed with a mean of 80.5 degrees and a standard deviation of 9 degrees. Likewise, assume that on days without extra magic morning hours the historic high temperature is normally distributed with a mean of 82 degrees and a standard deviation of 9 degrees. We can convert these to 'unit variance' normally distributed variables by dividing by that standard deviation, 9 (sometimes we refer to this as *standardizing* our variable); this gives us a standardized mean of 8.94 for days with extra magic morning hours and 9.11 for the others, or a mean difference of -0.17. Hold on to this number, we'll use it in conjunction with the next section for our sensitivity analysis.
+Our unmeasured confounder here, 'Historic high temperature', is continuous. For this example, let's assume it is normally distributed. We say to assume "unit variance" (a variance of 1), because it makes it easier to talk about the impact of the confounder in standard-deviation terms. Let's assume that on days with extra magic morning hours the historic high temperature is normally distributed with a mean of 80.5 degrees and a standard deviation of 9 degrees. Likewise, assume that on days without extra magic morning hours the historic high temperature is normally distributed with a mean of 82 degrees and a standard deviation of 9 degrees. We can convert these to 'unit variance' normally distributed variables by dividing by that standard deviation, 9 (sometimes we refer to this as *standardizing* our variable); this gives us a standardized mean of 8.94 for days with extra magic morning hours and 9.11 for the others, or a mean difference of -0.17. Hold on to this number; we'll use it in conjunction with the next section for our sensitivity analysis.
 
 #### Unmeasured confounder-outcome effect