A Meta-Analysis of Missing Data and Non-Compliance Data in Clinical Endpoint Bioequivalence Studies

被引:5
|
作者
Sun, Wanjie [1 ]
Zhou, Lingjie [2 ]
Grosser, Stella [1 ]
Kim, Carol [3 ]
机构
[1] US FDA, CDER, Off Biostat, 10903 New Hampshire Ave, Silver Spring, MD 20993 USA
[2] George Washington Univ, Dept Stat, Washington, DC 20052 USA
[3] US FDA, CDER, Off Gener Drugs, 10903 New Hampshire Ave, Silver Spring, MD 20993 USA
来源
关键词
Bioequivalence; Meta-analysis; Missing data; Non-compliance data; TRIALS;
D O I
10.1080/19466315.2016.1201000
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Missing data and noncompliance data questions are especially important in evaluating locally acting generic drugs because primary equivalence analyses in clinical endpoint bioequivalence (BE) studies are based on the per-protocol (PP) population (generally, completers and compliers). A meta-analysis using six clinical endpoint BE studies for topical drugs reveals the following: (1) An average of 22% (95% CI: 15-29%) of randomized subjects are excluded from the PP population. (2) Of these excluded subjects, half (10.6%, 95% CI: 8.3-12.8%) dropped out. Most who dropped out (6.9%, 95% CI: 5.0-8.8%) did not specify reasons. (3) Noncompliance categories include out-of-window visits (7.7%, 95% CI: 5.5%-9.8%), dosing noncompliance (<75% or >125% of dose) (5%, 95% CI: 2.7%-7.4%), and restricted medication use (3.2%, 95% CI: 1.8%-4.7%). (4) Drop out and noncompliance are not completely at random: a better treatment effect is associated with less drop out and less noncompliance. (5) Drop out and noncompliance are correlated: noncompliers are more likely to drop out, and vice versa. These results will help regulators better understand the extent and pattern of drop out and noncompliance and shed light on designing appropriate analysis population, endpoints, estimands, and investigating primary and sensitivity methods for equivalence in clinical endpoint BE studies in presence of missing and noncompliance data.
引用
下载
收藏
页码:334 / 344
页数:11
相关论文
共 50 条
  • [11] Imputation methods for missing outcome data in meta-analysis of clinical trials
    Higgins, Julian P. T.
    White, Ian R.
    Wood, Angela M.
    CLINICAL TRIALS, 2008, 5 (03) : 225 - 239
  • [12] Missing Data in Systematic Reviews and Meta-Analysis
    Chandelia, Sudha
    PEDIATRIC CRITICAL CARE MEDICINE, 2022, 23 (08) : E400 - E400
  • [13] Dealing with missing outcome data in meta-analysis
    Mavridis, Dimitris
    White, Ian R.
    RESEARCH SYNTHESIS METHODS, 2020, 11 (01) : 2 - 13
  • [14] Addressing missing outcome data in meta-analysis
    Mavridis, Dimitris
    Chaimani, Anna
    Efthimiou, Orestis
    Leucht, Stefan
    Salanti, Georgia
    EVIDENCE-BASED MENTAL HEALTH, 2014, 17 (03) : 85 - +
  • [15] Patient 'non-compliance' and 'missing data' in quality of life research: Where does the problem lie?
    Moynihan, C
    EUROPEAN JOURNAL OF CANCER, 1998, 34 (01) : 9 - 11
  • [16] A graphical model approach to systematically missing data in meta-analysis of observational studies
    Kovacic, Jelena
    Varnai, Veda Marija
    STATISTICS IN MEDICINE, 2016, 35 (24) : 4443 - 4458
  • [17] UNCOVERING REASONS OF NON-COMPLIANCE IN EPRO STUDIES TO IMPROVE PRO DATA COLLECTION
    Ross, J.
    Marcovitz, M.
    Peck, R.
    VALUE IN HEALTH, 2010, 13 (03) : A82 - A82
  • [18] A score test for binary data with patient non-compliance
    Branson, M
    Whitehead, J
    STATISTICS IN MEDICINE, 2003, 22 (20) : 3115 - 3132
  • [19] Systematically missing data in causally interpretable meta-analysis
    Steingrimsson, Jon A.
    Barker, David H.
    Bie, Ruofan
    Dahabreh, Issa J.
    BIOSTATISTICS, 2023, 25 (02) : 289 - 305
  • [20] Missing Data in Systematic Reviews and Meta-Analysis Reply
    Lehr, Anab Rebecca
    d'Astous, Soha Rached
    Tsampalieros, Anne
    Barrowman, Nick
    Menon, Kusum
    PEDIATRIC CRITICAL CARE MEDICINE, 2022, 23 (08) : E401 - E401