I've not had much academic coursework on imputation, and I can't find anything online or in any texts regarding how one could handle missing data where there are two (or more, possibly?) variables with the same missingness pattern.
For example, suppose that the data set I'm looking at has two blood pressure variables which takes in systolic and diastolic readings as two separate variables, but if a blood pressure reading wasn't taken for whatever reason (patient refusal, patient only in to speak with a doctor, staff forgot to take blood pressure, etc.) it would be regarded as missing.
Furthermore, suppose that as a researcher, you were asked to analyze this data. The standard approach, from what I recall from school and my research, is to perform multiple imputation, but that would generally be best on independent variables that have differing missingness patterns. If you did run a multiple imputation command on the data set (like mice/mi in R, or proc mi/mianalyze in SAS) to impute those missing blood pressure values, what effect would that have on the estimates? Are there any approaches to handling this missing data that would be better? I know listwise or pairwise deletion are some other options, but those are better for when the missing data is a small portion of the whole data set, right?; I'm curious on a more general level... suppose that nearly 40% of the blood pressure values were missing, or something that would make you feel as if list- or pairwise deletion would be inappropriate for forming accurate estimates.
I don't have a data set in mind or at hand, so I'm generate a set to help illustrate the issue I'm explaining:
ID Age Gender Systolic Diastolic 1 45 M (1) 125 76 2 33 F (0) 101 67 3 27 M (1) NA NA 4 51 M (1) 120 79 5 38 F (0) 119 77 6 64 M (1) NA NA 7 48 F (0) NA NA 8 83 F (0) 130 81 9 27 M (1) 99 66 10 55 F (1) NA NA . . . . . . . . . . . . . . .