1. Statistical Methods for Missing Data Roberta Harnett MAR 550 October 30, 2007

2. Outline When do we see missing data? Types of missing data Traditional approaches Deletion Substitution Modern Approaches Maximum likelihood and Bayes Software

3. Missing Data Medical studies, nonresponse in surveys or censuses, dropouts in clinical trials, censored data Loss of information, power Bias in results due to differences in missing and observed data Complicated analysis with standard software

4. Types of missing data MCAR MAR MNAR

5. MCAR Missing Completely at Random Probability that x i is missing doesn’t depend on its value or on value of other variables Doesn’t matter if it is associated with other “missingness”

6. MAR Missing at Random Missingness doesn’t depend on x i after controlling for other variable This is not great, but we can deal with it

7. MNAR Missing Not at Random Not MCAR or MAR (anything else) BAD!! Model missingness

8. Traditional Approaches Deletion List-wise Unbiased, but loses power Alternatives are really replacements for list-wise Pair-wise (also called “unwise”) deletion Leads to different sample sizes for different parts of analysis Can be a disaster

9. Traditional cont… Single Imputation Hot deck Census Bureau vs. Cold deck Mean substitution Regression substitution Stochastic regression substitution

10. Modern Methods Maximum Likelihood EM algorithm Estimate parameters  Listwise deletion, add some error Predict missing data (M): Maximize likelihood. Repeat. NORM (http://www.stat.psu.edu/~jls/misoftwa.html)

11. Modern Methods Multiple Imputation Simple and general – works for any type of analysis Validity of method depends on how imputation is carried out Should reasonably predict missing data, but should also reflect uncertainty in predictions Using a “sensible” imputation model

12. “Random Imputation” Predict missing values, then add error component drawn randomly from residual distribution of the variable Repeat several times to improve error estimates

13. Multiple Imputation Use Bayesian arguments to impute data: Parametric model for data Ignorable missing data Non-ignorable missing data Apply prior for unknown model parameters Simulate m independent draws from distribution of Y mis given Y obs Calculate values explicitly or through MCMC

14. MI procedure Simulate a random draw of unknown parameters from observed-data posterior Simulate a random draw of missing values from conditional predictive distribution Repeat, obtaining new parameter estimates from “complete” data set until stabilizes Do 3-5 times total (Rubin) MCMC: data augmentation algorithm of Tanner and Wong (1987)‏

15. Parameter Estimates Calculate parameter Q from m data sets Estimate of Q is just average of m values of Q Variance of Q is T = (1+m -1 ) B + U Where U is the mean within-imputation variance and B is B = (1/m) Σ (Q l -Q ave ) 2 The between-imputation variability. As m → ∞, T = B + U and you don’t need to correct B for low numbers of imputations.

16. MI Imputation is computationally distinct from analysis Problem if assumptions of imputation are not compatible with analysis assumptions Loss of power if imputation makes fewer assumptions than analysis “Superefficient” if imputation is based on more (valid) assumptions than analysis

17. MI Inconsistent if imputation makes invalid assumptions that are not included in analysis Ex: interaction terms Imputation needs to preserve features of data that will be included in analysis

18. ABB Approximate Bayesian Bootstrap (Rubin, 1987)‏ Fancier version of Hot deck imputation

19. Comparison of Methods Removing entries with missing data vs. MI Imputing once vs. MI Number of imputations Efficiency is (1+λ/m) -1 MI vs. EM

20. Nonignorable nonresponse Ignorable if data are MAR MI can be used when there is nonignorable nonresponse Missing-data mechanism

21. Programs For S-PLUS: www.stat.psu.edu/~jls/misoftwa.html For R: Amelia (II) (surveys and time-series data) Norm (for multivariate normal data) SOLAS (tested by Allison, 2000) For windows

22. References Little, R.J.A. and Rubin, D.B. (1987) Statistical Analysis with Missing Data. J. Wiley & Sons, New York. Schafer, J.L. (1999) Multiple imputation: a primer. Statistical Methods in Medical Research, 8, 3-15. Barnard, J. and X. Meng. (1999) Applications of multiple imputation in medical studies: from AIDS to NHANES. Statistical Methods in Medical Research, 8, 17-36. http://www.uvm.edu/~dhowell/StatPages/More_Stuff/Missing_Data/Missing.html http://www.stat.psu.edu/~jls/mifaq.html#em Allison, P.D. (2000) Multiple Imputation for Missing Data: A Cautionary Tale. Sociological Methods and Research, 28 (3), 301-309.

23. MI Example (Tu et al, 1993)‏ AIDS survival time with reporting-delay (1) Survival-time model (2) Reporting-lag model using available information (3) Multiply impute delayed cases using model from step 2 (4) Compute estimates of survival-time model parameters (5) Combine estimates using repeated-imputation rules

24. Milwaukee Parental Choice Program (MPCP)‏ Effects of school choice on achievement tests (public vs. private schools)‏ School vouchers to attend “choice” schools, participating private schools Only households with less than 1.75 times poverty line could participate

25. Milwaukee Parental Choice Program (MPCP)‏ Randomized block design Outcome variables were scores from ITBS Maximum of 4 years observed (1990-1994)‏ Higher levels of missingness than in typical medical study Pattern in missing data was not monotone