Uploaded April 2024 | Updated September 2026, 2 weeks ago
Abstract: Missing data is a common feature of many clinical studies. In this talk I show how we handled this using multiple imputation in a health and wellbeing study and reported the results. In particular, multiple imputation is more robust when there are large amounts of missingness as it takes sampling variables into account.
Bio: Peter Watson has been providing statistical support in various ways to the research at the MRC CBU since 1994, and before that fulfilling a similar role at the MRC Age and Cognitive Performance Research Centre in Manchester. He has also been secretary, since 1996, of the Cambridge Statistics Discussion Group and chair and meetings organiser for the SPSS users group (ASSESS) since 2001.
Abstract: Missing data is a common feature of many clinical studies. In this talk I show how we handled this using multiple imputation in a health and wellbeing study and reported the results. In particular, multiple imputation is more robust when there are large amounts of missingness as it takes sampling variables into account.
Bio: Peter Watson has been providing statistical support in various ways to the research at the MRC CBU since 1994, and before that fulfilling a similar role at the MRC Age and Cognitive Performance Research Centre in Manchester. He has also been secretary, since 1996, of the Cambridge Statistics Discussion Group and chair and meetings organiser for the SPSS users group (ASSESS) since 2001.
