Statistical Science

Michèle Widmer et al. (2024) ‘Group-based progressive functional, high-intensity training in adolescents and young adults with unilateral cerebral palsy – a tool to improve gross motor function, endurance and gait? – a pilot study’, Developmental Neurorehabilitation. 23.09.2024, 7(27), pp. 235–242. Available at: https://doi.org/10.1080/17518423.2024.2398151.

Roethlisberger, Michel et al. (2024) ‘Long-term tumor control in Koos grade IV vestibular schwannomas without the need for gross-total resection’, Journal of Neurosurgery, 140(6), pp. 1591–1604. Available at: https://doi.org/10.3171/2023.9.jns231316.

Bartels, C. et al. (2024) ‘Nonlinear mixed-effects modeling as a method for causal inference to predict exposures under desired within-subject dose titration schemes’, CPT: Pharmacometrics and Systems Pharmacology [Preprint]. Available at: https://doi.org/10.1002/psp4.13239.

Möckli, Nathalie et al. (2024) ‘How regulatory frameworks drive differences in home-care agencies: Results from a national multicenter cross-sectional study in Switzerland’, International Journal of Health Planning and Management, 39, pp. 477–501. Available at: https://doi.org/10.1002/hpm.3744.

Moffa, G. et al. (2023) ‘Sexual abuse and psychotic phenomena: A directed acyclic graph analysis of affective symptoms using English national psychiatric survey data’, Psychological Medicine, 53(16), pp. 7817–7826. Available at: https://doi.org/10.1017/S003329172300185X.

Bayer, F. et al. (2023) ‘Network-based clustering unveils interconnected landscapes of genomic and clinical features across myeloid malignancies’. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/2023.10.25.563992.

Giudice, E., Kuipers, J. and Moffa, G. (2023) ‘The dual PC algorithm and the role of Gaussianity for structure learning of Bayesian networks’, International Journal of Approximate Reasoning, 161. Available at: https://doi.org/10.1016/j.ijar.2023.108975.

Ali, F. et al. (2023) Uncovering network structure relating academic achievement, motivation, and schooling experience using structure learning of directed acyclic graphs. Center for Open Science. Available at: https://doi.org/10.35542/osf.io/3e7za.

Moffa Giusi et al. (2023) ‘Longitudinal symptomatic interactions in long-standing schizophrenia: A novel five-point analysis based on directed acyclic graphs’, Psychological Medicine, 53(4), pp. 1371–1378. Available at: https://doi.org/10.1017/S0033291721002920.

Suter, P. et al. (2023) ‘Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG’, Journal of Statistical Software, 105(9), pp. 1–31. Available at: https://doi.org/10.18637/jss.v105.i09.

Haak, Fabian et al. (2022) ‘Incidence of Liver Resection Following the Introduction of Caseload Requirements for Liver Surgery in Switzerland’, World Journal of Surgery, 46(6), pp. 1457–1464. Available at: https://doi.org/10.1007/s00268-022-06509-w.

Schmitt AM et al. (2022) ‘Characteristics and survival of patients with cancer with intended off-label use - A cohort study’, BMJ Open, 12(5), p. e060453. Available at: https://doi.org/10.1136/bmjopen-2021-060453.

Schmitt, Andreas Michael et al. (2022) ‘Characteristics and survival of patients with cancer with intended off-label use - A cohort study’, BMJ Open, 12(5), p. e060453. Available at: https://doi.org/10.1136/bmjopen-2021-060453.

Giudice, Enrico, Kuipers, Jack and Moffa, Giusi (2022) ‘The Dual PC Algorithm for Structure Learning’. ML Research Press, pp. 301–312.

Gryaznov, D. et al. (2022) ‘Reporting quality of clinical trial protocols: a repeated cross-sectional study about the Adherence to SPIrit recommendations in Switzerland, CAnada and GErmany (ASPIRE-SCAGE)’, BMJ Open, 12(5), p. e053417. Available at: https://doi.org/10.1136/bmjopen-2021-053417.

Kuipers, Jack and Moffa, Giusi (2022) ‘The variance of causal effect estimators for binary v-structures’, Journal of Causal Inference, 10(1), pp. 90–105. Available at: https://doi.org/10.1515/jci-2021-0025.

Kuipers, Jack, Suter, Polina and Moffa, Giusi (2022) ‘Efficient Sampling and Structure Learning of Bayesian Networks’, Journal of Computational and Graphical Statistics, 31(3), pp. 639–650. Available at: https://doi.org/10.1080/10618600.2021.2020127.

Sharma, Narayan et al. (2022) ‘The effect of time-varying capacity utilization on 14-day in-hospital mortality: a retrospective longitudinal study in Swiss general hospitals’, BMC Health Services Research, 22(1), p. 1551. Available at: https://doi.org/10.1186/s12913-022-08950-y.

Kappos, Elisabeth A et al. (2021) ‘Prepectoral versus subpectoral implant-based breast reconstruction after skin-sparing mastectomy or nipple-sparing mastectomy (OPBC-02/ PREPEC): A pragmatic, multicentre, randomised, superiority trial’, BMJ Open, 11(9), p. e045239. Available at: https://doi.org/10.1136/bmjopen-2020-045239.

Kaech M et al. (2021) ‘Influence of the introduction of caseload requirements on indication for visceral cancer surgery in Switzerland’, European Journal of Surgical Oncology, 47(6), pp. 1324–1331. Available at: https://doi.org/10.1016/j.ejso.2021.04.006.

Glinz, Dominik et al. (2021) ‘Antibiotic prescription monitoring and feedback in primary care in Switzerland: Design and rationale of a nationwide pragmatic randomized controlled trial’, Contemporary Clinical Trials Communications, 21, p. 100712. Available at: https://doi.org/10.1016/j.conctc.2021.100712.

Deutschmann, Elisabeth et al. (2021) ‘Prevalence of Potential Drug-Drug Interactions in Patients of the Swiss HIV Cohort Study in the Era of HIV Integrase Inhibitors’, Clinical Infectious Diseases, 73(7), pp. e2145–e2152. Available at: https://doi.org/10.1093/cid/ciaa918.

Herbrand, Amanda Katherina et al. (2021) ‘Association of Supporting Trial Evidence and Reimbursement for Off-Label Use of Cancer Drugs’, Jama Network Open, 4(3), p. e210380. Available at: https://doi.org/10.1001/jamanetworkopen.2021.0380.

Luo, Xiang Ge, Moffa, Giusi and Kuipers, Jack (2021) ‘Learning Bayesian networks from ordinal data’, Journal of Machine Learning Research, 22.

Rios, Felix L., Moffa, Giusi and Kuipers, Jack (2021) ‘Benchpress: a scalable and platform-independent workflow for benchmarking structure learning algorithms for graphical models’. arXiv. Available at: https://arxiv.org/abs/2107.03863.

Gryaznov, D. et al. (2020) ‘Rationale and design of repeated cross-sectional studies to evaluate the reporting quality of trial protocols: the Adherence to SPIrit REcommendations (ASPIRE) study and associated projects’, Trials, 21(1), p. 896. Available at: https://doi.org/10.1186/s13063-020-04808-y.

Kasenda, Benjamin et al. (2020) ‘Prediction of RECRUITment In Randomized Clinical Trials (RECRUIT-IT) - Rationale and Design for an International Collaborative Study’, Trials, 21(1), p. 731. Available at: https://doi.org/10.1186/s13063-020-04666-8.

Livio, Françoise et al. (2020) ‘Analysis of inappropriate prescribing in elderly patients of the Swiss HIV Cohort Study reveals gender inequity’, The journal of antimicrobial chemotherapy, p. dkaa505. Available at: https://doi.org/10.1093/jac/dkaa505.

von Strauss und Torney, Marco et al. (2020) ‘Risk of Emergency Surgery or Death After Initial Nonoperative Management of Complicated Diverticulitis in Scotland and Switzerland’, JAMA Surgery, 155(7), pp. 600–606. Available at: https://doi.org/10.1001/jamasurg.2020.0757.

Kuipers, J. et al. (2019) ‘Links between Psychotic and Neurotic Symptoms in the General Population: An Analysis of Longitudinal British National Survey Data Using Directed Acyclic Graphs’, Psychological medicine, 49(3), pp. 388–395. Available at: https://doi.org/10.1017/s0033291718000879.

Schaefer, Thorsten et al. (2019) ‘Regulation of Glioma Cell Invasion by 3q26 Gene Products PIK3CA, SOX2 and OPA1’, Brain Pathology, 29(3), pp. 336–350. Available at: https://doi.org/10.1111/bpa.12670.

Speich, Benjamin et al. (2019) ‘Reporting Quality of Journal Abstracts for Surgical Randomized Controlled Trials Before and After the Implementation of the CONSORT Extension for Abstracts’, World Journal of Surgery, 43(10), pp. 2371–2378. Available at: https://doi.org/10.1007/s00268-019-05064-1.

Steffen, Lukas et al. (2019) ‘Secondary Hyperparathyroidism: Recurrence after Total Parathyroidectomy with Autotransplantation’, Swiss Medical Weekly, 149, p. w20160. Available at: https://doi.org/10.4414/smw.2019.20160.

Kuipers, Jack et al. (2018) ‘Mutational Interactions Define Novel Cancer Subgroups’, Nature Communications, 9(1), p. 4353. Available at: https://doi.org/10.1038/s41467-018-06867-x.

Roethlisberger, Michel et al. (2018) ‘Effectiveness of a Chlorhexidine Dressing on Silver-coated External Ventricular Drain-associated Colonization and Infection: A Prospective Single-blinded Randomized Controlled Clinical Trial’, Clinical Infectious Diseases, 67(12), pp. 1868–1877. Available at: https://doi.org/10.1093/cid/ciy393.

Kuipers, J. et al. (2017) Mutational interactions define novel cancer subgroups. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/187260.

Kuipers, Jack and Moffa, Giusi (2017) ‘Partition MCMC for Inference on Acyclic Digraphs’, Journal of the American Statistical Association, 112(417), pp. 282–299. Available at: https://doi.org/10.1080/01621459.2015.1133426.

Moffa, Giusi et al. (2017) ‘Using Directed Acyclic Graphs in Epidemiological Research in Psychosis: An Analysis of the Role of Bullying in Psychosis’, Schizophrenia Bulletin, 43(6), pp. 1273–1279. Available at: https://doi.org/10.1093/schbul/sbx013.

Moffa, Giusi et al. (2016) ‘Refining Pathways: A Model Comparison Approach’, PLoS ONE, 11(6), p. e0155999. Available at: https://doi.org/10.1371/journal.pone.0155999.

Kuipers, Jack and Moffa, Giusi (2015) ‘Uniform Random Generation of Large Acyclic Digraphs’, Statistics and Computing, 25, pp. 227–242. Available at: https://doi.org/10.1007/s11222-013-9428-y.

Kuipers, Jack, Moffa, Giusi and Heckerman, David (2014) ‘Addendum on the Scoring of Gaussian Directed Acyclic Graphical Models’, The Annals of Statistics, 42(4), pp. 1689–1691. Available at: https://doi.org/10.1214/14-aos1217.

Moffa, Giusi and Kuipers, Jack (2014) ‘Sequential Monte Carlo EM for Multivariate Probit Models’, Computational Statistics & Data Analysis, 72, pp. 252–272. Available at: https://doi.org/10.1016/j.csda.2013.10.019.