spsMOSAICS
spsMOSAICS finds latent subgroups in patient cohorts, allowing for prospective responder identification in clinical trials. It's your unique tool for improving drug targeting and transforming patient outcomes.
While Saddle Point Signature constructs treatment response scores, if required, these are limited to those scenarios where their available covariates carry (possibly complex) information on the likelihood of an individual’s treatment response. The Bayesian pipeline Saddle Point Mosaics goes further. It also detects responder subgroups in those cases where the measured covariates are not informative. It uses Bayesian model selection to infer more generally:
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The number and size of statistically significant patient subgroups in a given clinical data set.
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Whether these subgroups differ in frailties, risk associations, base hazard rates or combinations of these three.
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What are the sizes and the quantitative characteristics of each subgroup.
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What are the probabilities for individual patients in the data set to belong to each of the detected subgroups.
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Whether and how subgroup membership can be predicted a priori from the covariates.
If (some of) the subgroups are distinct in terms of their association with the treatment variable, this results in responder identification. spsMOSAICS thus informs the user on (i) the extent to which a cohort is stratifiable, (ii) what are the characteristics of the strata, and (iii) a rational tool for finding stratifying biomarkers (which would be variables that correlate with the reported class membership probabilities). The pipeline can handle multiple risks, and decontaminate survival predictions for competing risk effects, and generates fully automated analysis reports.
Conditions
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All prices are in Euro (€), and exclusive of 21% value added tax (VAT)
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Single-user trial licenses are only available for Windows builds
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All other licenses are available for Windows, UNIX/LINUX and MacOS platforms
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Collaborator licenses are available to partners in formal research collaborations with Saddle Point Science Europe BV, of which results are to be published under joint authorship
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Students enrolled at degree-awarding institutions can purchase single-user licenses only, proof of student status (e.g. copy of a university ID card) will be required
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Support includes:
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one-to-one installation
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demonstration/introduction (on-site or remotely)
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software support
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results interpretation (remotely)
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Software manuals are made available as electronic (PDF) documents
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SaddlePoint DatasetManager included in all packages (Free of charge)
Features
per user
License type | Duration | Support | Manual | Multi-core |
|---|---|---|---|---|
Trial | 2 months | ─ | ✓ | ✓ |
Student | 1 year | ✓ | ✓ | ─ |
Collaborator | 1 year | ✓ | ✓ | ✓ |
Public sector | 1 year | ✓ | ✓ | ✓ |
Commercial | 1 year | ✓ | ✓ | ✓ |
Costs
per user
License type | Duration | 1 user | 2 users | Additional users |
|---|---|---|---|---|
Trial | 2 months | free | ─ | ─ |
Student | 1 year | € 1.309,00 | ─ | ─ |
Collaborator | 1 year | € 9.282,00 | € 13.923,00 | €3.094,00 each |
Public sector | 1 year | € 17.255,00 | € 25.883,00 | €5.212,00 each |
Commercial | 1 year | € 23.324,00 | € 34.986,00 | €7.771,00 each |
Publications in which the pipeline was used
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Häggström, C., Rowley, M., Liedberg, F., Coolen, A. C. C., & Holmberg, L. (2023). Latent heterogeneity of muscle‐invasive bladder cancer in patient characteristics and survival: A population‐based nation‐wide study in the Bladder Cancer Data Base Sweden (BladderBaSe). Cancer Medicine, 12(12), 13856–13864. https://doi.org/10.1002/cam4.5981
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Barber, P. R., Weitsman, G., Lawler, K., Barrett, J. E., Rowley, M., Rodríguez-Justo, M., Fisher, D. J., Gao, F., Tullis, I. D. C., Deng, J., Brown, L., Kaplan, R., Hochhauser, D., Adams, R., Maughan, T., Vojnovic, B., Coolen, A. C. C., & Ng, T. (2019). HER2-HER3 Heterodimer Quantification by FRET-FLIM and Patient Subclass Analysis of the COIN Colorectal Trial. JNCI: Journal Of The National Cancer Institute, 112(9), 944–954. https://doi.org/10.1093/jnci/djz231
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Häggström, C., Van Hemelrijck, M., Garmo, H., Robinson, D., Stattin, P., Rowley, M., Coolen, A. C. C., & Holmberg, L. (2018). Heterogeneity in risk of prostate cancer: A Swedish population‐based cohort study of competing risks and Type 2 diabetes mellitus. International Journal Of Cancer, 143(8), 1868–1875. https://doi.org/10.1002/ijc.31587
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Rowley, M., Garmo, H., Van Hemelrijck, M., Wulaningsih, W., Grundmark, B., Zethelius, B., Hammar, N., Walldius, G., Inoue, M., Holmberg, L., & Coolen, A. C. C. (2017). A latent class model for competing risks. Statistics in Medicine, 36(13), 2100–2119. https://doi.org/10.1002/sim.7246
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Wulaningsih, W., Vahdaninia, M., Rowley, M., Holmberg, L., Garmo, H., Malmström, H., Lambe, M., Hammar, N., Walldius, G., Jungner, I., Coolen, A. C. C., & Van Hemelrijck, M. (2015). Prediagnostic serum glucose and lipids in relation to survival in breast cancer patients: a competing risk analysis. BMC Cancer, 15(1). https://doi.org/10.1186/s12885-015-1928-z
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