Accounting for dropout in xenografted tumour efficacy studies: integrated endpoint analysis, reduced bias and better use of animalsCitation formats

TY - JOUR

T1 - Accounting for dropout in xenografted tumour efficacy studies: integrated endpoint analysis, reduced bias and better use of animals

AU - Martin, Emma

AU - Aarons, Leon

AU - WT Yates, James

PY - 2016/7

Y1 - 2016/7

N2 - PurposeXenograft studies are commonly used to assess the efficacy of new compounds and characterise their dose–response relationship. Analysis often involves comparing the final tumour sizes across dose groups. This can cause bias, as often in xenograft studies a tumour burden limit (TBL) is imposed for ethical reasons, leading to the animals with the largest tumours being excluded from the final analysis. This means the average tumour size, particularly in the control group, is underestimated, leading to an underestimate of the treatment effect.MethodsFour methods to account for dropout due to the TBL are proposed, which use all the available data instead of only final observations: modelling, pattern mixture models, treating dropouts as censored using the M3 method and joint modelling of tumour growth and dropout. The methods were applied to both a simulated data set and a real example.ResultsAll four proposed methods led to an improvement in the estimate of treatment effect in the simulated data. The joint modelling method performed most strongly, with the censoring method also providing a good estimate of the treatment effect, but with higher uncertainty. In the real data example, the dose–response estimated using the censoring and joint modelling methods was higher than the very flat curve estimated from average final measurements.ConclusionsAccounting for dropout using the proposed censoring or joint modelling methods allows the treatment effect to be recovered in studies where it may have been obscured due to dropout caused by the TBL.

AB - PurposeXenograft studies are commonly used to assess the efficacy of new compounds and characterise their dose–response relationship. Analysis often involves comparing the final tumour sizes across dose groups. This can cause bias, as often in xenograft studies a tumour burden limit (TBL) is imposed for ethical reasons, leading to the animals with the largest tumours being excluded from the final analysis. This means the average tumour size, particularly in the control group, is underestimated, leading to an underestimate of the treatment effect.MethodsFour methods to account for dropout due to the TBL are proposed, which use all the available data instead of only final observations: modelling, pattern mixture models, treating dropouts as censored using the M3 method and joint modelling of tumour growth and dropout. The methods were applied to both a simulated data set and a real example.ResultsAll four proposed methods led to an improvement in the estimate of treatment effect in the simulated data. The joint modelling method performed most strongly, with the censoring method also providing a good estimate of the treatment effect, but with higher uncertainty. In the real data example, the dose–response estimated using the censoring and joint modelling methods was higher than the very flat curve estimated from average final measurements.ConclusionsAccounting for dropout using the proposed censoring or joint modelling methods allows the treatment effect to be recovered in studies where it may have been obscured due to dropout caused by the TBL.

KW - Dropout Joint models Xenograft Tumour growth models

U2 - 10.1007/s00280-016-3059-x

DO - 10.1007/s00280-016-3059-x

M3 - Article

VL - 78

SP - 131

EP - 141

JO - Cancer Chemotherapy and Pharmacology

JF - Cancer Chemotherapy and Pharmacology

SN - 0344-5704

IS - 1

ER -