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1. Retinoblastoma with and without Extraocular Tumor Extension

Author: Swathi Kaliki, MD, Vijitha S. Vempuluru, MD, Ido Didi Fabian, MD, Elhassan Abdallah, MD, Shehu U. Abdullahi, MD, Rula A. Abdulqader, MD, Aminatu A. Abdulrahaman, MD, Sherif Abouelnaga, MD, Dupe S. Ademola-Popoola, FMCOph, FWACS, Adedayo Adio, FWACS, Mahmoud A. Afifi, MD, Armin R. Afshar, MD, Priyanka Aggarwal, MD, Ada E. Aghaji, FMCOph MSc, Alia Ahmad, MRCPCH UK, Marliyanti N.R. Akib, MD, Adeseye M. Akinsete, MBBS, Lamis Al Harby, MD, Saleh A. Al Mesfer, MD, Mouroge H. Al Ani, MD, Silvia Alarcón Portabella, MD, Safaa A.F. Al-Badri, MD, Ana Patricia A. Alcasabas, MD, Saad A. Al-Dahmash, MD, Amanda Alejos, MD, Ernesto Alemany-Rubio, MD, Amadou I. Alfa Bio, MD, Yvania Alfonso Carreras, MD, Christiane E. Al-Haddad, MD, Hamoud H.Y. Al-Hussaini, MD, MSc, Amany M. Ali, MD, Donjeta B. Alia, MD, Mazin F. Al-Jadiry, MD, Usama Al-Jumaly, MD, Hind M. Alkatan, MD, Charlotta All-Eriksson, MD, PhD, Ali A.R.M. Al-Mafrachi, FIBMS, Argentino A. Almeida, MD, Khalifa M. Alsawidi, MD, Athar A.S.M. Al-Shaheen, MD, Entissar H. Al-Shammary, MD, Doreen Amankwaa-Frempong, MBChB, Primawita O. Amiruddin, MD, Inggar Armytasari, MD, Nicholas J. Astbury, FRCS, FRCOphth, Hatice T. Atalay, MD, Eda Ataseven, MD, La-ongsri Atchaneeyasakul, MD, Rose Atsiaya, OCO, Rudolf Autrata, MD, PhD, Julia Balaguer, MD, PhD, Ruhengiz Balayeva, PhD, Honorio Barranco, MD, PhD, Paulina Bartoszek, MD, Katarina Bartuma, MD, PhD, Covadonga Bascaran, MD, MSc, Nikolaos E. Bechrakis, MD, Maja Beck Popovic, MD, Ainura S. Begimkulova, MD, Sarra Benmiloud, MD, Rokia C. Berete, MD, PhD, Jesse L. Berry, MD, Anirban Bhaduri, MD, Sunil Bhat, MBBS, MD, Arpita Bhattacharyya, MD, Eva M. Biewald, MD, Elaine Binkley, MD, Sharon Blum, MD, Nadia Bobrova, MD, H. Culver Boldt, MD, Maria Teresa B.C. Bonanomi, MD, PhD, Gabrielle C. Bouda, MD, Hédi Bouguila, MD, PhD, Rachel C. Brennan, MD, Bénédicte G. Brichard, MD, PhD, Jassada Buaboonnam, MD, Aléine Budiongo, MD, Matthew Burton, FRCOphth, Patricia Calderón-Sotelo, MD, Doris A. Calle Jara, MD, Jayne E. Camuglia, FRANZCO, Miriam R. Cano, MD, MSc, Michael Capra, FRCPI, Shani Caspi, MD, Nathalie Cassoux, MD, PhD, Guilherme Castela, MD, Luis Castillo, MD, Jaume Català-Mora, MD, PhD, Isabel Caviedes, MD, Arthika Chandramohan, MD, Guillermo L. Chantada, MD, PhD, Shabana Chaudhry, MD, Bhavna Chawla, MD, Wensi Chen, MD, Faraja S. Chiwanga, MSc, Tsengelmaa Chuluunbat, MD, PhD, Krzysztof Cieslik, MD, Antony Clark, FRANZCO, Ruellyn L. Cockcroft, MB ChB , M Med Paed, Codruta Comsa, MD, Maria G. Correa Llano, MD, Timothy W. Corson, PhD, Line Couitchere, MD, Kristin E. Cowan-Lyn, MD, MBBS, Monika Csóka, MD, PhD, Wantanee Dangboon, MD, Anirban Das, MD, Pranab Das, MD, Sima Das, MS, Jacquelyn M. Davanzo, BSN, BSPH, Alan Davidson, MBChB, MPhil, Sonia De Francesco, MD, Patrick De Potter, MD, PhD, Karina Q. Delgado, MD, PhD, Hakan Demirci, MD, Laurence Desjardins, MD, Rosdali Y. Diaz Coronado, MD, Helen Dimaras, PhD, Andrew J. Dodgshun, M Phil, Carla R. Donato Macedo, MD, Monica D. Dragomir, MD, PhD, Yi Du, MD, Magritha Du Bruyn, MD, Johannes P. Du Plessis, MMed (Paed), Gagan Dudeja, MBBS, MS, Katrin Eerme, MD, I Wayan Eka Sutyawan, MD, Asmaa El Kettani, MD, Amal M. Elbahi, MD, James E. Elder, MBBS, Alaa M. Elhaddad, MD, PhD, Moawia M.A. Elhassan, MD, Mahmoud M. Elzembely, MD, Connor Ericksen, MD, Vera A. Essuman, FWACS, Ted Grimbert A. Evina, MD, Ifeoma R. Ezegwui, FMCOph, FWACS, FAEH, Zehra Fadoo, MBBS, Adriana C. Fandiño, MD, Mohammad Faranoush, MD, Oluyemi Fasina, FWACS, Delia D.P.G. Fernández, MSc, Ana Fernández-Teijeiro, MD, PhD, Allen Foster, FRCOphth, Shahar Frenkel, MD, PhD, Ligia D. Fu, MD, Soad L. Fuentes-Alabi, MD, MPH, Juan L. Garcia, MSc, David García Aldana, MD, Henry N. Garcia Pacheco, MD, Jennifer A. Geel, MBChB, MMed, Fariba Ghassemi, MD, Ana V. Girón, MD, Marco A. Goenz, MD, Aaron S. Gold, OD, Hila Golberg, MD, Glen A. Gole, MD, FRANZCO, Nir Gomel, MD, Efren Gonzalez, MD, Graciela Gonzalez Perez, MD, Liudmira González-Rodríguez, MD, Malka Gorfine, PhD, Jaime Graells, MD, Pernille A. Gregersen, MD, Nathalia D.A.K. Grigorovski, MD, Koffi M. Guedenon, MD, D Sanjeeva Gunasekera, MD, Ahmet K. Gündüz, MD, Himika Gupta, MD, Sanjiv Gupta, MS, Vineeta Gupta, MD, Theodora Hadjistilianou, MD, Patrick Hamel, MD, Syed A. Hamid, FCPS, Norhafizah Hamzah, MSc, Eric D. Hansen, MD, J William Harbour, MD, M. Elizabeth Hartnett, MD, Murat Hasanreisoglu, MD, Sadiq Hassan, MD, FWACS, Shadab Hassan, FRCS, FCPS, Wojciech Hautz, MD, Huda A. Haydar, CHD, Stanislava Hederova, MD, Laila Hessissen, MD, Hoby Lalaina, MD, Suradej Hongeng, MD, Diriba F. Hordofa, MD, G. Baker Hubbard, MD, Marlies Hummlen, MD, Kristina Husakova, MD, Allawi N. Hussein Al-Janabi, MD, Affiong A. Ibanga, MB.BCh, FMCOph, Russo Ida, MD, Vesna R. Ilic, MD, Ziyavuddin Islamov, MD, Vivekaraj Jairaj, DNB, Teyyeb A. Janjua, MD, FCPS, FRCSEd, Irfan Jeeva, FRCOphth, Xunda Ji, MD, Dong Hyun Jo, MD, PhD, Michael M. Jones, MD, PhD, FRANZCO, Theophile B. Amani Kabesha, MD, PhD, Rolande L. Kabore, MD, Abubakar Kalinaki, MD, Pius Kamsang, MD, Mehmet Kantar, MD, Noa Kapelushnik, MD, Tamar Kardava, PhD, Rejin Kebudi, MD, Jonny Keomisy, MD, Tomas Kepak, MD, Petra Ketteler, MD, Zohora J. Khan, MD, Hussain A. Khaqan, MD, Vikas Khetan, FRCS, FACS, Alireza Khodabande, MD, Zaza Khotenashvili, MD, Jonathan W. Kim, MD, Jeong Hun Kim, MD, PhD, Hayyam Kiratli, MD, Tero T. Kivelä, MD, Artur Klett, MD, PhD, Irem Koç, MD, Jess Elio Kosh Komba Palet, MD, Dalia Krivaitiene, MD, PhD, Mariana Kruger, Mmed Paed, PhD, Kittisak Kulvichit, MD, Mayasari W. Kuntorini, MD, Alice Kyara, BA, Geoffrey C. Lam, FRANZCO, Scott A. Larson, MD, Slobodanka Latinović, MD, PhD, Kelly D. Laurenti, MD, Yotam Lavi, MD, PhD, Alenka Lavric Groznik, MD, Amy A. Leverant, MD, Cairui Li, MD, Kaijun Li, MD, Ben Limbu, MD, Chun-Hsiu Liu, MD, Quah Boon Long, FRCS (Ed), MMed ( Ophth), FAMS, Juan P. López, MD, Robert M. Lukamba, MD, Sandra Luna-Fineman, MD, Delfitri Lutfi, MD, Lesia Lysytsia, MD, Shiran Madgar, MD, George N. Magrath, MD, Amita Mahajan, MD, Puja Maitra, MD, Erika Maka, MD, Emil K. Makimbetov, MD, Azza M.Y. Maktabi, MD, Carlos Maldonado, MD, Ashwin Mallipatna, MD, Rebecca Manudhane, MD, Lyazat Manzhuova, MD, Nieves Martín Begue, MD, PhD, Sidra Masud, MBBS, Ibrahim O. Matende, MD, M. Med (Oph), Clarissa C.D.S. Mattosinho, MD, Marchelo Matua, BAPH, Ismail Mayet, MD, Freddy B. Mbumba, MD, MMed Paed, John D. McKenzie, MD, Azim Mehrvar, MD, Aemero A. Mengesha, MD, Vikas Menon, MD, Gary John V.D.D. Mercado, MD, Marilyn B. Mets, MD, Edoardo Midena, MD, PhD, Audra Miller, MD, Divyansh K.C. Mishra, DNB, Furahini G. Mndeme, MD, Ahmed A. Mohamedani, FRCPath, Mona T. Mohammad, MD, FRCS, Annette C. Moll, MD, PhD, Margarita M. Montero, MD, Claude Moreira, MD, PhD, Prithvi Mruthyunjaya, MD, MHS, Mchikirwa S. Msina, MMed Ophth, Gerald Msukwa, MMed Ophth, Sangeeta S. Mudaliar, DNB Pediatric, Hassan Muhammad, MD, Kangwa I. Muma, MMed Ophth, FCOphth, Francis L. Munier, MD, Timothy G. Murray, MD, MBA, Kareem O. Musa, FWACS, FMCOphth, FICO, Asma Mushtaq, MD, Anne A. Musika, MD, Hamzah Mustak, MD, Tajudeen Mustapha, MBBS, FWACS, Okwen M. Muyen, MD, Khumo H. Myezo, Msc, Gita Naidu, MMed Paed, PhD, Natasha Naidu, MBCHB, FCS Ophthalmol, Akshay Gopinathan Nair, MD, Sundaram Natarajan, FRCS, Larisa Naumenko, MD, PhD, Paule Aïda Ndoye Roth, MD PhD, Yetty M. Nency, MD, Vladimir Neroev, MD, PhD, Yvonne Ng, MBChB ( Auckland) , FRANZCO, Marina Nikitovic, MD, PhD, Elizabeth D. Nkanga, FMCOph, Henry E. Nkumbe, MD, Marcel N. Numbi, MD, Kalle Nummi, MD, Murtuza Nuruddin, FRCS, Mutale Nyaywa, MD, MMed Ophth, FCOphth, Chinsisi Nyirenda, MD, Ghislaine Obono-Obiang, MD, Scott C.N. Oliver, MD, Joaquin Ooporto, MD, Miriam Ortega-Hernández, MD, Alexander Oscar, MD, Diego Ossandon, MD, Halimah Pagarra, MD, PhD, Vivian Paintsil, FWACP, Luisa Paiva, MD, Mahesh Shanmugam Palanivelu, FRCSED, Ruzanna Papyan, MD, Raffaele Parrozzani, MD, PhD, Claudia R. Pascual Morales, MD, Katherine E. Paton, MD, FRCSC, Jacob Pe'er, MD, Jesús Peralta Calvo, MD, Sanja Perić, MD, PhD, Chau T.M. Pham, MD, Remezo Philbert, MD, David A. Plager, MD, Pavel Pochop, MD, PhD, Rodrigo A. Polania, MD, Vladimir Polyakov, MD, Jimena Ponce, MD, Ali O. Qadir, MD, Seema Qayyum, FCPS, Jiang Qian, MD, Ardizal Rahman, MD, Purnima Rajkarnikar, MD, Rajesh Ramanjulu, MD, Aparna Ramasubramanian, MD, Marco A. Ramirez-Ortiz, MD, MPH, Jasmeen K. Randhawa, BA, Léa Raobela, MD, Riffat Rashid, MS, M. Ashwin Reddy, FRCOphth, Lorna A. Renner, FRCPCH (UK), David Reynders, MD, Dahiru Ribadu, FMCOph, Petra Ritter-Sovinz, MD, Anna Rogowska, MD, Duangnate Rojanaporn, MD, Livia Romero, MD, Soma R. Roy, DCO, Raya H. Saab, MD, Svetlana Saakyan, MD, PhD, Ahmed H. Sabhan, MD, Mandeep S. Sagoo, FRCS (Ed), Azza M.A. Said, MD, Rohit Saiju, MD, Beatriz Salas, MD, Sonsoles San Román Pacheco, MD, Gissela L. Sánchez, MD, Alma Janeth Sanchez Orozco, MD, Phayvanh Sayalith, MD, Trish A. Scanlan, MRCPI, MSc, Christoph Schwab, MD, Ahad Sedaghat, MD, Rachna Seth, DNB MNAMS, Mariana Sgroi, MD, Ankoor S. Shah, MD, PhD, Shawkat A. Shakoor, MS, Manoj K. Sharma, MD, Sadik T. Sherief, MD, Carol L. Shields, MD, David Sia, MB ChB, FRANZCO, Sorath Noorani Siddiqui, MD, Sidi Sidi cheikh, MD, PhD, Sónia Silva, MD, Arun D. Singh, MD, Usha Singh, MS, Penny Singha, MD, Rita S. Sitorus, MD, PhD, Alison H. Skalet, MD, PhD, Hendrian D. Soebagjo, MD, PhD, Tetyana Sorochynska, MD, PhD, Grace Ssali, MD, Andrew W. Stacey, MD, Sandra E. Staffieri, PhD, Erin D. Stahl, MD, David M. Steinberg, PhD, David K. Stones, MBChB, FCPaed, Caron Strahlendorf, MD, Maria Estela Coleoni Suarez, MD, Sadia Sultana, FCPS, Xiantao Sun, MD, Rosanne Superstein, MD, Eddy Supriyadi, MD, PhD, Supawan Surukrattanaskul, MD, Shigenobu Suzuki, MD, PhD, Karel Svojgr, MD, PhD, Fatoumata Sylla, MD, Gevorg Tamamyan, MD, PhD, Deborah Tan, MBBS, Alketa Tandili, MD, PhD, Jing Tang, MD, Fanny F. Tarrillo Leiva, MD, Maryam Tashvighi, MD, Bekim Tateshi, MD, PhD, Kok Hoi Teh, MD, Edi S. Tehuteru, MD, Luiz F. Teixeira, MD, Manca Tekavcic Pompe, MD, PhD, Abdullah Dahan M. Thawaba, MD, Tuyisabe Theophile, MSc, Helen Toledano, MBChB, Doan L. Trang, MD, Fousseyni Traoré, MD, Devjyoti Tripathy, MD, Samuray Tuncer, MD, Harba Tyau-Tyau, MD, Ali B. Umar, MD, FMCPath, Emel Unal, MD, Ogul E. Uner, BA, Steen F. Urbak, MD, PhD, Tatiana L. Ushakova, MD, Rustam H. Usmanov, MD, Sandra Valeina, MD, Paola Valente, MD, Milo van Hoefen Wijsard, MD, Jacqueline Karina Vasquez Anchaya, MD, Leon O. Vaughan, FRCS (Ed), Nevyana V. Veleva-Krasteva, MD, PhD, Nishant Verma, MD, Andi A. Victor, MD, PhD, Maris Viksnins, MD, Edwin G. Villacís Chafla, MD, Victor M. Villegas, MD, Victoria Vishnevskia-Dai, MD, Keith Waddell, DM, FRCP, FRCS, FRCOphth, Amina H. Wali, MD, FMCOph Nigeria, Yi-Zhuo Wang, MD, Nutsuchar Wangtiraumnuay, MD, FICO, Julie A. Wetter, MMed Rad Onc, FCRad Onc, Widiarti P. Riono, MD, Matthew W. Wilson, MD, Amelia D.C. Wime, MD, Atchareeya Wiwatwongwana, MD, Damrong Wiwatwongwana, MD, Charlotte Wolley Dod, MD, Emily S. Wong, FCOphth HK, FHKAM, Phanthipha Wongwai, MD, PhD, Si-qi Wu, MSc, Daoman Xiang, MD, PhD, Yishuang Xiao, MSc, Bing Xu, MD, Kang Xue, MD, Antonio Yaghy, MD, Jason C. Yam, FRCSEd, Huasheng Yang, MD, Jenny M. Yanga, MD, Muhammad A. Yaqub, MD, FCPS, FRCSEd, Vera A. Yarovaya, MD, Andrey A. Yarovoy, MD, PhD, Huijing Ye, MD, Roberto I. Yee, MD, Yacoub A. Yousef, MD, Putu Yuliawati, MD, Arturo M. López, MD, Ekhtelbenina Zein, MD, Yi Zhang, MD, PhD, Katsiaryna Zhilyaeva, MD, Nida Zia, MBBS, MCPS, Othman A.O. Ziko, MD, PhD, Marcia Zondervan, MBA, Sabrina Schlüter, MD, and Richard Bowman, FRCOphth
Subjects: External beam radiotherapy, Extraocular extension, Multimodal treatment, Retinoblastoma, Tumor, Ophthalmology, RE1-994
Abstract: Purpose: To study the treatment and outcomes of children with retinoblastoma (RB) with extraocular tumor extension (RB-EOE) and compare them with RB without extraocular tumor extension (RB-w/o-EOE). Design: Multicenter intercontinental collaborative prospective study from 2017 to 2020. RB-EOE cases included those with overt orbital tumor extension in treatment-naive patients. Cases with microscopic orbital extension detected postenucleation were excluded from the study. Participants: A total of 319 children with RB-EOE and 3116 children with RB-w/o-EOE. Intervention: Chemotherapy, enucleation, exenteration, radiotherapy. Main Outcome Measures: Systemic metastasis and death. Results: Of the 3435 RB patients included in this study, 309 (9%) were from low-income countries (LIC), 1448 (42%) from lower-middle income, 1012 (29%) from upper-middle income, and 666 (19%) patients from high-income countries. There was an inverse relationship between the percentage of RB-EOE and national income level, with 96 (31%) patients from LIC, 197 (6%) lower-middle income, 20 (2%) upper-middle income, and 6 (1%) patients from high-income countries (P = 0.0001). The outcomes were statistically significant for RB-EOE compared with RB-w/o-EOE: systemic metastasis (32% vs. 4% respectively; P = 0.0001) and metastasis-related death (63% vs. 6% respectively; P = 0.0001). Multimodal treatment was the most common form of treatment (n = 177; 54%) for RB-EOE, with most cases undergoing a combination of intravenous chemotherapy and enucleation (n = 97; 30%). Adjuvant external beam radiotherapy (EBRT) after surgery (enucleation/orbital exenteration) was given in only 68 (21%) cases. Kaplan–Meier analysis for systemic metastasis and metastasis-related death in RB-EOE was 28% and 57% at 1 year, 29% and 60% at 2 years, and 29% and 61% at 3 years, respectively. Cox regression analysis revealed that the risk of death from RB-EOE was greater in patients aged >4 years than
Published: 2025
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2. Hospital load and increased COVID-19 related mortality in Israel

Author: Hagai Rossman, Tomer Meir, Jonathan Somer, Smadar Shilo, Rom Gutman, Asaf Ben Arie, Eran Segal, Uri Shalit, and Malka Gorfine
Subjects: Science
Abstract: COVID-19 has caused many healthcare systems to become overwhelmed, potentially impacting patient care. Here, the authors show that COVID-19-related in-hospital mortality rates in Israel increased in periods of moderate or high hospital load, independent of patient characteristics.
Published: 2021
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3. General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv

Author: John V. Monaco, Malka Gorfine, and Li Hsu
Subjects: shared frailty model, survival analysis, clustered data, frailtysurv, Statistics, HA1-4737
Abstract: The R package frailtySurv for simulating and fitting semi-parametric shared frailty models is introduced. Package frailtySurv implements semi-parametric consistent estimators for a variety of frailty distributions, including gamma, log-normal, inverse Gaussian and power variance function, and provides consistent estimators of the standard errors of the parameters' estimators. The parameters' estimators are asymptotically normally distributed, and therefore statistical inference based on the results of this package, such as hypothesis testing and confidence intervals, can be performed using the normal distribution. Extensive simulations demonstrate the flexibility and correct implementation of the estimator. Two case studies performed with publicly available datasets demonstrate applicability of the package. In the Diabetic Retinopathy Study, the onset of blindness is clustered by patient, and in a large hard drive failure dataset, failure times are thought to be clustered by the hard drive manufacturer and model.
Published: 2018
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4. Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome.

Author: Malka Gorfine, Sonja I Berndt, Jenny Chang-Claude, Michael Hoffmeister, Loic Le Marchand, John Potter, Martha L Slattery, Nir Keret, Ulrike Peters, and Li Hsu
Subjects: Medicine, Science
Abstract: The popular Genome-wide Complex Trait Analysis (GCTA) software uses the random-effects models for estimating the narrow-sense heritability based on GWAS data of unrelated individuals without knowing and identifying the causal loci. Many methods have since extended this approach to various situations. However, since the proportion of causal loci among the variants is typically very small and GCTA uses all variants to calculate the similarities among individuals, the estimation of heritability may be unstable, resulting in a large variance of the estimates. Moreover, if the causal SNPs are not genotyped, GCTA sometimes greatly underestimates the true heritability. We present a novel narrow-sense heritability estimator, named HERRA, using well-developed ultra-high dimensional machine-learning methods, applicable to continuous or dichotomous outcomes, as other existing methods. Additionally, HERRA is applicable to time-to-event or age-at-onset outcome, which, to our knowledge, no existing method can handle. Compared to GCTA and LDAK for continuous and binary outcomes, HERRA often has a smaller variance, and when causal SNPs are not genotyped, HERRA has a much smaller empirical bias. We applied GCTA, LDAK and HERRA to a large colorectal cancer dataset using dichotomous outcome (4,312 cases, 4,356 controls, genotyped using Illumina 300K), the respective heritability estimates of GCTA, LDAK and HERRA are 0.068 (SE = 0.017), 0.072 (SE = 0.021) and 0.110 (SE = 5.19 x 10-3). HERRA yields over 50% increase in heritability estimate compared to GCTA or LDAK.
Published: 2017
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5. A novel host-proteome signature for distinguishing between acute bacterial and viral infections.

Author: Kfir Oved, Asi Cohen, Olga Boico, Roy Navon, Tom Friedman, Liat Etshtein, Or Kriger, Ellen Bamberger, Yura Fonar, Renata Yacobov, Ron Wolchinsky, Galit Denkberg, Yaniv Dotan, Amit Hochberg, Yoram Reiter, Moti Grupper, Isaac Srugo, Paul Feigin, Malka Gorfine, Irina Chistyakov, Ron Dagan, Adi Klein, Israel Potasman, and Eran Eden
Subjects: Medicine, Science
Abstract: Bacterial and viral infections are often clinically indistinguishable, leading to inappropriate patient management and antibiotic misuse. Bacterial-induced host proteins such as procalcitonin, C-reactive protein (CRP), and Interleukin-6, are routinely used to support diagnosis of infection. However, their performance is negatively affected by inter-patient variability, including time from symptom onset, clinical syndrome, and pathogens. Our aim was to identify novel viral-induced host proteins that can complement bacterial-induced proteins to increase diagnostic accuracy. Initially, we conducted a bioinformatic screen to identify putative circulating host immune response proteins. The resulting 600 candidates were then quantitatively screened for diagnostic potential using blood samples from 1002 prospectively recruited patients with suspected acute infectious disease and controls with no apparent infection. For each patient, three independent physicians assigned a diagnosis based on comprehensive clinical and laboratory investigation including PCR for 21 pathogens yielding 319 bacterial, 334 viral, 112 control and 98 indeterminate diagnoses; 139 patients were excluded based on predetermined criteria. The best performing host-protein was TNF-related apoptosis-inducing ligand (TRAIL) (area under the curve [AUC] of 0.89; 95% confidence interval [CI], 0.86 to 0.91), which was consistently up-regulated in viral infected patients. We further developed a multi-protein signature using logistic-regression on half of the patients and validated it on the remaining half. The signature with the highest precision included both viral- and bacterial-induced proteins: TRAIL, Interferon gamma-induced protein-10, and CRP (AUC of 0.94; 95% CI, 0.92 to 0.96). The signature was superior to any of the individual proteins (P
Published: 2015
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6. Function of cancer associated genes revealed by modern univariate and multivariate association tests.

Author: Malka Gorfine, Boaz Goldstein, Alla Fishman, Ruth Heller, Yair Heller, and Ayelet T Lamm
Subjects: Medicine, Science
Abstract: Copy number variation (CNV) plays a role in pathogenesis of many human diseases, especially cancer. Several whole genome CNV association studies have been performed for the purpose of identifying cancer associated CNVs. Here we undertook a novel approach to whole genome CNV analysis, with the goal being identification of associations between CNV of different genes (CNV-CNV) across 60 human cancer cell lines. We hypothesize that these associations point to the roles of the associated genes in cancer, and can be indicators of their position in gene networks of cancer-driving processes. Recent studies show that gene associations are often non-linear and non-monotone. In order to obtain a more complete picture of all CNV associations, we performed omnibus univariate analysis by utilizing dCov, MIC, and HHG association tests, which are capable of detecting any type of association, including non-monotone relationships. For comparison we used Spearman and Pearson association tests, which detect only linear or monotone relationships. Application of dCov, MIC and HHG tests resulted in identification of twice as many associations compared to those found by Spearman and Pearson alone. Interestingly, most of the new associations were detected by the HHG test. Next, we utilized dCov's and HHG's ability to perform multivariate analysis. We tested for association between genes of unknown function and known cancer-related pathways. Our results indicate that multivariate analysis is much more effective than univariate analysis for the purpose of ascribing biological roles to genes of unknown function. We conclude that a combination of multivariate and univariate omnibus association tests can reveal significant information about gene networks of disease-driving processes. These methods can be applied to any large gene or pathway dataset, allowing more comprehensive analysis of biological processes.
Published: 2015
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7. Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning.

Author: Asaf Ben Arie and Malka Gorfine
Published: 2024
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8. Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study.

Author: Michael Roimi, Rom Gutman, Jonathan Somer, Asaf Ben Arie, Ido Calman, Yaron Bar-Lavie, Udi Gelbshtein, Sigal Liverant-Taub, Arnona Ziv, Danny Eytan, Malka Gorfine, and Uri Shalit
Published: 2021
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9. PyDTS: A Python Package for Discrete Time Survival Analysis with Competing Risks.

Author: Tomer Meir, Rom Gutman, and Malka Gorfine
Published: 2022
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10. Analyzing Big EHR Data - Optimal Cox Regression Subsampling Procedure with Rare Events

Author: Nir Keret and Malka Gorfine
Subjects: Statistics and Probability, Statistics, Probability and Uncertainty
Published: 2023
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11. PyMSM: Python package for Competing Risks and Multi-State models for Survival Data.

Author: Hagai Rossman, Ayya Keshet, and Malka Gorfine
Published: 2022
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12. An accelerated failure time regression model for illness–death data: A frailty approach

Author: Lea Kats and Malka Gorfine
Subjects: Statistics and Probability, General Immunology and Microbiology, Applied Mathematics, General Medicine, General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology
Published: 2023
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13. Consistent Distribution-Free $K$-Sample and Independence Tests for Univariate Random Variables.

Author: Ruth Heller, Yair Heller, Shachar Kaufman, Barak Brill, and Malka Gorfine
Published: 2016

14. The Role of 68Ga-FAPI PET/CT in Detection of Metastatic Lobular Breast cancer

Author: Yael Eshet, Noam Tau, Sara Apter, Noam Nissan, Keren Levanon, Rinat Bernstein-Molho, Ofer Globus, Amit Itay, Tal Shapira, Cecilie Oedegaard, Malka Gorfine, Michal Eifer, Tima Davidson, Einav Gal-Yam, and Liran Domachevsky
Subjects: Radiology, Nuclear Medicine and imaging, General Medicine
Published: 2023
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15. Causal inference for semi-competing risks data

Author: Daniel Nevo and Malka Gorfine
Subjects: FOS: Computer and information sciences, Statistics and Probability, Apolipoprotein E4, Mathematics - Statistics Theory, Statistics Theory (math.ST), Articles, General Medicine, Statistics - Applications, 01 natural sciences, Methodology (stat.ME), Causality, 010104 statistics & probability, 03 medical and health sciences, Apolipoproteins E, 0302 clinical medicine, Alzheimer Disease, FOS: Mathematics, Humans, Applications (stat.AP), 0101 mathematics, Statistics, Probability and Uncertainty, Statistics - Methodology, Alleles, 030217 neurology & neurosurgery
Abstract: An emerging challenge for time-to-event data is studying semi-competing risks, namely when two event times are of interest: a non-terminal event time (e.g. age at disease diagnosis), and a terminal event time (e.g. age at death). The non-terminal event is observed only if it precedes the terminal event, which may occur before or after the non-terminal event. Studying treatment or intervention effects on the dual event times is complicated because for some units, the non-terminal event may occur under one treatment value but not under the other. Until recently, existing approaches (e.g., the survivor average causal effect) generally disregarded the time-to-event nature of both outcomes. More recent research focused on principal strata effects within time-varying populations under Bayesian approaches. In this paper, we propose alternative non time-varying estimands, based on a single stratification of the population. We present a novel assumption utilizing the time-to-event nature of the data, which is weaker than the often-invoked monotonicity assumption. We derive results on partial identifiability, suggest a sensitivity analysis approach, and give conditions under which full identification is possible. Finally, we present non-parametric and semi-parametric estimation methods for right-censored data., 35 pages, 3 figure, 3 tables
Published: 2021
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16. General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv.

Author: John V. Monaco, Malka Gorfine, and Li Hsu
Published: 2017

17. COVID-19 mRNA Vaccination: Age and Immune Status and Its Association with Axillary Lymph Node PET/CT Uptake

Author: Yousef Alhoubani, Nayroz Kanana, Michal Eifer, Malka Gorfine, Yael Eshet, Liran Domachevsky, Nir Keret, Noam Tau, and Jala Shams
Subjects: Adult, Male, medicine.medical_specialty, Axillary lymph nodes, Gastroenterology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Positron Emission Tomography Computed Tomography, Positive axillary lymph node, Internal medicine, Axillary Lymphadenopathy, Humans, Medicine, Radiology, Nuclear Medicine and imaging, Lymph node, Aged, Retrospective Studies, PET-CT, business.industry, COVID-19, Odds ratio, Middle Aged, Vaccination, medicine.anatomical_structure, 030220 oncology & carcinogenesis, Female, Lymph Nodes, Lymph, business
Abstract: With hundreds of millions of coronavirus disease 2019 (COVID-19) mRNA-based vaccine doses planned to be delivered worldwide in the upcoming months, it is important to recognize positron emission tomography with computed tomography (PET/CT) findings in recently vaccinated immunocompetent or immunocompromised patients. We aimed to assess PET/CT uptake in the deltoid muscle and axillary lymph nodes of patients that received a COVID-19 mRNA-based vaccine, and to evaluate its association with patients' age and immune status. Methods: All consecutive adult subjects undergoing PET/CT scans with any radiotracer at our center during the first month of a national COVID-19 vaccination rollout (between 23 December 2020 and January 27, 2021) were included. Data regarding clinical status, laterality and time interval from recent COVID-19 mRNA vaccination was prospectively collected and retrospectively analyzed, and correlated with deltoid muscle and axillary lymph nodes uptake. Results: Of 426 eligible, recently vaccinated, subjects (median age, 67±12 years; 49% female), 377 (88%) underwent PET/CT with F-18-fluorodeoxyglucose (FDG) and positive axillary lymph node uptake was seen in 45% of them. Multivariate logistic regression analysis revealed a strong inverse association between positive FDG uptake in ipsilateral lymph nodes and patients' age (Odds Ratio [OR]=0.57, 95% CI, 0.45-0.72; p
Published: 2021
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18. COVID-19 dynamics after a national immunization program in Israel

Author: Malka Gorfine, Smadar Shilo, Hagai Rossman, Eran Segal, Tomer Meir, and Uri Shalit
Subjects: 0301 basic medicine, Prioritization, Younger age, Coronavirus disease 2019 (COVID-19), business.industry, Retrospective cohort study, General Medicine, General Biochemistry, Genetics and Molecular Biology, Vaccination, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, 030220 oncology & carcinogenesis, Pandemic, Medicine, Immunization program, Christian ministry, business, Demography
Abstract: Studies on the real-life effect of the BNT162b2 vaccine for Coronavirus Disease 2019 (COVID-19) prevention are urgently needed. In this study, we conducted a retrospective analysis of data from the Israeli Ministry of Health collected between 28 August 2020 and 24 February 2021. We studied the temporal dynamics of the number of new COVID-19 cases and hospitalizations after the vaccination campaign, which was initiated on 20 December 2020. To distinguish the possible effects of the vaccination on cases and hospitalizations from other factors, including a third lockdown implemented on 8 January 2021, we performed several comparisons: (1) individuals aged 60 years and older prioritized to receive the vaccine first versus younger age groups; (2) the January lockdown versus the September lockdown; and (3) early-vaccinated versus late-vaccinated cities. A larger and earlier decrease in COVID-19 cases and hospitalization was observed in individuals older than 60 years, followed by younger age groups, by the order of vaccination prioritization. This pattern was not observed in the previous lockdown and was more pronounced in early-vaccinated cities. Our analysis demonstrates the real-life effect of a national vaccination campaign on the pandemic dynamics.
Published: 2021
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19. Hospital load and increased COVID-19 related mortality in Israel

Author: Asaf Ben Arie, Smadar Shilo, Hagai Rossman, Jonathan Somer, Rom Gutman, Tomer Meir, Eran Segal, Malka Gorfine, and Uri Shalit
Subjects: Adult, Male, 2019-20 coronavirus outbreak, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), Epidemiology, Science, General Physics and Astronomy, 030204 cardiovascular system & hematology, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, 0302 clinical medicine, Patient Load, medicine, Computational models, Humans, 030212 general & internal medicine, Hospital Mortality, Israel, Epidemics, Aged, Aged, 80 and over, Multidisciplinary, business.industry, SARS-CoV-2, Mortality rate, COVID-19, General Chemistry, Middle Aged, Hospitals, Hospitalization, Patient population, Standard error, Viral infection, Emergency medicine, Christian ministry, Female, business, Monte Carlo Method, Healthcare system
Abstract: The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases. Here, we study the ramifications of hospital load due to COVID-19 morbidity on in-hospital mortality of patients with COVID-19 by analyzing records of all 22,636 COVID-19 patients hospitalized in Israel from mid-July 2020 to mid-January 2021. We show that even under moderately heavy patient load (>500 countrywide hospitalized severely-ill patients; the Israeli Ministry of Health defined 800 severely-ill patients as the maximum capacity allowing adequate treatment), in-hospital mortality rate of patients with COVID-19 significantly increased compared to periods of lower patient load (250–500 severely-ill patients): 14-day mortality rates were 22.1% (Standard Error 3.1%) higher (mid-September to mid-October) and 27.2% (Standard Error 3.3%) higher (mid-December to mid-January). We further show this higher mortality rate cannot be attributed to changes in the patient population during periods of heavier load., COVID-19 has caused many healthcare systems to become overwhelmed, potentially impacting patient care. Here, the authors show that COVID-19-related in-hospital mortality rates in Israel increased in periods of moderate or high hospital load, independent of patient characteristics.
Published: 2021

20. Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study

Author: Yaron Bar-Lavie, Rom Gutman, Arnona Ziv, Sigal Liverant-Taub, Asaf Ben Arie, Michael Roimi, Malka Gorfine, Uri Shalit, Ido Calman, Jonathan Somer, Udi Gelbshtein, and Danny Eytan
Subjects: Adult, Male, R software, medicine.medical_specialty, AcademicSubjects/SCI01060, Coronavirus disease 2019 (COVID-19), Hospital bed, Health Informatics, Research and Applications, survival analysis, Machine Learning, Humans, Medicine, Hospital utilization, Registries, Israel, AcademicSubjects/MED00580, Survival analysis, Aged, Proportional Hazards Models, Aged, 80 and over, Models, Statistical, Illness trajectory, business.industry, Proportional hazards model, hospital utilization, COVID-19, Length of Stay, Middle Aged, Prognosis, Hospitals, multistate model, Hospitalization, healthcare facilities, Emergency medicine, Female, AcademicSubjects/SCI01530, business, Resource utilization
Abstract: Objective The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patient characteristics. Materials and Methods We develop a model of patient clinical course based on an advanced multistate survival model. The model predicts the patient's disease course in terms of clinical states—critical, severe, or moderate. The model also predicts hospital utilization on the level of entire hospitals or healthcare systems. We cross-validated the model using a nationwide registry following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1 to May 2, 2020 (n = 2703). Results Per-day mean absolute errors for predicted total and critical care hospital bed utilization were 4.72 ± 1.07 and 1.68 ± 0.40, respectively, over cohorts of 330 hospitalized patients; areas under the curve for prediction of critical illness and in-hospital mortality were 0.88 ± 0.04 and 0.96 ± 0.04, respectively. We further present the impact of patient influx scenarios on day-by-day healthcare system utilization. We provide an accompanying R software package. Discussion The proposed model accurately predicts total and critical care hospital utilization. The model enables evaluating impacts of patient influx scenarios on utilization, accounting for the state of currently hospitalized patients and characteristics of incoming patients. We show that accurate hospital load predictions were possible using only a patient’s age, sex, and day-by-day clinical state (critical, severe, or moderate). Conclusions The multistate model we develop is a powerful tool for predicting individual-level patient outcomes and hospital-level utilization.
Published: 2021
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21. Sensitivity analysis for complex ecological models - A new approach.

Author: Vardit Makler-Pick, Gideon Gal, Malka Gorfine, Matthew R. Hipsey, and Yohay Carmel
Published: 2011
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22. Marginalized Frailty-Based Illness-Death Model: Application to the UK-Biobank Survival Data

Author: Nir Keret, Li Hsu, Asaf Ben Arie, Malka Gorfine, and David M. Zucker
Subjects: FOS: Computer and information sciences, Statistics and Probability, Gerontology, business.industry, 05 social sciences, Medical information, Delayed entry, Health resource, Statistics - Applications, 01 natural sciences, Biobank, 3. Good health, Methodology (stat.ME), 010104 statistics & probability, Survival data, Model application, 0502 economics and business, Medicine, Applications (stat.AP), 0101 mathematics, Statistics, Probability and Uncertainty, business, Statistics - Methodology, 050205 econometrics
Abstract: The UK Biobank is a large-scale health resource comprising genetic, environmental and medical information on approximately 500,000 volunteer participants in the UK, recruited at ages 40--69 during the years 2006--2010. The project monitors the health and well-being of its participants. This work demonstrates how these data can be used to estimate in a semi-parametric fashion the effects of genetic and environmental risk factors on the hazard functions of various diseases, such as colorectal cancer. An illness-death model is adopted, which inherently is a semi-competing risks model, since death can censor the disease, but not vice versa. Using a shared-frailty approach to account for the dependence between time to disease diagnosis and time to death, we provide a new illness-death model that assumes Cox models for the marginal hazard functions. The recruitment procedure used in this study introduces delayed entry to the data. An additional challenge arising from the recruitment procedure is that information coming from both prevalent and incident cases must be aggregated. Lastly, we do not observe any deaths prior to the minimal recruitment age, 40. In this work we provide an estimation procedure for our new illness-death model that overcomes all the above challenges.
Published: 2020
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23. K-sample omnibus non-proportional hazards tests based on right-censored data

Author: Malka Gorfine, Li Hsu, and Matan Schlesinger
Subjects: FOS: Computer and information sciences, Statistics and Probability, Hazard (logic), Epidemiology, Nonparametric statistics, Sample (statistics), 01 natural sciences, Methodology (stat.ME), 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Health Information Management, 030220 oncology & carcinogenesis, Resampling, Statistics, Combined test, 0101 mathematics, Non proportional, Statistics - Methodology, Mathematics
Abstract: This work presents novel and powerful tests for comparing non-proportional hazard functions, based on sample-space partitions. Right censoring introduces two major difficulties which make the existing sample-space partition tests for uncensored data non-applicable: (i) the actual event times of censored observations are unknown; and (ii) the standard permutation procedure is invalid in case the censoring distributions of the groups are unequal. We overcome these two obstacles, introduce invariant tests, and prove their consistency. Extensive simulations reveal that under non-proportional alternatives, the proposed tests are often of higher power compared with existing popular tests for non-proportional hazards. Efficient implementation of our tests is available in the R package KONPsurv, which can be freely downloaded from {https://github.com/matan-schles/KONPsurv
Published: 2020
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24. Revisiting the Cumulative Incidence Function With Competing Risks Data

Author: David M Zucker and Malka Gorfine
Subjects: Statistics and Probability, Methodology (stat.ME), FOS: Computer and information sciences, 62N02, Statistics, Probability and Uncertainty, Statistics - Methodology
Abstract: We consider estimation of the cumulative incidence function (CIF) in the competing risks Cox model. We study three methods. Methods 1 and 2 are existing methods while Method 3 is a newly-proposed method. Method 3 is constructed so that the sum of the CIF's across all event types at the last observed event time is guaranteed, assuming no ties, to be equal to 1. The performance of the methods is examined in a simulation study, and the methods are illustrated on a data example from the field of computer code comprehension. The newly-proposed Method 3 exhibits performance comparable to that of Methods 1 and 2 in terms of bias, variance, and confidence interval coverage rates. Thus, with our newly-proposed estimator, the advantage of having the end-of-study total CIF equal to 1 is achieved with no price to be paid in terms of performance.
Published: 2022

25. Predicting personal risk of developing breast and prostate cancer from routine check-up data using survival analysis trees

Author: Dan Coster, Eyal Fisher, Shani Shenhar-Tsarfaty, Tehillah Menes, Shlomo Berliner, Ori Rogowski, David Zeltser, Itzhak Shapira, Eran Halperin, Saharon Rosset, Malka Gorfine, and Ron Shamir
Abstract: The challenge of survival prediction is ubiquitous in industry and medicine. Few methods are available for survival prediction of time varying data. Here we propose a novel method for this problem, using a random forest of survival trees for left truncated and right-censored data. We demonstrated the advantage of our method on prediction of breast cancer and prostate gland cancer risk among healthy individuals by analyzing routine laboratory measurements, vital signs and age. We analyzed electronic medical records of 20,317 healthy individuals who underwent routine checkups and identified those who later developed cancer. In cross-validation, our method predicted future prostate and breast cancers six months before diagnosis with an area under the ROC curve of 0.62±0.05 and 0.6±0.03 respectively, outperforming standard random forest, Cox-regression model and a single survival tree. Our results suggest that computational analysis of data on healthy individuals can improve the detection of those at risk of future cancer development.
Published: 2022
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26. Association Between BNT162b2 Vaccination and Incidence of SARS-CoV-2 Infection in Pregnant Women

Author: Malka Gorfine, Gabriel Chodick, Ran S Rotem, Daniel Nevo, David M. Steinberg, Inbal Goldshtein, and Yaakov Segal
Subjects: Adult, Risk, medicine.medical_specialty, COVID-19 Vaccines, Time Factors, Gestational Age, Kaplan-Meier Estimate, Lower risk, Interquartile range, Pregnancy, Internal medicine, Confidence Intervals, Medicine, Humans, Israel, Pregnancy Complications, Infectious, BNT162 Vaccine, Retrospective Studies, Original Investigation, business.industry, Incidence (epidemiology), Incidence, Hazard ratio, Vaccination, Gestational age, COVID-19, Retrospective cohort study, General Medicine, Case-Control Studies, Cohort, Regression Analysis, Female, Pregnant Women, business
Abstract: IMPORTANCE: Data on BNT162b2 messenger RNA (mRNA) vaccine (Pfizer-BioNTech) effectiveness and safety in pregnancy are currently lacking because pregnant women were excluded from the phase 3 trial. OBJECTIVE: To assess the association between receipt of BNT162b2 mRNA vaccine and risk of SARS-CoV-2 infection among pregnant women. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective cohort study within the pregnancy registry of a large state-mandated health care organization in Israel. Pregnant women vaccinated with a first dose from December 19, 2020, through February 28, 2021, were 1:1 matched to unvaccinated women by age, gestational age, residential area, population subgroup, parity, and influenza immunization status. Follow-up ended on April 11, 2021. EXPOSURES: Exposure was defined by receipt of the BNT162b2 mRNA vaccine. To maintain comparability, nonexposed women who were subsequently vaccinated were censored 10 days after their exposure, along with their matched pair. MAIN OUTCOMES AND MEASURES: The primary outcome was polymerase chain reaction–validated SARS-CoV-2 infection at 28 days or more after the first vaccine dose. RESULTS: The cohort included 7530 vaccinated and 7530 matched unvaccinated women, 46% and 33% in the second and third trimester, respectively, with a mean age of 31.1 years (SD, 4.9 years). The median follow-up for the primary outcome was 37 days (interquartile range, 21-54 days; range, 0-70). There were 118 SARS-CoV-2 infections in the vaccinated group and 202 in the unvaccinated group. Among infected women, 88 of 105 (83.8%) were symptomatic in the vaccinated group vs 149 of 179 (83.2%) in the unvaccinated group (P ≥ .99). During 28 to 70 days of follow-up, there were 10 infections in the vaccinated group and 46 in the unvaccinated group. The hazards of infection were 0.33% vs 1.64% in the vaccinated and unvaccinated groups, respectively, representing an absolute difference of 1.31% (95% CI, 0.89%-1.74%), with an adjusted hazard ratio of 0.22 (95% CI, 0.11-0.43). Vaccine-related adverse events were reported by 68 patients; none was severe. The most commonly reported symptoms were headache (n = 10, 0.1%), general weakness (n = 8, 0.1%), nonspecified pain (n = 6
Published: 2021

27. Efficient study design to estimate population means with multiple measurement instruments

Author: Malka Gorfine, Michal Bitan, Laura J. Rosen, and David M. Steinberg
Subjects: Statistics and Probability, education.field_of_study, Epidemiology, Computer science, Population, Tobacco smoke exposure, Outcome (probability), Statistical power, Sample size determination, Research Design, Sample Size, Statistics, Measuring instrument, Errors-in-variables models, Humans, Sensitivity (control systems), education
Abstract: Outcomes from studies assessing exposure often use multiple measurements. In previous work, using a model first proposed by Buonoccorsi (1991), we showed that combining direct (eg, biomarkers) and indirect (eg, self-report) measurements provides a more accurate picture of true exposure than estimates obtained when using a single type of measurement. In this article, we propose a tool for efficient design of studies that include both direct and indirect measurements of a relevant outcome. Based on data from a pilot or preliminary study, the tool, which is available online as a shiny app at https://michalbitan.shinyapps.io/shinyApp/, can be used to compute: (1) the sample size required for a statistical power analysis, while optimizing the percent of participants who should provide direct measures of exposure (biomarkers) in addition to the indirect (self-report) measures provided by all participants; (2) the ideal number of replicates; and (3) the allocation of resources to intervention and control arms. In addition we show how to examine the sensitivity of results to underlying assumptions. We illustrate our analysis using studies of tobacco smoke exposure and nutrition. In these examples, a near-optimal allocation of the resources can be found even if the assumptions are not precise.
Published: 2021

28. COVID-19 dynamics after a national immunization program in Israel

Author: Hagai, Rossman, Smadar, Shilo, Tomer, Meir, Malka, Gorfine, Uri, Shalit, and Eran, Segal
Subjects: Adult, Male, COVID-19 Vaccines, SARS-CoV-2, Vaccination, COVID-19, Middle Aged, Hospitalization, Communicable Disease Control, Humans, Israel, Pandemics, BNT162 Vaccine, Aged, Retrospective Studies
Abstract: Studies on the real-life effect of the BNT162b2 vaccine for Coronavirus Disease 2019 (COVID-19) prevention are urgently needed. In this study, we conducted a retrospective analysis of data from the Israeli Ministry of Health collected between 28 August 2020 and 24 February 2021. We studied the temporal dynamics of the number of new COVID-19 cases and hospitalizations after the vaccination campaign, which was initiated on 20 December 2020. To distinguish the possible effects of the vaccination on cases and hospitalizations from other factors, including a third lockdown implemented on 8 January 2021, we performed several comparisons: (1) individuals aged 60 years and older prioritized to receive the vaccine first versus younger age groups; (2) the January lockdown versus the September lockdown; and (3) early-vaccinated versus late-vaccinated cities. A larger and earlier decrease in COVID-19 cases and hospitalization was observed in individuals older than 60 years, followed by younger age groups, by the order of vaccination prioritization. This pattern was not observed in the previous lockdown and was more pronounced in early-vaccinated cities. Our analysis demonstrates the real-life effect of a national vaccination campaign on the pandemic dynamics.
Published: 2021

29. Patterns of COVID-19 pandemic dynamics following deployment of a broad national immunization program

Author: Uri Shalit, Smadar Shilo, Hagai Rossman, Malka Gorfine, Eran Segal, and Tomer Meir
Subjects: education.field_of_study, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), Population level, business.industry, Public health, Population, Vaccination, Pandemic, Medicine, Immunization program, Christian ministry, business, education, Demography
Abstract: Studies on the real-life impact of the BNT162b2 vaccine, recently authorized for the prevention of coronavirus disease 2019 (COVID-19), are urgently needed. Here, we analysed the temporal dynamics of the number of new COVID-19 cases and hospitalization in Israel following a vaccination campaign initiated on December 20th, 2020. We conducted a retrospective analysis of data originating from the Israeli Ministry of Health (MOH) from March 2020 to February 2021. In order to distill the possible effect of the vaccinations from other factors, including a third lockdown imposed in Israel on January 2021, we compared the time-dependent changes in number of COVID-19 cases and hospitalizations between (1) individuals aged 60 years and older, eligible to receive the vaccine earlier and younger individuals (0-59 years old); (2) early-vaccinated cities compared to late-vaccinated cities; (3) early-vaccinated geographical statistical areas (GSAs) compared to late-vaccinated GSAs; and (4) the current lockdown versus the previous lockdown, imposed on September 2020. By February 6th 2021, 45.3% and 29.7% of the entire Israeli population (89.9% and 80% of individuals older than 60 years old) received the first dose or both doses of the vaccine, respectively, or recovered from COVID-19. In mid-January, the number of COVID-19 cases and hospitalization started to decline, with a larger and earlier decrease among older individuals. This trend was more evident in early-vaccinated compared to late-vaccinated cities. Such a pattern was not observed in the previous lockdown. Our analysis demonstrates evidence for the real-life effectiveness of a national vaccination campaign in Israel on the pandemic dynamics. This report reflects a snapshot of a fast-changing situation. However, we believe our findings have major public health implications in the struggle against the COVID-19 pandemic, including the public’s perception of the need for and benefit of nationwide vaccination campaigns. More studies aimed at assessing the effectiveness of vaccination both on the individual and on the population level, with larger followup are needed.
Published: 2021
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30. Early detection of prostate gland and breast cancer risk based on routine check-up data using survival analysis trees for left-truncated and right-censored data

Author: David Zeltser, Itzhak Shapira, Saharon Rosset, E. Fischer, Shlomo Berliner, Tehillah S. Menes, Eran Halperin, Shani Shenhar-Tsarfaty, Malka Gorfine, Ron Shamir, Dan Coster, and Ori Rogowski
Subjects: Oncology, Multivariate statistics, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Medical record, Vital signs, Cancer, medicine.disease, Random forest, Breast cancer, Internal medicine, medicine, Mammography, business, Survival analysis
Abstract: ObjectiveTo predict breast cancer (BC) and prostate gland cancer (PGC) risk among healthy individuals by analyzing routine laboratory measurements, vital signs and age.Materials and MethodsWe analyzed electronic medical records of 20,317 healthy individuals who underwent routine checkups, encompassing more than 600 parameters per visit, and identified those who later developed cancer. We developed a novel ensemble method for risk prediction of multivariate time series data using a random forest model of survival trees for left truncated and right-censored data.ResultsUsing cross-validation, our method predicted future PGC and BC 6 months before diagnosis, achieving an area under the ROC curve of 0.62±0.05 and 0.6±0.03 respectively, better than standard random forest, Cox-regression model and a single survival tree. Our method can complement existing screening tests such as clinical breast examination and mammography for BC, and help in detection of subjects that were missed by these tests.DiscussionComputational analysis of results of routine checkups of healthy individuals can improve the detection of those at risk of cancer development.ConclusionOur method may assist in early detection of breast and prostate gland cancer.
Published: 2021
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31. Hospital load and increased COVID-19 related mortality - a nationwide study in Israel

Author: Asaf Ben Arie, Tomer Meir, Rom Gutman, Smadar Shilo, Hagai Rossman, Eran Segal, Jonathan Somer, Malka Gorfine, and Uri Shalit
Subjects: medicine.medical_specialty, 2019-20 coronavirus outbreak, Patient population, Standard error, Coronavirus disease 2019 (COVID-19), Patient Load, business.industry, Mortality rate, Emergency medicine, medicine, Christian ministry, business, Healthcare system
Abstract: The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases within a short period of time. We explore the ramifications of hospital load due to COVID-19 morbidity on COVID-19 in-hospital patient mortality. We address this question with a nationwide study based on the records of all 22,636 COVID-19 patients hospitalized in Israel from mid-July 2020 to mid-January 2021. We show that even under moderately heavy patient load (>500 countrywide hospitalized severely-ill patients; the Israeli Ministry of Health defined 800 severely-ill patients as the maximum capacity allowing adequate treatment), in-hospital mortality rate of patients with COVID-19 significantly increased compared to periods of lower patient load (250-500 severely-ill patients): 14-day mortality rates were 22.1% (Standard Error 3.1%) higher (mid-September to mid-October) and 27.2% (Standard Error 3.3%) higher (mid-December to mid-January). We further show this higher mortality rate cannot be attributed to changes in the patient population during periods of heavier load.
Published: 2021
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32. Change-point detection for infinite horizon dynamic treatment regimes

Author: Mila Orlovsky, Malka Gorfine, Alexis Mitelpunkt, Moshe Pollak, Ahuva Weiss-Meilik, and Yair Goldberg
Subjects: Blood Glucose, Statistics and Probability, Mathematical optimization, Epidemiology, Computer science, Clinical Decision-Making, 01 natural sciences, 010104 statistics & probability, 03 medical and health sciences, Dynamic treatment regime, 0302 clinical medicine, Health Information Management, Sepsis, 030225 pediatrics, Diabetes Mellitus, Humans, Point (geometry), Medical history, Precision Medicine, 0101 mathematics, Time point, Set (psychology), Infant, Newborn, Fasting, Decision rule, Trajectory, Infant, Premature, Change detection
Abstract: A dynamic treatment regime is a set of decision rules for how to treat a patient at multiple time points. At each time point, a treatment decision is made depending on the patient’s medical history up to that point. We consider the infinite-horizon setting in which the number of decision points is very large. Specifically, we consider long trajectories of patients’ measurements recorded over time. At each time point, the decision whether to intervene or not is conditional on whether or not there was a change in the patient’s trajectory. We present change-point detection tools and show how to use them in defining dynamic treatment regimes. The performance of these regimes is assessed using an extensive simulation study. We demonstrate the utility of the proposed change-point detection approach using two case studies: detection of sepsis in preterm infants in the intensive care unit and detection of a change in glucose levels of a diabetic patient.
Published: 2017
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33. Propensity scores with misclassified treatment assignment: a likelihood-based adjustment

Author: Nils D. Arvold, Corwin M. Zigler, Giovanni Parmigiani, Malka Gorfine, Danielle Braun, and Francesca Dominici
Subjects: Statistics and Probability, Matching (statistics), Computer science, Comparative effectiveness research, Context (language use), 01 natural sciences, Treatment and control groups, 010104 statistics & probability, 0504 sociology, Statistics, Humans, 0101 mathematics, Information bias, Propensity Score, Likelihood Functions, Models, Statistical, Brain Neoplasms, 05 social sciences, 050401 social sciences methods, Articles, General Medicine, United States, Outcome and Process Assessment, Health Care, Propensity score matching, Errors-in-variables models, Observational study, Medicare Part A, Statistics, Probability and Uncertainty
Abstract: Propensity score methods are widely used in comparative effectiveness research using claims data. In this context, the inaccuracy of procedural or billing codes in claims data frequently misclassifies patients into treatment groups, that is, the treatment assignment (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$T$\end{document}) is often measured with error. In the context of a validation data where treatment assignment is accurate, we show that misclassification of treatment assignment can impact three distinct stages of a propensity score analysis: (i) propensity score estimation; (ii) propensity score implementation; and (iii) outcome analysis conducted conditional on the estimated propensity score and its implementation. We examine how the error in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$T$\end{document} impacts each stage in the context of three common propensity score implementations: subclassification, matching, and inverse probability of treatment weighting (IPTW). Using validation data, we propose a two-step likelihood-based approach which fully adjusts for treatment misclassification bias under subclassification. This approach relies on two common measurement error-assumptions; non-differential measurement error and transportability of the measurement error model. We use simulation studies to assess the performance of the adjustment under subclassification, and also investigate the method’s performance under matching or IPTW. We apply the methods to Medicare Part A hospital claims data to estimate the effect of resection versus biopsy on 1-year mortality among \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$10\,284$\end{document} Medicare beneficiaries diagnosed with brain tumors. The ICD9 billing codes from Medicare Part A inaccurately reflect surgical treatment, but SEER-Medicare validation data are available with more accurate information.
Published: 2017
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34. Simulation method for stochastic generation of domestic wastewater discharges and the effect of greywater reuse on gross solid transport

Author: Malka Gorfine, Manfred Schütze, Eran Friedler, and Roni Penn
Subjects: Engineering, business.industry, 0208 environmental biotechnology, Geography, Planning and Development, Environmental engineering, Greywater reuse, 02 engineering and technology, STREAMS, 010501 environmental sciences, Sedimentation, 01 natural sciences, 020801 environmental engineering, Wastewater, Faecal matter, Sanitary sewer, Water saving, business, 0105 earth and related environmental sciences, Water Science and Technology
Abstract: Together with significant water saving, due to lower wastewater discharges, greywater reuse (GWR) may also increase the impacts of faecal matter and other gross solids (GS) on sewer systems (sedimentation, blocking). Modelling and assessments of these effects require detailed descriptions of individual domestic wastewater streams. A novel stochastic methodology to generate such streams was developed and validated. The generator was developed while considering dependencies between in-house water-use events, and their varying degree of elasticity. These generated streams served as input to a sewer model, where scenarios of GWR were simulated and their effects on GS transport were examined. Extensive GWR decreases GS movement mostly in upstream links. Nevertheless, transient high flows, characterising these links, may move stationary GS and prevent blockages. The developed generator can be adopted to other sewer-related studies. Intelligent implementation of the results can assist in introducing GWR,...
Published: 2017
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35. Report of the Editors-2017

Author: Malka Gorfine, Michael J. Daniels, Stijn Vansteelandt, Geert Molenberghs, Debashis Ghosh, and Marie Davidian
Subjects: Statistics and Probability, General Immunology and Microbiology, Applied Mathematics, General Medicine, General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology
Published: 2018
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36. Practical implementation of frailty models in Mendelian risk prediction

Author: Malka Gorfine, Danielle Braun, Giovanni Parmigiani, Li Hsu, and Theodore Huang
Subjects: Male, Epidemiology, Computer science, Population, Genes, BRCA2, Genes, BRCA1, Breast Neoplasms, Article, Cancer Genetics Network, 03 medical and health sciences, symbols.namesake, Breast cancer, Risk Factors, Genotype, Econometrics, medicine, Humans, Computer Simulation, Genetic Predisposition to Disease, education, Genetics (clinical), 030304 developmental biology, 0303 health sciences, education.field_of_study, Models, Statistical, Models, Genetic, 030305 genetics & heredity, Cancer, Statistical model, medicine.disease, Residual risk, Mutation, Mendelian inheritance, symbols, Female
Abstract: There are numerous statistical models used to identify individuals at high risk of cancer due to inherited mutations. Mendelian models predict future risk of cancer by using family history with estimated cancer penetrances (age- and sex-specific risk of cancer given the genotype of the mutations) and mutation prevalences. However, there is often residual risk heterogeneity across families even after accounting for the mutations in the model, due to environmental or unobserved genetic risk factors. We aim to improve Mendelian risk prediction by incorporating a frailty model that contains a family-specific frailty vector, impacting the cancer hazard function, to account for this heterogeneity. We use a discrete uniform population frailty distribution and implement a marginalized approach that averages each family's risk predictions over the family's frailty distribution. We apply the proposed approach to improve breast cancer prediction in BRCAPRO, a Mendelian model that accounts for inherited mutations in the BRCA1 and BRCA2 genes to predict breast and ovarian cancer. We evaluate the proposed model's performance in simulations and real data from the Cancer Genetics Network and show improvements in model calibration and discrimination. We also discuss alternative approaches for incorporating frailties and their strengths and limitations.
Published: 2019

37. On Estimation of the Hazard Function from Population-based Case-Control Studies

Author: Li Hsu, Malka Gorfine, and David M. Zucker
Subjects: Statistics and Probability, Hazard (logic), education.field_of_study, Proportional hazards model, business.industry, Hazard ratio, Population, Absolute risk reduction, Logistic regression, 01 natural sciences, Article, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Statistics, Covariate, Econometrics, Medicine, 0101 mathematics, Statistics, Probability and Uncertainty, Risk factor, education, business
Abstract: The population-based case-control study design has been widely used for studying the etiology of chronic diseases. It is well established that the Cox proportional hazards model can be adapted to the case-control study and hazard ratios can be estimated by (conditional) logistic regression model with time as either a matched set or a covariate (Prentice and Breslow, 1978). However, the baseline hazard function, a critical component in absolute risk assessment, is unidentifiable, because the ratio of cases and controls is controlled by the investigators and does not reflect the true disease incidence rate in the population. In this paper we propose a simple and innovative approach, which makes use of routinely collected family history information, to estimate the baseline hazard function for any logistic regression model that is fit to the risk factor data collected on cases and controls. We establish that the proposed baseline hazard function estimator is consistent and asymptotically normal and show via simulation that it performs well in finite samples. We illustrate the proposed method by a population-based case-control study of prostate cancer where the association of various risk factors is assessed and the family history information is used to estimate the baseline hazard function.
Published: 2019

38. Fully nonparametric estimation of the marginal survival function based on case-control clustered data

Author: David M. Zucker and Malka Gorfine
Subjects: Statistics and Probability, Estimation, Nonparametric estimator, Local linear, nonparametric estimator, Nonparametric statistics, Multivariate survival data, Case-control, local linear, Survival function, 62N02, Statistics, Clustered data, family study, multivariate survival data, Statistics, Probability and Uncertainty, Control (linguistics), 62G20, Mathematics
Abstract: A case-control family study is a study where individuals with a disease of interest (case probands) and individuals without the disease (control probands) are randomly sampled from a well-defined population. Possibly right-censored age at onset and disease status are observed for both probands and their relatives. Correlation among the outcomes within a family is induced by factors such as inherited genetic susceptibility, shared environment, and common behavior patterns. For this setting, we present a nonparametric estimator of the marginal survival function, based on local linear estimation of conditional survival functions. Asymptotic theory for the estimator is provided, making this paper the first to present for this data setting a fully nonparametric estimator with proven consistency. Simulation results are presented showing that the method performs well. The method is illustrated on data from a prostate cancer study.
Published: 2019
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39. A fully nonparametric estimator of the marginal survival function based on case–control clustered age-at-onset data

Author: Malka Gorfine, Li Hsu, and Nadia Bordo
Subjects: 0301 basic medicine, Statistics and Probability, Population, Biology, 01 natural sciences, Correlation, 010104 statistics & probability, 03 medical and health sciences, Prostate cancer, Statistics, medicine, Humans, 0101 mathematics, education, Survival analysis, education.field_of_study, Nonparametric statistics, Case-control study, Estimator, Articles, General Medicine, medicine.disease, Survival Analysis, 030104 developmental biology, Survival function, Case-Control Studies, Data Interpretation, Statistical, Statistics, Probability and Uncertainty
Abstract: Consider a popular case-control family study where individuals with a disease under study (case probands) and individuals who do not have the disease (control probands) are randomly sampled from a well-defined population. Possibly right-censored age at onset and disease status are observed for both probands and their relatives. For example, case probands are men diagnosed with prostate cancer, control probands are men free of prostate cancer, and the prostate cancer history of the fathers of the probands is also collected. Inherited genetic susceptibility, shared environment, and common behavior lead to correlation among the outcomes within a family. In this article, a novel nonparametric estimator of the marginal survival function is provided. The estimator is defined in the presence of intra-cluster dependence, and is based on consistent smoothed kernel estimators of conditional survival functions. By simulation, it is shown that the proposed estimator performs very well in terms of bias. The utility of the estimator is illustrated by the analysis of case-control family data of early onset prostate cancer. To our knowledge, this is the first article that provides a fully nonparametric marginal survival estimator based on case-control clustered age-at-onset data.
Published: 2016
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40. Estimating the intervention effect in calibration substudies

Author: Michal Talitman, David M. Steinberg, and Malka Gorfine
Subjects: Statistics and Probability, Likelihood Functions, Observational error, Epidemiology, Calibration (statistics), Computer science, Variance (accounting), Maximization, Environmental Exposure, Risk Assessment, Statistics, Expectation–maximization algorithm, Calibration, Biomarker (medicine), Humans, Fraction (mathematics), Computer Simulation, Biomarkers, Exposure assessment
Abstract: Exposure assessment is often subject to measurement errors. We consider here the analysis of studies aimed at reducing exposure to potential health hazards, in which exposure is the outcome variable. In these studies, the intervention effect may be estimated using either biomarkers or self-report data, but it is not common to combine these measures of exposure. Bias in the self-reported measures of exposure is a well-known fact; however, only few studies attempt to correct it. Recently, Keogh et al addressed this problem, presenting a model for measurement error in this setting and investigating how self-report and biomarker data can be combined. Keogh et al find the maximum likelihood estimate for the intervention effect in their model via direct numerical maximization of the likelihood. Here, we exploit an alternative presentation of the model that leads us to a closed formula for the MLE and also for its variance, when the number of biomarker replicates is the same for all subjects in the substudy. The variance formula enables efficient design of such intervention studies. When the number of biomarker replicates is not constant, our approach can be used along with the EM-algorithm to quickly compute the MLE. We compare the MLE to Buonaccorsi's method (Buonaccorsi, 1996) and find that they have similar efficiency when most subjects have biomarker data, but that the MLE has clear advantages when only a small fraction of subjects has biomarker data. This conclusion extends the findings of Keogh et al (2016) and has practical importance for efficiently designing studies.
Published: 2018

41. General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv

Author: Malka Gorfine, Li Hsu, John V. Monaco, and Google
Subjects: FOS: Computer and information sciences, Statistics and Probability, Computer science, Statistics - Computation, 01 natural sciences, Article, survival analysis, Normal distribution, Inverse Gaussian distribution, 010104 statistics & probability, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Statistical inference, shared frailty model, clustered data, 0101 mathematics, lcsh:Statistics, lcsh:HA1-4737, Computation (stat.CO), Statistical hypothesis testing, Estimation, frailtySurv, R, Estimator, Confidence interval, Standard error, symbols, Computer Science - Mathematical Software, Statistics, Probability and Uncertainty, Mathematical Software (cs.MS), Algorithm, 030217 neurology & neurosurgery, Software
Abstract: The article of record as published may be found at https://doi.org/10.18637/jss.v086.i04 The R package frailtySurv for simulating and fitting semi-parametric shared frailty models is introduced. Package frailtySurv implements semi-parametric consistent estimators for a variety of frailty distributions, including gamma, log-normal, inverse Gaussian and power variance function, and provides consistent estimators of the standard errors of the parameters’ estimators. The parameters’ estimators are asymptotically normally distributed, and therefore statistical inference based on the results of this package, such as hypothesis testing and confidence intervals, can be performed using the normal distribution. Extensive simulations demonstrate the flexibility and correct implementation of the estimator. Two case studies performed with publicly available datasets demonstrate applicability of the package. In the Diabetic Retinopathy Study, the onset of blindness is clustered by patient, and in a large hard drive failure dataset, failure times are thought to be clustered by the hard drive manufacturer and model. The authors would like to thank Google, which partially funded development of frailtySurv through the 2015 Google Summer of Code, and NIH grants (R01CA195789 and P01CA53996). The authors would like to thank Google, which partially funded development of frailtySurv through the 2015 Google Summer of Code, and NIH grants (R01CA195789 and P01CA53996).
Published: 2018
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42. The impact of covariate measurement error on risk prediction

Author: Malka Gorfine, Donna Spiegelman, David M. Zucker, and Polyna Khudyakov
Subjects: Statistics and Probability, Heteroscedasticity, Observational error, Conditional independence, Brier score, Receiver operating characteristic, Epidemiology, Computer science, Homoscedasticity, Covariate, Statistics, Econometrics, Logistic regression
Abstract: In the development of risk prediction models, predictors are often measured with error. In this paper, we investigate the impact of covariate measurement error on risk prediction. We compare the prediction performance using a costly variable measured without error, along with error-free covariates, to that of a model based on an inexpensive surrogate along with the error-free covariates. We consider continuous error-prone covariates with homoscedastic and heteroscedastic errors, and also a discrete misclassified covariate. Prediction performance is evaluated by the area under the receiver operating characteristic curve (AUC), the Brier score (BS), and the ratio of the observed to the expected number of events (calibration). In an extensive numerical study, we show that (i) the prediction model with the error-prone covariate is very well calibrated, even when it is mis-specified; (ii) using the error-prone covariate instead of the true covariate can reduce the AUC and increase the BS dramatically; (iii) adding an auxiliary variable, which is correlated with the error-prone covariate but conditionally independent of the outcome given all covariates in the true model, can improve the AUC and BS substantially. We conclude that reducing measurement error in covariates will improve the ensuing risk prediction, unless the association between the error-free and error-prone covariates is very high. Finally, we demonstrate how a validation study can be used to assess the effect of mismeasured covariates on risk prediction. These concepts are illustrated in a breast cancer risk prediction model developed in the Nurses' Health Study.
Published: 2015
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43. Nonparametric Adjustment for Measurement Error in Time-to-Event Data: Application to Risk Prediction Models

Author: Hormuzd A. Katki, Malka Gorfine, Argyrios Ziogas, Danielle Braun, and Giovanni Parmigiani
Subjects: Statistics and Probability, Mean squared error, Calibration (statistics), Mismeasured Covariates, Statistics & Probability, Context (language use), Bioengineering, 01 natural sciences, Article, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Statistics, Breast Cancer, Econometrics, 0101 mathematics, Family History, Mathematics, Event (probability theory), Cancer, Demography, Observational error, Carrier Status Prediction, Prevention, Nonparametric statistics, Estimator, Smoothed Kaplan-Meier Estimator, Survival Analysis, 030220 oncology & carcinogenesis, Monte Carlo integration, Statistics, Probability and Uncertainty
Abstract: Mismeasured time-to-event data used as a predictor in risk prediction models will lead to inaccurate predictions. This arises in the context of self-reported family history, a time-to-event predictor often measured with error, used in Mendelian risk prediction models. Using validation data, we propose a method to adjust for this type of error. We estimate the measurement error process using a nonparametric smoothed Kaplan–Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian and multivariate survival prediction models. Simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our method mitigates the effects of measurement error mainly by improving calibration and total accuracy. We illustrate our method in the context of Mendelian risk prediction models focusing on misreporting of breast cancer, fitting the measurement error model on data from the University of California at Irvine, and applying our method to counselees from the Cancer Genetics Network. We show that our method improves overall calibration, especially in low risk deciles. Supplementary materials for this article are available online.
Published: 2017

44. Frailty Models for Familial Risk With Application to Breast Cancer

Author: Giovanni Parmigiani, Malka Gorfine, and Li Hsu
Subjects: Statistics and Probability, Absolute risk reduction, Family aggregation, Context (language use), Disease, Biology, Familial risk, medicine.disease, Article, symbols.namesake, Breast cancer, Statistics, Mendelian inheritance, symbols, medicine, Statistics, Probability and Uncertainty, Cancer risk, Demography
Abstract: In evaluating familial risk for disease we have two main statistical tasks: assessing the probability of carrying an inherited genetic mutation conferring higher risk; and predicting the absolute risk of developing diseases over time, for those individuals whose mutation status is known. Despite substantial progress, much remains unknown about the role of genetic and environmental risk factors, about the sources of variation in risk among families that carry high-risk mutations, and about the sources of familial aggregation beyond major Mendelian effects. These sources of heterogeneity contribute substantial variation in risk across families. In this paper we present simple and efficient methods for accounting for this variation in familial risk assessment. Our methods are based on frailty models. We implemented them in the context of generalizing Mendelian models of cancer risk, and compared our approaches to others that do not consider heterogeneity across families. Our extensive simulation study demonstrates that when predicting the risk of developing a disease over time conditional on carrier status, accounting for heterogeneity results in a substantial improvement in the area under the curve of the receiver operating characteristic. On the other hand, the improvement for carriership probability estimation is more limited. We illustrate the utility of the proposed approach through the analysis of BRCA1 and BRCA2 mutation carriers in the Washington Ashkenazi Kin-Cohort Study of Breast Cancer.
Published: 2013
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45. Calibrated predictions for multivariate competing risks models

Author: Li Hsu, Malka Gorfine, David M. Zucker, and Giovanni Parmigiani
Subjects: Male, Risk, education.field_of_study, Multivariate statistics, Models, Statistical, Multivariate analysis, Computer science, Proportional hazards model, Applied Mathematics, Population, Kaplan-Meier Estimate, General Medicine, Disease, Risk factor (computing), Article, Neoplasms, Multivariate Analysis, Econometrics, Humans, Female, Family history, education, Predictive modelling, Proportional Hazards Models
Abstract: Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.
Published: 2013
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46. A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error

Author: Malka Gorfine, Yi Li, Mahlet G. Tadesse, David M. Zucker, and Donna Spiegelman
Subjects: Statistics and Probability, Polynomial regression, Multivariate adaptive regression splines, Proper linear model, General Immunology and Microbiology, Applied Mathematics, Local regression, General Medicine, Logistic regression, General Biochemistry, Genetics and Molecular Biology, Covariate, Statistics, Statistics::Methodology, Segmented regression, General Agricultural and Biological Sciences, Regression diagnostic, Mathematics
Abstract: present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses’ Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer.
Published: 2013
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47. Conditional and marginal estimates in case-control family data – extensions and sensitivity analyses

Author: Malka Gorfine, Rottem De-Picciotto, and Li Hsu
Subjects: Statistics and Probability, Estimation, Statistics::Theory, Dependency (UML), Statistics::Applications, Proportional hazards model, Applied Mathematics, Article, Interpretation (model theory), Inverse Gaussian distribution, symbols.namesake, Distribution (mathematics), Modeling and Simulation, Statistics, symbols, Econometrics, Probability distribution, Statistics, Probability and Uncertainty, Sensitivity analyses, Mathematics
Abstract: This work considers two specific estimation techniques for the family-specific proportional hazards model and for the population-averaged proportional hazards model. So far, these two estimation procedures were presented and studied under the gamma frailty distribution mainly because of its simple interpretation and mathematical tractability. Modifications of both procedures for other frailty distributions, such as the inverse Gaussian, positive stable and a specific case of discrete distribution, are presented. By extensive simulations, it is shown that under the family-specific proportional hazards model, the gamma frailty model appears to be robust to frailty distribution mis-specification in both bias and efficiency loss in the marginal parameters. The population-averaged proportional hazards model, is found to be robust under the gamma frailty model mis-specification only under moderate or weak dependency within cluster members.
Published: 2012
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48. Sensitivity analysis for complex ecological models – A new approach

Author: Malka Gorfine, Matthew R. Hipsey, Vardit Makler-Pick, Gideon Gal, and Yohay Carmel
Subjects: General linear model, Variable (computer science), Environmental Engineering, Data collection, Ecosystem model, Ecology, Ecological Modeling, Lake ecosystem, Environmental science, Recursive partitioning, Sensitivity (control systems), Software, Regression
Abstract: A strategy for global sensitivity analysis of a multi-parameter ecological model was developed and used for the hydrodynamic-ecological model (DYRESM-CAEDYM, DYnamic REservoir Simulation Model-Computational Aquatic Ecosystem Dynamics Model) applied to Lake Kinneret (Israel). Two different methods of sensitivity analysis, RPART (Recursive Partitioning And Regression Trees) and GLM (General Linear Model) were applied in order to screen a subset of significant parameters. All the parameters which were found significant by at least one of these methods were entered as input to a GBM (Generalized Boosted Modeling) analysis in order to provide a quantitative measure of the sensitivity of the model variables to these parameters. Although the GBM is a general and powerful machine learning algorithm, it has substantial computational costs in both storage requirements and CPU time. Employing the screening stage reduces this cost. The results of the analysis highlighted the role of particulate organic material in the lake ecosystem and its impact on the over all lake nutrient budget. The GBM analysis established, for example, that parameters such as particulate organic material diameter and density were particularly important to the model outcomes. The results were further explored by lumping together output variables that are associated with sub-components of the ecosystem. The variable lumping approach suggested that the phytoplankton group is most sensitive to parameters associated with the dominant phytoplankton group, dinoflagellates, and with nanoplankton (Chlorophyta), supporting the view of Lake Kinneret as a bottom-up system. The study demonstrates the effectiveness of such procedures for extracting useful information for model calibration and guiding further data collection.
Published: 2011
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49. Frailty-Based Competing Risks Model for Multivariate Survival Data

Author: Malka Gorfine and Li Hsu
Subjects: Risk, Statistics and Probability, Likelihood Functions, Class (computer programming), Multivariate analysis, Dependency (UML), General Immunology and Microbiology, Biometrics, Computer science, Applied Mathematics, Estimator, Contrast (statistics), General Medicine, Survival Analysis, Article, General Biochemistry, Genetics and Molecular Biology, Multivariate Analysis, Statistics, Covariate, Econometrics, Cluster Analysis, Humans, Computer Simulation, General Agricultural and Biological Sciences, Parametric statistics
Abstract: In this work, we provide a new class of frailty-based competing risks models for clustered failure times data. This class is based on expanding the competing risks model of Prentice et al. (1978, Biometrics 34, 541–554) to incorporate frailty variates, with the use of cause-specific proportional hazards frailty models for all the causes. Parametric and nonparametric maximum likelihood estimators are proposed. The main advantages of the proposed class of models, in contrast to the existing models, are: (1) the inclusion of covariates; (2) the flexible structure of the dependency among the various types of failure times within a cluster; and (3) the unspecified within-subject dependency structure. The proposed estimation procedures produce the most efficient parametric and semiparametric estimators and are easy to implement. Simulation studies show that the proposed methods perform very well in practical situations.
Published: 2010
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50. Combining longitudinal discriminant analysis and partial area under the ROC curve to predict non-response to treatment for hepatitis C virus

Author: Avidan U. Neumann, Esther Lukasiewicz, Malka Gorfine, and Laurence S. Freedman
Subjects: Statistics and Probability, Index (economics), Epidemiology, Hepatitis C virus, Treatment outcome, medicine.disease_cause, Antiviral Agents, Health Information Management, Statistics, Confidence Intervals, medicine, Humans, Longitudinal Studies, Treatment Failure, Randomized Controlled Trials as Topic, Mathematics, Models, Statistical, Receiver operating characteristic, Discriminant Analysis, Hepatitis C, Chronic, Viral Load, Linear discriminant analysis, Response to treatment, Treatment Outcome, ROC Curve, Area Under Curve, Linear Models, RNA, Viral, Bootstrap confidence interval, Area under the roc curve
Abstract: A longitudinal discriminant analysis is applied to build predictive models based on repeated measurements of serum hepatitis C virus RNA. These models are evaluated through the partial area under the receiver operating curve index (PA index) and, the final selection of the best model is based on cross-validated estimates of the PA index. Models are compared by building 95% bootstrap confidence interval for the difference in PA index between two models. Data from a randomised trial, in which chronic HCV patients were enrolled, are used to illustrate the application of the proposed method to predict treatment outcome.
Published: 2010
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