Dhiman, Paula

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 4,718

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,069

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 852

Antidepressant use and risk of adverse outcomes in older people: population based cohort study

Coupland, C., Dhiman, P., Morriss, R., Arthur, A., Barton, G., & Hippisley-Cox, J. (2011) · BMJ

Pharmaceutical Practices and Patient OutcomesTreatment of Major Depression

Cited by 833

Evaluation of clinical prediction models (part 1): from development to external validation

Collins, G. S., Dhiman, P., Ma, J., Schlussel, M. M., Archer, L., Van Calster, B., Harrell, F. E., Martin, G. P., Moons, K. G. M., van Smeden, M., et al. (2024) · BMJ

Statistical Methods in EpidemiologyArtificial Intelligence in Healthcare

Cited by 798

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

Andaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ

Statistical Methods in EpidemiologyMeta-analysis and systematic reviews

Cited by 415

Multiple cereblon genetic changes are associated with acquired resistance to lenalidomide or pomalidomide in multiple myeloma

Gooding, S., Ansari-Pour, N., Towfic, F., Ortiz Estévez, M., Chamberlain, P. P., Tsai, K.-T., Flynt, E., Hirst, M., Rozelle, D., Dhiman, P., et al. (2021) · Blood

Multiple Myeloma Research and TreatmentsProtein Degradation and Inhibitors

Cited by 172

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2022) · BMC Medical Research Methodology

Statistical Methods in EpidemiologyArtificial Intelligence in Healthcare

Cited by 140

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Andaur Navarro, C. L., Damen, J. A. A., van Smeden, M., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., et al. (2023) · Journal of Clinical Epidemiology

Artificial Intelligence in HealthcareStatistical Methods in Epidemiology

Cited by 121

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology

Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2022) · Diagnostic and Prognostic Research

Statistical Methods in EpidemiologyMachine Learning in Healthcare

Cited by 79

ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies

Barreñada, L., Ledger, A., Dhiman, P., Collins, G., Wynants, L., Verbakel, J. Y., Timmerman, D., Valentin, L., & Van Calster, B. (2024) · BMJ Medicine

Ovarian cancer diagnosis and treatmentBRCA gene mutations in cancer

Cited by 42

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2023) · Journal of Clinical Epidemiology

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 41

Reminding Peer Reviewers of Reporting Guideline Items to Improve Completeness in Published Articles: Primary Results of 2 Randomized Trials

Speich, B., Mann, E., Schönenberger, C. M., Mellor, K., Griessbach, A. N., Dhiman, P., Gandhi, P., Lohner, S., Agarwal, A., Odutayo, A., et al. (2023) · JAMA Network Open

Meta-analysis and systematic reviewsAcademic Publishing and Open Access

Cited by 41

Clinical prediction models using machine learning in oncology: challenges and recommendations

Collins, G. S., Chester-Jones, M., Gerry, S., Ma, J., Matos, J., Sehjal, J., Tsegaye, B., & Dhiman, P. (2025) · BMJ Oncology

Machine Learning in HealthcareStatistical Methods in Epidemiology

Cited by 27

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning

Collins, G. S., Whittle, R., Bullock, G. S., Logullo, P., Dhiman, P., de Beyer, J. A., Riley, R. D., & Schlussel, M. M. (2024) · Journal of Clinical Epidemiology

Research Data Management PracticesAcademic Publishing and Open Access

Cited by 21

Up Front and Open? Shrouded in Secrecy? Or Somewhere in Between? A Meta-Research Systematic Review of Open Science Practices in Sport Medicine Research

Bullock, G. S., Ward, P., Impellizzeri, F. M., Kluzek, S., Hughes, T., Hillman, C., Waterman, B. R., Danelson, K., Henry, K., Barr, E., et al. (2023) · Journal of Orthopaedic & Sports Physical Therapy

Academic Publishing and Open AccessResearch Data Management Practices

Cited by 15