| Title |
Decisions on Further Research for Predictive Biomarkers of High-Dose Alkylating Chemotherapy in Triple-Negative Breast Cancer: A Value of Information Analysis
|
|---|---|
| Published in |
Value in Health (Elsevier Science), April 2016
|
| DOI | 10.1016/j.jval.2016.01.015 |
| Pubmed ID | |
| Authors |
Anna Miquel-Cases, Valesca P. Retèl, Wim H. van Harten, Lotte M.G. Steuten |
| Abstract |
To inform decisions about the design and priority of further studies of emerging predictive biomarkers of high-dose alkylating chemotherapy (HDAC) in triple-negative breast cancer (TNBC) using value-of-information analysis. A state transition model compared treating women with TNBC with current clinical practice and four biomarker strategies to personalize HDAC: 1) BRCA1-like profile by array comparative genomic hybridization (aCGH) testing; 2) BRCA1-like profile by multiplex ligation-dependent probe amplification (MLPA) testing; 3) strategy 1 followed by X-inactive specific transcript gene (XIST) and tumor suppressor p53 binding protein (53BP1) testing; and 4) strategy 2 followed by XIST and 53BP1 testing, from a Dutch societal perspective and a 20-year time horizon. Input data came from literature and expert opinions. We assessed the expected value of partial perfect information, the expected value of sample information, and the expected net benefit of sampling for potential ancillary studies of an ongoing randomized controlled trial (RCT; NCT01057069). The expected value of partial perfect information indicated that further research should be prioritized to the parameter group including "biomarkers' prevalence, positive predictive value (PPV), and treatment response rates (TRRs) in biomarker-negative patients and patients with TNBC" (€639 million), followed by utilities (€48 million), costs (€40 million), and transition probabilities (TPs) (€30 million). By setting up four ancillary studies to the ongoing RCT, data on 1) TP and MLPA prevalence, PPV, and TRR; 2) aCGH and aCGH/MLPA plus XIST and 53BP1 prevalence, PPV, and TRR; 3) utilities; and 4) costs could be simultaneously collected (optimal size = 3000). Further research on predictive biomarkers for HDAC should focus on gathering data on TPs, prevalence, PPV, TRRs, utilities, and costs from the four ancillary studies to the ongoing RCT. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 64 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 13% |
| Researcher | 6 | 9% |
| Student > Doctoral Student | 6 | 9% |
| Student > Bachelor | 6 | 9% |
| Unspecified | 5 | 8% |
| Other | 15 | 23% |
| Unknown | 18 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 16% |
| Biochemistry, Genetics and Molecular Biology | 9 | 14% |
| Economics, Econometrics and Finance | 8 | 13% |
| Unspecified | 5 | 8% |
| Computer Science | 3 | 5% |
| Other | 13 | 20% |
| Unknown | 16 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 May 2017.
All research outputs
#16,109,035
of 25,461,852 outputs
Outputs from Value in Health (Elsevier Science)
#2,463
of 4,145 outputs
Outputs of similar age
#173,601
of 315,929 outputs
Outputs of similar age from Value in Health (Elsevier Science)
#72
of 143 outputs
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