Bland-Altman Plots for Evaluating Agreement Between Solid Tumor Measurements

Rationale and Objectives. Solid tumor measurements are regularly used in clinical trials of anticancer therapeutic agents and in clinical practice managing patients\u27 care. Consequently studies evaluating the reproducibility of solid tumor measurements are important as lack of reproducibility may directly affect patient management. The authors propose utilizing a modified Bland-Altman plot with a difference metric that lends itself naturally to this situation and facilitates interpretation. Materials and Methods. The modification to the Bland-Altman plot involves replacing the difference plotted on the vertical axis with the relative percent change (RC) between the two measurements. This quantity is the same one used in assessing tumor response to therapeutic agents and is very familiar to radiologists and clinicians working with cancer patients.The distribution of the RC is explored and revised equations for the limits of agreement (LoA) are presented. These methods are applied to positron emission tomography (PET) data studying two radiotracers. Results. The RC can be calculated separately for each lesion measured or at the patient level by summing over lesions within patient. In both cases, the distribution of the RC is highly skewed and is approximated by a negative shifted lognormal distribution. The standard equations for the 95% LoA assume the differences are approximately normally distributed and are not appropriate for the RC. Conclusions. The modified Bland-Altman plot with correctly calculated LoA can aid in evaluating agreement between solid tumor measurements

Comparing the Predictive Values of Diagnostic Tests: Sample Size and Analysis for Paired Study Designs

In this paper we consider the design and analysis of studies comparing the positive and negative predictive values of two diagnostic tests that are measured on all subjects. Although statistical methodology is well developed for comparing diagnostic tests in terms of their sensitivities and specificities, comparative inference about predictive values is not. We derive analytic variance expressions for the relative predictive values. Sample size formulas for study design ensue. In addition, two new methods for analyzing the resulting data are presented and compared with an existing marginal regression methodology

Inferential Methods to Assess the Difference in the Area Under the Curve From Nested Binary Regression Models

The area under the curve (AUC) is the most common statistical approach to evaluate the discriminatory power of a set of factors in a binary regression model. A nested model framework is used to ascertain whether the AUC increases when new factors enter the model. Two statistical tests are proposed for the difference in the AUC parameters from these nested models. The asymptotic null distributions for the two test statistics are derived from the scenarios: (A) the difference in the AUC parameters is zero and the new factors are not associated with the binary outcome, (B) the difference in the AUC parameters is less than a strictly positive value. A confidence interval for the difference in AUC parameters is developed. Simulations are generated to determine the finite sample operating characteristics of the tests and a pancreatic cancer data example is used to illustrate this approach

Estimating the Empirical Lorenz Curve and Gini Coefficient in the Presence of Error

The Lorenz curve is a graphical tool that is widely used to characterize the concentration of a measure in a population, such as wealth. It is frequently the case that the measure of interest used to rank experimental units when estimating the empirical Lorenz curve, and the corresponding Gini coefficient, is subject to random error. This error can result in an incorrect ranking of experimental units which inevitably leads to a curve that exaggerates the degree of concentration (variation) in the population. We explore this bias and discuss several widely available statistical methods that have the potential to reduce or remove the bias in the empirical Lorenz curve. The properties of these methods are examined and compared in a simulation study. This work is motivated by a health outcomes application which seeks to assess the concentration of black patient visits among primary care physicians. The methods are illustrated on data from this study

Subsequent Female Breast Cancer Risk Associated With Anthracycline Chemotherapy for Childhood Cancer

Anthracycline-based chemotherapy is associated with increased subsequent breast cancer (SBC) risk in female childhood cancer survivors, but the current evidence is insufficient to support early breast cancer screening recommendations for survivors treated with anthracyclines. In this study, we pooled individual patient data of 17,903 survivors from six well-established studies, of whom 782 (4.4%) developed a SBC, and analyzed dose-dependent effects of individual anthracycline agents on developing SBC and interactions with chest radiotherapy. A dose-dependent increased SBC risk was seen for doxorubicin (hazard ratio (HR) per 100 mg 

Subsequent female breast cancer risk associated with anthracycline chemotherapy for childhood cancer.

Anthracycline-based chemotherapy is associated with increased subsequent breast cancer (SBC) risk in female childhood cancer survivors, but the current evidence is insufficient to support early breast cancer screening recommendations for survivors treated with anthracyclines. In this study, we pooled individual patient data of 17,903 survivors from six well-established studies, of whom 782 (4.4%) developed a SBC, and analyzed dose-dependent effects of individual anthracycline agents on developing SBC and interactions with chest radiotherapy. A dose-dependent increased SBC risk was seen for doxorubicin (hazard ratio (HR) per 100 mg m-2: 1.24, 95% confidence interval (CI): 1.18-1.31), with more than twofold increased risk for survivors treated with ≥200 mg m-2 cumulative doxorubicin dose versus no doxorubicin (HR: 2.50 for 200-299 mg m-2, HR: 2.33 for 300-399 mg m-2 and HR: 2.78 for ≥400 mg m-2). For daunorubicin, the associations were not statistically significant. Epirubicin was associated with increased SBC risk (yes/no, HR: 3.25, 95% CI: 1.59-6.63). For patients treated with or without chest irradiation, HRs per 100 mg m-2 of doxorubicin were 1.11 (95% CI: 1.02-1.21) and 1.26 (95% CI: 1.17-1.36), respectively. Our findings support that early initiation of SBC surveillance may be reasonable for survivors who received ≥200 mg m-2 cumulative doxorubicin dose and should be considered in SBC surveillance guidelines for survivors and future treatment protocols

Cohort profile: Risk and risk factors for female breast cancer after treatment for childhood and adolescent cancer: an internationally pooled cohort.

PURPOSE The International Consortium for Pooled Studies on Subsequent Malignancies after Childhood and Adolescent Cancer was established in 2018 to address gaps in knowledge of risk and risk factors for breast cancer subsequent to childhood/adolescent cancer by pooling individual patient data from seven cohorts. Initially, the pooled cohort will focus on three clinically relevant questions regarding treatment-related subsequent breast cancer risk in female survivors, which are the risk related to low-dose radiotherapy exposure to the chest, specific chemotherapy agents and attained age. PARTICIPANTS The consortium database includes pooled data on 21 892 female survivors from seven cohorts in North America and Europe with a primary cancer diagnosis at <21 years of age, and survival ≥5 years from diagnosis. FINDINGS TO DATE This is a newly established pooled study. The cohort profile summarised the data collected from each included cohort, including childhood cancer diagnosis information and treatment details (ie, radiotherapy fields and cumulative doses, and chemotherapy agents and cumulative doses for each agent). Included cohorts' follow-up started 1951-1981 and ended 2013-2021, respectively, for a median follow-up duration of 24.3 (IQR 18.0-32.8) years since primary cancer diagnosis. The median age at primary cancer diagnosis was 5.4 (IQR 2.5-11.9) years. And the median attained age at last follow-up was 32.2 (IQR 24.0-40.4) years. In all, 4240 (19.4%) survivors were treated with radiotherapy to the chest and 9308 (42.5%) with anthracyclines. At the end of the follow-up, 835 females developed a first subsequent breast cancer, including 635 invasive breast cancer only, 184 carcinomas in situ only (172 ductal carcinomas in situ and 12 lobular carcinomas in situ), and 16 with both an invasive and in situ diagnosis at the same moment. The cumulative incidences of subsequent breast cancer (both invasive and in situ) 25 and 35 years after primary cancer diagnosis were 2.2% and 6.2%, respectively. FUTURE PLANS The consortium is intended to serve as a model and robust source of childhood/adolescent cancer survivor data for elucidating other knowledge gaps on subsequent breast cancer risk, and risk of other subsequent malignancies (including data on males) in the future

Subsequent female breast cancer risk associated with anthracycline chemotherapy for childhood cancer

Anthracycline-based chemotherapy is associated with increased subsequent breast cancer (SBC) risk in female childhood cancer survivors, but the current evidence is insufficient to support early breast cancer screening recommendations for survivors treated with anthracyclines. In this study, we pooled individual patient data of 17,903 survivors from six well-established studies, of whom 782 (4.4%) developed a SBC, and analyzed dose-dependent effects of individual anthracycline agents on developing SBC and interactions with chest radiotherapy. A dose-dependent increased SBC risk was seen for doxorubicin (hazard ratio (HR) per 100 mg m−2: 1.24, 95% confidence interval (CI): 1.18–1.31), with more than twofold increased risk for survivors treated with ≥200 mg m−2 cumulative doxorubicin dose versus no doxorubicin (HR: 2.50 for 200–299 mg m−2, HR: 2.33 for 300–399 mg m−2 and HR: 2.78 for ≥400 mg m−2). For daunorubicin, the associations were not statistically significant. Epirubicin was associated with increased SBC risk (yes/no, HR: 3.25, 95% CI: 1.59–6.63). For patients treated with or without chest irradiation, HRs per 100 mg m−2 of doxorubicin were 1.11 (95% CI: 1.02–1.21) and 1.26 (95% CI: 1.17–1.36), respectively. Our findings support that early initiation of SBC surveillance may be reasonable for survivors who received ≥200 mg m−2 cumulative doxorubicin dose and should be considered in SBC surveillance guidelines for survivors and future treatment protocols

Quantifying and Comparing the Accuracy of Binary Biomarkers When Predicting a Failure Time Outcome

The positive and negative predictive value are standard measures used to quantify the predictive accuracy of binary biomarkers when the outcome being predicted is also binary. When the biomarkers are instead being used to predict a failure time outcome, there is no standard way of quantifying predictive accuracy. We propose a natural extension of the traditional predictive values to accommodate censored survival data. We discuss not only quantifying predictive accuracy using these extended predictive values, but also rigorously comparing the accuracy of two biomarkers in terms of their predictive values. Using a marginal regression framework, we describe how to estimate differences in predictive accuracy and how to test whether the observed difference is statistically significant

Predicting Adverse Health Outcomes in Long-Term Survivors of a Childhood Cancer

More than 80% of children and young adults diagnosed with invasive cancer will survive five or more years beyond their cancer diagnosis. This population has an increased risk for serious illness- and treatment-related morbidity and premature mortality. A number of these adverse health outcomes, such as cardiovascular disease and some second primary neoplasms, either have modifiable risk factors or can be successfully treated if detected early. Absolute risk models that project a personalized risk of developing a health outcome can be useful in patient counseling, in designing intervention studies, in forming prevention strategies, and in deciding upon surveillance programs. Here, we review existing absolute risk prediction models that are directly applicable to survivors of a childhood cancer, discuss the concepts and interpretation of absolute risk models, and examine ways in which these models can be used applied in clinical practice and public health