The Amyotrophic Lateral Sclerosis Assessment Questionnaire (ALSAQ‐40): Evidence for a method of imputing missing data

Abstract

The Amyotrophic Lateral Sclerosis Assessment Questionnaire (ALSAQ‐40) is the most widely validated measure of health status for use with patients diagnosed with motor neuron disease/amyotrophic lateral sclerosis (MND/ALS). The questionnaire was designed to be used in studies evaluating treatment regimes where missing data may cause problems with data analyses.The purpose of this paper is to evaluate an algorithm for the imputation of missing dimension scores on the ALSAQ‐40. We used a postal survey of patients diagnosed with MND/ALS. One thousand, nine hundred and seventy‐nine patients were surveyed and 1093 (55.2%) questionnaires returned. Of these, complete ALSAQ‐40 data was available in 854 (85.8%) cases. Data were deleted from this complete dataset, and in a randomly selected subset of 100 cases, and then imputed using the Expectation Maximization (EM) algorithm: results were then compared to the dataset prior to data deletion. Descriptive statistics, mean scores and spread of scores were almost identical between original and imputed datasets. Furthermore, the two datasets were highly correlated (intra‐class correlation coefficient = 0.95 or greater), and mean differences were small (±1.00). We concluded that EM imputation for the ALSAQ‐40 provides data that closely mirrors the original when this has been deliberately removed. Consequently, EM is likely to be appropriate for studies using the ALSAQ‐40 that contain missing data points.

• motor neuron disease
• amyotrophic lateral sclerosis assessment questionnaire
• ALSAQ‐40
• missing data
• data imputation