Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study
Abstract Background The cohort multiple randomised controlled trial (cmRCT) design provides an opportunity to incorporate the benefits of randomisation within clinical practice; thus reducing costs, integrating electronic healthcare records, and improving external validity. This study aims to addres...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2017-01-01
|
| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12874-017-0295-7 |
| _version_ | 1818108547659988992 |
|---|---|
| author | Jane Candlish Alexander Pate Matthew Sperrin Tjeerd van Staa on behalf of GetReal Work Package 2 |
| author_facet | Jane Candlish Alexander Pate Matthew Sperrin Tjeerd van Staa on behalf of GetReal Work Package 2 |
| author_sort | Jane Candlish |
| collection | DOAJ |
| description | Abstract Background The cohort multiple randomised controlled trial (cmRCT) design provides an opportunity to incorporate the benefits of randomisation within clinical practice; thus reducing costs, integrating electronic healthcare records, and improving external validity. This study aims to address a key concern of the cmRCT design: refusal to treatment is only present in the intervention arm, and this may lead to bias and reduce statistical power. Methods We used simulation studies to assess the effect of this refusal, both random and related to event risk, on bias of the effect estimator and statistical power. A series of simulations were undertaken that represent a cmRCT trial with time-to-event endpoint. Intention-to-treat (ITT), per protocol (PP), and instrumental variable (IV) analysis methods, two stage predictor substitution and two stage residual inclusion, were compared for various refusal scenarios. Results We found the IV methods provide a less biased estimator for the causal effect when refusal is present in the intervention arm, with the two stage residual inclusion method performing best with regards to minimum bias and sufficient power. We demonstrate that sample sizes should be adapted based on expected and actual refusal rates in order to be sufficiently powered for IV analysis. Conclusion We recommend running both an IV and ITT analyses in an individually randomised cmRCT as it is expected that the effect size of interest, or the effect we would observe in clinical practice, would lie somewhere between that estimated with ITT and IV analyses. The optimum (in terms of bias and power) instrumental variable method was the two stage residual inclusion method. We recommend using adaptive power calculations, updating them as refusal rates are collected in the trial recruitment phase in order to be sufficiently powered for IV analysis. |
| first_indexed | 2024-12-11T02:17:06Z |
| format | Article |
| id | doaj.art-383269f2eab8477fab37b991a4ae1fdd |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-12-11T02:17:06Z |
| publishDate | 2017-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-383269f2eab8477fab37b991a4ae1fdd2022-12-22T01:24:10ZengBMCBMC Medical Research Methodology1471-22882017-01-0117111010.1186/s12874-017-0295-7Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation studyJane Candlish0Alexander Pate1Matthew Sperrin2Tjeerd van Staa3on behalf of GetReal Work Package 2Health eResearch Centre, Farr Institute for Health Informatics Research, University of ManchesterHealth eResearch Centre, Farr Institute for Health Informatics Research, University of ManchesterHealth eResearch Centre, Farr Institute for Health Informatics Research, University of ManchesterHealth eResearch Centre, Farr Institute for Health Informatics Research, University of ManchesterAbstract Background The cohort multiple randomised controlled trial (cmRCT) design provides an opportunity to incorporate the benefits of randomisation within clinical practice; thus reducing costs, integrating electronic healthcare records, and improving external validity. This study aims to address a key concern of the cmRCT design: refusal to treatment is only present in the intervention arm, and this may lead to bias and reduce statistical power. Methods We used simulation studies to assess the effect of this refusal, both random and related to event risk, on bias of the effect estimator and statistical power. A series of simulations were undertaken that represent a cmRCT trial with time-to-event endpoint. Intention-to-treat (ITT), per protocol (PP), and instrumental variable (IV) analysis methods, two stage predictor substitution and two stage residual inclusion, were compared for various refusal scenarios. Results We found the IV methods provide a less biased estimator for the causal effect when refusal is present in the intervention arm, with the two stage residual inclusion method performing best with regards to minimum bias and sufficient power. We demonstrate that sample sizes should be adapted based on expected and actual refusal rates in order to be sufficiently powered for IV analysis. Conclusion We recommend running both an IV and ITT analyses in an individually randomised cmRCT as it is expected that the effect size of interest, or the effect we would observe in clinical practice, would lie somewhere between that estimated with ITT and IV analyses. The optimum (in terms of bias and power) instrumental variable method was the two stage residual inclusion method. We recommend using adaptive power calculations, updating them as refusal rates are collected in the trial recruitment phase in order to be sufficiently powered for IV analysis.http://link.springer.com/article/10.1186/s12874-017-0295-7Trials within cohortsCohort multiple randomised controlled trialPragmatic trialInstrumental variable |
| spellingShingle | Jane Candlish Alexander Pate Matthew Sperrin Tjeerd van Staa on behalf of GetReal Work Package 2 Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study BMC Medical Research Methodology Trials within cohorts Cohort multiple randomised controlled trial Pragmatic trial Instrumental variable |
| title | Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study |
| title_full | Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study |
| title_fullStr | Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study |
| title_full_unstemmed | Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study |
| title_short | Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study |
| title_sort | evaluation of biases present in the cohort multiple randomised controlled trial design a simulation study |
| topic | Trials within cohorts Cohort multiple randomised controlled trial Pragmatic trial Instrumental variable |
| url | http://link.springer.com/article/10.1186/s12874-017-0295-7 |
| work_keys_str_mv | AT janecandlish evaluationofbiasespresentinthecohortmultiplerandomisedcontrolledtrialdesignasimulationstudy AT alexanderpate evaluationofbiasespresentinthecohortmultiplerandomisedcontrolledtrialdesignasimulationstudy AT matthewsperrin evaluationofbiasespresentinthecohortmultiplerandomisedcontrolledtrialdesignasimulationstudy AT tjeerdvanstaa evaluationofbiasespresentinthecohortmultiplerandomisedcontrolledtrialdesignasimulationstudy AT onbehalfofgetrealworkpackage2 evaluationofbiasespresentinthecohortmultiplerandomisedcontrolledtrialdesignasimulationstudy |