Limits...
A re-randomisation design for clinical trials.

Kahan BC, Forbes AB, Doré CJ, Morris TP - BMC Med Res Methodol (2015)

Bottom Line: We describe a re-randomisation design for these scenarios, which allows each patient to be independently randomised on multiple occasions.We discuss the circumstances in which this design can be used.The re-randomisation design will give asymptotically unbiased estimates of treatment effect and correct type I error rates under the following conditions: (a) patients are only re-randomised after the follow-up period from their previous randomisation is complete; (b) randomisations for the same patient are performed independently; and (c) the treatment effect is constant across all randomisations.

View Article: PubMed Central - PubMed

Affiliation: Pragmatic Clinical Trials Unit, Queen Mary University of London, London, E1 2AB, UK. b.kahan@qmul.ac.uk.

ABSTRACT

Background: Recruitment to clinical trials is often problematic, with many trials failing to recruit to their target sample size. As a result, patient care may be based on suboptimal evidence from underpowered trials or non-randomised studies.

Methods: For many conditions patients will require treatment on several occasions, for example, to treat symptoms of an underlying chronic condition (such as migraines, where treatment is required each time a new episode occurs), or until they achieve treatment success (such as fertility, where patients undergo treatment on multiple occasions until they become pregnant). We describe a re-randomisation design for these scenarios, which allows each patient to be independently randomised on multiple occasions. We discuss the circumstances in which this design can be used.

Results: The re-randomisation design will give asymptotically unbiased estimates of treatment effect and correct type I error rates under the following conditions: (a) patients are only re-randomised after the follow-up period from their previous randomisation is complete; (b) randomisations for the same patient are performed independently; and (c) the treatment effect is constant across all randomisations. Provided the analysis accounts for correlation between observations from the same patient, this design will typically have higher power than a parallel group trial with an equivalent number of observations.

Conclusions: If used appropriately, the re-randomisation design can increase the recruitment rate for clinical trials while still providing an unbiased estimate of treatment effect and correct type I error rates. In many situations, it can increase the power compared to a parallel group design with an equivalent number of observations.

No MeSH data available.


Related in: MedlinePlus

Simulation results across different re-randomisation proportions We compared two methods of analysis: (a) an unadjusted analysis (ignoring patient effects); and (b) an adjusted analysis (accounting for patient effects using a mixed-effects model). The ICC was set to 0.50 for all scenarios. The estimated treatment effect was unbiased for both methods of analysis. Standard errors for the estimated type I error rate and power are 0.3 % and 0.6 % respectively (assuming true values of 5 % and 80 %)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4634916&req=5

Fig4: Simulation results across different re-randomisation proportions We compared two methods of analysis: (a) an unadjusted analysis (ignoring patient effects); and (b) an adjusted analysis (accounting for patient effects using a mixed-effects model). The ICC was set to 0.50 for all scenarios. The estimated treatment effect was unbiased for both methods of analysis. Standard errors for the estimated type I error rate and power are 0.3 % and 0.6 % respectively (assuming true values of 5 % and 80 %)

Mentions: Results are shown in Figs. 3 and 4. An analysis which treated all observations as independent (unadjusted analysis) led to unbiased estimates of treatment effect, correct type I error rates, and identical power to a parallel group design in all scenarios.Fig. 3


A re-randomisation design for clinical trials.

Kahan BC, Forbes AB, Doré CJ, Morris TP - BMC Med Res Methodol (2015)

Simulation results across different re-randomisation proportions We compared two methods of analysis: (a) an unadjusted analysis (ignoring patient effects); and (b) an adjusted analysis (accounting for patient effects using a mixed-effects model). The ICC was set to 0.50 for all scenarios. The estimated treatment effect was unbiased for both methods of analysis. Standard errors for the estimated type I error rate and power are 0.3 % and 0.6 % respectively (assuming true values of 5 % and 80 %)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4634916&req=5

Fig4: Simulation results across different re-randomisation proportions We compared two methods of analysis: (a) an unadjusted analysis (ignoring patient effects); and (b) an adjusted analysis (accounting for patient effects using a mixed-effects model). The ICC was set to 0.50 for all scenarios. The estimated treatment effect was unbiased for both methods of analysis. Standard errors for the estimated type I error rate and power are 0.3 % and 0.6 % respectively (assuming true values of 5 % and 80 %)
Mentions: Results are shown in Figs. 3 and 4. An analysis which treated all observations as independent (unadjusted analysis) led to unbiased estimates of treatment effect, correct type I error rates, and identical power to a parallel group design in all scenarios.Fig. 3

Bottom Line: We describe a re-randomisation design for these scenarios, which allows each patient to be independently randomised on multiple occasions.We discuss the circumstances in which this design can be used.The re-randomisation design will give asymptotically unbiased estimates of treatment effect and correct type I error rates under the following conditions: (a) patients are only re-randomised after the follow-up period from their previous randomisation is complete; (b) randomisations for the same patient are performed independently; and (c) the treatment effect is constant across all randomisations.

View Article: PubMed Central - PubMed

Affiliation: Pragmatic Clinical Trials Unit, Queen Mary University of London, London, E1 2AB, UK. b.kahan@qmul.ac.uk.

ABSTRACT

Background: Recruitment to clinical trials is often problematic, with many trials failing to recruit to their target sample size. As a result, patient care may be based on suboptimal evidence from underpowered trials or non-randomised studies.

Methods: For many conditions patients will require treatment on several occasions, for example, to treat symptoms of an underlying chronic condition (such as migraines, where treatment is required each time a new episode occurs), or until they achieve treatment success (such as fertility, where patients undergo treatment on multiple occasions until they become pregnant). We describe a re-randomisation design for these scenarios, which allows each patient to be independently randomised on multiple occasions. We discuss the circumstances in which this design can be used.

Results: The re-randomisation design will give asymptotically unbiased estimates of treatment effect and correct type I error rates under the following conditions: (a) patients are only re-randomised after the follow-up period from their previous randomisation is complete; (b) randomisations for the same patient are performed independently; and (c) the treatment effect is constant across all randomisations. Provided the analysis accounts for correlation between observations from the same patient, this design will typically have higher power than a parallel group trial with an equivalent number of observations.

Conclusions: If used appropriately, the re-randomisation design can increase the recruitment rate for clinical trials while still providing an unbiased estimate of treatment effect and correct type I error rates. In many situations, it can increase the power compared to a parallel group design with an equivalent number of observations.

No MeSH data available.


Related in: MedlinePlus