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
  • [21] Intention-to-treat analysis with treatment discontinuation and missing data in clinical trials
    Little, Roderick
    Kang, Shan
    STATISTICS IN MEDICINE, 2015, 34 (16) : 2381 - 2390
  • [22] Move over LOCF: Principled methods for handling missing data in sleep disorder trials
    Olsen, Maren K.
    Stechuchak, Karen M.
    Edinger, Jack D.
    Ulmer, Christi S.
    Woolson, Robert F.
    SLEEP MEDICINE, 2012, 13 (02) : 123 - 132
  • [23] Missing tumor measurement (TM) data in the search for alternative TM-based endpoints in cancer clinical trials
    An, Ming-Wen
    Tang, Jun
    Grothey, Axel
    Sargent, Daniel J.
    Ou, Fang-Shu
    Mandrekar, Sumithra J.
    CONTEMPORARY CLINICAL TRIALS COMMUNICATIONS, 2020, 17
  • [24] Efficient and alternative approaches for imputing missing data to estimate population mean
    Pandey A.K.
    Singh G.N.
    Bhattacharyya D.
    Singh P.K.
    Quality & Quantity, 2024, 58 (6) : 5883 - 5897
  • [25] Are We Missing the Importance of Missing Values in HIV Prevention Randomized Clinical Trials? Review and Recommendations
    Harel, Ofer
    Pellowski, Jennifer
    Kalichman, Seth
    AIDS AND BEHAVIOR, 2012, 16 (06) : 1382 - 1393
  • [26] Are We Missing the Importance of Missing Values in HIV Prevention Randomized Clinical Trials? Review and Recommendations
    Ofer Harel
    Jennifer Pellowski
    Seth Kalichman
    AIDS and Behavior, 2012, 16 : 1382 - 1393
  • [27] Missing data imputation in two phase III trials treating HIV1 infection
    Huson, L. W.
    Chung, J.
    Salgo, M.
    JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2007, 17 (01) : 159 - 172
  • [28] Methods for handling missing binary data in substance use disorder trials
    Ren, Boyu
    Lipsitz, Stuart R.
    Weiss, Roger D.
    Fitzmaurice, Garrett M.
    DRUG AND ALCOHOL DEPENDENCE, 2023, 250
  • [29] Assessing and interpreting treatment effects in longitudinal clinical trials with missing data
    Mallinckrodt, CH
    Sanger, TM
    Dubé, S
    DeBrota, DJ
    Molenberghs, G
    Carroll, RJ
    Potter, WZ
    Tollefson, GD
    BIOLOGICAL PSYCHIATRY, 2003, 53 (08) : 754 - 760
  • [30] A cautionary tale: dealing with missing data in clinical trials for rheumatic diseases
    Song, J.
    Boscardin, W. J.
    Furst, D. E.
    Khanna, D.
    Wong, W. K.
    CLINICAL AND EXPERIMENTAL RHEUMATOLOGY, 2014, 32 (06) : S122 - S126