Is treatment with interferon-α effective in all patients with metastatic renal carcinoma? A new approach to the investigation of interactions

Royston, Patrick; Sauerbrei, Willi; Ritchie, A.

doi:10.1038/sj.bjc.6601622

Cited by 31 publications

(26 citation statements)

References 25 publications

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“…These include inhibitors of vascular endothelial growth factor function (21), inhibitors of receptor tyrosine kinases with promiscuous targets including VEGFR1 and VEGFR2, PDGFR, C-Kit, Raf kinase, and mammalian target of rapamycin (22 -25). In addition, IFN has been shown in several phase III studies to exhibit a modest survival advantage (26,27). It would be of interest to determine the role of CAIX staining in predicting benefit to each of these agents.…”

Section: Caix and Il-2 For Renal Cell Carcinomamentioning

confidence: 99%

Carbonic Anhydrase IX Expression Predicts Outcome of Interleukin 2 Therapy for Renal Cancer

Atkins

Regan

McDermott

et al. 2005

Clinical Cancer Research

382

213

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Purpose: Renal cancer response to interleukin 2 (IL-2) therapy and patient survival has been correlated with tumor histology and carbonic anhydrase IX (CAIX) expression. In an effort to confirm and expand these observations, we examined CAIX expression in pathology specimens from renal cancer patients who had previously received IL-2 therapy. Experimental Design: Paraffin-embedded tissue sections of renal cancer were immunostained with the MN-75 monoclonal antibody to CAIX and expression levels were correlated with histologic findings and clinical outcome. Results:Tissue specimens were obtained from 66 patients; 27 of whom (41%) had responded to IL-2^based therapy. Fifty-eight specimens were assessed as clear cell, with 56, 33, and 4 having alveolar, granular, and papillary features, respectively.Twenty-four (36%), 31 (47%), and11 (17%) were classified into good, intermediate, and poor prognosis groups according to the Upton pathology model. Forty-one specimens (62 %) had high CAIX expression. Twenty-one of 27 (78%) responding patients had high CAIX expressing tumors compared with 20 of 39 (51%) nonresponders (odds ratio, 3.3; P = 0.04). Median survival was prolonged (P = 0.04) and survival >5 years was only seen in high CAIX expressers. In patients with intermediate pathologic prognosis, all nine responders had high CAIX expression versus 11 of 22 nonresponders. A resultant group with good pathologic prognosis alone or with intermediate pathologic prognosis and high CAIX contained 26 of 27 (96%) responders compared with18 of 39 (46%) nonresponders (odds ratio, 30; P < 0.01) and exhibited longer median survival (P < 0.01).Conclusions: CAIX expression seems to be an important predictor of outcome in renal cell carcinoma patients receiving IL-2^based therapy and may enhance prognostic information obtained from pathology specimens.

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Section: Caix and Il-2 For Renal Cell Carcinomamentioning

confidence: 99%

Carbonic Anhydrase IX Expression Predicts Outcome of Interleukin 2 Therapy for Renal Cancer

Atkins

Regan

McDermott

et al. 2005

Clinical Cancer Research

382

213

View full text Add to dashboard Cite

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“…These data will be used here for further investigations. No interaction was found for another nine prognostic variables [22].…”

Section: Treatment-covariate Interaction the Issue Of Considering Inmentioning

confidence: 94%

“…In the first paper based on 335 patients and 236 deaths, a 28 per cent reduction in the risk of death in the IFN group was reported (MRCRCC, 1999 [21]). Royston et al [22] used updated data with 322 deaths in 347 patients and found a significant interaction with white cell count (WCC) by using the MFPI procedure. These data will be used here for further investigations.…”

Section: Treatment-covariate Interaction the Issue Of Considering Inmentioning

confidence: 99%

Modelling to extract more information from clinical trials data: On some roles for the bootstrap

Sauerbrei

Royston

2007

Statistics in Medicine

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Despite its importance in the theoretical literature, the bootstrap appears to play a negligible role in pharmaceutical research, as will be demonstrated by a brief literature review. As will be shown by examples, the bootstrap is a useful tool in the planning and analysis of clinical trials. The first example shows that some important information required in the design of a study can best be gained by using the bootstrap. It is argued from two further examples that more information can be extracted from large clinical trials by data-dependent modelling. This is shown by identifying a prognostic factor that may play a role as an inclusion criterion of a new study and by an interaction of a continuous predictor with treatment. To protect against erroneous conclusions from data-dependent modelling in a multivariable context, detailed checks of the results and stability analyses should be performed by the bootstrap. To conclude, a discussion of the future of modelling and the future of the bootstrap is given.

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“…The plot shows that the proportion of unfavorable treatment e↵ect due to interferon-alpha treatment increases with increasing white blood cell count. Detailed information on the dataset was documented Royston et al in 2004 [31]. A horizontal line for a benchmark can be drawn for the decision of the threshold.…”

Section: Modelling the Functional Form Of The Interactionmentioning

confidence: 99%

“…The package can be implemented in linear, logistic, and Cox regression. In the following we will describe main parts of the R comments for cbpti by using an example from Medical Research Council RE01 phase III randomized controlled trial [31].…”

Section: R Cbpti Vignettementioning

confidence: 99%

Graphical Presentation of Patient-Treatment Interaction Elucidated by Continuous Biomarkers

Shen¹,

Le²,

Wilson³

et al. 2017

Methods Inf Med

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Summary Background: Biomarkers providing evidence for patient-treatment interaction are key in the development and practice of personalized medicine. Knowledge that a patient with a specific feature – as demonstrated through a biomarker – would have an advantage under a given treatment vs. a competing treatment can aid immensely in medical decision-making. Statistical strategies to establish evidence of continuous biomarkers are complex and their formal results are thus not easy to communicate. Good graphical representations would help to translate such findings for use in the clinical community. Although general guidelines on how to present figures in clinical reports are available, there remains little guidance for figures elucidating the role of continuous biomarkers in patient-treatment interaction (CBPTI). Objectives: To combat the current lack of comprehensive reviews or adequate guides on graphical presentation within this topic, our study proposes presentation principles for CBPTI plots. In order to understand current practice, we review the development of CBPTI methodology and how CBPTI plots are currently used in clinical research. Methods: The quality of a CBPTI plot is determined by how well the presentation provides key information for clinical decision-making. Several criteria for a good CBPTI plot are proposed, including general principles of visual display, use of units presenting absolute outcome measures, appropriate quantification of statistical uncertainty, correct display of benchmarks, and informative content for answering clinical questions especially on the quantitative advantage for an individual patient with regard to a specific treatment. We examined the development of CBPTI methodology from the years 2000-2014, and reviewed how CBPTI plots were currently used in clinical research in six major clinical journals from 2013-2014 using the principle of theoretical saturation. Each CBPTI plot found was assessed for appropriateness of its presentation and clinical utility. Results: In our review, a total of seven methodological papers and five clinical reports used CBPTI plots which we categorized into four types: those that distinguish the outcome effect for each treatment group; those that show the outcome differences between treatment groups (by either partitioning all individuals into subpopulations or modelling the functional form of the interaction); those that evaluate the proportion of population impact of the biomarker; and those that show the classification accuracy of the biomarker. The current practice of utilizing CBPTI plots in clinical reports suffers from methodological shortcomings: the lack of presentation of statistical uncertainty, the outcome measure scaled by relative unit instead of absolute unit, incorrect use of benchmarks, and being non-informative in answering clinical questions. Conclusions: There is considerable scope for improvement in the graphical representation of CBPTI in clinical reports. The current challenge is to develop instruments for high-quality graphical plots which not only convey quantitative concepts to readers with limited statistical knowledge, but also facilitate medical decision-making.

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Is treatment with interferon-α effective in all patients with metastatic renal carcinoma? A new approach to the investigation of interactions

Cited by 31 publications

References 25 publications

Carbonic Anhydrase IX Expression Predicts Outcome of Interleukin 2 Therapy for Renal Cancer

Carbonic Anhydrase IX Expression Predicts Outcome of Interleukin 2 Therapy for Renal Cancer

Modelling to extract more information from clinical trials data: On some roles for the bootstrap

Graphical Presentation of Patient-Treatment Interaction Elucidated by Continuous Biomarkers

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