Missing Values

Missing values in your data? 🚨 Why does this happen and how you can handle this problem?

Reasons for Missing Values

⛔ There are many different reasons why values can be missing. For example, a patient didn't show up to his appointment or even left the study completely. In the worst case, a patient may even have died.

Challenges

⚠️ Missing values can lead to bias.

⚠️ If values are not missing at random, but systematically, this is especially problematic.

Statistical Methods

📊 We can estimate the missing values from the rest of the data. This is called imputation. The easiest form of imputation is the single value imputation, where we replace the missing value by a single value. There are different methods how to do this, e.g. last observation carried forward or mean imputation.

🛠️ Single value imputation is a rather easy approach. There are much more complex ways of imputation, for example multiple imputation. You can learn more about that in our next post!

📢 Stay tuned and continue Building Statistical Confidence For Market Access