期刊
BEHAVIOR THERAPY
卷 43, 期 3, 页码 679-685出版社
ELSEVIER INC
DOI: 10.1016/j.beth.2011.10.001
关键词
autocorrelation; case-based; missing observations; power sensitivity; time-series
资金
- NIMH NIH HHS [T32 MH020012, T32 MH020012-14, MH20012] Funding Source: Medline
The case-based time-series design is a viable methodology for treatment outcome research. However, the literature has not fully addressed the problem of missing observations with such autocorrelated data streams. Mainly, to what extent do missing observations compromise inference when observations are not independent? Do the available missing data replacement procedures preserve inferential integrity? Does the extent of autocorrelation matter? We use Monte Carlo simulation modeling of a single-subject intervention study to address these questions. We find power sensitivity to be within acceptable limits across four proportions of missing observations (10%, 20%, 30%, and 40%) when missing data are replaced using the Expectation-Maximization Algorithm, more commonly known as the EM Procedure (Dempster, Laird, & Rubin, 1977). This applies to data streams with lag-1 autocorrelation estimates under 0.80. As autocorrelation estimates approach 0.80, the replacement procedure yields an unacceptable power profile. The implications of these findings and directions for future research are discussed.
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