A comparison of methods for fixed effects meta-analysis of individual patient data with time to event outcomes

被引:37
|
作者
Smith, Catrin Tudur [1 ]
Williamson, Paula Ruth [1 ]
机构
[1] Univ Liverpool, Ctr Med Stat & Hlth Evaluat, Canc Res UK Liverpool Canc Trials Unit, Liverpool L69 3BX, Merseyside, England
关键词
HETEROGENEITY; LEVEL; BIAS;
D O I
10.1177/1740774507085276
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
Background Alternative methods for individual patient data (lPD) meta-analysis of time-to-event outcomes have been established and utilized in practice. The most common approach is a stratified log-rank analysis. The IPD approach is considered to be the gold standard approach for meta-analysis and is becoming increasingly more popular but the performance of different methods has not been studied previously. Purpose To compare commonly used methods for fixed effects meta-analysis of individual patient time-to-event data. Methods The stratified log-rank analysis, an inverse variance weighted average of Cox model estimates, and the stratified Cox regression model are compared. First, a theoretical comparison of approaches is undertaken. Second, the bias and coverage are assessed for the pooled hazard ratio using simulated data under commonly encountered meta-analysis conditions. Finally, a comparison is presented using empirical data from four separate systematic reviews of anti-epileptic drug trials where IPD are available for two time-to-event outcomes. Results For hazard ratio close to 1 with minimal heterogeneity between trials, theoretical results suggest similar results should be expected from all the three methods. Results for empirical and simulated data are in keeping with the theoretical results and show all the three methods perform well under these conditions. When there is no heterogeneity and the proportional hazards assumption holds, the stratified Cox model and inverse variance weighted average produce similar estimates of the pooled treatment effect and are to be preferred to the stratified log-rank analysis when the underlying treatment effect is large. Coverage values diminish for all the three methods and are below 95% for low or moderate heterogeneity. The low coverage values highlight the need for models that appropriately account for or explore the between trial variation. Limitations Until larger simulations can be undertaken, conclusions based on the simulated and empirical data should only be applied to small meta-analyses of four or five trials. Conclusions These investigations suggest that under normal conditions all three methods provide similar results. For moderate heterogeneity coverage for all the three fixed effects models depreciates.
引用
收藏
页码:621 / 630
页数:10
相关论文
共 50 条
  • [1] Investigating heterogeneity in an individual patient data meta-analysis of time to event outcomes
    Smith, CT
    Williamson, PR
    Marson, AG
    STATISTICS IN MEDICINE, 2005, 24 (09) : 1307 - 1319
  • [2] An overview of methods and empirical comparison of aggregate data and individual patient data results for investigating heterogeneity in meta-analysis of time-to-event outcomes
    Smith, CT
    Williamson, PR
    Marson, AG
    JOURNAL OF EVALUATION IN CLINICAL PRACTICE, 2005, 11 (05) : 468 - 478
  • [3] Network meta-analysis of (individual patient) time to event data alongside (aggregate) count data
    Saramago, Pedro
    Chuang, Ling-Hsiang
    Soares, Marta O.
    BMC MEDICAL RESEARCH METHODOLOGY, 2014, 14
  • [4] Penalized Poisson model for network meta-analysis of individual patient time-to-event data
    Ollier, Edouard
    Blanchard, Pierre
    Le Teuff, Gwenael
    Michiels, Stefan
    STATISTICS IN MEDICINE, 2022, 41 (02) : 340 - 355
  • [5] Systematic review of methods for individual patient data meta- analysis with binary outcomes
    Thomas, Doneal
    Radji, Sanyath
    Benedetti, Andrea
    BMC MEDICAL RESEARCH METHODOLOGY, 2014, 14
  • [6] Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis
    Seo, Michael
    White, Ian R.
    Furukawa, Toshi A.
    Imai, Hissei
    Valgimigli, Marco
    Egger, Matthias
    Zwahlen, Marcel
    Efthimiou, Orestis
    STATISTICS IN MEDICINE, 2021, 40 (06) : 1553 - 1573
  • [7] Individual patient data meta-analysis of survival data using Poisson regression models
    Crowther, Michael J.
    Riley, Richard D.
    Staessen, Jan A.
    Wang, Jiguang
    Gueyffier, Francois
    Lambert, Paul C.
    BMC MEDICAL RESEARCH METHODOLOGY, 2012, 12
  • [8] The devil is in the details ... or not? A primer on individual patient data meta-analysis
    Sud, Sachin
    Douketis, James
    ANNALS OF INTERNAL MEDICINE, 2009, 151 (02)
  • [9] Systematic review of methods for individual patient data meta- analysis with binary outcomes
    Doneal Thomas
    Sanyath Radji
    Andrea Benedetti
    BMC Medical Research Methodology, 14
  • [10] Individual participant data meta-analysis of intervention studies with time-to-event outcomes: A review of the methodology and an applied example
    de Jong, Valentijn M. T.
    Moons, Karel G. M.
    Riley, Richard D.
    Tudur Smith, Catrin
    Marson, Anthony G.
    Eijkemans, Marinus J. C.
    Debray, Thomas P. A.
    RESEARCH SYNTHESIS METHODS, 2020, 11 (02) : 148 - 168