Ewoud Schuit was born on the 14th of April 1985 in Gorinchem. In 2003 he completed pre-university education at the Sint-Oelbertgymnasium in Oosterhout. The same year he started as a student at the department of Biomedical Engineering at the Eindhoven University of Technology. He obtained his Bachelor of Science degree in 2007 and his Master of Science degree in 2009. After graduation, Ewoud started an internship at the Center of Medical Decision Sciences at the Erasmus Medical Center under supervision of Prof. Ewout W Steyerberg in collaboration with the Netherlands Perinatal Registry. After completion of this internship he started his PhD studies at the Julius Center for Health Sciences and Primary Care under supervision of Prof. Karel GM Moons in close collaboration with the nationwide consortium for women?s health research by person of Prof. Ben Willem J Mol. His work on the one hand included empirical studies aimed at enhancing personalized management. He also conducted several large international individual participant data (IPD) meta-analyses with a focus on subgroup effects, and reviewed, developed and validated numerous clinical prediction models. On the other hand his work focused on methodology for prediction research and IPD meta-analysis, specifically for subgroup analysis. In 2012 he obtained his Master of Science degree after completion of the Postgraduate Master of Clinical Epidemiology at the Utrecht University, and in March 2013 he obtained his PhD. Additionally he serves as a methodologist in Dutch Consortium for Healthcare Evaluation and Research in Obstetrics and Gynaecology ( Here he assists principal investigators with methodological issues relating to and statistical analyses of their clinical trial, and supervises secondary analyses including prediction research and IPD meta-analysis.
One year after he obtained a Rubicon Grant (#1 score out of 37 applications) by the Netherlands Organisation for Scientific Research (NWO) which allowed him to conduct research at Stanford University under supervision of Prof. John PA Ioannidis. He focuses on methodology for the use and application of individual participant data of treatment effects in network meta-analysis, specifically on differential treatment effects. He also have various methodological ongoing projects.
Ewoud aims to improve personalised medicine through the use of (network) meta-analysis, especially using individual patient data and focusing on subgroups, and by using prediction models.

Honors & Awards

  • Rubicon grant (120.000 EUR) - personal grant, Netherlands Organisation for Scientific Research (2014)
  • R21 grant (275.000 USD) - Co-applicant, National Institutes of Health (2014)
  • Project Grant (75.000 AUD) - Co-applicant, Women's and Children's Hospital Foundation (2015)
  • Doelmatigheidsonderzoek grant (83.000 EUR) - Co-applicant, ZonMW (2014)
  • Harold A. Kaminetzky Prize Paper 2011 - co-author, Obstetrics & Gynecology journal (2012)
  • Special citation for the Publication prize Junior Researcher, The Netherlands Epidemiological Society (2014)

Professional Education

  • Master of Science, Utrecht University (2012)
  • Bachelor of Science, Technische Universiteit Eindhoven (2007)
  • Doctor, Utrecht University (2013)
  • Master of Science, Technische Universiteit Eindhoven (2009)

Stanford Advisors

Research & Scholarship

Current Research and Scholarly Interests

I aim to improve personalized medicine in two ways. One, through the use of (network) meta-analysis in which I use individual participant data (raw data from primary trials) to assess whether differential effects exist in specific subgroups of patients. Two, by using multivariable prediction models that predict a certain outcome (e.g. treatment response) based on multiple characteristics of individual patients.
Many of my current ongoing research focuses on these two types of research. I'm currently involved in several randomized clinical trials, (network) meta-analyses, and prediction studies. Most of these projects focus on the field of obstetrics, but some also involve gynecology, depression, and cardiovascular disease.
Additionally, within the Meta-analysis Research Innovation Center at Stanford (METRICS) I focus on transparency, redundancy and reproducability of (network) meta-analyses.


All Publications

  • Preventing Preterm Birth with Progesterone in Women with a Short Cervical Length from a Low-Risk Population: A Multicenter Double-Blind Placebo-Controlled Randomized Trial AMERICAN JOURNAL OF PERINATOLOGY van Os, M. A., van der Ven, A. J., Kleinrouweler, C. E., Schuit, E., Kazemier, B. M., Verhoeven, C. J., de Miranda, E., van Wassenaer-Leemhuis, A. G., Sikkema, J. M., Woiski, M. D., Bossuyt, P. M., Pajkrt, E., de Groot, C. J., Mol, B. W., Haak, M. C. 2015; 32 (10): 993-1000
  • Risk stratification with cervical length and fetal fibronectin in women with threatened preterm labor before 34weeks and not delivering within 7days ACTA OBSTETRICIA ET GYNECOLOGICA SCANDINAVICA Hermans, F. J., Bruijn, M. M., Vis, J. Y., Wilms, F. F., Oudijk, M. A., Porath, M. M., Scheepers, H. C., Bloemenkamp, K. W., Bax, C. J., Cornette, J. M., Bijvanck, B. W., Franssen, M. T., Vandenbussche, F. P., Kok, M., Grobman, W. A., van der Post, J. A., Bossuyt, P. M., Opmeer, B. C., Mol, B. W., Schuit, E., van Baaren, G. 2015; 94 (7): 715-721


    To stratify the risk of spontaneous preterm delivery using cervical length (CL) and fetal fibronectin (fFN) in women with threatened preterm labor who remained pregnant after 7 days.Prospective observational study.Nationwide cohort of women with threatened preterm labor from the Netherlands.Women with threatened preterm labor between 24 and 34 weeks with a valid CL and fFN measurement and remaining pregnant 7 days after admission.Kaplan-Meier and Cox proportional hazards models were used to estimate cumulative percentages and hazard ratios (HR) for spontaneous delivery.Spontaneous delivery between 7 and 14 days after initial presentation and spontaneous preterm delivery before 34 weeks.The risk of delivery between 7 and 14 days was significantly increased for women with a CL < 15 mm or a CL ?15 to <30 mm and a positive fFN, compared with women with a CL ?30 mm: HR 22.3 [95% confidence interval (CI) 2.6-191] and 14 (95% CI 1.8-118), respectively. For spontaneous preterm delivery before 34 weeks the risk was increased for women with a CL < 15 mm [HR 6.3 (95% CI 2.6-15)] or with a CL ?15 to <30 mm with either positive fFN [HR 3.6 (95% CI 1.5-8.7)] or negative fFN [HR 3.0 (95% CI 1.2-7.1)] compared with women with a CL ? 30 mm.In women remaining pregnant 7 days after threatened preterm labor, CL and fFN results can be used in risk stratification for spontaneous delivery.

    View details for DOI 10.1111/aogs.12643

    View details for Web of Science ID 000355868600007

    View details for PubMedID 25845495

  • Recurrence of hypertensive disorders of pregnancy: an individual patient data metaanalysis AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY van Oostwaard, M. F., Langenveld, J., Schuit, E., Papatsonis, D. N., Brown, M. A., Byaruhanga, R. N., Bhattacharya, S., Campbell, D. M., Chappell, L. C., Chiaffarino, F., Crippa, I., Facchinetti, F., Ferrazzani, S., Ferrazzi, E., Figueiro-Filho, E. A., Gaugler-Senden, I. P., Haavaldsen, C., Lykke, J. A., Mbah, A. K., Oliveira, V. M., Poston, L., Redman, C. W., Salim, R., Thilaganathan, B., Vergani, P., Zhang, J., Steegers, E. A., Mol, B. W., Ganzevoort, W. 2015; 212 (5)


    We performed an individual participant data (IPD) metaanalysis to calculate the recurrence risk of hypertensive disorders of pregnancy (HDP) and recurrence of individual hypertensive syndromes.We performed an electronic literature search for cohort studies that reported on women experiencing HDP and who had a subsequent pregnancy. The principal investigators were contacted and informed of our study; we requested their original study data. The data were merged to form one combined database. The results will be presented as percentages with 95% confidence interval (CI) and odds ratios with 95% CI.Of 94 eligible cohort studies, we obtained IPD of 22 studies, including a total of 99,415 women. Pooled data of 64 studies that used published data (IPD where available) showed a recurrence rate of 18.1% (n=152,213; 95% CI, 17.9-18.3%). In the 22 studies that are included in our IPD, the recurrence rate of a HDP was 20.7% (95% CI, 20.4-20.9%). Recurrence manifested as preeclampsia in 13.8% of the studies (95% CI,13.6-14.1%), gestational hypertension in 8.6% of the studies (95% CI, 8.4-8.8%) and hemolysis, elevated liver enzymes and low platelets (HELLP) syndrome in 0.2% of the studies (95% CI, 0.16-0.25%). The delivery of a small-for-gestational-age child accompanied the recurrent HDP in 3.4% of the studies (95% CI, 3.2-3.6%). Concomitant HELLP syndrome or delivery of a small-for-gestational-age child increased the risk of recurrence of HDP. Recurrence increased with decreasing gestational age at delivery in the index pregnancy. If the HDP recurred, in general it was milder, regarding maximum diastolic blood pressure, proteinuria, the use of oral antihypertensive and anticonvulsive medication, the delivery of a small-for-gestational-age child, premature delivery, and perinatal death. Normotensive women experienced chronic hypertension after pregnancy more often after experiencing recurrence (odds ratio, 3.7; 95% CI, 2.3-6.1).Among women that experience hypertension in pregnancy, the recurrence rate in a next pregnancy is relatively low, and the course of disease is milder for most women with recurrent disease. These reassuring data should be used for shared decision-making in women who consider a new pregnancy after a pregnancy that was complicated by hypertension.

    View details for DOI 10.1016/j.ajog.2015.01.009

    View details for Web of Science ID 000353598500019

    View details for PubMedID 25582098

  • Meta-analyses triggered by previous (false-)significant findings: problems and solutions. Systematic reviews Schuit, E., Roes, K. C., Mol, B. W., Kwee, A., Moons, K. G., Groenwold, R. H. 2015; 4: 57-?


    Meta-analyses are typically triggered by a (potentially false-significant) finding in one of the preceding primary studies. We studied consequences of meta-analysis investigating effects when primary studies that triggered such meta-analysis are also included.We analytically determined the bias of the treatment effect estimates obtained by meta-analysis, conditional on the number of included primary and false-significant studies. The type I error rate and power of the meta-analysis were assessed using simulations. We applied a method for bias-correction, by subtracting an analytically derived bias from the treatment effect estimated in meta-analysis.Bias in meta-analytical effects and type I error rates increased when increasing numbers of primary studies with false-significant effects were included. When 20% of the primary studies showed false-significant effects, the bias was 0.33 (z-score) instead of 0, and the type I error rate was 23% instead of 5%. After applying a bias-correction, the type I error rate became indeed 5%.Inclusion of primary studies with false-significant effects leads to biased effect estimates and inflated type I error rates in the meta-analysis, depending on the number of false-significant studies. This bias can be adjusted for.

    View details for DOI 10.1186/s13643-015-0048-9

    View details for PubMedID 25908184

  • Impact of provision of cardiovascular disease risk estimates to healthcare professionals and patients: a systematic review BMJ OPEN Usher-Smith, J. A., Silarova, B., Schuit, E., Moons, K. G., Griffin, S. J. 2015; 5 (10)

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