Sibbald B, Roland M. Understanding controlled trials. Why are randomised controlled trials important? BMJ (Clinical research ed.). 1998;316(7126):201.
CAS
Google Scholar
Stuart EA, Bradshaw CP, Leaf PJ. Assessing the generalizability of randomized trial results to target populations. Prev Sci. 2015;16(3):475–85.
PubMed
PubMed Central
Google Scholar
Sarfati D, Koczwara B, Jackson C. The impact of comorbidity on cancer and its treatment. CA Cancer J Clin. 2016;66(4):337–50.
PubMed
Google Scholar
Malatestinic W, et al. Characteristics and medication use of psoriasis patients who may or may not qualify for randomized controlled trials. J Manag Care Spec Pharm. 2017;23(3):370–81.
PubMed
Google Scholar
Hutchinson-Jaffe AB, et al. Comparison of baseline characteristics, management and outcome of patients with non-ST-segment elevation acute coronary syndrome in versus not in clinical trials. Am J Cardiol. 2010;106(10):1389–96.
PubMed
Google Scholar
Dalela D, et al. Generalizability of the prostate cancer intervention versus observation trial (PIVOT) results to contemporary north American men with prostate cancer. Eur Urol. 2017;71(4):511–4.
PubMed
Google Scholar
Kennedy-Martin T, et al. A literature review on the representativeness of randomized controlled trial samples and implications for the external validity of trial results. Trials. 2015;16:495.
PubMed
PubMed Central
Google Scholar
Sanson-Fisher RW, et al. Limitations of the randomized controlled trial in evaluating population-based health interventions. Am J Prev Med. 2007;33(2):155–61.
PubMed
Google Scholar
Krauss A. Why all randomised controlled trials produce biased results. Ann Med. 2018;50(4):312–22.
PubMed
Google Scholar
Black N. Why we need observational studies to evaluate the effectiveness of health care. BMJ. 1996;312(7040):1215–8.
CAS
PubMed
PubMed Central
Google Scholar
Booth CM, Tannock IF. Randomised controlled trials and population-based observational research: partners in the evolution of medical evidence. Br J Cancer. 2014;110(3):551–5.
CAS
PubMed
PubMed Central
Google Scholar
Campbell JR, et al. Phase II evaluation of clinical coding schemes: completeness, taxonomy, mapping, definitions, and clarity. CPRI Work Group on Codes and Structures. J Am Med Inform Assoc. 1997;4(3):238–51.
CAS
PubMed
PubMed Central
Google Scholar
de Lusignan S, et al. Call for consistent coding in diabetes mellitus using the Royal College of General Practitioners and NHS pragmatic classification of diabetes. Inform Prim Care. 2012;20(2):103–13.
PubMed
Google Scholar
Powell GA, et al. Using routinely recorded data in the UK to assess outcomes in a randomised controlled trial: the trials of access. Trials. 2017;18(1):389.
CAS
PubMed
PubMed Central
Google Scholar
Ad N, et al. Practice changes in blood glucose management following open heart surgery: from a prospective randomized study to everyday practice. Eur J Cardiothorac Surg. 2015;47(4):733–9.
PubMed
Google Scholar
Hadley J, et al. Comparative effectiveness of prostate cancer treatments: evaluating statistical adjustments for confounding in observational data. J Natl Cancer Inst. 2010;102(23):1780–93.
PubMed
PubMed Central
Google Scholar
Zumsteg ZS, Zelefsky MJ. Improved survival with surgery in prostate cancer patients without medical comorbidity: a self-fulfilling prophecy? Eur Urol. 2013;64(3):381–3. https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(16)30102-4/fulltext.
Tree AC, van As NJ, Dearnaley DP. Re: Christopher J.D. Wallis, Refik Saskin, Richard Choo, et al. Surgery versus radiotherapy for clinically-localized prostate cancer: a systematic review and meta-analysis. Eur Urol 2016;70:21–30. Eur Urol. 2016;70(1):e10.
PubMed
Google Scholar
Dickerman BA, et al. Avoidable flaws in observational analyses: an application to statins and cancer. Nat Med. 2019;25(10):1601–6.
CAS
PubMed
Google Scholar
Kilburn LS, et al. Can routine data be used to support cancer clinical trials? A historical baseline on which to build: retrospective linkage of data from the TACT (CRUK 01/001) breast cancer trial and the National Cancer Data Repository. Trials. 2017;18(1):561.
PubMed
PubMed Central
Google Scholar
Gray CM, Wyke S, Zhang R, et al. Long-term weight loss following a randomised controlled trial of a weight management programme for men delivered through professional football clubs: the Football Fans in Training follow-up study. Southampton: NIHR Journals Library; 2018. (Public Health Research, No. 6.9.) Chapter 6, Data linkage utility and feasibility. Available from: https://www.ncbi.nlm.nih.gov/books/NBK513433/. Acccessed 19 June 2020.
Google Scholar
Mc Cord KA, et al. Routinely collected data for randomized trials: promises, barriers, and implications. Trials. 2018;19(1):29.
PubMed
PubMed Central
Google Scholar
Lewsey JD, et al. Using routine data to complement and enhance the results of randomised controlled trials. Health Technol Assess. 2000;4(22):1–55.
CAS
PubMed
Google Scholar
Lyons RA, et al. The SAIL databank: linking multiple health and social care datasets. BMC Med Inform Decis Mak. 2009;9:3.
PubMed
PubMed Central
Google Scholar
Padmanabhan S, et al. Approach to record linkage of primary care data from Clinical Practice Research Datalink to other health-related patient data: overview and implications. Eur J Epidemiol. 2019;34(1):91–9.
PubMed
Google Scholar
Bradley CJ, et al. Health services research and data linkages: issues, methods, and directions for the future. Health Serv Res. 2010;45(5 Pt 2):1468–88.
PubMed
PubMed Central
Google Scholar
Adami H-O. A paradise for epidemiologists? Lancet. 1996;347(9001):588–9.
Google Scholar
Dearnaley D, et al. Conventional versus hypofractionated high-dose intensity-modulated radiotherapy for prostate cancer: 5-year outcomes of the randomised, non-inferiority, phase 3 CHHiP trial. Lancet Oncol. 2016;17(8):1047–60.
PubMed
PubMed Central
Google Scholar
Wilkins A, et al. Hypofractionated radiotherapy versus conventionally fractionated radiotherapy for patients with intermediate-risk localised prostate cancer: 2-year patient-reported outcomes of the randomised, non-inferiority, phase 3 CHHiP trial. Lancet Oncol. 2015;16(16):1605–16.
PubMed
PubMed Central
Google Scholar
LENT SOMA tables. Radiother Oncol. 1995;35:17–60.
Correa A, et al. Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) sentinel network: a cohort profile. BMJ Open. 2016;6:e011092. https://doi.org/10.1136/bmjopen-2016-011092.
de Lusignan S, et al. RCGP Research and Surveillance Centre: 50 years’ surveillance of influenza, infections, and respiratory conditions. Br J Gen Pract. 2017;67(663):440–1.
PubMed
PubMed Central
Google Scholar
Lemanska A, et al. Linking CHHiP prostate cancer RCT with GP records: a study proposal to investigate the effect of co-morbidities and medications on long-term symptoms and radiotherapy-related toxicity. Tech Innov Patient Support Radiat Oncol. 2017;2(Supplement C):5–12.
PubMed
PubMed Central
Google Scholar
Mills S, et al. Unique health identifiers for universal health coverage. J Health Popul Nutr. 2019;38(Suppl 1):22.
PubMed
PubMed Central
Google Scholar
Holm S, Ploug T. Big data and health research-the governance challenges in a mixed data economy. J Bioeth Inq. 2017;14(4):515–25.
PubMed
Google Scholar
Vayena E, et al. Digital health: meeting the ethical and policy challenges. Swiss Med Wkly. 2018;148:w14571.
PubMed
Google Scholar
Vissers PA, et al. The impact of having both cancer and diabetes on patient-reported outcomes: a systematic review and directions for future research. J Cancer Surviv. 2016;10(2):406–15.
PubMed
Google Scholar
Skwarchuk MW, et al. Late rectal toxicity after conformal radiotherapy of prostate cancer (I): multivariate analysis and dose-response. Int J Radiat Oncol Biol Phys. 2000;47(1):103–13.
CAS
PubMed
Google Scholar
van der Veen SJ, et al. ACE inhibition attenuates radiation-induced cardiopulmonary damage. Radiother Oncol. 2015;114(1):96–103.
PubMed
Google Scholar
Wedlake LJ, et al. Evaluating the efficacy of statins and ACE-inhibitors in reducing gastrointestinal toxicity in patients receiving radiotherapy for pelvic malignancies. Eur J Cancer. 2012;48(14):2117–24.
CAS
PubMed
Google Scholar
Kollmeier MA, et al. Improved biochemical outcomes with statin use in patients with high-risk localized prostate cancer treated with radiotherapy. Int J Radiat Oncol Biol Phys. 2011;79(3):713–8.
CAS
PubMed
Google Scholar
Ostrau C, et al. Lovastatin attenuates ionizing radiation-induced normal tissue damage in vivo. Radiother Oncol. 2009;92(3):492–9.
CAS
PubMed
Google Scholar
de Lusignan S, Van Weel C. The use of routinely collected computer data for research in primary care: opportunities and challenges. Fam Pract. 2006;23(2):253–63.
PubMed
Google Scholar
Jha AK, et al. The use of health information technology in seven nations. Int J Med Inform. 2008;77(12):848–54.
PubMed
Google Scholar
Lemanska, A., et al., Extracting primary care records for prostate cancer patients in the CHHiP multicentre randomised control trial: a healthcare data linkage study. 2018. doi.org/https://doi.org/10.23889/ijpds.v3i4.741.
Liyanage H, et al. Ontologies in big health data analytics: application to routine clinical data. Stud Health Technol Inform. 2018;255:65–9.
PubMed
Google Scholar
Khan NF, et al. Long-term health outcomes in a British cohort of breast, colorectal and prostate cancer survivors: a database study. Br J Cancer. 2011;105(Suppl 1):S29–37.
PubMed
PubMed Central
Google Scholar
The Transforming Cancer Services Team for London (TSCT), Tower Hamlets CCG and Tower Hamlets Clinical Effectiveness Group. Guidance on clinical coding of cancer patients in primary care (2019). https://www.healthylondon.org/wp-content/uploads/2019/07/Guidance-on-clinical-coding-of-cancer-patients-in-primary-care.pdf. Accessed Feb 2020..
Google Scholar
A Bhuiya (2017). London cancer and Macmillan cancer support: a guide to quality coding and safety netting in the context of cancer. http://londoncancer.org/wp-content/uploads/2017/03/Guide-to-coding-and-safety-netting-in-cancer-by-Dr-A-Bhuiya_V5-Feb-17.pdf. Accessed Aug 2019.
Google Scholar
National Information Board (2014). Personalised health and care 2020. Using data and technology to transform outcomes for patients and citizens: a framework for action. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/384650/NIB_Report.pdf. Accessed Feb 2020.
Google Scholar
Vezyridis P, Timmons S. Evolution of primary care databases in UK: a scientometric analysis of research output. BMJ Open. 2016;6(10):e012785.
PubMed
PubMed Central
Google Scholar
Kopcke F, et al. Secondary use of routinely collected patient data in a clinical trial: an evaluation of the effects on patient recruitment and data acquisition. Int J Med Inform. 2013;82(3):185–92.
PubMed
Google Scholar
Cornelius VR, et al. Automated recruitment and randomisation for an efficient randomised controlled trial in primary care. Trials. 2018;19(1):341.
PubMed
PubMed Central
Google Scholar
Brooks CJ, et al. Use of a patient linked data warehouse to facilitate diabetes trial recruitment from primary care. Prim Care Diabetes. 2009;3(4):245–8.
CAS
PubMed
Google Scholar