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Statistical Methods for Missing Data - PowerPoint PPT Presentation

Roberta Harnett MAR 550 October 30, 2007. Statistical Methods for Missing Data. Outline. When do we see missing data? Types of missing data Traditional approaches Deletion Substitution Modern Approaches Maximum likelihood and Bayes Software. Missing Data.

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MAR 550

October 30, 2007

Statistical Methods for Missing Data

When do we see missing data?

Types of missing data

Deletion

Substitution

Modern Approaches

Maximum likelihood and Bayes

Software

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

MCAR

MAR

MNAR

Missing Completely at Random

Probability that xi is missing doesn’t depend on its value or on value of other variables

Doesn’t matter if it is associated with other “missingness”

Missing at Random

Missingness doesn’t depend on xi after controlling for other variable

This is not great, but we can deal with it

Missing Not at Random

Not MCAR or MAR (anything else)

Model missingness

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

Single Imputation

Hot deck

Census Bureau

vs. Cold deck

Mean substitution

Regression substitution

Stochastic regression substitution

• 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)

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

• Predict missing values, then add error component drawn randomly from residual distribution of the variable

• Repeat several times to improve error estimates

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 Ymis given Yobs

Calculate values explicitly or through MCMC

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)‏

• 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 (Ql-Qave)2

The between-imputation variability.

• As m →∞, T = B + U and you don’t need to correct B for low numbers of imputations.

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

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

Approximate Bayesian Bootstrap (Rubin, 1987)‏

Fancier version of Hot deck imputation

Removing entries with missing data vs. MI

Imputing once vs. MI

Number of imputations

Efficiency is (1+λ/m)-1

MI vs. EM

Ignorable if data are MAR

MI can be used when there is nonignorable nonresponse

Missing-data mechanism

• 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

• 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.

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

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

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