Cited by 219Statistical Methods in EpidemiologyStatistical Methods and Bayesian InferenceMeta-analysis and systematic reviews

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines.

Open access

Burton, A, Altman, D G·2004·British Journal of Cancer

DOI 10.1038/sj.bjc.6601907PMC PMC2364743

Open this paper in CoChat — free account Open PDF

Abstract

Prognostic models play a crucial role in the clinical decision-making process. Unfortunately, missing covariate data impede the construction of valid and reliable models, potentially introducing bias, if handled inappropriately. The extent of missing covariate data within reported cancer prognostic studies, the current handling and the quality of reporting this missing covariate data are unknown. Therefore, a review was conducted of 100 articles reporting multivariate survival analyses to assess potential prognostic factors, published within seven cancer journals in 2002. Missing covariate data is a common occurrence in studies performing multivariate survival analyses, being apparent in 81 of the 100 articles reviewed. The percentage of eligible cases with complete data was obtainable in 39 articles, and was <90% in 17 of these articles. The methods used to handle incomplete covariates were obtainable in 32 of the 81 articles with known missing data and the most commonly reported approaches were complete case and available case analysis. This review has highlighted deficiencies in the reporting of missing covariate data. Guidelines for presenting prognostic studies with missing covariate data are proposed, which if followed should clarify and standardise the reporting in future articles.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/sj.bjc.6601907

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Figure 1 Flow diagram showing the presence or absence of missing covariate data.CC BY · PubMed Central

Figure 2 Guidelines for reporting prognostic studies with missing covariate data.CC BY · PubMed Central

Figure 3 Flow diagram showing adherence to proposed guidelines.CC BY · PubMed Central

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012
Analysis of Incomplete Multivariate Data
Booth, D. E., & Schafer, J. L. (2000) · Technometrics · doi:10.2307/1271466 · Cited by 5,627
Statistical Analysis with Missing Data.
Chatfield, C., Little, R. J. A., & Rubin, D. B. (1988) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982783 · Cited by 2,203
Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates
Peduzzi, P., Concato, J., Feinstein, A. R., & Holford, T. R. (1995) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(95)00048-8 · Cited by 2,154
A critical look at methods for handling missing covariates in epidemiologic regression analyses.
Greenland, S., & Finkle, W. D. (1995) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a117592 · Cited by 891 · cited 2×
“Other techniques, utilising all cases, include analysing the missing data as a separate category (Greenland and Finkle, 1995), single imputation (Little and Rubin, 1987), in which a single value is substituted for each missing value, and multiple imputation (Rubin, 1987; Schafer, 1997), where more than one independent…”
Statistical aspects of prognostic factor studies in oncology
Simon, R. M., & Altman, D. G. (1994) · British Journal of Cancer · doi:10.1038/bjc.1994.192 · Cited by 586

Show all 18 references

Sorted by citations

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Self-citation
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)
McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/dji237 · Cited by 2,162
Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
REporting recommendations for tumour MARKer prognostic studies (REMARK).
for the Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics, McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · British Journal of Cancer · doi:10.1038/sj.bjc.6602678 · Cited by 1,394
Self-citation
A catalogue of reporting guidelines for health research
Simera, I., Moher, D., Hoey, J., Schulz, K. F., & Altman, D. G. (2010) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2009.02234.x · Cited by 1,047
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration
Altman, D. G., McShane, L. M., Sauerbrei, W., & Taube, S. E. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001216 · Cited by 1,034
Registries for Evaluating Patient Outcomes: A User’s Guide
(2020) · doi:10.23970/ahrqepcregistries4 · Cited by 974
External validation of a Cox prognostic model: principles and methods
Royston, P. J., & Altman, D. G. (2013) · BMC Medical Research Methodology · doi:10.1186/1471-2288-13-33 · Cited by 911
Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines
Marshall, A., Altman, D. G., HOLDER, R. L., & Royston, P. J. (2009) · BMC Medical Research Methodology · doi:10.1186/1471-2288-9-57 · Cited by 883
REporting recommendations for tumor MARKer prognostic studies (REMARK)
McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2006) · Breast Cancer Research and Treatment · doi:10.1007/s10549-006-9242-8 · Cited by 803
Much Ado About Nothing
Horton, N. J., & Kleinman, K. (2007) · The American Statistician · doi:10.1198/000313007x172556 · Cited by 829
Reporting Recommendations for Tumor Marker Prognostic Studies
McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2004.01.0454 · Cited by 779
A guide to systematic review and meta-analysis of prognostic factor studies
Riley, R. D., Moons, K. G. M., Snell, K. I. E., Ensor, J., Hooft, L., Altman, D. G., Hayden, J. A., Collins, P. G. S., & Debray, T. P. A. (2019) · BMJ · doi:10.1136/bmj.k4597 · Cited by 771
External validation of multivariable prediction models: a systematic review of methodological conduct and reporting
Collins, G. S., de Groot, J. A., Dutton, S., Omar, O., Shanyinde, M., Tajar, A., Voysey, M., Wharton, R., Yu, L.-M., Moons, K. G., et al. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-40 · Cited by 729
Self-citation
Basic statistical reporting for articles published in Biomedical Journals: The “Statistical Analyses and Methods in the Published Literature” or the SAMPL Guidelines
Lang, T. A., & Altman, D. G. (2014) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2014.09.006 · Cited by 662
Reporting and Methods in Clinical Prediction Research: A Systematic Review
Bouwmeester, W., Zuithoff, N. P. A., Mallett, S., Geerlings, M. I., Vergouwe, Y., Steyerberg, E. W., Altman, D. G., & Moons, K. G. M. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001221 · Cited by 600
Missing covariate data in medical research: To impute is better than to ignore
Janssen, K. J. M., Donders, A. R. T., Harrell, F. E., Vergouwe, Y., Chen, Q., Grobbee, D. E., & Moons, K. G. M. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.12.008 · Cited by 593
REporting recommendations for tumor MARKer prognostic studies (REMARK)
McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · Nature Clinical Practice Oncology · doi:10.1038/ncponc0252 · Cited by 563
Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis
Groenwold, R. H. H., White, I. R., Donders, A. R. T., Carpenter, J. R., Altman, D. G., & Moons, K. G. M. (2012) · Canadian Medical Association Journal · doi:10.1503/cmaj.110977 · Cited by 538
Self-citation
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): An Abridged Explanation and Elaboration
Sauerbrei, W., Taube, S. E., McShane, L. M., Cavenagh, M. M., & Altman, D. G. (2018) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djy088 · Cited by 500
Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting
Collins, G. S., Mallett, S., Omar, O., & Yu, L.-M. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-103 · Cited by 484
Per-Oral Endoscopic Myotomy: A Series of 500 Patients
Inoue, H., Sato, H., Ikeda, H., Onimaru, M., Sato, C., Minami, H., Yokomichi, H., Kobayashi, Y., Grimes, K. L., & Kudo, S. (2015) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2015.03.057 · Cited by 481
The rise of multiple imputation: a review of the reporting and implementation of the method in medical research
Rezvan, P. H., Lee, K. J., & Simpson, J. A. (2015) · BMC Medical Research Methodology · doi:10.1186/s12874-015-0022-1 · Cited by 483
Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration
Altman, D. G., McShane, L. M., Sauerbrei, W., & Taube, S. E. (2012) · BMC Medicine · doi:10.1186/1741-7015-10-51 · Cited by 378
Evolution of Translational Omics: Lessons Learned and the Path Forward
Micheel, C., Nass, S. J., & Omenn, G. S. (2013) · Cited by 365
Dealing With Missing Data in Developmental Research
Enders, C. K. (2013) · Child Development Perspectives · doi:10.1111/cdep.12008 · Cited by 330
REporting recommendations for tumour MARKer prognostic studies (REMARK)
McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · European Journal of Cancer · doi:10.1016/j.ejca.2005.03.032 · Cited by 315
Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study
Marshall, A., Altman, D. G., Royston, P. J., & HOLDER, R. L. (2010) · BMC Medical Research Methodology · doi:10.1186/1471-2288-10-7 · Cited by 248
Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2
Collins, P. G. S., & Altman, D. G. (2012) · BMJ · doi:10.1136/bmj.e4181 · Cited by 245
Use of classical and novel biomarkers as prognostic risk factors for localised prostate cancer: a systematic review
Sutcliffe, P., Hummel, S., Simpson, E., Young, T., Rees, A., Wilkinson, A., Hamdy, F. C., Clarke, N. W., & Staffurth, J. (2009) · Health Technology Assessment · doi:10.3310/hta13050 · Cited by 237
Missing Data
Eekhout, I. E., de Boer, M. R., Twisk, J. W. R., de Vet, H. C. W., & Heymans, M. W. (2012) · Epidemiology · doi:10.1097/ede.0b013e3182576cdb · Cited by 235
A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome
Ambler, G., Omar, R. Z., & Royston, P. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074466 · Cited by 233
Almost all articles on cancer prognostic markers report statistically significant results
Kyzas, P., Denaxa-Kyza, D., & Ioannidis, J. P. A. (2007) · European Journal of Cancer · doi:10.1016/j.ejca.2007.08.030 · Cited by 233
Best practices for addressing missing data through multiple imputation
Woods, A. D., Gerasimova, D., Dusen, B. V., Nissen, J., Bainter, S. A., Uzdavines, A., Davis-Kean, P. E., Halvorson, M. A., King, K. M., Logan, J. A. R., et al. (2023) · Infant and Child Development · doi:10.1002/icd.2407 · Cited by 251
Reporting the Use of Multiple Imputation for Missing Data in Higher Education Research
Manly, C. A., & Wells, R. S. (2015) · Research in Higher Education · doi:10.1007/s11162-014-9344-9 · Cited by 210
Reporting methods in studies developing prognostic models in cancer: a review
Mallett, S., Royston, P., Dutton, S., Waters, R., & Altman, D. G. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-20 · Cited by 206
“Several search strings have been developed [9-11] but other studies have used hand-searching [12].”
Missing data
Altman, D. G., & Bland, M. (2007) · BMJ · doi:10.1136/bmj.38977.682025.2c · Cited by 196
A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures
Karahalios, A., Baglietto, L., Carlin, J. B., English, D. R., & Simpson, J. A. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-96 · Cited by 197
Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example
Knol, M. J., Janssen, K. J. M., Donders, A. R. T., Egberts, T. C. G., Heerdink, E. R., Grobbee, D. E., Moons, K. G. M., & Geerlings, M. I. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.08.028 · Cited by 190
Variable selection under multiple imputation using the bootstrap in a prognostic study
Heymans, M. W., van Buuren, S., Knol, D. L., van Mechelen, W., & de Vet, H. C. W. (2007) · BMC Medical Research Methodology · doi:10.1186/1471-2288-7-33 · Cited by 172
On the dark side of work: a longitudinal analysis using self-determination theory
Olafsen, A. H., Niemiec, C. P., Halvari, H., Deci, E. L., & Williams, G. C. (2016) · European Journal of Work and Organizational Psychology · doi:10.1080/1359432x.2016.1257611 · Cited by 220
Prognostic markers in cancer: the evolution of evidence from single studies to meta-analysis, and beyond
Riley, R. D., Sauerbrei, W., & Altman, D. G. (2009) · British Journal of Cancer · doi:10.1038/sj.bjc.6604999 · Cited by 164
Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values
García-Laencina, P. J., Abreu, P. H., Abreu, M. H., & Afonoso, N. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.02.006 · Cited by 161
Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions
Zygmont, C. S., & Smith, M. R. (2014) · The Quantitative Methods for Psychology · doi:10.20982/tqmp.10.1.p040 · Cited by 149
Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study
Marshall, A., Altman, D. G., & HOLDER, R. L. (2010) · BMC Medical Research Methodology · doi:10.1186/1471-2288-10-112 · Cited by 146
Specialized and high-volume care leads to better outcomes of ovarian cancer treatment in the Netherlands
Vernooij, F., Heintz, A. P. M., Coebergh, J. W. W., Massuger, L. F. A. G., Witteveen, P. O., & van der Graaf, Y. (2009) · Gynecologic Oncology · doi:10.1016/j.ygyno.2008.11.011 · Cited by 138
Missing Data in the American College of Surgeons National Surgical Quality Improvement Program Are Not Missing at Random: Implications and Potential Impact on Quality Assessments
Hamilton, B. H., Ko, C. Y., Richards, K., & Hall, B. L. (2010) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2009.10.021 · Cited by 134
Prognostic Models: A Methodological Framework and Review of Models for Breast Cancer
Altman, D. G. (2009) · Cancer Investigation · doi:10.1080/07357900802572110 · Cited by 129
Reporting of prognostic studies of tumour markers: a review of published articles in relation to REMARK guidelines.
Mallett, S., Timmer, A., Sauerbrei, W., & Altman, D. G. (2010) · British Journal of Cancer · doi:10.1038/sj.bjc.6605462 · Cited by 122
Self-citation
A systematic review of the quality of clinical prediction models in in vitro fertilisation
Ratna, M. B., Bhattacharya, S., Abdulrahim, B., & McLernon, D. J. (2019) · Human Reproduction · doi:10.1093/humrep/dez258 · Cited by 94
Quality of reporting of cancer prognostic marker studies: association with reported prognostic effect.
Kyzas, P., Denaxa-Kyza, D., & Ioannidis, J. (2007) · Journal of the National Cancer Institute · doi:10.1093/jnci/djk032 · Cited by 95
Inflammatory Cytokines in Chronic Heart Failure: Interleukin-8 is Associated with Adverse Outcome. Results from CORONA
Nymo, S. H., Hulthe, J., Ueland, T., McMurray, J. J. V., Wikstrand, J., Askevold, E. T., Yndestad, A., Gullestad, L. L., & Aukrust, P. (2013) · European Journal of Heart Failure · doi:10.1093/eurjhf/hft125 · Cited by 94
Determining prognosis in acute exacerbation of COPD
Flattet, Y., Garin, N., Serratrice, J., Perrier, A., Stirnemann, J., & Carballo, S. (2017) · International Journal of COPD · doi:10.2147/copd.s122382 · Cited by 94
Systematic review of prediction models for gestational hypertension and preeclampsia
Antwi, E., Amoakoh‐Coleman, M., Vieira, D. L., Madhavaram, S., Koram, K. A., Grobbee, D. E., Agyepong, I. A., & Klipstein‐Grobusch, K. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230955 · Cited by 101
The Use of (Network) Meta-Analysis in Clinical Oncology
ter Veer, E., van Oijen, M. G. H., & van Laarhoven, H. W. M. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00822 · Cited by 83
“Inconsistent reporting of the baseline characteristics hampers adequate between-study comparison (32), and calls for standard sets of characteristics to report in clinical studies.”
Primer: an evidence-based approach to prognostic markers
Altman, D. G., & Riley, R. D. (2005) · Nature Clinical Practice Oncology · doi:10.1038/ncponc0287 · Cited by 76
Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey
Shishvan, O. R., Zois, D., & Soyata, T. (2018) · IEEE Access · doi:10.1109/access.2018.2866049 · Cited by 73
Sexual orientation differences in psychological treatment outcomes for depression and anxiety: National cohort study.
Rimes, K. A., Ion, D., Wingrove, J., & Carter, B. (2019) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000416 · Cited by 73
Letter to the Editor
Riley, R. D., Ridley, G. F., Williams, K. J., Altman, D. G., Hayden, J. A., & de Vet, H. C. W. (2007) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.02.004 · Cited by 71
The use of artificial neural networks to stratify the length of stay of cardiac patients based on preoperative and initial postoperative factors
Rowan, M. J., Ryan, T., Hegarty, F., & O'Hare, N. J. (2007) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2007.04.005 · Cited by 70
Incidence of Missing Item Scores in Personality Measurement, and Simple Item-Score Imputation
van Ginkel, J. R., Sijtsma, K., van der Ark, L. A., & Vermunt, J. K. (2010) · Methodology · doi:10.1027/1614-2241/a000003 · Cited by 65
A Sightability Model for Mountain Goats
Rice, C. G., Jenkins, K. J., & Chang, W. (2009) · Journal of Wildlife Management · doi:10.2193/2008-196 · Cited by 59
Identifying patients with undetected colorectal cancer: an independent validation of QCancer (Colorectal)
Collins, G. S., & Altman, D. (2012) · British Journal of Cancer · doi:10.1038/bjc.2012.266 · Cited by 60
Statistical Analyses and Methods in the Published Literature: The SAMPL Guidelines*
Lang, T. A., & Altman, D. G. (2014) · Guidelines for Reporting Health Research: A User's Manual · doi:10.1002/9781118715598.ch25 · Cited by 57
Combined effect of modifiable and non-modifiable risk factors for colorectal cancer risk in a pooled analysis of 11 population-based studies
Wang, X., O’Connell, K., Jeon, J., Song, M., Hunter, D. J., Hoffmeister, M., Lin, Y., Berndt, S. I., Brenner, H., Chan, A. T., et al. (2019) · BMJ Open Gastroenterology · doi:10.1136/bmjgast-2019-000339 · Cited by 62
Early Childhood Assessments of Community Pediatric Professionals Predict Autism Spectrum and Attention Deficit Hyperactivity Problems
Jaspers, M., de Winter, A. F., Buitelaar, J. K., Verhulst, F. C., Reijneveld, S. A., & Hartman, C. A. (2012) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-012-9653-4 · Cited by 56
An empirical evaluation of multifarious outcomes in pharmacogenetics: beta-2 adrenoceptor gene polymorphisms in asthma treatment
Contopoulos‐Ioannidis, D. G., Alexiou, G. Α., Gouvias, T. C., & Ioannidis, J. P. A. (2006) · Pharmacogenetics and Genomics · doi:10.1097/01.fpc.0000236332.11304.8f · Cited by 54
Predicting childhood overweight and obesity using maternal and early life risk factors: a systematic review
Ziauddeen, N., Roderick, P., Macklon, N., & Alwan, N. A. (2017) · Obesity Reviews · doi:10.1111/obr.12640 · Cited by 56
Fracture Risk Assessment: State of the Art, Methodologically Unsound, or Poorly Reported?
Collins, G. S., & Michaëlsson, K. (2012) · Current Osteoporosis Reports · doi:10.1007/s11914-012-0108-1 · Cited by 51
Transcending data gaps: a framework to reduce inferential errors in ecological analyses
Kim, S. W., Blomberg, S. P., & Pandolfi, J. M. (2018) · Ecology Letters · doi:10.1111/ele.13089 · Cited by 46
Existing models, but not neutrophil-to-lymphocyte ratio, are prognostic in malignant mesothelioma
Meniawy, T. M., Creaney, J., Lake, R., & Nowak, A. K. (2013) · British Journal of Cancer · doi:10.1038/bjc.2013.504 · Cited by 47
Sensitivity analysis within multiple imputation framework using delta-adjustment: Application to Longitudinal Study of Australian Children
Rezvan, P. H., Lee, K. J., & Simpson, J. A. (2018) · Longitudinal and Life Course Studies · doi:10.14301/llcs.v9i3.503 · Cited by 43
Population pharmacokinetic modelling of gentamicin and vancomycin in patients with unstable renal function following cardiothoracic surgery
Staatz, C. E., Byrne, C., & Thomson, A. H. (2005) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.2005.02547.x · Cited by 43
Prognostic DNA methylation markers for sporadic colorectal cancer: a systematic review
Draht, M. X. G., Goudkade, D., Koch, A., Grabsch, H. I., Weijenberg, M. P., van Engeland, M., Melotte, V., & Smits, K. M. (2018) · Clinical Epigenetics · doi:10.1186/s13148-018-0461-8 · Cited by 41
Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration—the PINNACLE trial protocol
Sutton, J., Menten, M. J., Riedl, S., Bogunović, H., Leingang, O., Anders, P., Hagag, A. M., Waldstein, S. M., Wilson, A., Cree, A. J., et al. (2022) · Eye · doi:10.1038/s41433-022-02097-0 · Cited by 43
Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening
Han, G., Wei, Z., Song, X., Ng, P. K., Karam, J. A., Jonasch, E. W., Mills, G. B., Zhao, Z., Ding, Z., & Jia, P. (2017) · BMC Genomics · doi:10.1186/s12864-017-4026-6 · Cited by 40
The impact of girl child marriage on the completion of the first cycle of secondary education in Zimbabwe: A propensity score analysis
Bengesai, A. V., Amusa, L. B., & Makonye, F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0252413 · Cited by 49
Graphical models illustrated complex associations between variables describing human functioning
Strobl, R., Stucki, G., Grill, E., Müller, M., & Mansmann, U. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.01.018 · Cited by 37
Health Behaviors of Cancer Survivors in Nationwide Cross-Sectional Survey in Korea
Park, B., Kong, S., Kim, J., Kim, Y., Park, I. H., Jung, S., & Lee, E. S. (2015) · Medicine · doi:10.1097/md.0000000000001214 · Cited by 36
The practical utility of incorporating model selection uncertainty into prognostic models for survival data
Augustin, N. H., Sauerbrei, W., & Schumacher, M. C. (2005) · Statistical Modelling · doi:10.1191/1471082x05st089oa · Cited by 36
Predicting Residual Disease in Incidental Gallbladder Cancer: Risk Stratification for Modified Treatment Strategies
Creasy, J. M., Goldman, D. A., Gönen, M., Dudeja, V., Askan, G., Baştürk, O., Balachandran, V. P., Allen, P. J., DeMatteo, R. P., D'Angelica, M. I., et al. (2017) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-017-3436-8 · Cited by 35
A Deep Learning Approach for Missing Data Imputation of Rating Scales Assessing Attention-Deficit Hyperactivity Disorder
Cheng, C.-Y., Tseng, W., Chang, C., Chang, C.-H., & Gau, S. S. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00673 · Cited by 35
Limits to forecasting in personalized medicine: An overview
Ioannidis, J. P. A. (2009) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2009.05.003 · Cited by 31
Treatment and Prognosis of AIDS-Related Lymphoma in the Era of Highly Active Antiretroviral Therapy: Findings from the Swiss HIV Cohort Study
Simcock, M., Blasko, M., Karrer, U. W., Bertisch, B., Pless, M., Blumer, L., Vora, S., Robinson, J. O., Bernasconi, E., Beretta‐Piccoli, B. T., et al. (2007) · Antiviral Therapy · doi:10.1177/135965350701200609 · Cited by 31
Estimates of cancer incidence, mortality and survival in aboriginal people from NSW, Australia
Morrell, S. L., You, H., & Baker, D. F. (2012) · BMC Cancer · doi:10.1186/1471-2407-12-168 · Cited by 30
Estudios de precisión diagnóstica (STARD) y pronóstica (REMARK)
Altman, D. G., & Bossuyt, P. M. (2005) · Medicina Clínica · doi:10.1016/s0025-7753(05)72210-7 · Cited by 32
Association Between Hemoglobin A1c and Development of Cardiovascular Disease in Canadian Men and Women Without Diabetes at Baseline: A Population‐Based Study of 608 474 Adults
Butalia, S., Chu, L. M., Dover, D. C., Lau, D., Yeung, R. O., Eurich, D. T., Senior, P. A., & Kaul, P. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.123.031095 · Cited by 33
Top ten errors of statistical analysis in observational studies for cancer research
Carmona‐Bayonas, A., Jiménez‐Fonseca, P., Fernández‐Somoano, A., Alvarez-Manceñido, F., Castañón, E., Custodio, A., de la Peña, F. A., Martín‐Payo, R., & Valiente, L. P. (2017) · Clinical & Translational Oncology · doi:10.1007/s12094-017-1817-9 · Cited by 33
Reporting guidelines for oncology research: helping to maximise the impact of your research
MacCarthy, A., Kirtley, S., de Beyer, J. A., Altman, D. G., & Simera, I. (2018) · British Journal of Cancer · doi:10.1038/bjc.2017.407 · Cited by 29
Defining the expected 30-day mortality for patients undergoing palliative radiotherapy: A meta-analysis
Kutzko, J. H., Dadwal, P., Holt, T., Rahman, M. A., Zahir, S. F., & Hickey, B. E. (2022) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.01.030 · Cited by 29
Overweight and perception of overweight as predictors of smokeless tobacco use and of cigarette smoking in a cohort of Swedish adolescents.
Carìa, M., Bellocco, R., Zambon, A., Horton, N. J., & Galanti, M. (2009) · Addiction · doi:10.1111/j.1360-0443.2009.02506.x · Cited by 33
Can we afford to ignore missing data in cost-effectiveness analyses?
Marshall, A., Billingham, L. J., & Bryan, S. (2008) · The European Journal of Health Economics · doi:10.1007/s10198-008-0129-y · Cited by 29
Meta-analyzing the multiverse: A peek under the hood of selective reporting.
Olsson-Collentine, A., van Aert, R. C. M., Bakker, M., & Wicherts, J. M. (2023) · Psychological Methods · doi:10.1037/met0000559 · Cited by 40
Portal Vein Embolization Followed by Right-Side Hemihepatectomy for Hepatocellular Carcinoma Patients: A Japanese Multi-Institutional Study
Beppu, T., Okabe, H., Okuda, K., Eguchi, S., Kitahara, K., Taniai, N., Ueno, S., Shirabe, K., Ohta, M., Kondo, K., et al. (2016) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2016.03.023 · Cited by 27
Missing data, imputation, and endogeneity
McDonough, I. K., & Millimet, D. L. (2017) · Journal of Econometrics · doi:10.1016/j.jeconom.2017.05.006 · Cited by 27
Trends in study design and the statistical methods employed in a leading general medicine journal
Gosho, M., Sato, Y., Nagashima, K., & Takahashi, S. (2018) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1111/jcpt.12605 · Cited by 26
Prognostic Value of Circulating Cytokines on Overall Survival and Disease-Free Survival in Cancer Patients
Visconti, L. M., Nelissen, K., Deckx, L., van den Akker, M., Adriaensen, W. J., Daniëls, L., Matheï, C., Linsen, L., Hellings, N., Stinissen, P., et al. (2014) · Biomarkers in Medicine · doi:10.2217/bmm.13.122 · Cited by 26
Analyzing Incomplete Item Scores in Longitudinal Data by Including Item Score Information as Auxiliary Variables
Eekhout, I. E., Enders, C. K., Twisk, J. W. R., de Boer, M. R., de Vet, H. C. W., & Heymans, M. W. (2015) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2014.937670 · Cited by 26
Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
Exarchos, T. P., Papadopoulos, A. N., & Fotiadis, D. I. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-314-2 · Cited by 25
Advanced Stage at Presentation Remains a Major Factor Contributing to Breast Cancer Survival Disparity between Public and Private Hospitals in a Middle-Income Country
Kong, Y., Bhoo‐Pathy, N., Subramaniam, S., Bhoo-Pathy, N. T., Taib, N. A. M., Jamaris, S., Kaur, K., See, M., Ho, G. F., & Yip, C. H. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14040427 · Cited by 24

Authors: Burton, A., & Altman, D. G.
Affiliations: Cancer Research UK (GB)
Corresponding author: Burton, A
ORCID: Burton, A
Altman, D G
Venue: British Journal of Cancer
Publisher: Springer Science and Business Media LLC
Published: 2004-06-08
Volume: 91
Issue: 1
Pages: 4-8
ISSN: 0007-0920 (ISSN-L)
1532-1827
DOI: 10.1038/sj.bjc.6601907
PMC: PMC2364743
OpenAlex: W2040958097
Cited by: 219
References: 18
Citations by year: 2026: 3 · 2025: 9 · 2024: 9 · 2023: 11 · 2022: 10 · 2021: 7 · 2020: 12 · 2019: 11 · 2018: 13 · 2017: 13 · 2016: 7 · 2015: 9 · 2014: 16 · 2013: 7 · 2012: 20
FWCI: 8.78
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-sa
www.springer.com/tdm (unspecified)
Locations: British Journal of Cancer (publishedVersion, cc-by-nc-sa)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Funders: Cancer Research UK
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords: missing covariates, reporting quality, multivariate survival analysis, complete case analysis, available case analysis, missing data handling, reporting guidelines, prognostic factors
MeSH terms: Decision Making, Humans, Models, Theoretical, Neoplasms, Prognosis, Reproducibility of Results, Multivariate Analysis, Guidelines as Topic, Sample Size

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines.

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks