15 results on '"Sauerbrei, Willi"'
Search Results
2. Confidence intervals for the effect of a prognostic factor after selection of an 'optimal' cutpoint.
- Author
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Holländer N, Sauerbrei W, and Schumacher M
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- Breast Neoplasms genetics, Breast Neoplasms pathology, Computer Simulation, DNA, Neoplasm chemistry, DNA, Neoplasm genetics, Disease-Free Survival, Female, Flow Cytometry, Humans, Predictive Value of Tests, S Phase genetics, Confidence Intervals, Data Interpretation, Statistical, Prognosis
- Abstract
When investigating the effects of potential prognostic or risk factors that have been measured on a quantitative scale, values of these factors are often categorized into two groups. Sometimes an 'optimal' cutpoint is chosen that gives the best separation in terms of a two-sample test statistic. It is well known that this approach leads to a serious inflation of the type I error and to an overestimation of the effect of the prognostic or risk factor in absolute terms. In this paper, we illustrate that the resulting confidence intervals are similarly affected. We show that the application of a shrinkage procedure to correct for bias, together with bootstrap resampling for estimating the variance, yields confidence intervals for the effect of a potential prognostic or risk factor with the desired coverage., (Copyright 2004 John Wiley & Sons, Ltd.)
- Published
- 2004
- Full Text
- View/download PDF
3. Structured reporting to improve transparency of analyses in prognostic marker studies.
- Author
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Sauerbrei, Willi, Haeussler, Tim, Balmford, James, and Huebner, Marianne
- Subjects
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PROGNOSIS , *SURVIVAL rate , *TUMOR markers , *STATISTICS , *SAMPLE size (Statistics) - Abstract
Background: Factors contributing to the lack of understanding of research studies include poor reporting practices, such as selective reporting of statistically significant findings or insufficient methodological details. Systematic reviews have shown that prognostic factor studies continue to be poorly reported, even for important aspects, such as the effective sample size. The REMARK reporting guidelines support researchers in reporting key aspects of tumor marker prognostic studies. The REMARK profile was proposed to augment these guidelines to aid in structured reporting with an emphasis on including all aspects of analyses conducted. Methods: A systematic search of prognostic factor studies was conducted, and fifteen studies published in 2015 were selected, three from each of five oncology journals. A paper was eligible for selection if it included survival outcomes and multivariable models were used in the statistical analyses. For each study, we summarized the key information in a REMARK profile consisting of details about the patient population with available variables and follow-up data, and a list of all analyses conducted. Results: Structured profiles allow an easy assessment if reporting of a study only has weaknesses or if it is poor because many relevant details are missing. Studies had incomplete reporting of exclusion of patients, missing information about the number of events, or lacked details about statistical analyses, e.g., subgroup analyses in small populations without any information about the number of events. Profiles exhibit severe weaknesses in the reporting of more than 50% of the studies. The quality of analyses was not assessed, but some profiles exhibit several deficits at a glance. Conclusions: A substantial part of prognostic factor studies is poorly reported and analyzed, with severe consequences for related systematic reviews and meta-analyses. We consider inadequate reporting of single studies as one of the most important reasons that the clinical relevance of most markers is still unclear after years of research and dozens of publications. We conclude that structured reporting is an important step to improve the quality of prognostic marker research and discuss its role in the context of selective reporting, meta-analysis, study registration, predefined statistical analysis plans, and improvement of marker research. [ABSTRACT FROM AUTHOR]
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- 2022
- Full Text
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4. Multivariate Analysis of Prognostic Factors in Patients with Glioblastoma
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Lutterbach, Johannes, Sauerbrei, Willi, and Guttenberger, Roland
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- 2003
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5. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
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Wolff, Robert F, Moons, Karel GM, Riley, Richard D, Whiting, Penny F, Westwood, Marie, Collins, Gary S, Reitsma, Johannes B, Kleijnen, Jos, Mallett, Sue, Altman, Doug, Bossuyt, Patrick, Cook, Nancy R, D'Amico, Gennaro, Debray, Thomas PA, Deeks, Jon, De Groot, Joris, Di Angelantonio, Emanuele, Fahey, Tom, Harrell, Frank, Hayden, Jill A, Heymans, Martijn W, Hooft, Lotty, Hyde, Chris, Ioannidis, John, Iorio, Alfonso, Kaptoge, Stephen, Knottnerus, Andre, Leeflang, Mariska, Nixon, Frances, Perel, Pablo, Phillips, Bob, Raatz, Heike, Riemsma, Rob, Rovers, Maroeska, Rutjes, Anne WS, Sauerbrei, Willi, Sauerland, Stefan, Scheibler, Fueloep, Scholten, Rob, Schuit, Ewoud, Steyerberg, Ewout, Tan, Toni, Ter Riet, Gerben, Van Der Windt, Danielle, Vergouwe, Yvonne, Vickers, Andrew, Wood, Angela M, Grp, PROBAST, Grp, PROBAST Steering, Grp, PROBAST Delphi, Family Medicine, RS: CAPHRI - R6 - Promoting Health & Personalised Care, RS: CAPHRI - R5 - Optimising Patient Care, Bureau FHML, Public Health, Di Angelantonio, Emanuele [0000-0001-8776-6719], Kaptoge, Stephen [0000-0002-1155-4872], Wood, Angela [0000-0002-7937-304X], and Apollo - University of Cambridge Repository
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Delphi Technique ,Delphi method ,MEDLINE ,01 natural sciences ,Decision Support Techniques ,03 medical and health sciences ,All institutes and research themes of the Radboud University Medical Center ,0302 clinical medicine ,Bias ,Diagnosis ,Internal Medicine ,Medicine ,Humans ,030212 general & internal medicine ,0101 mathematics ,computer.programming_language ,Models, Statistical ,business.industry ,CARDIOVASCULAR RISK ,Explanation ,010102 general mathematics ,DIAGNOSIS TRIPOD ,INDIVIDUAL PROGNOSIS ,General Medicine ,Evidence-based medicine ,Prognosis ,R1 ,Data science ,Critical appraisal ,Systematic review ,Research Design ,Urological cancers Radboud Institute for Health Sciences [Radboudumc 15] ,Model risk ,business ,RA ,computer ,Predictive modelling ,Delphi ,Systematic Reviews as Topic - Abstract
Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.
- Published
- 2019
6. The prognostic effect of histological tumor grade in node-negative breast cancer patients
- Author
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Schumacher, Martin, Schmoor, Claudia, Sauerbrei, Willi, Schauer, Alfred, Ummenhofer, Lucia, Gatzemeier, Wolfgang, and Rauschecker, Helmut
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- 1993
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7. Individual participant data meta-analysis of prognostic factor studies: state of the art?
- Author
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Abo-Zaid Ghada, Sauerbrei Willi, and Riley Richard D
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Meta-analysis ,Prognostic factor ,Prognosis ,Individual participant (patient) data ,Systematic review ,Reporting ,Medicine (General) ,R5-920 - Abstract
Abstract Background Prognostic factors are associated with the risk of a subsequent outcome in people with a given disease or health condition. Meta-analysis using individual participant data (IPD), where the raw data are synthesised from multiple studies, has been championed as the gold-standard for synthesising prognostic factor studies. We assessed the feasibility and conduct of this approach. Methods A systematic review to identify published IPD meta-analyses of prognostic factors studies, followed by detailed assessment of a random sample of 20 articles published from 2006. Six of these 20 articles were from the IMPACT (International Mission for Prognosis and Analysis of Clinical Trials in traumatic brain injury) collaboration, for which additional information was also used from simultaneously published companion papers. Results Forty-eight published IPD meta-analyses of prognostic factors were identified up to March 2009. Only three were published before 2000 but thereafter a median of four articles exist per year, with traumatic brain injury the most active research field. Availability of IPD offered many advantages, such as checking modelling assumptions; analysing variables on their continuous scale with the possibility of assessing for non-linear relationships; and obtaining results adjusted for other variables. However, researchers also faced many challenges, such as large cost and time required to obtain and clean IPD; unavailable IPD for some studies; different sets of prognostic factors in each study; and variability in study methods of measurement. The IMPACT initiative is a leading example, and had generally strong design, methodological and statistical standards. Elsewhere, standards are not always as high and improvements in the conduct of IPD meta-analyses of prognostic factor studies are often needed; in particular, continuous variables are often categorised without reason; publication bias and availability bias are rarely examined; and important methodological details and summary results are often inadequately reported. Conclusions IPD meta-analyses of prognostic factors are achievable and offer many advantages, as displayed most expertly by the IMPACT initiative. However such projects face numerous logistical and methodological obstacles, and their conduct and reporting can often be substantially improved.
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- 2012
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8. Did the reporting of prognostic studies of tumour markers improve since the introduction of REMARK guideline? A comparison of reporting in published articles
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Sekula, Peggy, Mallett, Susan, Altman, Douglas G., and Sauerbrei, Willi
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Research Report ,Biomedical Research ,Writing ,Cancer Treatment ,lcsh:Medicine ,Guidelines as Topic ,Research and Analysis Methods ,Biochemistry ,Mathematical and Statistical Techniques ,Diagnostic Medicine ,Breast Tumors ,Breast Cancer ,Medicine and Health Sciences ,Biomarkers, Tumor ,Humans ,Statistical Methods ,lcsh:Science ,Publishing ,lcsh:R ,Biology and Life Sciences ,Cancers and Neoplasms ,Research Assessment ,Prognosis ,Oncology ,Research Design ,Physical Sciences ,Research Reporting Guidelines ,lcsh:Q ,Biomarkers ,Mathematics ,Statistics (Mathematics) ,Research Article - Abstract
Although biomarkers are perceived as highly relevant for future clinical practice, few biomarkers reach clinical utility for several reasons. Among them, poor reporting of studies is one of the major problems. To aid improvement, reporting guidelines like REMARK for tumour marker prognostic (TMP) studies were introduced several years ago. The aims of this project were to assess whether reporting quality of TMP-studies improved in comparison to a previously conducted study assessing reporting quality of TMP-studies (PRE-study) and to assess whether articles citing REMARK (citing group) are better reported, in comparison to articles not citing REMARK (not-citing group). For the POST-study, recent articles citing and not citing REMARK (53 each) were identified in selected journals through systematic literature search and evaluated in same way as in the PRE-study. Ten of the 20 items of the REMARK checklist were evaluated and used to define an overall score of reporting quality. The observed overall scores were 53.4% (range: 10%-90%) for the PRE-study, 57.7% (range: 20%-100%) for the not-citing group and 58.1% (range: 30%-100%) for the citing group of the POST-study. While there is no difference between the two groups of the POST-study, the POST-study shows a slight but not relevant improvement in reporting relative to the PRE-study. Not all the articles of the citing group, cited REMARK appropriately. Irrespective of whether REMARK was cited, the overall score was slightly higher for articles published in journals requesting adherence to REMARK than for those published in journals not requesting it: 59.9% versus 51.9%, respectively. Several years after the introduction of REMARK, many key items of TMP-studies are still very poorly reported. A combined effort is needed from authors, editors, reviewers and methodologists to improve the current situation. Good reporting is not just nice to have but is essential for any research to be useful.
- Published
- 2017
9. Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example
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Winzer, Klaus-Jürgen, Buchholz, Anika, Schumacher, Martin, and Sauerbrei, Willi
- Subjects
Cancer Treatment ,lcsh:Medicine ,Breast Neoplasms ,Receptors, Cell Surface ,Surgical and Invasive Medical Procedures ,Kaplan-Meier Estimate ,600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit ,Research and Analysis Methods ,Severity of Illness Index ,Biochemistry ,Polynomials ,Lymphatic System ,Mathematical and Statistical Techniques ,Diagnostic Medicine ,Breast Tumors ,Breast Cancer ,Biomarkers, Tumor ,Medicine and Health Sciences ,Humans ,Statistical Methods ,lcsh:Science ,Proportional Hazards Models ,lcsh:R ,Biology and Life Sciences ,Cancers and Neoplasms ,Reference Standards ,Prognosis ,Quality Improvement ,Hormones ,Tumor Burden ,Algebra ,Oncology ,Lymphatic Metastasis ,Multivariate Analysis ,Physical Sciences ,lcsh:Q ,Female ,Lymph Nodes ,Neoplasm Grading ,Anatomy ,Mathematics ,Statistics (Mathematics) ,Research Article - Abstract
Background Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases.
- Published
- 2015
10. Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): An Abridged Explanation and Elaboration.
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Sauerbrei, Willi, Taube, Sheila E, McShane, Lisa M, Cavenagh, Margaret M, and Altman, Douglas G
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TUMOR markers , *CANCER , *BIOMARKERS , *TUMORS , *TUMOR antigens , *TUMOR diagnosis , *COMPARATIVE studies , *EXPERIMENTAL design , *RESEARCH methodology , *MEDICAL cooperation , *MEDICAL protocols , *MEDICAL research , *PROGNOSIS , *PUBLISHING , *RESEARCH , *EVALUATION research , *STANDARDS - Abstract
The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) were developed to address widespread deficiencies in the reporting of such studies. The REMARK checklist consists of 20 items to report for published tumor marker prognostic studies. A detailed paper was published explaining the rationale behind checklist items, providing positive examples and giving empirical evidence of the quality of reporting. REMARK provides a comprehensive overview to educate on good reporting and provide a valuable reference for the many issues to consider when designing, conducting, and analyzing tumor marker studies and prognostic studies in medicine in general. Despite support for REMARK from major cancer journals, prognostic factor research studies remain poorly reported. To encourage dissemination and uptake of REMARK, we have produced this considerably abridged version of the detailed explanatory manuscript, which may also serve as a brief guide to key issues for investigators planning tumor marker prognostic studies. To summarize the current situation, more recent papers investigating the quality of reporting and related reporting guidelines are cited, but otherwise the literature is not updated. Another important impetus for this paper is that it serves as a basis for literal translations into other languages. Translations will help to bring key information to a larger audience world-wide. Many more details can be found in the original paper. [ABSTRACT FROM AUTHOR]
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- 2018
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11. Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example.
- Author
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Winzer, Klaus-Jürgen, Buchholz, Anika, Schumacher, Martin, and Sauerbrei, Willi
- Subjects
BREAST cancer prognosis ,BREAST cancer patients ,BREAST cancer treatment ,DISEASE management ,MEDICAL informatics ,TUMOR grading - Abstract
Background: Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings: Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions: The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases. [ABSTRACT FROM AUTHOR]
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- 2016
- Full Text
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12. Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration.
- Author
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Altman, Douglas G., McShane, Lisa M., Sauerbrei, Willi, and Taube, Sheila E.
- Subjects
TUMOR markers ,CANCER treatment ,PROGNOSIS - Abstract
The article presents a study which provides detailed explanations on the checklist items in the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). It is inferred that the recommendations were developed by the European Organization for Research and Treatment of Cancer and the National Cancer Institute. An overview on the proper method of reporting prognostic marker research is provided.
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- 2012
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13. Evidence-Based Assessment and Application of Prognostic Markers: The Long Way from Single Studies to Meta-Analysis.
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Sauerbrei, Willi, Holländer, Norbert, Riley, RichardD., and Altman, DouglasG.
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META-analysis , *PROGNOSIS , *BIOMARKERS , *MULTIVARIATE analysis , *MATHEMATICAL statistics - Abstract
The identification and assessment of prognostic markers constitutes one of the major tasks in clinical research. Despite huge research effort, the prognostic value of most traditional factors under discussion is uncertain and the usefulness of many specific markers, prognostic indices, and classification schemes is still unproven. Results from different studies are often contradictory, and a general assessment of the usefulness of a specific marker is very difficult. One reason is that systematic reviews of prognostic marker studies have received rather little attention in the literature. It is obvious that a clinically useful and sensible systematic review of a prognostic marker is only possible if the published studies reflect the true nature of the marker and if sufficient details are given in each report. An important goal of a systematic review is to produce a quantitative summary of an effect of interest by a meta-analysis, a statistical approach that combines the results of individual primary studies by a weighted average. For observational studies, an estimate from a univariate model is only of limited interest; a multivariable approach is absolutely essential to derive an estimate that is adjusted for other factors. However, even when “adjusted estimates” are presented, it is common for different studies to use different variables for adjustment, and specific “adjustment” variables may be measured in different ways or may be used with different scales. These difficulties are partly caused by the large variety of available statistical methods of analyzing prognostic marker studies. In three related papers published in a proceedings volume, Holländer and Sauerbrei (2006), Riley et al. (2006), and Altman et al. (2006) discuss statistical approaches for multivariable analysis, issues of reporting of primary studies, and the feasibility of obtaining individual patient data from multiple studies on prognosis. Holländer and Sauerbrei (2006) show that the specific statistical method can have a strong influence on the final multivariable model and on the interpretation of the effect of a specific factor. Possible approaches to help improve reporting standards are discussed in the paper by Riley et al. (2006), which also considers other important issues such as how to improve the design and clinical relevance of primary prognostic studies. For a sensible summary assessment, individual patient data (IPD) and a close collaboration between different study groups seem to be essential. However, Altman et al. (2006) discuss in their paper practical problems in using the IPD approach to evaluate evidence relating to a prognostic marker. Here the three papers are summarized with the aim of demonstrating difficulties and making some recommendations to improve future research in evidence-based assessment of prognostic markers. For many more details we refer to the original papers. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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14. The practical utility of incorporating model selection uncertainty into prognostic models for survival data.
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Augustin, Nicole, Sauerbrei, Willi, and Schumacher, Martin
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- *
PROGNOSIS , *DIAGNOSIS , *STATISTICAL sampling , *SURVIVAL , *CONFIDENCE intervals , *STATISTICAL hypothesis testing - Abstract
Predictions of disease outcome in prognostic factor models are usually based on one selected model. However, often several models fit the data equally well, but these models might differ substantially in terms of included explanatory variables and might lead to different predictions for individual patients. For survival data, we discuss two approaches to account for model selection uncertainty in two data examples, with the main emphasis on variable selection in a proportional hazard Cox model. The main aim of our investigation is to establish the ways in which either of the two approaches is useful in such prognostic models. The first approach is Bayesian model averaging (BMA) adapted for the proportional hazard model, termed 'approx. BMA' here. As a new approach, we propose a method which averages over a set of possible models using weights estimated from bootstrap resampling as proposed by Buckland et al., but in addition, we perform an initial screening of variables based on the inclusion frequency of each variable to reduce the set of variables and corresponding models. For some necessary parameters of the procedure, investigations concerning sensible choices are still required. The main objective of prognostic models is prediction, but the interpretation of single effects is also important and models should be general enough to ensure transportability to other clinical centres. In the data examples, we compare predictions of our new approach with approx. BMA, with 'conventional' predictions from one selected model and with predictions from the full model. Confidence intervals are compared in one example. Comparisons are based on the partial predictive score and the Brier score. We conclude that the two model averaging methods yield similar results and are especially useful when there is a high number of potential prognostic factors, most likely some of them without influence in a multivariable context. Although the method based on bootstrap resampling lacks formal justification and requires some ad hoc decisions, it has the additional positive effect of achieving model parsimony by reducing the number of explanatory variables and dealing with correlated variables in an automatic fashion. [ABSTRACT FROM AUTHOR]
- Published
- 2005
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15. Reply: Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK).
- Author
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McShane, Lisa M., Altman, Douglas G., Sauerbrei, Willi, Taube, Sheila E., Gion, Massimo, and Clark, Gary M.
- Subjects
TUMOR reporting ,GUIDELINES ,TUMOR markers ,TUMORS ,PROGNOSIS - Abstract
The article presents a response to the creation reporting guidelines for tumor marker prognostic studies. The authors cited that there are serious differences in the quality of reporting of tumor marker studies. He wants to maintain his position that, more is better, only when the data are of good quality.
- Published
- 2005
- Full Text
- View/download PDF
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