Search results
The best possible method of handling the missing data is to prevent the problem by well-planning the study and collecting the data carefully [5, 6]. The following are suggested to minimize the amount of missing data in the clinical research [7]. First, the study design should limit the collection of data to those who are participating in the study.
When there are only missing data in the outcome (which may be cross sectional or longitudinal), Subsection 5.1 showed that maximising the likelihood of the observed data gives valid inference (provided we choose an appropriate covariance structure).
- Step 2: Establish What Data Are Missing For The Chosen Estimand
- ‘Pandemic- Free World’ Estimand
- ‘World Including A Pandemic’ Estimand
- Step 4: Perform Sensitivity Analysis Under Alternative Plausible Assumptions
We can only begin to think about missing data in any trial setting once the treatment estimand has been defined: only once we know exactly what we are aiming to estimate can we know whether we have the data required to estimate it. We define missing data as data that are required to estimate the estimand of interest, but that are unavailable. Some ...
Participants directly and indirectly clinically affected by a pandemic
Outcome data that are clinically affected by a pandemic either directly (e.g. via participant infection with Covid-19), or indirectly, are treated as missing for the ‘pandemic-free world’ estimand and can be handled in the same way as each other. For such data, an MAR assumption —conditional on randomised treatment arm and all observed variables expected to be associated with boththe trial outcome and being missing (i.e. being directly or indirectly affected) — may be the most reasonable assu...
Participants lost to follow-up during pandemic times
Follow-up may continue for participants whose trials outcomes are not impacted directly or indirectly during a pandemic (remotely or in person), but only some of their outcomes may be observed. Here, relative to non-pandemic times, there may be different factors that are expected to be associated with both outcome being missing (due to pandemic follow-up interruptions) and the trial outcome to consider to justify an MAR assumption. These factors may differ depending on the precise mode of fol...
Participants lost to follow-up during non-pandemic times
In ASCOT, and likely in any trial overlapping the pandemic, there will inevitably also be data missing from participants in non-pandemic times. Trialists should consider whether the same missing data assumption is relevant for data missing pre- (or post-) pandemic and during the pandemic. Where prior to the pandemic, a reduced set of factors related to outcome and missingness were considered appropriate for analysis under MAR (e.g., in ASCOT, treatment arm, baseline vision and 3 month vision)...
In a world including a pandemic, participant outcomes that are clinically affected by the pandemic (i. directly with the disease or ii. indirectly via changes to treatment/standard care) may either be observed – or expected – to be quite different to those observable under non-pandemic circumstances. To estimate the ‘world including a pandemic’ est...
Any missing data assumption is unverifiable, so sensitivity analyses under alternative plausible missing data assumptions should be conducted, regardless of the type of missing data assumption employed for primary analysis. Sensitivity analysis should address the same question as the primary analysis . If it can be shown that the inference is robus...
- Suzie Cro, Tim P Morris, Brennan C Kahan, Victoria R Cornelius, James R Carpenter
- 2020
Feb 24, 2021 · When there are only missing data in the outcome (which may be cross sectional or longitudinal), Subsection 5.1 showed that maximising the likelihood of the observed data gives valid inference (provided we choose an appropriate covariance structure).
- James R Carpenter, Melanie Smuk
- 2021
Feb 1, 2024 · Missing data are data that we planned to collect to answer a research question, such as participant characteristics at the start of the study or their health outcomes after receiving some treatments, but for some reason we were not able to. In practice there are various ways in which missing data can arise.
May 10, 2018 · Missing outcome information: It should be noted that up to this point, this article has focused primarily on missing covariate information. That is because when there are missing outcome data, it has been argued that the complete case analysis is more appropriate as imputed outcome data can lead to misleading results [14, 15]. Single imputation ...
People also ask
How do we handle missing outcome data?
Are missing outcomes Mar?
What happens if the outcome data is missing at random?
What is a missing outcome scenario?
What percentage of trials have missing outcome data?
What is missing data?
Dec 23, 2011 · Although missing outcome data are an important problem in randomized trials and observational studies, methods to address this issue can be difficult to apply. Using simulated data, the authors compared 3 methods to handle missing outcome data: 1) complete case analysis; 2) single imputation; and 3) multiple imputation (all 3 with and without covariate adjustment).
