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      Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial

      research-article
      , Prof, FRCOG a , b , ah , * , * , , Prof, FRCOG a , * , ** , , PhD a , , PhD a , , PhD a , , PhD a , , Prof, PhD c , , PhD a , , FRCPath d , , FRCOG g , , BSN a , , RGN a , , PhD e , , FRCOG h , , DCR(R) a , , MSc a , i , , FRCOG j , , MSc a , , PhD a , k , , PhD a , l , , MRCOG m , n , , FRCOG o , , , BSc a , , FRCOG p , , MSc a , a , , FRCOG j , q , , FRCOG f , r , , FRCOG s , , FRCOG t , , MSc u , , FRCOG t , , FRCOG o , , FRCOG v , w , , PhD a , x , , FRCPath y , , MSc a , , MSc a , z , , Prof a , , FRCOG aa , , MD a , ab , , Prof, DPhil ac , , Prof, PhD ad , , Prof, DSc ae , , Prof, DPhil af , , , PhD u , ag ,
      Lancet (London, England)
      Elsevier
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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Background

          Ovarian cancer has a poor prognosis, with just 40% of patients surviving 5 years. We designed this trial to establish the effect of early detection by screening on ovarian cancer mortality.

          Methods

          In this randomised controlled trial, we recruited postmenopausal women aged 50–74 years from 13 centres in National Health Service Trusts in England, Wales, and Northern Ireland. Exclusion criteria were previous bilateral oophorectomy or ovarian malignancy, increased risk of familial ovarian cancer, and active non-ovarian malignancy. The trial management system confirmed eligibility and randomly allocated participants in blocks of 32 using computer-generated random numbers to annual multimodal screening (MMS) with serum CA125 interpreted with use of the risk of ovarian cancer algorithm, annual transvaginal ultrasound screening (USS), or no screening, in a 1:1:2 ratio. The primary outcome was death due to ovarian cancer by Dec 31, 2014, comparing MMS and USS separately with no screening, ascertained by an outcomes committee masked to randomisation group. All analyses were by modified intention to screen, excluding the small number of women we discovered after randomisation to have a bilateral oophorectomy, have ovarian cancer, or had exited the registry before recruitment. Investigators and participants were aware of screening type. This trial is registered with ClinicalTrials.gov, number NCT00058032.

          Findings

          Between June 1, 2001, and Oct 21, 2005, we randomly allocated 202 638 women: 50 640 (25·0%) to MMS, 50 639 (25·0%) to USS, and 101 359 (50·0%) to no screening. 202 546 (>99·9%) women were eligible for analysis: 50 624 (>99·9%) women in the MMS group, 50 623 (>99·9%) in the USS group, and 101 299 (>99·9%) in the no screening group. Screening ended on Dec 31, 2011, and included 345 570 MMS and 327 775 USS annual screening episodes. At a median follow-up of 11·1 years (IQR 10·0–12·0), we diagnosed ovarian cancer in 1282 (0·6%) women: 338 (0·7%) in the MMS group, 314 (0·6%) in the USS group, and 630 (0·6%) in the no screening group. Of these women, 148 (0·29%) women in the MMS group, 154 (0·30%) in the USS group, and 347 (0·34%) in the no screening group had died of ovarian cancer. The primary analysis using a Cox proportional hazards model gave a mortality reduction over years 0–14 of 15% (95% CI −3 to 30; p=0·10) with MMS and 11% (−7 to 27; p=0·21) with USS. The Royston-Parmar flexible parametric model showed that in the MMS group, this mortality effect was made up of 8% (−20 to 31) in years 0–7 and 23% (1–46) in years 7–14, and in the USS group, of 2% (−27 to 26) in years 0–7 and 21% (−2 to 42) in years 7–14. A prespecified analysis of death from ovarian cancer of MMS versus no screening with exclusion of prevalent cases showed significantly different death rates (p=0·021), with an overall average mortality reduction of 20% (−2 to 40) and a reduction of 8% (−27 to 43) in years 0–7 and 28% (−3 to 49) in years 7–14 in favour of MMS.

          Interpretation

          Although the mortality reduction was not significant in the primary analysis, we noted a significant mortality reduction with MMS when prevalent cases were excluded. We noted encouraging evidence of a mortality reduction in years 7–14, but further follow-up is needed before firm conclusions can be reached on the efficacy and cost-effectiveness of ovarian cancer screening.

          Funding

          Medical Research Council, Cancer Research UK, Department of Health, The Eve Appeal.

          Related collections

          Most cited references45

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          A Proportional Hazards Model for the Subdistribution of a Competing Risk

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            Reduced lung-cancer mortality with low-dose computed tomographic screening.

            (2011)
            The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer. From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02). Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385.).
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              Stan: A Probabilistic Programming Language

              Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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                Author and article information

                Contributors
                Journal
                Lancet
                Lancet
                Lancet (London, England)
                Elsevier
                0140-6736
                1474-547X
                05 March 2016
                05 March 2016
                : 387
                : 10022
                : 945-956
                Affiliations
                [a ]Department of Women's Cancer, Institute for Women's Health, University College London, London, UK
                [b ]University of New South Wales, Sydney, NSW, Australia
                [c ]Obstetrics and Gynaecology, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
                [d ]Research Department of Pathology, Cancer Institute, University College London Hospital, London, UK
                [e ]Clinical Biochemistry, University College London Hospital, London, UK
                [f ]Department of Gynaecological Oncology, University College London Hospital, London, UK
                [g ]Department of Gynaecological Oncology, James Cook University Hospital, Middlesbrough, UK
                [h ]Department of Gynaecological Oncology, Belfast City Hospital, Belfast, UK
                [i ]Malomatia (Information, Communication and Technology QATAR) Qatari Shareholding Company, Qatar
                [j ]Northern Gynaecological Oncology Centre, Queen Elizabeth Hospital, Gateshead, Tyne and Wear, UK
                [k ]Medical Research Council Centre for Neuromuscular Diseases, National Hospital for Neurology and Neurosurgery, London, UK
                [l ]School of Medical Sciences, Bangor University, Bangor, Gwynedd, UK
                [m ]Department of Gynaecology, Liverpool Women's Hospital, Liverpool, UK
                [n ]Women's Hospital, Hamad Medical Corporation, Doha, Qatar
                [o ]Department of Gynaecological Oncology, Royal Derby Hospital, Derby, UK
                [p ]Department of Gynaecological Oncology, Llandudno Hospital, Gwynedd, UK
                [q ]Derriford Hospital, Plymouth, Devon, UK
                [r ]Department of Gynaecological Oncology, Royal Free Hospital, London
                [s ]Department of Gynaecological Oncology, St Michael's Hospital, Bristol, UK
                [t ]Department of Gynaecological Oncology, St Bartholomew's Hospital, London, UK
                [u ]Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA
                [v ]Central Manchester Foundation Trust, St Mary's Hospital, Manchester, UK
                [w ]Institute of Cancer Sciences, University of Manchester, Manchester, UK
                [x ]Department of Gynaecological Oncology, University Hospital of Wales, Heath Park, Cardiff, UK
                [y ]Department of Pathology, Barts Health National Health Service Trust, London, UK
                [z ]Public Health England, London, UK
                [aa ]Department of Gynaecological Oncology, Nottingham City Hospital, Nottingham, UK
                [ab ]Department of Gynaecological Oncology, Queen Alexandra Hospital, Portsmouth, Hampshire, UK
                [ac ]Sussex Health Outcomes Research and Education in Cancer, Brighton and Sussex Medical School, University of Sussex, Sussex, UK
                [ad ]Department of Social Policy, London School of Economics, London, UK
                [ae ]Create Health Clinic, London, UK
                [af ]Medical Research Council Clinical Trials Unit at University College London, London, UK
                [ag ]Harvard Medical School, Boston, MA, USA
                [ah ]Centre for Women's Health, Institute of Human Development, University of Manchester, Manchester, UK
                Author notes
                [* ]Correspondence to: Prof Ian J Jacobs, University of New South Wales, Sydney, NSW 2052, AustraliaCorrespondence to: Prof Ian J JacobsUniversity of New South WalesSydneyNSW2052Australia i.jacobs@ 123456unsw.edu.au
                [** ]Prof Usha Menon, Department of Women's Cancer, Institute for Women's Health, University College London, London W1T 7DN, UKProf Usha MenonDepartment of Women's CancerInstitute for Women's HealthUniversity College LondonLondonW1T 7DNUK u.menon@ 123456ucl.ac.uk
                [*]

                Contributed equally

                [†]

                Contributed equally

                [‡]

                Mr Jenkins died in June, 2015

                Article
                S0140-6736(15)01224-6
                10.1016/S0140-6736(15)01224-6
                4779792
                26707054
                7dbdf1ec-9d6c-4c78-acd0-86407016e676
                © 2016 Jacobs Menon et al. Open Access article published under the terms of CC BY
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