%0 Journal Article %1 Li2015 %A Li, Peng %A Stuart, Elizabeth A. %A Allison, David B. %D 2015 %J JAMA %K imported %N 18 %P 1966-7 %R 10.1001/jama.2015.15281 %T Multiple Imputation: A Flexible Tool for Handling Missing Data. %U http://www.ncbi.nlm.nih.gov/pubmed/26547468 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4638176 %V 314 %X In this issue of JAMA, Asch et al1 report results of a cluster randomized clinical trial designed to evaluate the effects of physician financial incentives, patient incentives, or shared physician and patient incentives on low-density lipoprotein cholesterol (LDL-C) levels among patients with high cardiovascular risk. Because 1 or more follow-up LDL-C measurements were missing for approximately 7% of participants, Asch et al used multiple imputation (MI) to analyze their data and concluded that shared financial incentives for physicians and patients, but not incentives to physicians or patients alone, resulted in the patients having lower LDL-C levels. Imputation is the process of replacing missing data with 1 or more specific values, to allow statistical analysis that includes all participants and not just those who do not have any missing data. %@ 1538-3598 (Electronic)\r0098-7484 (Linking)

@article{Li2015, abstract = {In this issue of JAMA, Asch et al1 report results of a cluster randomized clinical trial designed to evaluate the effects of physician financial incentives, patient incentives, or shared physician and patient incentives on low-density lipoprotein cholesterol (LDL-C) levels among patients with high cardiovascular risk. Because 1 or more follow-up LDL-C measurements were missing for approximately 7% of participants, Asch et al used multiple imputation (MI) to analyze their data and concluded that shared financial incentives for physicians and patients, but not incentives to physicians or patients alone, resulted in the patients having lower LDL-C levels. Imputation is the process of replacing missing data with 1 or more specific values, to allow statistical analysis that includes all participants and not just those who do not have any missing data.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Li, Peng and Stuart, Elizabeth A. and Allison, David B.}, biburl = {https://www.bibsonomy.org/bibtex/24c5950d35517ab88daab52541c7cd13b/jepcastel}, doi = {10.1001/jama.2015.15281}, interhash = {0c471ee052c14972bef5074f4e510963}, intrahash = {4c5950d35517ab88daab52541c7cd13b}, isbn = {1538-3598 (Electronic)\r0098-7484 (Linking)}, issn = {1538-3598}, journal = {JAMA}, keywords = {imported}, month = {11}, note = {Dades censurades; Imputació múltiple; Introductori}, number = 18, pages = {1966-7}, pmid = {26547468}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Multiple Imputation: A Flexible Tool for Handling Missing Data.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/26547468 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4638176}, volume = 314, year = 2015 }