Inverse probability weighting to estimate causal effect of a singular phase in a multiphase randomized clinical trial for multiple myeloma.
Randomization procedure in randomized controlled trials (RCTs) permits an unbiased estimation of causal effects. However, in clinical practice, differential compliance between arms may cause a strong violation of randomization balance and biased treatment effect among those who comply. We evaluated the effect of the consolidation phase on disease-free survival of patients with multiple myeloma in an RCT designed for another purpose, adjusting for potential selection bias due to different compliance to previous treatment phases.



We computed two propensity scores (PS) to model two different selection processes: the first to undergo autologous stem cell transplantation, the second to begin consolidation therapy. Combined stabilized inverse probability treatment weights were then introduced in the Cox model to estimate the causal effect of consolidation therapy miming an ad hoc RCT protocol.



We found that the effect of consolidation therapy was restricted to the first 18 months of the phase (HR: 0.40, robust 95 % CI: 0.17-0.96), after which it disappeared.



PS-based methods could be a complementary approach within an RCT context to evaluate the effect of the last phase of a complex therapeutic strategy, adjusting for potential selection bias caused by different compliance to the previous phases of the therapeutic scheme, in order to simulate an ad hoc randomization procedure.



ClinicalTrials.gov: NCT01134484 May 28, 2010 (retrospectively registered) EudraCT: 2005-003723-39 December 17, 2008 (retrospectively registered).
Istituto di Ematologia Seràgnoli, Università degli Studi di Bologna, Policlinico Sant'Orsola-Malpighi, Via Massarenti 9, 40138, Bologna, BO, Italy.
Annalisa
A
Pezzi
Istituto di Ematologia Seràgnoli, Università degli Studi di Bologna, Policlinico Sant'Orsola-Malpighi, Via Massarenti 9, 40138, Bologna, BO, Italy.
Michele
M
Cavo
Dipartimento di Statistica, Informatica, Applicazioni "Giuseppe Parenti" (DiSIA), Università di Firenze, Via Morgagni 57-59, 50134, Firenze, FI, Italy.|SC Biostatistica, Istituto per lo studio e la prevenzione oncologica (ISPO), Via delle Oblate 2, 50126, Firenze, FI, Italy.
Annibale
A
Biggeri
Istituto di Ematologia Seràgnoli, Università degli Studi di Bologna, Policlinico Sant'Orsola-Malpighi, Via Massarenti 9, 40138, Bologna, BO, Italy.
Elena
E
Zamagni
Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via Maroncelli 40, 47014, Meldola, FC, Italy. oriana.nanni@irst.emr.it.
Oriana
0000-0001-7338-2896
O
Nanni
D000293:Adolescent; D000328:Adult; D000368:Aged; D017326:Clinical Trials, Phase III as Topic; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D009101:Multiple Myeloma / Q000628:therapy*; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data; D057216:Propensity Score; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D012107:Research Design / Q000592:standards; D013997:Time Factors; D055815:Young Adult
Causal effect; Compliance; Propensity score; RCT; Selection bias; Weighting sample