Jason Oke

• The use of statistical methodology to determine the accuracy of grading within a diabetic retinopathy screening programme

  Oke JL. et al, (2016), Diabetic Medicine, 33, 896 - 903

• Comment on: 'Trends in the lifetime risk of developing cancer in Great Britain: comparison of risk for those born from 1930 to 1960' - Cancer predictions need more context

  Oke JL. et al, (2015), British Journal of Cancer, 113

• Management of smoking in UK primary care in patients diagnosed with bladder, lung and head & neck cancer compared with coronary heart disease (CHD): a Clinical Practice Research Database (CPRD) study

  Farley A. et al, (2015), EUROPEAN JOURNAL OF CANCER CARE, 24, 26 - 26

• Development of a cost-effectiveness model for optimisation of the screening interval in diabetic retinopathy screening

  Scanlon PH. et al, (2015), Health Technology Assessment, 19, 1 - 116

• People's willingness to accept overdetection in cancer screening: Population survey OPEN ACCESS

  Van Den Bruel A. et al, (2015), BMJ (Online), 350

• Trends in serum creatinine testing in Oxfordshire, UK, 1993-2013: A population-based cohort study

  Oke J. et al, (2015), BMJ Open, 5

• Cancer survival figures are misleading

  Oke JL., (2014), BMJ (Online), 348

• Optimal strategies for identifying kidney disease in diabetes: Properties of screening tests, progression of renal dysfunction and impact of treatment - Systematic review and modelling of progression and cost-effectiveness

  Farmer AJ. et al, (2014), Health Technology Assessment, 18, 1 - 127

• Monitoring young people with type 1 diabetes for diabetic nephropathy: Potential errors of annual ACR testing

  Oke J. et al, (2013), Diabetes Research and Clinical Practice, 99, 307 - 314

• Monitoring young people with type 1 diabetes for diabetic nephropathy: Potential errors of annual ACR testing

  Oke J. et al, (2013), Diabetes Research and Clinical Practice

• An explanatory randomised controlled trial of a nurse-led, consultation-based intervention to support patients with adherence to taking glucose lowering medication for type 2 diabetes

  Farmer A. et al, (2012), BMC Family Practice, 13

• HbA(1c) before and after starting treatment with metformin in a large diabetes type 2 cohort

  Vazquez-Montes M. et al, (2012), DIABETOLOGIA, 55, S325 - S325

• Meta-analysis of individual patient data in randomised trials of self monitoring of blood glucose in people with non-insulin treated type 2 diabetes

  Farmer AJ. et al, (2012), BMJ (Online), 344

• Metaanalysis of "individual date of 2552 patients" issed in 6 randomized trials on the efficiency of ASG in non-insulin treated type 2 diabetic patients

  Guerci B. et al, (2012), DIABETES & METABOLISM, 38, A10 - A10

• Establishing an evidence base for frequency of monitoring glycated haemoglobin levels in patients with Type 2 diabetes: Projections of effectiveness from a regression model

  Oke JL. et al, (2012), Diabetic Medicine, 29, 266 - 271

• Differences in insulin treatment satisfaction following randomized addition of biphasic, prandial or basal insulin to oral therapy in type 2 diabetes

  Farmer AJ. et al, (2011), Diabetes, Obesity and Metabolism, 13, 1136 - 1141

• Statistical models for the control phase of clinical monitoring

  Stevens RJ. et al, (2010), Statistical Methods in Medical Research, 19, 394 - 414

Recent Publications

• The use of statistical methodology to determine the accuracy of grading within a diabetic retinopathy screening programme

  Oke JL. et al, (2016), Diabetic Medicine, 33, 896 - 903

• Comment on: 'Trends in the lifetime risk of developing cancer in Great Britain: comparison of risk for those born from 1930 to 1960' - Cancer predictions need more context

  Oke JL. et al, (2015), British Journal of Cancer, 113

• Management of smoking in UK primary care in patients diagnosed with bladder, lung and head & neck cancer compared with coronary heart disease (CHD): a Clinical Practice Research Database (CPRD) study

  Farley A. et al, (2015), EUROPEAN JOURNAL OF CANCER CARE, 24, 26 - 26

• Development of a cost-effectiveness model for optimisation of the screening interval in diabetic retinopathy screening

  Scanlon PH. et al, (2015), Health Technology Assessment, 19, 1 - 116

• People's willingness to accept overdetection in cancer screening: Population survey OPEN ACCESS

  Van Den Bruel A. et al, (2015), BMJ (Online), 350