Issues in multiple imputation of missing data for large general practice clinical databases

Multiple Imputation for Nonresponse in Surveys

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Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

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Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies

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Mortality in relation to smoking: 50 years' observations on male British doctors

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Risk of myocardial infarction in patients with psoriasis

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Risk of Myocardial Infarction and Stroke after Acute Infection or Vaccination

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Mortality in relation to smoking: 40 years' observations on male British doctors

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Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study

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Multiple Imputation of Missing Values: Update of Ice

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Alcohol Dosing and Total Mortality in Men and Women: An Updated Meta-analysis of 34 Prospective Studies

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A critical look at methods for handling missing covariates in epidemiologic regression analyses.

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Relative Risk of Cardiovascular and Cancer Mortality in People With Severe Mental Illness From the United Kingdom's General Practice Research Database

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Feasibility study and methodology to create a quality-evaluated database of primary care data

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Use of Multiple Imputation in the Epidemiologic Literature

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Missing data in randomised controlled trials: a practical guide

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The British Regional Heart Study 1975-2004

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Contribution of changes in incidence and mortality to trends in the prevalence of coronary heart disease in the UK: 1996 2005

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Panic disorder and risk of new onset coronary heart disease, acute myocardial infarction, and cardiac mortality: cohort study using the general practice research database

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Agreement between GPRD smoking data: a survey of general practitioners and a population‐based survey

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Low identification of alcohol use disorders in general practice in England

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Lifestyle variables and the risk of myocardial infarction in the General Practice Research Database

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Validating the effects of drug treatment on blood pressure in the General Practice Research Database

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Hormone therapy and coronary heart disease in young women

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Multiple imputation of missing blood pressure covariates in survival analysis

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The Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement.

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Missing data and multiple imputation in clinical epidemiological research

Pedersen, A. B., Mikkelsen, E. M., Cronin-Fenton, D. P., Kristensen, N. R., Pham, T. M., Pedersen, L. H., & Petersen, I. (2017) · Clinical Epidemiology · doi:10.2147/clep.s129785 · Cited by 982 · see citing works

Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study

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External validation of multivariable prediction models: a systematic review of methodological conduct and reporting

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Chronic Inflammatory Disorders and Risk of Type 2 Diabetes Mellitus, Coronary Heart Disease, and Stroke: A Population-Based Cohort Study

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Data Resource Profile: Cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER)

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Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)

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Serum Bilirubin and Risk of Respiratory Disease and Death

Horsfall, L. (2011) · JAMA · doi:10.1001/jama.2011.124 · Cited by 210 · see citing works

Representativeness and optimal use of body mass index (BMI) in the UK Clinical Practice Research Datalink (CPRD)

Bhaskaran, K., Forbes, H. J., Douglas, I., Leon, D. A., & Smeeth, L. L. (2013) · BMJ Open · doi:10.1136/bmjopen-2013-003389 · Cited by 156 · see citing works

Real-world associations of renin-angiotensin-aldosterone system inhibitor dose, hyperkalemia, and adverse clinical outcomes in a cohort of patients with new-onset chronic kidney disease or heart failure in the United Kingdom

Linde, C., Bakhai, A., Furuland, H., Evans, M., McEwan, P., Ayoubkhani, D., & Qin, L. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.012655 · Cited by 110 · see citing works

Smoker, ex-smoker or non-smoker? The validity of routinely recorded smoking status in UK primary care: a cross-sectional study

Marston, L., Carpenter, J. R., Walters, K. R., Morris, R. W., Nazareth, I., White, I. R., & Petersen, I. (2014) · BMJ Open · doi:10.1136/bmjopen-2014-004958 · Cited by 105 · see citing works

Das RECORD-Statement zum Berichten von Beobachtungsstudien, die routinemäßig gesammelte Gesundheitsdaten verwenden

Benchimol, E. I., Smeeth, L. L., Guttmann, A., Harron, K. L., Hemkens, L. G., Moher, D., Petersen, I., Sørensen, H. T., von Elm, E. B., & Langan, S. (2016) · Zeitschrift für Evidenz Fortbildung und Qualität im Gesundheitswesen · doi:10.1016/j.zefq.2016.07.010 · Cited by 97 · see citing works

Data Mining Applications in Engineering and Medicine

Adem Karahoca, A. (2012) · InTech eBooks · doi:10.5772/2616 · Cited by 85 · see citing works

Patient Factors Influencing the Prescribing of Lipid Lowering Drugs for Primary Prevention of Cardiovascular Disease in UK General Practice: A National Retrospective Cohort Study

Wu, J., Zhu, S., Yao, G., Mohammed, M. A., & Marshall, T. P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067611 · Cited by 69 · see citing works

Deep learning for clustering of multivariate clinical patient trajectories with missing values

de Jong, J., Emon, M. A., Wu, P., Karki, R., Sood, M., Godard, P., Ahmad, A., Vrooman, H. A., Hofmann‐Apitius, M., & Fröhlich, H. (2019) · GigaScience · doi:10.1093/gigascience/giz134 · Cited by 65 · see citing works

Collaborative, pooled and harmonized study designs for epidemiologic research: challenges and opportunities

Lesko, C. R., Jacobson, L. P., Althoff, K. N., Abraham, A. G., Gange, S. J., Moore, R. D., Modur, S. P., & Lau, B. M. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyx283 · Cited by 67 · see citing works

Consumer perception of price fairness and dynamic pricing: Evidence from Booking.com

Alderighi, M., Nava, C. R., Calabrese, M., Christille, J.-M., & Salvemini, C. B. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.017 · Cited by 63 · see citing works

Long-term body mass index changes in overweight and obese adults and the risk of heart failure, cardiovascular disease and mortality: a cohort study of over 260,000 adults in the UK

Iyen, B., Weng, S. F., Vinogradova, Y., Akyea, R. K., Qureshi, N., & Kai, J. (2021) · BMC Public Health · doi:10.1186/s12889-021-10606-1 · Cited by 64 · see citing works

Insights into social disparities in smoking prevalence using Mosaic, a novel measure of socioeconomic status: an analysis using a large primary care dataset

Sharma, A., Lewis, S., & Szatkowski, L. C. (2010) · BMC Public Health · doi:10.1186/1471-2458-10-755 · Cited by 62 · see citing works

Evaluation of two-fold fully conditional specification multiple imputation for longitudinal electronic health record data

Welch, C., Petersen, I., Bartlett, J., White, I., Marston, L., Morris, R., Nazareth, I., Walters, K., & Carpenter, J. (2014) · Statistics in Medicine · doi:10.1002/sim.6184 · Cited by 60 · see citing works

Using Structural Equation Modeling to Examine the Influence of Social, Behavioral, and Nutritional Variables on Health Outcomes Based on NHANES Data: Addressing Complex Design, Nonnormally Distributed Variables, and Missing Information

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Can data from primary care medical records be used to monitor national smoking prevalence?

Szatkowski, L. C., Lewis, S., McNeill, A. D., Huang, Y. S., & Coleman, T. J. (2011) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2010.120154 · Cited by 55 · see citing works

Health indicator recording in UK primary care electronic health records: key implications for handling missing data

Petersen, I., Welch, C. A., Nazareth, I., Walters, K. R., Marston, L., Morris, R. W., Carpenter, J. R., Morris, T. P., & Pham, T. M. (2019) · Clinical Epidemiology · doi:10.2147/clep.s191437 · Cited by 55 · see citing works

Relative incidence of common cancers in people with severe mental illness. Cohort study in the United Kingdom THIN primary care database

Osborn, D., Limburg, H., Walters, K. R., Petersen, I., King, M., Green, J., Watson, J. R., & Nazareth, I. (2012) · Schizophrenia Research · doi:10.1016/j.schres.2012.11.009 · Cited by 54 · see citing works

Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study

Dennis, J., Young, K., McGovern, A. P., Mateen, B. A., Vollmer, S. J., Simpson, M. D., Henley, W., Holman, R. R., Sattar, N. A., Pearson, E. R., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00174-1 · Cited by 53 · see citing works

CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening

Himmelreich, J. C. L., Lucassen, W. A. M., Harskamp, R. E., Aussems, C., van Weert, H. C. P. M., & Nielen, M. M. J. (2021) · Open Heart · doi:10.1136/openhrt-2020-001459 · Cited by 49 · see citing works

Incidence of osteoarthritis, osteoporosis and inflammatory musculoskeletal diseases in adults with cerebral palsy: A population-based cohort study

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Key Concepts to Assess the Readiness of Data for International Research: Data Quality, Lineage and Provenance, Extraction and Processing Errors, Traceability, and Curation

de Lusignan, S., Liaw, S., Krause, P., Ćurčin, V., Vicente, M., Michalakidis, G., Agréus, L., Leysen, P., Shaw, N. T., & Mendis, K. (2011) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1638748 · Cited by 47 · see citing works

Noncommunicable disease among adults with cerebral palsy

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Ongoing impairments following transient ischaemic attack: retrospective cohort study

Turner, G., Calvert, M., Feltham, M. G., Ryan, R. P., & Marshall, T. P. (2016) · European Journal of Neurology · doi:10.1111/ene.13088 · Cited by 44 · see citing works

The positivity assumption and marginal structural models: the example of warfarin use and risk of bleeding

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Availability and Quality of Coronary Heart Disease Family History in Primary Care Medical Records: Implications for Cardiovascular Risk Assessment

Dhiman, P., Kai, J., Horsfall, L., Walters, K. R., & Qureshi, N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0081998 · Cited by 41 · see citing works

Risk of Melanoma and Nonmelanoma Skin Cancer in People with Vitiligo: United Kingdom Population−Based Cohort Study

Ferguson, J., Eleftheriadou, V., & Nesnas, J. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.04.013 · Cited by 38 · see citing works

Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model

Tibble, H., Tsanas, A., Horne, E., Horne, R., Mizani, M., Simpson, C., & Sheikh, A. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-028375 · Cited by 30 · see citing works

Risk of venous thromboembolism in immune-mediated inflammatory diseases: a UK matched cohort study

Galloway, J. B., Barrett, K. J., Irving, P. M., Khavandi, K., Nijher, M., Nicholson, R., de Lusignan, S., & Buch, M. H. (2020) · RMD Open · doi:10.1136/rmdopen-2020-001392 · Cited by 38 · see citing works

Mortality and Medical Care after Bereavement: A General Practice Cohort Study

King, M., Vasanthan, M., Petersen, I., Jones, L., Marston, L., & Nazareth, I. (2013) · PLoS ONE · doi:10.1371/journal.pone.0052561 · Cited by 38 · see citing works

Imputing missing data of function and disease activity in rheumatoid arthritis registers: what is the best technique?

Mongin, D., Lauper, K., Turesson, C., Hetland, M. L., Kristianslund, E. K., Kvien, T. K., Santos, M. J., Pavelká, K., Iannone, F., Finckh, A., et al. (2019) · RMD Open · doi:10.1136/rmdopen-2019-000994 · Cited by 37 · see citing works

Can analyses of electronic patient records be independently and externally validated? The effect of statins on the mortality of patients with ischaemic heart disease: a cohort study with nested case–control analysis

Reeves, D., Springate, D. A., Ashcroft, D. M., Ryan, R. P., Doran, T., Morris, R. W., Olier, I., & Kontopantelis, E. (2014) · BMJ Open · doi:10.1136/bmjopen-2014-004952 · Cited by 35 · see citing works

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine

Venkatasubramaniam, A., Mateen, B. A., Shields, B. M., Hattersley, A. T., Jones, A. G., Vollmer, S. J., & Dennis, J. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02207-2 · Cited by 34 · see citing works

Analyzing partially missing confounder information in comparative effectiveness and safety research of therapeutics

Toh, S., Rodríguez, L. A. G., & Hernán, M. Á. (2012) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.3248 · Cited by 33 · see citing works

β-blocker prescription is associated with lower cumulative risk of knee osteoarthritis and knee pain consultations in primary care: a propensity score–matched cohort study

Nakafero, G., Grainge, M. J., Valdes, A. M., Townsend, N. P., Mallen, C. D., Zhang, W., Doherty, M., Mamas, M. A., & Abhishek, A. (2021) · Rheumatology · doi:10.1093/rheumatology/keab234 · Cited by 29 · see citing works

The RECORD reporting guidelines: meeting the methodological and ethical demands of transparency in research using routinely-collected health data

Nicholls, S. G., Langan, S., Sørensen, H. T., Petersen, I., & Benchimol, E. I. (2016) · Clinical Epidemiology · doi:10.2147/clep.s110528 · Cited by 27 · see citing works

Generalizing Randomized Clinical Trial Results: Implementation and Challenges Related to Missing Data in the Target Population

Hong, J., Funk, M. J., LoCasale, R. J., Dempster, S. E., Cole, S. R., Webster‐Clark, M., Edwards, J. K., & Stürmer‎, T. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx287 · Cited by 29 · see citing works

Can analyses of electronic patient records be independently and externally validated? Study 2—the effect of β-adrenoceptor blocker therapy on cancer survival: a retrospective cohort study

Springate, D. A., Ashcroft, D. M., Kontopantelis, E., Doran, T., Ryan, R. P., & Reeves, D. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-007299 · Cited by 28 · see citing works

Population‐calibrated multiple imputation for a binary/categorical covariate in categorical regression models

Pham, T. M., Carpenter, J. R., Morris, T. P., Wood, A. M., & Petersen, I. (2018) · Statistics in Medicine · doi:10.1002/sim.8004 · Cited by 27 · see citing works

Access to publicly funded weight management services in England using routine data from primary and secondary care (2007–2020): An observational cohort study

Coulman, K. D., Margelyte, R., Jones, T., Blazeby, J. M., Macleod, J., Owen‐Smith, A., Parretti, H. M., Welbourn, R., Redaniel, M. T., & Judge, A. (2023) · PLoS Medicine · doi:10.1371/journal.pmed.1004282 · Cited by 27 · see citing works

Population-calibrated multiple imputation for a binary/categorical covariate in categorical regression models.

Pham, T. M., Carpenter, J. R., Morris, T. P., Wood, A., & Petersen, I. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.33210 · Cited by 26 · see citing works

Using nationwide ‘big data’ from linked electronic health records to help improve outcomes in cardiovascular diseases: 33 studies using methods from epidemiology, informatics, economics and social science in the ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER) programme

Hemingway, H., Feder, G. S., Fitzpatrick, N. K., Denaxas, S., Shah, A. D., & Timmis, A. (2017) · Programme Grants for Applied Research · doi:10.3310/pgfar05040 · Cited by 27 · see citing works

Can primary care data be used to monitor regional smoking prevalence? An analysis of The Health Improvement Network primary care data

Langley, T. E., Szatkowski, L. C., Wythe, S., & Lewis, S. (2011) · BMC Public Health · doi:10.1186/1471-2458-11-773 · Cited by 24 · see citing works

Survival Benefits of Statins for Primary Prevention: A Cohort Study

Gitsels, L. A., Kulinskaya, E., & Steel, N. (2016) · PLoS ONE · doi:10.1371/journal.pone.0166847 · Cited by 22 · see citing works

Analyzing Missing Data in Perinatal Pharmacoepidemiology Research: Methodological Considerations to Limit the Risk of Bias

Lupattelli, A., Wood, M. E., & Nordeng, H. (2019) · Clinical Therapeutics · doi:10.1016/j.clinthera.2019.11.003 · Cited by 22 · see citing works

The mental health associations of vitiligo: UK population-based cohort study

Thompson, A. R., Eleftheriadou, V., & Nesnas, J. (2022) · BJPsych Open · doi:10.1192/bjo.2022.591 · Cited by 22 · see citing works

Epilepsy and mortality: a retrospective cohort analysis with a nested case–control study identifying causes and risk factors from primary care and linkage-derived data

Wojewodka, G., Gulliford, M. C., Ashworth, M. J., Richardson, M. P., & Ridsdale, L. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-052841 · Cited by 21 · see citing works

Temporal and within practice variability in the health improvement network

Haynes, K., Bilker, W. B., Tenhave, T., Strom, B. L., & Lewis, J. D. (2011) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.2191 · Cited by 21 · see citing works

The use of electronic healthcare records for colorectal cancer screening referral decisions and risk prediction model development

Cooper, J., Ryan, R. P., Parsons, N., Stinton, C., Marshall, T. P., & Taylor‐Phillips, S. (2020) · BMC Gastroenterology · doi:10.1186/s12876-020-01206-1 · Cited by 20 · see citing works

Non-benzodiazepine hypnotic use for sleep disturbance in people aged over 55 years living with dementia: a series of cohort studies

Richardson, K., Savva, G. M., Boyd, P. J., Aldus, C. F., Maidment, I. D., Pakpahan, E., Loke, Y. K., Arthur, A. J., Steel, N., Ballard, C. G., et al. (2021) · Health Technology Assessment · doi:10.3310/hta25010 · Cited by 21 · see citing works

Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms

Garies, S., Cummings, M., Quan, H., McBrien, K., Drummond, N., Manca, D. P., & Williamson, T. S. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1068-5 · Cited by 17 · see citing works

Incident Type 2 Diabetes and Risk of Fracture: A Comparative Cohort Analysis Using U.K. Primary Care Records

Davie, G. S., Pal, K., Orton, E., Tyrrell, E. G., & Petersen, I. (2020) · Diabetes Care · doi:10.2337/dc20-1220 · Cited by 17 · see citing works

Initiation rates of statin therapy for the primary prevention of cardiovascular disease: an assessment of differences between countries of the UK and between regions within England

O’Keeffe, A. G., Petersen, I., & Nazareth, I. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-007207 · Cited by 15 · see citing works

Can the Use of Bayesian Analysis Methods Correct for Incompleteness in Electronic Health Records Diagnosis Data? Development of a Novel Method Using Simulated and Real-Life Clinical Data

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Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models

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Measuring the Association Between Body Mass Index and All-Cause Mortality in the Presence of Missing Data: Analyses From the Scottish National Diabetes Register

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Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity

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Survival prospects after acute myocardial infarction in the UK: a matched cohort study 1987–2011

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The lifetime risk and impact of vitiligo across sociodemographic groups: a UK population-based cohort study

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Estimating the diagnostic accuracy of rheumatoid factor in UK primary care: a study using the Clinical Practice Research Datalink

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Weight loss as a predictor of cancer and serious disease in primary care: an ISAC-approved CPRD protocol for a retrospective cohort study using routinely collected primary care data from the UK

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Novel Data Mining Techniques for Incomplete Clinical Data in Diabetes Management

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Quality of diabetes care in breast, colorectal, and prostate cancer

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Mortality after Transplantation for Hepatocellular Carcinoma: A Study from the European Liver Transplant Registry

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Preventing unscheduled hospitalisations from asthma: a retrospective cohort study using routine primary and secondary care data in the UK (The PUSH-Asthma Study)—protocol paper

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Model‐Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial

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Missing Data Analysis Using Statistical and Machine Learning Methods in Facility-Based Maternal Health Records

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What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key

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Missing laboratory test data in electronic general practice records: analysis of rheumatoid factor recording in the clinical practice research datalink

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Evaluating the Effectiveness of an Additional Risk Minimization Measure to Reduce the Risk of Prescribing Mirabegron to Patients with Severe Uncontrolled Hypertension in Four European Countries

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Multiple Imputation for Robust Cluster Analysis to Address Missingness in Medical Data

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Managing missing and erroneous data in nurse staffing surveys

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A comparison of missing data procedures for addressing selection bias in HIV sentinel surveillance data

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Multiple Imputation to Deal with Missing Clinical Data in Rheumatologic Surveys: an Application in the WHO-ILAR COPCORD Study in Iran.

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Use of oral anticoagulants in older people with atrial fibrillation in UK general practice: protocol for a cohort study using the Clinical Practice Research Datalink (CPRD) database

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Can primary care data be used to evaluate the effectiveness of tobacco control policies?: data quality, method development and assessment of the impact of smokefree legislation using data from the Health Improvement Network

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Risk of venous thromboembolism in people with RA: a population-based study in the UK

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Subject-wise model generalization through pooling and patching for regression: Application on non-invasive systolic blood pressure estimation

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Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: An application for type 2 diabetes precision medicinePreprint

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Handling missing data in Stata - a whirlwind tour 2012 Italian Stata Users Group Meeting

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The Use of Multiple Imputation Techniques on Short-Term Clinical Complications of Patients Presenting with Traumatic Spinal Cord Injuries

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Association between body mass index, missing data, and mortality risk among critically ill patients: the role of missing-data imputation

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Exogenous sex steroid hormones and asthma phenotypes: a study protocol for a prospective cohort analysis with UK-wide primary care data

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Comparison of diabetes‐associated secondary healthcare utilization between alternative oral antihyperglycaemic dual therapy combinations with metformin in patients with type 2 diabetes: an observational cohort study

Strongman, H., D’Oca, K., Langerman, H., & Das, R. (2015) · Diabetes Obesity and Metabolism · doi:10.1111/dom.12458 · Cited by 1 · see citing works

La déclaration RECORD (Reporting of Studies Conducted Using Observational Routinely Collected Health Data) : directives pour la communication des études réalisées à partir de données de santé collectées en routine

Benchimol, E. I., Smeeth, L. L., Guttmann, A., Harron, K. L., Moher, D., Petersen, I., Sørensen, H. T., Januel, J., von Elm, E. B., & Langan, S. (2019) · Canadian Medical Association Journal · doi:10.1503/cmaj.181309 · Cited by 1 · see citing works

The Artificial Intelligence Based Diagnostic Assistant – AIDA

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Prognostic biomarkers in and selection of surgical patients with hepatocellular carcinoma

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Extending balance assessment for the generalized propensity score under multiple imputation

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How to Study Interactions between Challenging Behaviour and Space? Exploring the Relevance of Routinely Collected Data

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Health indicator recording in UK primary care electronic health records: key implications for handling missing data

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Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms

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Weighted multiple imputation of ethnicity data that are missing not at random in primary care databases

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Smoker, ex-smoker or non-smoker? The validity of routinely recorded smoking status in UK primary care: a cross-sectional study

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Ethnicity recording in primary care: multiple imputation of missing data in ethnicity recording using The Health Improvement Network (THIN) databasePreprint

Pham, T. M., Morris, T. P., & Petersen, I. (2015) · RePEc: Research Papers in Economics · Cited by 3 · see citing works