Implementing Multiple Imputation With Sensitivity Analysis In Large-scale Longitudinal Studies
Funder
National Health and Medical Research Council
Funding Amount
$473,507.00
Summary
Missing data arise in most research studies and if not handled appropriately can mean the study results are not correct. With researchers now conducting larger and longer studies the challenges posed by missing data are increasing. In this grant we study a powerful technique for handling missing data, which in its current form often cannot be applied effectively in large studies. By developing this approach we will improve the accuracy of results from large-scale epidemiological studies.
How Should We Analyse, Synthesize, And Interpret Evidence From Interrupted Time Series Studies? Making The Best Use Of Available Evidence
Funder
National Health and Medical Research Council
Funding Amount
$445,144.00
Summary
Interrupted time series (ITS) studies are frequently used to evaluate whether policy interventions are successful. The findings from these studies are often collated into systematic reviews, which are used to inform healthcare decisions by clinicians, consumers and policy makers. It is not known how the statistical methods, which underpin the findings from ITS studies, perform. This proposal will evaluate the statistical methods and provide guidance on how to analyse and interpret ITS studies.