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Finland, Juvenile Diabetes Research Foundation, and Sigrid Juselius

Foundation. The authors thank Pirjo Haavisto, Taina Lahti, and Katri Villa

from Turku University Hospital for assistance in the manual data

collection, and Aidan McGlinchey for checking the English language.

Appendix A. Supplementary data

Supplementary material related to this article can be

found, in the online version, at eururo.2017.01.043 .



Guinney J, Wang T, Laajala TD, et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: de- velopment of a prognostic model through a crowdsourced chal- lenge with open clinical trial data. Lancet Oncol 2017;18:132–42.


Sydes MR, Langley RE. Potential pitfalls in the design and reporting of clinical trials. Lancet Oncol 2010;11:694–700.


Templeton AJ, Vera-Badillo FE, Wang L, et al. Translating clinical trials to clinical practice: outcomes of men with metastatic castra- tion resistant prostate cancer treated with docetaxel and predni- sone in and out of clinical trials. Ann Oncol 2013;24:2972–7.


Howard DN, Chambers C, Cusano F. Efficacy vs. effectiveness— docetaxel and prednisone in hormone refractory prostate cancer. J Oncol Pharm Pract 2008;14:45–9.


Halabi S, Lin C-Y, Kelly WK, et al. Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castration-resistant prostate cancer. J Clin Oncol 2014;32:671–7.


Turku Centre for Biotechnology, Turku, Finland


Department of Mathematics and Statistics, University of Turku, Turku,



Department of Information Technology, University of Turku, Turku, Finland


Department of Oncology and Radiotherapy, Turku University Central

Hospital, Turku, Finland


Department of Clinical Oncology, University of Turku, Turku, Finland


Department of Pulmonary Diseases and Clinical Allergology, Turku

University Hospital and University of Turku, Turku, Finland


Centre for Clinical Informatics, Turku University Hospital, Turku, Finland

*Corresponding author. Computational Biomedicine and Bioinformatics,

Turku Centre for Biotechnology, Tykisto¨katu 6, FI-20520 Turku, Finland.

Tel. +358 2 333


8009; Fax: +358 2





E-mail address:

(L.L. Elo).

January 24, 2017

Methodological Considerations for Early-phase Development of

Immune Checkpoint Inhibitors in Urothelial Bladder Cancer

Andrea Necchi

* ,

Daniele Giardiello, Luigi Mariani

Early results from immunotherapy trials paved the way for

a revolutionary approach in urothelial carcinoma (UC).

However, the published single-arm studies on atezolizu-

mab and durvalumab, added to other published studies,

raised important hints on interpretation of results accord-

ing to their study design in a context involving substantial

uncertainties regarding PD-L1 immunohistochemical (IHC)



. In general, should any similar investigation

suggest that the target treatment benefit is more likely to

occur in a specific biomarker-defined subset of patients, any

further study might be more suitably decided in accordance

with an enrichment strategy. Consolidated statistical

criteria for supporting such a decision are lacking in the

context of noncomparative trials with binary endpoints,

such as those commonly adopted in phase 2 trials in

oncology. One solution with which we have had satisfactory

experience is based on the Bayesian approach of predictive

probability (PP) calculation.

As explained by Lee and Liu


, PP quantifies the

probability of reaching a positive result by the end of the

trial on the basis of cumulative information in the current

stage. Therefore, PP can be used to decide on early stopping

because of efficacy/futility or to continue the study when

the current data are not yet conclusive. Our approach to

biomarker-stratified studies is to calculate PP both for the

overall sample and separately for each stratum, and to

allow study continuation only when PP


30% (in which

case we denote the treatment as promising). Enrichment in

particular is considered when PP in the best-performing

stratum fulfills the 30% criterion and exceeds by


20% the

estimate for the competing stratum. At the same time,

enrichment is assessed in terms of the projected time for

study completion (biomarker-driven patient selection

implies a reduction in the accrual rate, which can be more

severe in the case of low feature prevalence) and finally

adopted if not too much penalizing. The PP approach can

also be used from scratch for sequential study planning



but this choice would not have the flexibility necessarily

required in biomarker-stratified studies.

As an example, we applied our approach to the

durvalumab data

[1] ,

hypothesizing an overall response rate

(ORR) of


20% as the efficacy criterion and a maximum

E U R O P E A N U R O L O G Y 7 1 ( 2 0 1 7 ) 8 3 7 – 8 4 3