Dealing with Missing Data in Health Statistics
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Questions and Answers

What is a common problem in statistical analyses involving real data sets?

  • Data imputation
  • Data duplication
  • Data normalization
  • Missing data (correct)

    • In which type of studies is missing data a common problem?

  • Randomized controlled trials
  • Non controlled, observational studies (correct)
  • Cross-sectional studies
  • Case-control studies

    • What statistical technique is used to estimate parameters in a population different from that in which the data was collected?

  • Multiple imputation
  • Multiple imputation by chained equations (MICE) algorithm
  • Inverse probability weighting (correct)
  • Regression imputation

    • What is the main limitation of regression imputation method?

    <p>Imputed values within each variable are not equal due to the values of the remaining variables.</p> Signup and view all the answers

    According to the text, what does IPW do when applied to deal with missing data?

    <p>It weights each observation by the inverse of the probability of being sampled.</p> Signup and view all the answers

    What assumption do MI techniques make about missing data?

    <p>Missing data can be replaced by predictions derived from observed data.</p> Signup and view all the answers

    What is the MICE algorithm used for in multiple imputation?

    <p>Imputing realistic values to the missing data and propagating the uncertainty.</p> Signup and view all the answers

    How are the missing values for each variable handled in the MICE algorithm?

    <p>By using predictions from fitted regression models after performing a regression model analysis.</p> Signup and view all the answers

    Which statistical technique uses inflation of weight for under-represented subjects due to a large degree of missing data?

    <p>Inverse probability weighting</p> Signup and view all the answers

    In the context of linear regression analysis, what is performed for each imputed dataset in multiple imputation?

    <p>A typical regression analysis with Yi as the dependent variable and all other variables as independent predictors.</p> Signup and view all the answers

    What can be performed using the mice package in R according to the text?

    <p>Multiple imputation analysis.</p> Signup and view all the answers

    What is Var(✓) MI used for in multiple imputation?

    <p>Captures the uncertainty of the imputations and inflates the error of the estimate accordingly.</p> Signup and view all the answers

    What is the consequence of missing data being MNAR?

    <p>Sample size reduction and biased parameter estimates.</p> Signup and view all the answers

    What method replaces each missing datum with the sample mean, median, or mode of the variable computed using available data?

    <p>Mean/median/mode substitution</p> Signup and view all the answers

    What is the primary assumption for estimates to be unbiased under complete cases analysis?

    <p>Data is missing completely at random (MCAR).</p> Signup and view all the answers

    Which type of missing data pattern implies that the probability of an observation being missing does not depend on the value of the observation or any other variables in the dataset?

    <p>MCAR (Missing completely at random)</p> Signup and view all the answers

    What is the consequence of using mean/median/mode substitution for missing data?

    <p>Unrealistic imputed values and underestimation of variance.</p> Signup and view all the answers

    What is the primary characteristic of MNAR (Missing not at random) data?

    <p>The probability of missing data depends only on the value of the observation itself.</p> Signup and view all the answers

    What are the consequences when using complete cases analysis?

    <p>Sample size reduction and biased parameter estimates.</p> Signup and view all the answers

    What characteristic differentiates MAR from MCAR?

    <p>The probability of missing Xi depends on observed data, not missing data.</p> Signup and view all the answers

    What is one of the potential consequences when data are MCAR?

    <p>Sample size reduction and statistical power reduction.</p> Signup and view all the answers

    What is a primary characteristic that defines MAR (Missing at random) data?

    <p>The probability of missing Xi depends on observed data, not missing data.</p> Signup and view all the answers

    What is one of the potential consequences when data are MNAR?

    <p>Sample size reduction and biased parameter estimates.</p> Signup and view all the answers

    Study Notes

    Statistical Analysis Challenges

    • Missing data is a prevalent issue in statistical analyses involving real data sets.
    • Commonly encountered in observational studies, surveys, and longitudinal studies.

    Parameter Estimation Techniques

    • Inverse Probability Weighting (IPW) is utilized to estimate parameters from different populations than the one from which data was collected.
    • Regression imputation method's main limitation is its assumption of relationships based solely on available data.

    Missing Data Management Techniques

    • IPW adjusts for missing data by inflating weights for under-represented subjects, ensuring a more accurate representation.
    • Multiple Imputation (MI) techniques assume that the missing data are missing at random (MAR).

    MICE Algorithm

    • MICE (Multivariate Imputation by Chained Equations) is employed for handling multiple imputation by generating multiple complete datasets.
    • It addresses missing values for each variable by utilizing regression models based on other variables in the dataset to predict and fill in missing entries.

    Statistical Techniques for Imputation

    • In multiple imputation, linear regression analysis is performed on each imputed dataset to derive estimates.
    • The mice package in R provides tools for implementing MICE, facilitating the completion of missing data tasks efficiently.
    • Var(✓) MI refers to the variance estimation of the imputed parameters in multiple imputation.

    Missing Data Patterns and Implications

    • Missing Not At Random (MNAR) occurs when the missingness of data is related to the unobserved data itself, complicating imputation efforts.
    • Mean, median, or mode substitution replaces missing values with central tendency measures derived from the observed data.
    • Complete cases analysis assumes that the remaining data can be unbiased; it typically requires the data to be Missing Completely At Random (MCAR).

    Types of Missing Data

    • MCAR implies that the probability of data being missing is independent of observed or unobserved values, potentially leading to biased results.
    • Missing At Random (MAR) means the missingness is related to observed data but not to the missing data itself, providing a more manageable scenario for imputation.
    • The transition from MAR to MNAR can lead to distorted analysis and unreliable conclusions if not appropriately addressed.

    Conclusion

    • Utilizing advanced imputation strategies like MICE and IPW helps to mitigate issues arising from missing data, enhancing the robustness of statistical conclusions.

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    Related Documents

    Statistics in Health Sciences, 2023/2024 PDF

    Description

    Learn about dealing with missing data in health statistics in this quiz based on the B.Sc. Degree in Applied Statistics. Explore the different types of missing data and methods to handle them, presented by Jose Barrera from ISGlobal Barcelona Institute for Global Health.

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