# How to handle data with 2 variables that have same missingness pattern?

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
.      .      .           .           .
.      .      .           .           .
.      .      .           .           .

• Probably you are lucky the missing values are on the same observations: you then keep more complete observations. However, this might hide a systematic error. Patients without measurement might be patients that feel too well to wait for yet another doctor, or nurse to do the measurements, or they might be transferred to emergency. – Dirk Horsten Jul 22 '15 at 21:16
• I was looking more for how one might analyze this data and/or any errors that might crop up in estimates from analyzing this data via multiple imputation. – Tyler Jul 27 '15 at 12:33