Balduzzi, Sara
(2016)

*Statistical Methods for Dealing with Selective Crossover in Randomised Controlled Trials*, [Dissertation thesis], Alma Mater Studiorum Università di Bologna.
Dottorato di ricerca in

Metodologia statistica per la ricerca scientifica, 27 Ciclo. DOI 10.6092/unibo/amsdottorato/7252.

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## Abstract

Background
Selective crossover (SCO) occurs when patients in a randomised controlled trial (RCT) are given the opportunity to switch from the arm in which they were initially allocated to the other arm. Problems in data analysis and interpretation may arise.
Objectives
Main objectives were to assess: (i) the prevalence of SCO in RCTs assessing the efficacy of therapies for breast cancer (BC); (ii) whether different statistical methods provide different results, in particular when the outcome of interest is a time-to-event outcome.
Methods
RCTs assessing the efficacy of therapies for BC were searched in the medical literature. Different methods for dealing with SCO exist. Among naïve methods, the Intention-To-Treat analysis (ITT), the censored analysis and the analysis considering the treatment as a time-varying covariate were contemplated; among more complex methods, the inverse probability of censoring weighting analysis, the Loeys and Goetghebeur estimator, and the rank preserving structural failure time models were considered. The methods were evaluated trough simulations, considering twelve scenarios which differed by the proportion of patients crossing over, their underlying prognosis, and the magnitude of true treatment effect.
Results
Crossover occurred in the 23.5% of RCTs identified. Simulations highlighted that ITT analysis, always presented, tend to underestimate the actual treatment effect across all scenarios. Other naïve methods are able to better estimate the treatment effect, but when the probability of crossover is assumed to depend on prognosis (i.e. patients with a poor prognosis cross over more frequently than patients with a good prognosis), they failed. More complex models work better, but each of them make assumptions not always verifiable or likely to occur in the considered context.
Conclusions
The present work aims to highlight the need for attention being paid on the phenomenon of SCO in RCTs, and underlines the concerns related to it.

Abstract

Background
Selective crossover (SCO) occurs when patients in a randomised controlled trial (RCT) are given the opportunity to switch from the arm in which they were initially allocated to the other arm. Problems in data analysis and interpretation may arise.
Objectives
Main objectives were to assess: (i) the prevalence of SCO in RCTs assessing the efficacy of therapies for breast cancer (BC); (ii) whether different statistical methods provide different results, in particular when the outcome of interest is a time-to-event outcome.
Methods
RCTs assessing the efficacy of therapies for BC were searched in the medical literature. Different methods for dealing with SCO exist. Among naïve methods, the Intention-To-Treat analysis (ITT), the censored analysis and the analysis considering the treatment as a time-varying covariate were contemplated; among more complex methods, the inverse probability of censoring weighting analysis, the Loeys and Goetghebeur estimator, and the rank preserving structural failure time models were considered. The methods were evaluated trough simulations, considering twelve scenarios which differed by the proportion of patients crossing over, their underlying prognosis, and the magnitude of true treatment effect.
Results
Crossover occurred in the 23.5% of RCTs identified. Simulations highlighted that ITT analysis, always presented, tend to underestimate the actual treatment effect across all scenarios. Other naïve methods are able to better estimate the treatment effect, but when the probability of crossover is assumed to depend on prognosis (i.e. patients with a poor prognosis cross over more frequently than patients with a good prognosis), they failed. More complex models work better, but each of them make assumptions not always verifiable or likely to occur in the considered context.
Conclusions
The present work aims to highlight the need for attention being paid on the phenomenon of SCO in RCTs, and underlines the concerns related to it.

Tipologia del documento

Tesi di dottorato

Autore

Balduzzi, Sara

Supervisore

Co-supervisore

Dottorato di ricerca

Scuola di dottorato

Scienze economiche e statistiche

Ciclo

27

Coordinatore

Settore disciplinare

Settore concorsuale

Parole chiave

Selective crossover
Switch
Intention-to-treat
Inverse probability of censoring weighting
Rank preserving structural failure time models

URN:NBN

DOI

10.6092/unibo/amsdottorato/7252

Data di discussione

4 Marzo 2016

URI

## Altri metadati

Tipologia del documento

Tesi di dottorato

Autore

Balduzzi, Sara

Supervisore

Co-supervisore

Dottorato di ricerca

Scuola di dottorato

Scienze economiche e statistiche

Ciclo

27

Coordinatore

Settore disciplinare

Settore concorsuale

Parole chiave

Selective crossover
Switch
Intention-to-treat
Inverse probability of censoring weighting
Rank preserving structural failure time models

URN:NBN

DOI

10.6092/unibo/amsdottorato/7252

Data di discussione

4 Marzo 2016

URI

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