Willi Sauerbrei

Reporting Recommendations for Tumor Marker Prognostic Studies

Lisa M. McShane, Biometric Research Branch, National Cancer Institute, Bethesda, MD Douglas G. Altman, Medical Statistics Group, Cancer Research UK, Centre for Statistics in Medicine, Wolfson College, Oxford, UK Willi Sauerbrei, Institut fuer Medizinische Biometrie und Medizinische Informatik, Universitaetsklinikum Freiburg, Freiburg, Germany Sheila E. Taube, Cancer Diagonisis Program, National Cancer Institute, Bethesda, MD Massimo Gion, Centro Regionale Indicatori Biochimici di Tumore, Ospedale Civile, Venezia, Italy Gary M. Clark, OSI Pharmaceuticals Inc, Boulder, CO For the Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics

REporting recommendations for tumour MARKer prognostic studies (REMARK).

Despite years of research and hundreds of reports on tumour markers in oncology, the number of markers that have emerged as clinically useful is pitifully small. Often initially reported studies of a marker show great promise, but subsequent studies on the same or related markers yield inconsistent conclusions or stand in direct contradiction to the promising results. It is imperative that we attempt to understand the reasons that multiple studies of the same marker lead to differing conclusions. A variety of methodological problems have been cited to explain these discrepancies. Unfortunately, many tumour marker studies have not been reported in a rigorous fashion, and published articles often lack sufficient information to allow adequate assessment of the quality of the study or the generalisability of the study results. The development of guidelines for the reporting of tumour marker studies was a major recommendation of the US National Cancer Institute and the European Organisation for Research and Treatment of Cancer (NCI-EORTC) First International Meeting on Cancer Diagnostics in 2000. Similar to the successful CONSORT initiative for randomised trials and the STARD statement for diagnostic studies, we suggest guidelines to provide relevant information about the study design, preplanned hypotheses, patient and specimen characteristics, assay methods, and statistical analysis methods. In addition, the guidelines suggest helpful presentations of data and important elements to include in discussions. The goal of these guidelines is to encourage transparent and complete reporting so that the relevant information will be available to others to help them to judge the usefulness of the data and understand the context in which the conclusions apply.

Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration

The REMARK “elaboration and explanation” guideline, by Doug Altman and colleagues, provides a detailed reference for authors on important issues to consider when designing, conducting, and analyzing tumor marker prognostic studies.

Assessment and comparison of prognostic classification schemes for survival data

Prognostic classification schemes have often been used in medical applications, but rarely subjected to a rigorous examination of their adequacy. For survival data, the statistical methodology to assess such schemes consists mainly of a range of ad hoc approaches, and there is an alarming lack of commonly accepted standards in this field. We review these methods and develop measures of inaccuracy which may be calculated in a validation study in order to assess the usefulness of estimated patient-specific survival probabilities associated with a prognostic classification scheme. These measures are meaningful even when the estimated probabilities are misspecified, and asymptotically they are not affected by random censorship. In addition, they can be used to derive R(2)-type measures of explained residual variation. A breast cancer study will serve for illustration throughout the paper.

Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials

To be useful to clinicians, prognostic and diagnostic indices must be derived from accurate models developed by using appropriate data sets. We show that fractional polynomials, which extend ordinary polynomials by including non-positive and fractional powers, may be used as the basis of such models. We describe how to fit fractional polynomials in several continuous covariates simultaneously, and we propose ways of ensuring that the resulting models are parsimonious and consistent with basic medical knowledge. The methods are applied to two breast cancer data sets, one from a prognostic factors study in patients with positive lymph nodes and the other from a study to diagnose malignant or benign tumours by using colour Doppler blood flow mapping. We investigate the problems of biased parameter estimates in the final model and overfitting using cross-validation calibration to estimate shrinkage factors. We adopt bootstrap resampling to assess model stability. We compare our new approach with conventional modelling methods which apply stepwise variables selection to categorized covariates. We conclude that fractional polynomial methodology can be very successful in generating simple and appropriate models.

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (http://www.strobe-statement.org/) should be helpful resources to improve reporting of observational research.

REporting recommendations for tumor MARKer prognostic studies (REMARK)

Despite the plethora of reports on tumor markers in oncology, the number of markers that have emerged as clinically useful is disappointing. There is considerable evidence that the quality of reporting of studies of biomarkers, particularly tumor prognostic markers, is generally poor. McShaneet al. present guidelines that provide helpful suggestions on study design, patient characteristics, statistical analysis methods, and guidance on how to present data. The authors advocate the importance of transparent and complete reporting of tumor marker prognostic studies in order to increase accessibility and interpretability of trial data, which should help to improve patient treatment and management.AbstractDespite years of research and hundreds of reports on tumor markers in oncology, the number of markers that have emerged as clinically useful is pitifully small. Often initially reported studies of a marker show great promise, but subsequent studies on the same or related markers yield inconsistent conclusions or stand in direct contradiction to the promising results. It is imperative that we attempt to understand the reasons why multiple studies of the same marker lead to differing conclusions. A variety of methodological problems have been cited to explain these discrepancies. Unfortunately, many tumor marker studies have not been reported in a rigorous fashion, and published articles often lack sufficient information to allow adequate assessment of the quality of the study or the generalizability of study results. The development of guidelines for the reporting of tumor marker studies was a major recommendation of the National Cancer Institute–European Organisation for Research and Treatment of Cancer (NCI–EORTC) First International Meeting on Cancer Diagnostics in 2000. As for the successful CONSORT initiative for randomized trials and for the STARD statement for diagnostic studies, we suggest guidelines to provide relevant information about the study design, preplanned hypotheses, patient and specimen characteristics, assay methods, and statistical analysis methods. In addition, the guidelines provide helpful suggestions on how to present data and important elements to include in discussions. The goal of these guidelines is to encourage transparent and complete reporting so that the relevant information will be available to others to help them to judge the usefulness of the data and understand the context in which the conclusions apply.boxed-text

Goserelin versus cyclophosphamide, methotrexate, and fluorouracil as adjuvant therapy in premenopausal patients with node-positive breast cancer: The Zoladex Early Breast Cancer Research Association Study

PURPOSE Current adjuvant therapies have improved survival for premenopausal patients with breast cancer but may have short-term toxic effects and long-term effects associated with premature menopause. PATIENTS AND METHODS The Zoladex Early Breast Cancer Research Association study assessed the efficacy and tolerability of goserelin (3.6 mg every 28 days for 2 years; n = 817) versus cyclophosphamide, methotrexate, and fluorouracil (CMF) chemotherapy (six 28-day cycles; n = 823) for adjuvant treatment in premenopausal patients with node-positive breast cancer. RESULTS Analysis was performed when 684 events had been achieved, and the median follow-up was 6 years. A significant interaction between treatment and estrogen receptor (ER) status was found (P =.0016). In ER-positive patients (approximately 74%), goserelin was equivalent to CMF for disease-free survival (DFS) (hazard ratio [HR], 1.01; 95% confidence interval [CI], 0.84 to 1.20). In ER-negative patients, goserelin was inferior to CMF for DFS (HR, 1.76; 95% CI, 1.27 to 2.44). Amenorrhea occurred in more than 95% of goserelin patients by 6 months versus 58.6% of CMF patients. Menses returned in most goserelin patients after therapy stopped, whereas amenorrhea was generally permanent in CMF patients (22.6% v 76.9% amenorrheic at 3 years). Chemotherapy-related side effects such as nausea/vomiting, alopecia, and infection were higher with CMF than with goserelin during CMF treatment. Side effects related to estrogen suppression were initially higher with goserelin, but when goserelin treatment stopped, reduced to a level below that observed in the CMF group. CONCLUSION Goserelin offers an effective, well-tolerated alternative to CMF in premenopausal patients with ER-positive and node-positive early breast cancer.

The Use of Resampling Methods to Simplify Regression Models in Medical Statistics

Summary. The number of variables in a regression model is often too large and a more parsimonious model may be preferred. Selection strategies (e.g. all-subset selection with various penalties for model complexity, or stepwise procedures) are widely used, but there are few analytical results about their properties. The problems of replication stability, model complexity, selection bias and an overoptimistic estimate of the predictive value of a model are discussed together with several proposals based on resampling methods. The methods are applied to data from a case-control study on atopic dermatitis and a clinical trial to compare two chemotherapy regimes by using a logistic regression and a Cox model. A recent proposal to use shrinkage factors to reduce the bias of parameter estimates caused by model building is extended to parameterwise shrinkage factors and is discussed as a further possibility to illustrate problems of models which are too complex. The results from the resampling approaches favour greater simplicity of the final regression model.

Prognosis research strategy (PROGRESS) 4: Stratified medicine research

In patients with a particular disease or health condition, stratified medicine seeks to identify those who will have the most clinical benefit or least harm from a specific treatment. In this article, the fourth in the PROGRESS series, the authors discuss why prognosis research should form a cornerstone of stratified medicine, especially in regard to the identification of factors that predict individual treatment response