BACKGROUND: Many cohort studies and clinical trials use repeated measurements of laboratory markers to track disease progression and to evaluate new therapies. A major problem in the analysis of such studies is that marker data are censored in some patients owing to withdrawal, loss to follow-up, or death. The objective of this paper is to evaluate the impact of selective dropouts attributable to death or disease progression on the estimates of marker change among different groups. METHODS: Data on CD4 cell count in human immunodeficiency virus 1-infected individuals from a clinical trial and a cohort study are used to illustrate this problem and a possible solution. Simulation studies are also presented. RESULTS: When the rate of dropout is greater in subjects whose marker status is declining rapidly, commonly used methods, like random effects models, that ignore informative dropouts lead to overoptimistic statements about the marker trends in all compared groups, because subjects with steeper marker drops tend to have shorter follow-up times and hence are weighted less in the estimation of the group rate of the average marker decline. CONCLUSIONS: The potential biases attributable to incomplete data require greater recognition in longitudinal studies. Sensitivity analyses to assess the effect of dropouts are important.
BACKGROUND: Many cohort studies and clinical trials use repeated measurements of laboratory markers to track disease progression and to evaluate new therapies. A major problem in the analysis of such studies is that marker data are censored in some patients owing to withdrawal, loss to follow-up, or death. The objective of this paper is to evaluate the impact of selective dropouts attributable to death or disease progression on the estimates of marker change among different groups. METHODS: Data on CD4 cell count in human immunodeficiency virus 1-infected individuals from a clinical trial and a cohort study are used to illustrate this problem and a possible solution. Simulation studies are also presented. RESULTS: When the rate of dropout is greater in subjects whose marker status is declining rapidly, commonly used methods, like random effects models, that ignore informative dropouts lead to overoptimistic statements about the marker trends in all compared groups, because subjects with steeper marker drops tend to have shorter follow-up times and hence are weighted less in the estimation of the group rate of the average marker decline. CONCLUSIONS: The potential biases attributable to incomplete data require greater recognition in longitudinal studies. Sensitivity analyses to assess the effect of dropouts are important.
Authors: N Jeanne Conley; Patricia B Pavlinac; Brandon L Guthrie; Romel D Mackelprang; Anthony N Muiru; Robert Y Choi; Rose Bosire; Ann Gatuguta; Carey Farquhar Journal: PLoS One Date: 2012-08-21 Impact factor: 3.240
Authors: Julie Henriques; Mar Pujades-Rodriguez; Megan McGuire; Elisabeth Szumilin; Jean Iwaz; Jean-François Etard; René Ecochard Journal: PLoS One Date: 2012-02-23 Impact factor: 3.240
Authors: Elena Losina; Hapsatou Touré; Lauren M Uhler; Xavier Anglaret; A David Paltiel; Eric Balestre; Rochelle P Walensky; Eugène Messou; Milton C Weinstein; François Dabis; Kenneth A Freedberg Journal: PLoS Med Date: 2009-10-27 Impact factor: 11.069
Authors: Constantin T Yiannoutsos; Ming-Wen An; Constantine E Frangakis; Beverly S Musick; Paula Braitstein; Kara Wools-Kaloustian; Daniel Ochieng; Jeffrey N Martin; Melanie C Bacon; Vincent Ochieng; Sylvester Kimaiyo Journal: PLoS One Date: 2008-12-02 Impact factor: 3.240