یک روش متغیر پنهان در مدل سازی همزمان داده های طولی و ترک تحصیل در آزمایشات اسکیزوفرنی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|36725||2013||7 صفحه PDF||سفارش دهید||4206 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : European Neuropsychopharmacology, Volume 23, Issue 11, November 2013, Pages 1570–1576
Dropouts impact clinical trial outcome analyses. Ignoring missing data is not an acceptable option when planning, conducting or interpreting the analysis of a clinical trial. Treatment related efficacy and safety data observed in the trial may not always be sufficient in explaining the dropouts' mechanism. Nevertheless, these dropout data may carry important treatment-related information and present as an outcome by itself. Traditional analyses involve the use of the time-to-event approach assuming that the dropouts' hazard is solely related to the efficacy or safety profiles observed in a study. A latent variable approach was developed to generalize this approach and to implement a more flexible dropout hazard function in a schizophrenia trial. This unobserved latent variable was used to jointly model the longitudinal efficacy data and dropout profiles across treatments. The analysis provides a framework to model informative dropouts simultaneously with primary efficacy outcomes and make intelligent decisions in drug development.
Dropout is an important outcome in randomized clinical trials (RCTs) because it may reflect drug tolerability, adverse effects, and lack of compliance. For this reason it is often used as an outcome measure in clinical trials of antipsychotic medications. For instance, in the recent clinical antipsychotic trials of intervention effectiveness (CATIE), study discontinuation was a primary outcome measure. Seventy four percent of CATIE trial participants discontinued their assigned study medication before study completion at 18 months (Lieberman et al., 2005). A recent meta-analysis of RCTs of antipsychotic medication was conducted to compare dropout rates for first- and second-generation antipsychotic drugs and to examine how a broad range of design features affect dropout. Ninety-three RCTs that met specific inclusion criteria were included. The analysis showed that dropout rates are lower for second- than first-generation antipsychotic drugs and appear to be partly explained by trial design features thus providing direction for future trial design (Rabinowitz et al., 2009).