Analysis of time-to-event and duration outcomes in neonatal clinical trials with twin births

被引:5
|
作者
Shaffer, Michele L. [1 ,2 ]
Hiriote, Sasiprapa [3 ]
机构
[1] Penn State Coll Med, Dept Publ Hlth Sci, Hershey, PA USA
[2] Penn State Coll Med, Dept Pediat, Hershey, PA USA
[3] Penn State Univ, Dept Stat, State Coll, PA USA
关键词
Correlated data; Frailty models; Robust variance; FRAILTY; MODEL;
D O I
10.1016/j.cct.2008.11.001
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
When conducting neonatal trials in pre-term and/or low-birth-weight infants, twins may represent 10-20% of the study sample. Frailty models and proportional hazards regression with a robust sandwich variance estimate are common approaches for handling correlated time-to-event data or duration outcomes that are subject to censoring. However, the operating characteristics of these methods for mixes of correlated and independent time-to-event data are not well established. Simulation studies were conducted to compare frailty models and proportional hazards regression models with a robust sandwich variance estimate to standard proportional hazards regression models to estimate the treatment effect in two-armed clinical trials. While overall frailty models showed the best performance, caution must be exercised as the interpretation of the parameters differs from the marginal models. Data from the National Institute of Child Health & Human Development sponsored PROPHET trial are used for illustration. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:150 / 154
页数:5
相关论文
共 50 条
  • [1] Analysis of neonatal clinical trials with twin births
    Shaffer, Michele L.
    Kunselman, Allen R.
    Watterberg, Kristi L.
    [J]. BMC MEDICAL RESEARCH METHODOLOGY, 2009, 9
  • [2] Analysis of neonatal clinical trials with twin births
    Michele L Shaffer
    Allen R Kunselman
    Kristi L Watterberg
    [J]. BMC Medical Research Methodology, 9
  • [3] Predicting study duration in clinical trials with a time-to-event endpoint
    Machida, Ryunosuke
    Fujii, Yosuke
    Sozu, Takashi
    [J]. STATISTICS IN MEDICINE, 2021, 40 (10) : 2413 - 2421
  • [4] A Bayesian approach for event predictions in clinical trials with time-to-event outcomes
    Aubel, Paul
    Antigny, Marine
    Fougeray, Ronan
    Dubois, Frederic
    Saint-Hilary, Gaelle
    [J]. STATISTICS IN MEDICINE, 2021, 40 (28) : 6344 - 6359
  • [5] A Bayesian Sequential Design for Clinical Trials With Time-to-Event Outcomes
    Zhu, Lin
    Yu, Qingzhao
    Mercante, Donald E.
    [J]. STATISTICS IN BIOPHARMACEUTICAL RESEARCH, 2019, 11 (04): : 387 - 397
  • [6] Assurance calculations for planning clinical trials with time-to-event outcomes
    Ren, Shijie
    Oakley, Jeremy E.
    [J]. STATISTICS IN MEDICINE, 2014, 33 (01) : 31 - 45
  • [7] Sizing clinical trials when comparing bivariate time-to-event outcomes
    Sugimoto, Tomoyuki
    Hamasaki, Toshimitsu
    Evans, Scott R.
    Sozu, Takashi
    [J]. STATISTICS IN MEDICINE, 2017, 36 (09) : 1363 - 1382
  • [8] A hybrid approach to predicting events in clinical trials with time-to-event outcomes
    Fang, Liang
    Su, Zheng
    [J]. CONTEMPORARY CLINICAL TRIALS, 2011, 32 (05) : 755 - 759
  • [9] Assessing Clinical Equivalence in Oncology Biosimilar Trials With Time-to-Event Outcomes
    Uno, Hajime
    Schrag, Deborah
    Kim, Dae Hyun
    Tang, Dejun
    Tian, Lu
    Rugo, Hope S.
    Wei, Lee-Jen
    [J]. JNCI CANCER SPECTRUM, 2019, 3 (04)
  • [10] Predicting events in clinical trials using two time-to-event outcomes
    Mu, Rongji
    Xu, Jin
    [J]. BIOMETRICAL JOURNAL, 2018, 60 (04) : 815 - 826