Missing data methods in HIV clinical trials: Regulatory guidance and alternative approaches

被引:1
|
作者
Kelleher, T
Thiry, A
Wilber, R
Cross, A
机构
[1] Bristol Myers Squibb Co, Dept 703, Biostat & Data Management, Pharmaceut Res Inst, Wallingford, CT 06492 USA
[2] Bristol Myers Squibb Co, Infect Dis Clin Res, Pharmaceut Res Inst, Wallingford, CT 06492 USA
来源
DRUG INFORMATION JOURNAL | 2001年 / 35卷 / 04期
关键词
missing data; HIV; clinical trials; regulatory guidance;
D O I
10.1177/009286150103500432
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Efficacy in HIV clinical trials is measured by changes in HIV RNA levels over time as well as the proportion of subjects with HIV RNA levels below an assay's threshold of reliable quantification at a single time point. Missing, data arise naturally due to missed visits and premature discontinuations of treatment. The available data are then analyzed using repeated measures models and univariate comparisons of proportions, assuming missing data occur at random or considering missing values as treatment failures (worst case scenario). These and other methods recently proposed by regulatory authorities are presented along with alternative approaches. Advantages and disadvantages of each method are discussed. Data from a recent comparison of 'standard-of-care' triple combination regimens are used for illustration.
引用
收藏
页码:1363 / 1371
页数:9
相关论文
共 50 条
  • [41] Missing data in clinical trials: from clinical assumptions to statistical analysis using patternmixture models
    Ratitch, Bohdana
    O'Kelly, Michael
    Tosiello, Robert
    PHARMACEUTICAL STATISTICS, 2013, 12 (06) : 337 - 347
  • [42] Handling of missing data in long-term clinical trials: a case study
    Janssens, Mark
    Molenberghs, Geert
    Kerstens, Rene
    PHARMACEUTICAL STATISTICS, 2012, 11 (06) : 442 - 448
  • [43] Incorporating and interpreting regulatory guidance on estimands in diabetes clinical trials: The PIONEER 1 randomized clinical trial as an example
    Aroda, Vanita R.
    Saugstrup, Trine
    Buse, John B.
    Donsmark, Morten
    Zacho, Jeppe
    Davies, Melanie J.
    DIABETES OBESITY & METABOLISM, 2019, 21 (10) : 2203 - 2210
  • [44] Sensitivity analysis using an imputation method for missing binary data in clinical trials
    Proschan, MA
    McMahon, RP
    Shih, JH
    Hunsberger, SA
    Geller, NL
    Knatterud, G
    Wittes, J
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2001, 96 (01) : 155 - 165
  • [45] Semi-parametric and non-parametric methods for clinical trials with incomplete data
    O'Brien, PC
    Zhang, D
    Bailey, KR
    STATISTICS IN MEDICINE, 2005, 24 (03) : 341 - 358
  • [46] Methods for Neuroscience Drug Development: Guidance on Standardization of the Process for Defining Clinical Outcome Strategies in Clinical Trials
    Domingo, Silvia Zaragoza
    Alonso, Jordi
    Ferrer, Montse
    Acosta, Maria T.
    Alphs, Larry
    Annas, Peter
    Balabanov, Pavel
    Berger, Anna -Karin
    Bishop, Kim I.
    Butlen-Ducuing, Florence
    Dorffner, Georg
    Edgar, Chris
    Blanco, Manuel de Gracia
    Harel, Brian
    Harrison, John
    Horan, William P.
    Jaeger, Judith
    Kottner, Jan
    Pinkham, Amy
    Tinoco, Daniella
    Vance, Monika
    Yavorsky, Christian
    EUROPEAN NEUROPSYCHOPHARMACOLOGY, 2024, 83 : 32 - 42
  • [47] Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide
    Cro, Suzie
    Morris, Tim P.
    Kenward, Michael G.
    Carpenter, James R.
    STATISTICS IN MEDICINE, 2020, 39 (21) : 2815 - 2842
  • [48] Methods to Analyze Treatment Effects in the Presence of Missing Data for a Continuous Heavy Drinking Outcome Measure When Participants Drop Out from Treatment in Alcohol Clinical Trials
    Witkiewitz, Katie
    Falk, Daniel E.
    Kranzler, Henry R.
    Litten, Raye Z.
    Hallgren, Kevin A.
    O'Malley, Stephanie S.
    Anton, Raymond F.
    ALCOHOLISM-CLINICAL AND EXPERIMENTAL RESEARCH, 2014, 38 (11) : 2826 - 2834
  • [49] Missing Data and Censoring in the Analysis of Progression-Free Survival in Oncology Clinical Trials
    Denne, J. S.
    Stone, A. M.
    Bailey-Iacona, R.
    Chen, T. -T.
    JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2013, 23 (05) : 951 - 970
  • [50] 'Intention-to-treat' meets 'missing data': implications of alternate strategies for analyzing clinical trials data
    Nich, C
    Carroll, KM
    DRUG AND ALCOHOL DEPENDENCE, 2002, 68 (02) : 121 - 130