Dustin J Rabideau1, Andrew A Nierenberg2, Louisa G Sylvia2, Edward S Friedman3, Charles L Bowden4, Michael E Thase5, Terence A Ketter6, Michael J Ostacher6, Noreen Reilly-Harrington2, Dan V Iosifescu7, Joseph R Calabrese8, Andrew C Leon9, David A Schoenfeld10. 1. Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA drabideau@partners.org. 2. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA. 3. Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA. 4. Department of Psychiatry, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. 5. Department of Psychiatry, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 6. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA. 7. Department of Psychiatry, Mount Sinai School of Medicine, New York, USA. 8. Department of Psychiatry, Case Western Reserve University, School of Medicine, Cleveland, OH, USA. 9. Psychiatry and Public Health, Weill Cornell Medical College, New York, USA. 10. Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA.
Abstract
BACKGROUND: Missing data are unavoidable in most randomized controlled clinical trials, especially when measurements are taken repeatedly. If strong assumptions about the missing data are not accurate, crude statistical analyses are biased and can lead to false inferences. Furthermore, if we fail to measure all predictors of missing data, we may not be able to model the missing data process sufficiently. In longitudinal randomized trials, measuring a patient's intent to attend future study visits may help to address both of these problems. Leon et al. developed and included the Intent to Attend assessment in the Lithium Treatment - Moderate dose Use Study (LiTMUS), aiming to remove bias due to missing data from the primary study hypothesis. PURPOSE: The purpose of this study is to assess the performance of the Intent to Attend assessment with regard to its use in a sensitivity analysis of missing data. METHODS: We fit marginal models to assess whether a patient's self-rated intent predicted actual study adherence. We applied inverse probability of attrition weighting (IPAW) coupled with patient intent to assess whether there existed treatment group differences in response over time. We compared the IPAW results to those obtained using other methods. RESULTS: Patient-rated intent predicted missed study visits, even when adjusting for other predictors of missing data. On average, the hazard of retention increased by 19% for every one-point increase in intent. We also found that more severe mania, male gender, and a previously missed visit predicted subsequent absence. Although we found no difference in response between the randomized treatment groups, IPAW increased the estimated group difference over time. LIMITATIONS: LiTMUS was designed to limit missed study visits, which may have attenuated the effects of adjusting for missing data. Additionally, IPAW can be less efficient and less powerful than maximum likelihood or Bayesian estimators, given that the parametric model is well specified. CONCLUSIONS: In LiTMUS, the Intent to Attend assessment predicted missed study visits. This item was incorporated into our IPAW models and helped reduce bias due to informative missing data. This analysis should both encourage and facilitate future use of the Intent to Attend assessment along with IPAW to address missing data in a randomized trial.
RCT Entities:
BACKGROUND: Missing data are unavoidable in most randomized controlled clinical trials, especially when measurements are taken repeatedly. If strong assumptions about the missing data are not accurate, crude statistical analyses are biased and can lead to false inferences. Furthermore, if we fail to measure all predictors of missing data, we may not be able to model the missing data process sufficiently. In longitudinal randomized trials, measuring a patient's intent to attend future study visits may help to address both of these problems. Leon et al. developed and included the Intent to Attend assessment in the Lithium Treatment - Moderate dose Use Study (LiTMUS), aiming to remove bias due to missing data from the primary study hypothesis. PURPOSE: The purpose of this study is to assess the performance of the Intent to Attend assessment with regard to its use in a sensitivity analysis of missing data. METHODS: We fit marginal models to assess whether a patient's self-rated intent predicted actual study adherence. We applied inverse probability of attrition weighting (IPAW) coupled with patient intent to assess whether there existed treatment group differences in response over time. We compared the IPAW results to those obtained using other methods. RESULTS:Patient-rated intent predicted missed study visits, even when adjusting for other predictors of missing data. On average, the hazard of retention increased by 19% for every one-point increase in intent. We also found that more severe mania, male gender, and a previously missed visit predicted subsequent absence. Although we found no difference in response between the randomized treatment groups, IPAW increased the estimated group difference over time. LIMITATIONS: LiTMUS was designed to limit missed study visits, which may have attenuated the effects of adjusting for missing data. Additionally, IPAW can be less efficient and less powerful than maximum likelihood or Bayesian estimators, given that the parametric model is well specified. CONCLUSIONS: In LiTMUS, the Intent to Attend assessment predicted missed study visits. This item was incorporated into our IPAW models and helped reduce bias due to informative missing data. This analysis should both encourage and facilitate future use of the Intent to Attend assessment along with IPAW to address missing data in a randomized trial.
Authors: Louisa G Sylvia; Noreen A Reilly-Harrington; Andrew C Leon; Christine I Kansky; Terence A Ketter; Joseph R Calabrese; Michael E Thase; Charles L Bowden; Edward S Friedman; Michael J Ostacher; Dan V Iosifescu; Joanne Severe; Michelle Keyes; Andrew A Nierenberg Journal: Clin Trials Date: 2011-11-10 Impact factor: 2.486
Authors: Jennifer Weuve; Eric J Tchetgen Tchetgen; M Maria Glymour; Todd L Beck; Neelum T Aggarwal; Robert S Wilson; Denis A Evans; Carlos F Mendes de Leon Journal: Epidemiology Date: 2012-01 Impact factor: 4.822
Authors: Trisha Suppes; Ellen B Dennehy; Robert M A Hirschfeld; Lori L Altshuler; Charles L Bowden; Joseph R Calabrese; M Lynn Crismon; Terence A Ketter; Gary S Sachs; Alan C Swann Journal: J Clin Psychiatry Date: 2005-07 Impact factor: 4.384
Authors: Andrew A Nierenberg; Louisa G Sylvia; Andrew C Leon; Noreen A Reilly-Harrington; Terence A Ketter; Joseph R Calabrese; Michael E Thase; Charles L Bowden; Edward S Friedman; Michael J Ostacher; Lena Novak; Dan V Iosifescu Journal: Clin Trials Date: 2009-11-23 Impact factor: 2.486