The importance of considering competing treatment affecting prognosis in the evaluation of therapy in trials: the example of renal transplantation in hemodialysis trials

被引:9
作者
Hazelbag, C. Marijn [1 ]
Peters, Sanne A. E. [1 ,2 ]
Blankestijn, Peter J. [3 ]
Bots, Michiel L. [1 ]
Canaud, Bernard [4 ,5 ]
Davenport, Andrew [6 ]
Grooteman, Muriel P. C. [7 ]
Kircelli, Fatih [8 ]
Locatelli, Francesco [9 ]
Maduell, Francisco [10 ]
Morena, Marion [5 ,11 ,12 ]
Nube, Menso J. [7 ]
Ok, Ercan [8 ]
Torres, Ferran [13 ,14 ]
Hoes, Arno W. [1 ]
Groenwold, Rolf H. H. [1 ]
机构
[1] UMC Utrecht, Julius Ctr Hlth Sci & Primary Care, Utrecht, Netherlands
[2] Univ Oxford, George Inst Global Hlth, Oxford, England
[3] Univ Med Ctr Utrecht, Dept Nephrol, Utrecht, Netherlands
[4] CHRU, Nephrol Dialysis & Intens Care Unit, Montpellier, France
[5] Dialysis Res & Training Inst, Montpellier, France
[6] UCL, Royal Free Hosp, Ctr Nephrol, London, England
[7] Vrije Univ Amsterdam, Med Ctr, Dept Nephrol, Amsterdam, Netherlands
[8] Ege Univ, Sch Med, Div Nephrol, Izmir, Turkey
[9] Alessandro Manzoni Hosp, Dept Nephrol, Lecce, Italy
[10] Hosp Clin Barcelona, Nephrol Dept, Barcelona, Spain
[11] CHRU, Biochem & Hormonol Dept Lab, Montpellier, France
[12] Univ Montpellier, CNRS, UMR 9214, ISERM U1046,PhyMedExp, Montpellier, France
[13] Univ Autonoma Barcelona, Sch Med, Biostat Unit, Barcelona, Spain
[14] Hosp Clin Barcelona, IDIBAPS, Biostat & Data Management Platform, Barcelona, Spain
关键词
end-stage renal disease; hemodiafiltration; randomized controlled trial; renal transplantation; time-varying exposure; ALL-CAUSE MORTALITY; IMMORTAL TIME BIAS; RANDOMIZED CONTROLLED-TRIALS; ONLINE HEMODIAFILTRATION; COHORT; OUTCOMES; RISKS;
D O I
10.1093/ndt/gfw458
中图分类号
R3 [基础医学]; R4 [临床医学];
学科分类号
1001 ; 1002 ; 100602 ;
摘要
Background. During the follow-up in a randomized controlled trial (RCT), participants may receive additional (non-randomly allocated) treatment that affects the outcome. Typically such additional treatment is not taken into account in evaluation of the results. Two pivotal trials of the effects of hemodiafiltration (HDF) versus hemodialysis (HD) on mortality in patients with end-stage renal disease reported differing results. We set out to evaluate to what extent methods to take other treatments (i.e. renal transplantation) into account may explain the difference in findings between RCTs. This is illustrated using a clinical example of two RCTs estimating the effect of HDF versus HD on mortality. Methods. Using individual patient data from the Estudio de Supervivencia de Hemodiafiltracion On-Line (ESHOL; n = 902) and The Dutch CONvective TRAnsport STudy (CONTRAST; n = 714) trials, five methods for estimating the effect of HDF versus HD on all-cause mortality were compared: intention-totreat (ITT) analysis (i.e. not taking renal transplantation into account), per protocol exclusion (PPexcl; exclusion of patients who receive transplantation), PPcens (censoring patients at the time of transplantation), transplantation-adjusted (TA) analysis and an extension of the TA analysis (TA(ext)) with additional adjustment for variables related to both the risk of receiving a transplant and the risk of an outcome (transplantation-outcome confounders). Cox proportional hazards models were applied. Results. Unadjusted ITT analysis of all-cause mortality led to differing results between CONTRAST and ESHOL: hazard ratio (HR) 0.95 (95% CI 0.75-1.20) and HR 0.76 (95% CI 0.59-0.97), respectively; difference between 5 and 24% risk reductions. Similar differences between the two trials were observed for the other unadjusted analytical methods (PPcens, PPexcl, TA) The HRs of HDF versus HD treatment became more similar after adding transplantation as a time-varying covariate and including transplantation-outcome confounders: HR 0.89 (95% CI 0.69-1.13) in CONTRAST and HR 0.80 (95% CI 0.62-1.02) in ESHOL. Conclusions. The apparent differences in estimated treatment effects between two dialysis trials were to a large extent attributable to differences in applied methodology for taking renal transplantation into account in their final analyses. Our results exemplify the necessity of careful consideration of the treatment effect of interest when estimating the therapeutic effect in RCTs in which participants may receive additional treatments.
引用
收藏
页码:31 / 39
页数:9
相关论文
共 22 条
[11]   ESHOL study reanalysis: All-cause mortality considered by competing risks and time-dependent covariates for renal transplantation [J].
Maduell, Francisco ;
Moreso, Francesc ;
Mora-Macia, Josep ;
Pons, Mercedes ;
Ramose, Rosa ;
Carreras, Jordi ;
Soler, Jordi ;
Torres, Ferran .
NEFROLOGIA, 2016, 36 (02) :156-163
[12]   High-Efficiency Postdilution Online Hemodiafiltration Reduces All-Cause Mortality in Hemodialysis Patients [J].
Maduell, Francisco ;
Moreso, Francesc ;
Pons, Mercedes ;
Ramos, Rosa ;
Mora-Macia, Josep ;
Carreras, Jordi ;
Soler, Jordi ;
Torres, Ferran ;
Campistol, Josep M. ;
Martinez-Castelao, Alberto .
JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY, 2013, 24 (03) :487-497
[13]   Clinical Evidence on Hemodiafiltration: A Systematic Review and a Meta- analysis [J].
Mostovaya, Ira M. ;
Blankestijn, Peter J. ;
Bots, Michiel L. ;
Covic, Adrian ;
Davenport, Andrew ;
Grooteman, Muriel P. C. ;
Hegbrant, Joergen ;
Locatelli, Francesco ;
Vanholder, Raymond ;
Nube, Menso J. .
SEMINARS IN DIALYSIS, 2014, 27 (02) :119-127
[14]   Effect of increased convective clearance by on-line hemodiafiltration on all cause and cardiovascular mortality in chronic hemodialysis patients -: the Dutch CONvective TRAnsport STudy (CONTRAST):: rationale and design of a randomised controlled trial [ISRCTN38365125] -: art. no. 8 [J].
Penne, EL ;
Blankestijn, PJ ;
Bots, ML ;
van den Dorpel, MA ;
Grooteman, MP ;
Nubé, MJ ;
van der Tweel, I ;
ter Wee, PM .
CURRENT CONTROLLED TRIALS IN CARDIOVASCULAR MEDICINE, 2005, 6 (1)
[15]   Haemodiafiltration and mortality in end-stage kidney disease patients: a pooled individual participant data analysis from four randomized controlled trials [J].
Peters, Sanne A. E. ;
Bots, Michiel L. ;
Canaud, Bernard ;
Davenport, Andrew ;
Grooteman, Muriel P. C. ;
Kircelli, Fatih ;
Locatelli, Francesco ;
Maduell, Francisco ;
Morena, Marion ;
Nube, Menso J. ;
Ok, Ercan ;
Torres, Ferran ;
Woodward, Mark ;
Blankestijn, Peter J. .
NEPHROLOGY DIALYSIS TRANSPLANTATION, 2016, 31 (06) :978-984
[16]   Immortal time bias in critical care research: Application of time-varying Cox regression for observational cohort studies [J].
Shintani, Ayumi K. ;
Girard, Timothy D. ;
Eden, Svetlana K. ;
Arbogast, Patrick G. ;
Moons, Karel G. M. ;
Ely, E. Wesley .
CRITICAL CARE MEDICINE, 2009, 37 (11) :2939-2945
[17]   Analysis of individual drug use as a time-varying determinant of exposure in prospective population-based cohort studies [J].
Stricker, B. H. Ch. ;
Stijnen, T. .
EUROPEAN JOURNAL OF EPIDEMIOLOGY, 2010, 25 (04) :245-251
[18]   Immortal time bias in observational studies of drug effects [J].
Suissa, Samy .
PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2007, 16 (03) :241-249
[19]   Online haemodiafiltration: definition, dose quantification and safety revisited [J].
Tattersall, James E. ;
Ward, Richard A. .
NEPHROLOGY DIALYSIS TRANSPLANTATION, 2013, 28 (03) :542-550
[20]  
Therneau TM., 2015, R TOP DOC, V128, P28