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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Multiple imputation for handling missing outcome data when estimating the relative risk.

Thomas R Sullivan1, Katherine J Lee2,3, Philip Ryan4

  • 1The University of Adelaide, School of Public Health, Adelaide, SA, Australia. thomas.sullivan@adelaide.edu.au.

BMC Medical Research Methodology
|September 8, 2017
PubMed
Summary
This summary is machine-generated.

Multiple imputation methods for handling missing data can bias relative risk estimates in medical research. Fully conditional specification is preferred over multivariate normal imputation, but further research is needed for optimal approaches.

Keywords:
Missing dataMultiple imputationRelative risk

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Area of Science:

  • Medical Statistics
  • Epidemiology
  • Biostatistics

Background:

  • Multiple imputation is widely used for missing data in medical research.
  • Its effectiveness for estimating relative risk is not well understood.
  • Standard imputation methods may not align with relative risk estimation models, potentially causing bias.

Purpose of the Study:

  • To evaluate the performance of multiple imputation techniques for estimating relative risk.
  • To assess the impact of imputation model misspecification on relative risk estimates.
  • To compare multivariate normal imputation and fully conditional specification in simulated scenarios.

Main Methods:

  • Simulated data with missing outcomes and exposure variables under missing at random mechanisms.
  • Evaluated multivariate normal imputation and fully conditional specification (logistic model).
  • Estimated adjusted relative risks using a correctly specified log binomial model.

Main Results:

  • Multivariate normal imputation yielded biased relative risk estimates, skewed towards the null.
  • Fully conditional specification also produced biased estimates, particularly with high outcome prevalence and large relative risks.
  • Removing imputed outcomes did not enhance the performance of fully conditional specification.

Conclusions:

  • Both tested multiple imputation methods resulted in biased relative risk estimates due to model misspecification.
  • Fully conditional specification is recommended over multivariate normal imputation, retaining imputed data in analysis.
  • Further research is necessary to determine optimal multiple imputation strategies for relative risk estimation.