%0 Journal Article %1 Overall:1999:J-Biopharm-Stat:10091918 %A Overall, J E %A Ahn, C %A Shivakumar, C %A Kalburgi, Y %D 1999 %J J Biopharm Stat %K CorrelatedData LongitudinalDataAnalysis RandomEffects sas statistics %N 1 %P 189-216 %R 10.1081/BIP-100101008 %T Problematic formulations of SAS PROC.MIXED models for repeated measurements %U https://www.ncbi.nlm.nih.gov/pubmed/10091918 %V 9 %X The work reported in this article was undertaken to evaluate the utility of SAS PROC.MIXED for testing hypotheses concerning GROUP and TIME x GROUP effects in repeated measurements designs with drop-outs. If dropouts are not completely at random, covariate control over informative individual differences on which dropout data patterns depend is widely recognized to be important. However, the inclusion of baseline scores and time-in-study as between-subject covariates in an otherwise well formulated SAS PROC.MIXED model resulted in inadequate control over type I error in simulated data with or without drop-outs present. The inadequate model formulations and resulting deviant test sizes are presented here as a warning for others who might be guided by the same information sources to employ similar model specifications when analyzing data from actual clinical trials. It is important that the complete model specification be provided in detail when reporting applications of the general linear mixed-model procedure. A single random-coefficients model produced appropriate test sizes, but it provided inferior power when informative covariates were added in the attempt to adjust for dropouts. As an alternative, the incorporation of covariate controls in simpler two-stage endpoint or random regression analyses is documented to be effective in dealing with dropouts under specifiable conditions.

@article{Overall:1999:J-Biopharm-Stat:10091918, abstract = {The work reported in this article was undertaken to evaluate the utility of SAS PROC.MIXED for testing hypotheses concerning GROUP and TIME x GROUP effects in repeated measurements designs with drop-outs. If dropouts are not completely at random, covariate control over informative individual differences on which dropout data patterns depend is widely recognized to be important. However, the inclusion of baseline scores and time-in-study as between-subject covariates in an otherwise well formulated SAS PROC.MIXED model resulted in inadequate control over type I error in simulated data with or without drop-outs present. The inadequate model formulations and resulting deviant test sizes are presented here as a warning for others who might be guided by the same information sources to employ similar model specifications when analyzing data from actual clinical trials. It is important that the complete model specification be provided in detail when reporting applications of the general linear mixed-model procedure. A single random-coefficients model produced appropriate test sizes, but it provided inferior power when informative covariates were added in the attempt to adjust for dropouts. As an alternative, the incorporation of covariate controls in simpler two-stage endpoint or random regression analyses is documented to be effective in dealing with dropouts under specifiable conditions.}, added-at = {2018-09-21T01:44:35.000+0200}, author = {Overall, J E and Ahn, C and Shivakumar, C and Kalburgi, Y}, biburl = {https://www.bibsonomy.org/bibtex/2e68be26222a63692e1beafffa5cd3cf1/jkd}, description = {Problematic formulations of SAS PROC.MIXED models for repeated measurements. - PubMed - NCBI}, doi = {10.1081/BIP-100101008}, interhash = {ad672842d1f22f07ed885e94dd16b14c}, intrahash = {e68be26222a63692e1beafffa5cd3cf1}, journal = {J Biopharm Stat}, keywords = {CorrelatedData LongitudinalDataAnalysis RandomEffects sas statistics}, month = mar, number = 1, pages = {189-216}, pmid = {10091918}, timestamp = {2018-10-03T05:31:55.000+0200}, title = {Problematic formulations of SAS PROC.MIXED models for repeated measurements}, url = {https://www.ncbi.nlm.nih.gov/pubmed/10091918}, volume = 9, year = 1999 }