Endpoint In Cancer Clinical Trials Research Articles

Patient-reported outcome measures for physical function in cancer patients: content comparison of the EORTC CAT Core, EORTC QLQ-C30, SF-36, FACT-G, and PROMIS measures using the International Classification of Functioning, Disability and Health

BackgroundPatient-reported physical function (PF) is a key endpoint in cancer clinical trials. Using complex statistical methods, common metrics have been developed to compare scores from different patient-reported outcome (PRO) measures, but such methods do not account for possible differences in questionnaire content. Therefore, the aim of our study was a content comparison of frequently used PRO measures for PF in cancer patients.MethodsRelying on the framework of the International Classification of Functioning, Disability and Health (ICF) we categorized the item content of the physical domains of the following measures: EORTC CAT Core, EORTC QLQ-C30, SF-36, PROMIS Cancer Item Bank for Physical Function, PROMIS Short Form for Physical Function 20a, and the FACT-G. Item content was linked to ICF categories by two independent reviewers.ResultsThe 118 items investigated were assigned to 3 components (‘d – Activities and Participation’, ‘b – Body Functions’, and ‘e – Environmental Factors’) and 11 first-level ICF categories. All PF items of the EORTC measures but one were assigned to the first-level ICF categories ‘d4 – Mobility’ and ‘d5 – Self-care’, all within the component ‘d – Activities and Participation’. The SF-36 additionally included item content related to ‘d9 – Community, social and civic life’ and the PROMIS Short Form for Physical Function 20a also included content related to ‘d6 – domestic life’. The PROMIS Cancer Item Bank (v1.1) covered, in addition, two first-level categories within the component ‘b – Body Functions’. The FACT-G Physical Well-being scale was found to be the most diverse scale with item content partly not covered by the ICF framework.DiscussionOur results provide information about conceptual differences between common PRO measures for the assessment of PF in cancer patients. Our results complement quantitative information on psychometric characteristics of these measures and provide a better understanding of the possibilities of establishing common metrics.

Evaluation considerations for using surrogate endpoints in anticancer clinical trials

The prolongation of patient's overall survival is the accepted as gold standard to prove clinical values of anti-cancer drugs. However, if overall survival is taken as the primary endpoint in clinical trials for cancer types with a relatively good prognosis in the process of new anti-cancer drug research and development, the time to market the drugs will be prolonged due to the long follow-up time. In addition, overall survival is often interfered by confounding factors such as follow-up treatment. Therefore, regulatory agencies have established an accelerated review model using surrogate endpoints for the approval of new anti-cancer drugs, but there are still some problems in the use of surrogate endpoints in cancer clinical trials. From the perspective of new drug review, the authors expounds the key points of confirming and rationally using surrogate endpoints in clinical trials of anti-cancer drugs, which will improve the level of clinical trials of new anti-cancer drugs and accelerate the development of anti-tumor drugs.

The heterogeneity effect of surveillance intervals on progression free survival

Progression-free survival (PFS) is an increasingly important surrogate endpoint in cancer clinical trials. However, the true time of progression is typically unknown if the evaluation of progression status is only scheduled at given surveillance intervals. In addition, comparison between treatment arms under different surveillance schema is not uncommon. Our aim is to explore whether the heterogeneity of the surveillance intervals may interfere with the validity of the conclusion of efficacy based on PFS, and the extent to which the variation would bias the results. We conduct comprehensive simulation studies to explore the aforementioned goals in a two-arm randomized control trial. We introduce three steps to simulate survival data with predefined surveillance intervals under different censoring rate considerations. We report the estimated hazard ratios and examine false positive rate, power and bias under different surveillance intervals, given different baseline median PFS, hazard ratio and censoring rate settings. Results show that larger heterogeneous lengths of surveillance intervals lead to higher false positive rate and overestimate the power, and the effect of the heterogeneous surveillance intervals may depend upon both the life expectancy of the tumor prognoses and the censoring proportion of the survival data. We also demonstrate such heterogeneity effect of surveillance intervals on PFS in a phase III metastatic colorectal cancer trial. In our opinions, adherence to consistent surveillance intervals should be favored in designing the comparative trials. Otherwise, it needs to be appropriately taken into account when analyzing data.

Joint modelling with competing risks of dropout for longitudinal analysis of health-related quality of life in cancer clinical trials.

Health-related quality of life (HRQoL) is an important endpoint in cancer clinical trials. Analysis of HRQoL longitudinal data is plagued by missing data, notably due to dropout. Joint models are increasingly receiving attention for modelling longitudinal outcomes and the time-to-dropout. However, dropout can be informative or non-informative depending on the cause. We propose using a joint model that includes a competing risks sub-model for the cause-specific time-to-dropout. We compared a competing risks joint model (CR JM) that distinguishes between two causes of dropout with a standard joint model (SJM) that treats all the dropouts equally. First, we applied the CR JM and SJM to data from 267 patients with advanced oesophageal cancer from the randomized clinical trial PRODIGE 5/ACCORD 17 to analyse HRQoL data in the presence of dropouts unrelated and related to a clinical event. Then, we compared the models using a simulation study. We showed that the CR JM performed as well as the SJM in situations where the risk of dropout was the same whatever the cause. In the presence of both informative and non-informative dropouts, only the SJM estimations were biased, impacting the HRQoL estimated parameters. The systematic collection of the reasons for dropout in clinical trials would facilitate the use of CR JMs, which could be a satisfactory approach to analysing HRQoL data in presence of both informative and non-informative dropout. This study is registered with ClinicalTrials.gov, number NCT00861094.

Updated Standardized Definitions for Efficacy End Points (STEEP) in Adjuvant Breast Cancer Clinical Trials: STEEP Version 2.0.

The Standardized Definitions for Efficacy End Points (STEEP) criteria, established in 2007, provide standardized definitions of adjuvant breast cancer clinical trial end points. Given the evolution of breast cancer clinical trials and improvements in outcomes, a panel of experts reviewed the STEEP criteria to determine whether modifications are needed. We conducted systematic searches of ClinicalTrials.gov for adjuvant systemic and local-regional therapy trials for breast cancer to investigate if the primary end points reported met STEEP criteria. On the basis of common STEEP deviations, we performed a series of simulations to evaluate the effect of excluding non-breast cancer deaths and new nonbreast primary cancers from the invasive disease-free survival end point. Among 11 phase III breast cancer trials with primary efficacy end points, three had primary end points that followed STEEP criteria, four used STEEP definitions but not the corresponding end point names, and four used end points that were not included in the original STEEP manuscript. Simulation modeling demonstrated that inclusion of second nonbreast primary cancer can increase the probability of incorrect inferences, can decrease power to detect clinically relevant efficacy effects, and may mask differences in recurrence rates, especially when recurrence rates are low. We recommend an additional end point, invasive breast cancer-free survival, which includes all invasive disease-free survival events except second nonbreast primary cancers. This end point should be considered for trials in which the toxicities of agents are well-known and where the risk of second primary cancer is small. Additionally, we provide end point recommendations for local therapy trials, low-risk populations, noninferiority trials, and trials incorporating patient-reported outcomes.

Quality of Life of Breast Cancer Patients

Background: Breast cancer control in a low-income country such a Bangladesh is a challenging endeavor influenced by a myriad of forces. Quality of life is now considered an important endpoint in cancer clinical trials. The study was conducted to determine the quality of life of breast cancer patients.
 Methods: This cross-sectional study was conducted among 142 breast cancer patients. Data were collected by faceto- face interview using European Organization for Research and Treatment of Cancer questionnaire from respondents once only ensuring the privacy, confidentiality and maintaining ethical issues. The study was conducted during the period from January 2012 to June 2012 in National Institute of Cancer Research and Hospital (NICRH), Mohakhali, Dhaka-1212, Bangladesh.
 Results: The study revealed that majority of the participants were housewife (76.1%) and the maximum were in the 41-50 age groups. Mean age of the respondents was 42.80(±8.95) years. Maximum respondents (30%) had passed SSC and primary educated were 27%. The average monthly income was Tk. 11,570.70. Better income group (Tk. 15,000.00 monthly) enjoy better quality of life than other group. Quality of life was better in women living in joint family rather than nuclear family. Among 142 respondents who heard the name of breast cancer, majority of the respondents heard it from Neighbors/ Relatives. Adequate monthly income (p=0.006) and small size family (p=0.02) improved the quality of life while treatment specific attributes and duration of disease sufferings worsen the quality of life of breast cancer patients
 Conclusion: The overall QOL score was considerably poor in this study. Early diagnosis with regular follow-up treatment could be a way of improving quality of life among breast cancer patients.
 JOPSOM 2020; 39(2): 20-33

Missing tumor measurement (TM) data in the search for alternative TM-based endpoints in cancer clinical trials

PurposeMissing data commonly occur in cancer clinical trials (CCT) and may hinder the search for alternative trial endpoints. We consider reasons for missing tumor measurement (TM) data in CCT and how missing TM data are typically handled. We explore the potential impact of missing TM data on predictive ability of a set of TM-based endpoints. MethodsLiterature review identifies reasons for and approaches to handling missing TM data. Data from 3 actual clinical trials were used for illustration. A sensitivity analysis of the potential impact of missing TM data was performed by comparing overall survival (OS) predictive ability of alternative endpoints using observed and imputed data. ResultsReasons for missing TM data in CCT are presented, based on the literature review and the three trials. Although missing TM data impacted individual objective status (e.g. 12-week status changed for 53% of patients in one imputation set), it surprisingly only minimally impacted endpoint predictive ability (e.g. median c-indices of 500 imputed datasets ranged from 0.566 to 0.570 for N9741, 0.592–0.616 for N9841, and 0.542–0.624 for N0026). ConclusionBy understanding the reasons for missingness, we can better anticipate them and minimize their occurrence. Our preliminary analysis suggests missing TM data may not impact endpoint predictive ability, but could impact objective response status classification; however these findings require further validation. With response status accepted as an important phase II endpoint in the development of new cancer therapies (including immunotherapy), we urge that in CCT complete TM data collection and adherence to protocol-defined disease evaluation as closely as possible be a priority.

Clinical Meaning and Operability of Endpoints in Cancer Clinical Trials: Example of Survey on RMST Threshold

Abstract Efficacy endpoints in clinical trial should have, as well as clinical meaning, high operability for measuring, summarizing and testing hypotheses by using statistical methodology. Constructing components of operability include surrogacy (for true endpoints as OS and QOL adjusted OS), less requirement for statistical assumptions (assuring comparative validity) or robustness against departure from assumptions, easiness of computation and high power or less required sample size. Recent interests in RMST(Restricted Mean Survival Time) lie in high requirement of clinical trials for patients with good prognosis and/or recognition to real situations where (hitherto implicitly postulated) proportional hazard assumption does not hold. In this presentation, significance and interpretation of RMST is discussed in the above context, and an example of survey on threshold of RMST to physicians who participated in the noninferiority clinical trial of trastuzumab (test treatment) vs trastuzumab+chemotherapy (standard therapy) for elderly women (RESPECT-trial).

Interpreting European Organisation for Research and Treatment for Cancer Quality of life Questionnaire core 30 scores as minimally importantly different for patients with malignant melanoma

IntroductionHealth-related quality of life (HRQOL) is increasingly recognised as an important end-point in cancer clinical trials. The concept of minimally important difference (MID) enables interpreting differences and changes in HRQOL scores in terms of clinical meaningfulness. We aimed to estimate MIDs for interpreting group-level change of European Organisation for Research and Treatment for Cancer Quality of life Questionnaire core 30 (EORTC QLQ-C30) scores in patients with malignant melanoma. MethodsData were pooled from three published melanoma phase III trials. Anchors relying on clinician's ratings, e.g. performance status, were selected using correlation strength and clinical plausibility of associating the anchor/EORTC QLQ-C30 scale pair. HRQOL change was evaluated between time periods that were common to all trials: start of treatment to end of treatment and end of treatment to end of follow-up. Three change status groups were formed: deteriorated by one anchor category, improved by one anchor category and no change. Patients with greater anchor change were excluded. The mean change method and linear regression were used to estimate MIDs for change in HRQOL scores within the group and between the groups of patients, respectively. ResultsMIDs varied according to QLQ-C30 scale, direction (improvement versus deterioration), anchor and period. MIDs for within-group change ranged from 4 to 18 points (improvement) and −16 to −4 points (deterioration), and MIDs for between-group change ranged from 3 to 16 points and from −16 to −3 points. MIDs for most of QLQ-C30 scales ranged from 5 to 10 points in absolute values. ConclusionsThese results are useful for interpreting changes in EORTC QLQ-C30 scores over time and for performing more accurate sample size calculations in adjuvant melanoma settings.

Item response models for the longitudinal analysis of health-related quality of life in cancer clinical trials

BackgroundThe use of health-related quality of life (HRQoL) as an endpoint in cancer clinical trials is growing rapidly. Hence, research into the statistical approaches used to analyze HRQoL data is of major importance, and could lead to a better understanding of the impact of treatments on the everyday life and care of patients. Amongst the models that are used for the longitudinal analysis of HRQoL, we focused on the mixed models from item response theory, to directly analyze raw data from questionnaires.MethodsWe reviewed the different item response models for ordinal responses, using a recent classification of generalized linear models for categorical data. Based on methodological and practical arguments, we then proposed a conceptual selection of these models for the longitudinal analysis of HRQoL in cancer clinical trials.ResultsTo complete comparison studies already present in the literature, we performed a simulation study based on random part of the mixed models, so to compare the linear mixed model classically used to the selected item response models. As expected, the sensitivity of the item response models to detect random effects with lower variance is better than that of the linear mixed model. We then used a cumulative item response model to perform a longitudinal analysis of HRQoL data from a cancer clinical trial.ConclusionsAdjacent and cumulative item response models seem particularly suitable for HRQoL analysis. In the specific context of cancer clinical trials and the comparison between two groups of HRQoL data over time, the cumulative model seems to be the most suitable, given that it is able to generate a more complete set of results and gives an intuitive illustration of the data.

Validation of surrogate endpoints in cancer clinical trials via principal stratification with an application to a prostate cancer trial.

Increasing attention has been focused on the use and validation of surrogate endpoints in cancer clinical trials. Previous literature on validation of surrogate endpoints are classified into four approaches: the proportion explained approach; the indirect effects approach; the meta-analytic approach; and the principal stratification approach. The mainstream in cancer research has seen the application of a meta-analytic approach. However, VanderWeele (2013) showed that all four of these approaches potentially suffer from the surrogate paradox. It was also shown that, if a principal surrogate satisfies additional criteria called one-sided average causal sufficiency, the surrogate cannot exhibit a surrogate paradox. Here, we propose a method for estimating principal effects under a monotonicity assumption. Specifically, we consider cancer clinical trials which compare a binary surrogate endpoint and a time-to-event clinical endpoint under two naturally ordered treatments (e.g. combined therapy vs. monotherapy). Estimation based on a mean score estimating equation will be implemented by the expectation-maximization algorithm. We will also apply the proposed method as well as other surrogacy criteria to evaluate the surrogacy of prostate-specific antigen using data from a phase III advanced prostate cancer trial, clarifying the complementary roles of both the principal stratification and meta-analytic approaches in the evaluation of surrogate endpoints in cancer. Copyright © 2017 John Wiley & Sons, Ltd.

Estimation of Growth Modulation Index Using Latent Variable and Accelerated Failure Time Model

Objectives For cytostatic cancer trials, Growth Modulation Index (GMI) defined by an intrapatient progression-free survival (PFS) ratio, has been proposed to evaluate the efficacy of new target agent. The purpose of this study was to suggest new methods for the estimation of GMI with censored data in the first PFS (PFS1) interval, and subsequent second PFS (PFS2) interval. Methods The proposed methods include latent variable approach based on Rank Preserving Structural Failure Time (RPSFT) model and Accelerated Failure Time (AFT) model. Simulations were conducted to compare the performance of proposed GMI estimates and estimates based on the Kaplan-Meier method in terms of bias and mean squared error (MSE) by varying dependency of two PFS and censoring rates. Results Simulation results show that new GMI estimates using latent variable approach and AFT model exhibited smaller bias and MSE than the previous estimates based on the Kaplan-Meier survival function. As censoring rates increased in PFS1, bias and MSE increased in the previous GMI estimates. When the AFT model was applied in the case of high censoring rates, bias was relatively higher than those of latent variable approach. Conclusions When using GMI as primary endpoint in cancer clinical trials, cautious statistical application and interpretation is needed, particularly for the presence of censored data in the first PFS interval. Key words: Cytostatic, Growth modulation index, Latent variable, Accelerated failure time model, Kaplan-Meier

Assessment of health related quality of life (QOL) in phase III trials in advanced colorectal cancer (CRC): Frequency, time trends, and QOL improvement.

e18209Background: QOL is often one of the endpoints in cancer clinical trials. We assessed the frequency and correlates of QOL usage as an endpoint in phase III trials in advanced CRC. Methods: We ...

Analysing data from patient-reported outcome and quality of life endpoints for cancer clinical trials: a start in setting international standards

Analysing data from patient-reported outcome and quality of life endpoints for cancer clinical trials: a start in setting international standards

Health-related quality of life in women with breast cancer: a literature-based review of psychometric properties of breast cancer-specific measures.

Breast cancer is one of the most common cancers in women in the world. Health-related quality of life (HRQL) at treatment endpoint in cancer clinical trials is widely considered to be increasingly important. The aim of this review was to provide a literature-based assessment of the validity, reliability and responsiveness of breast cancer-specific HRQL instruments in women breast cancer patients. The databases consulted were Medline, PubMed, and Embase. The inclusion criteria required studies to: (1) involve use of HRQL measures; (2) cover women with breast cancer under standard treatment (surgery, radiation therapy, chemotherapy, hormone therapy, and targeted therapy); (3) involve the validity, reliability, or responsiveness of HRQL; (4) deal with validation of breast cancer-specific HRQL instruments. A total of 16 studies were identified through the literature search that met the 4 inclusion criteria. Some seven instruments were assessed among these 16 studies: EORTC QLQ-BR23, FACT-B, FACT-ES, HFRDIS, LSQ- 32, QLICP-BR, and SLDS-BC. EORTC QLQ-BR23, FACT-B, LSQ-32, QLICP-BR, and SLDS-BC are more general breast cancer-specific HRQL instruments. FACT-EB is the endocrine subscale combined with FACT-B in order to measure the side effects and putative benefits of hormonal treatment administered in breast cancer patients. HFRDIS is the HRQL measure focusing on hot flash concerns. This paper provides an overall understanding on the currently available breast cancer-specific HRQL instruments in women breast cancer patients.

Endpoints in cancer clinical trials

Endpoints are measurable clinical and biological findings that are used for the development and assessment of treatment options. In the treatment of cancer, endpoints can be classified into two categories: "patient-centered clinical endpoints" including overall survival (OS) and health-related quality of life (QoL), and "tumor-centered clinical endpoints" such as progression-free survival. Surrogate endpoints are tumor-centered clinical endpoints that can be used as substitutes for patient-centered clinical endpoints, particularly OS. The choice of endpoints in oncology trials is a major problem. The published Consolidated Standards of Reporting Trials (CONSORT) best-practice guidelines encourage the reporting of clearly defined primary and secondary outcome measures. OS is the gold standard of endpoints but as increasing numbers of effective salvage treatments become available for many types of cancer, much larger numbers of patients are included; this requires a longer follow-up period and increases the cost of clinical trials. Thus, tumor-centered clinical endpoints that can be assessed earlier and used as surrogates for overall survival are increasingly studied, but most of them currently lack standardized definitions to enable cross comparison of results among different clinical trials and they have not been validated as surrogate endpoints. In addition, the variability of their definition can strongly impact the trial's conclusions by affecting both statistical power and estimation. In this context, QoL constitutes an available and useful surrogate endpoint for trials to ensure treatment benefit from both the patient and public health points of view. Methodological research should be pursued to develop standard outcome definitions for use in cancer clinical trials and to define a standardized longitudinal analysis of QoL data.

Recommendations for including multiple symptoms as endpoints in cancer clinical trials

The multiple symptoms arising from cancer and its treatment impose significant distress for patients. However, in clinical research, there is no agreed-upon way of assessing and presenting the effects of treatment on multiple symptoms, as either individual scores or a composite score. The ASCPRO (Assessing the Symptoms of Cancer Using Patient-Reported Outcomes) Multisymptom Task Force was established to make recommendations about measuring multiple symptoms as outcomes in cancer clinical trials. The Multisymptom Task Force addressed how to choose the symptoms to be assessed and how multiple individual symptom scores or composite scores of several symptoms might be used as clinical trial outcomes. Consensus was reached on a definition of a multisymptom outcome, the problem of source attribution, and the need for a hypothesis-driven conceptual framework to measure multisymptom outcomes. Validated single-item and multi-item measures currently available or that can be easily generated for oncology use were deemed sufficient for measuring multiple symptoms. The relative value of a composite score versus a set of individual symptom scores was discussed, along with issues in developing and deploying such a composite measure. The results indicated that more research on combining scores of different symptoms is needed. Symptom data should be a required component of cancer clinical trials. Patient-reported symptoms provide a unique patient perspective on treatment benefit and risk that goes beyond clinician-reported adverse events. A representation of changes in multiple symptoms would clarify the impact of treatment and enhance the interpretation of cancer clinical trials for clinicians, patients, and those who make health care policy.

Quality of life outcomes in patients with breast cancer

Health-related quality of life is now considered an important endpoint in cancer clinical trials. It has been shown that assessing quality of life in cancer patients could contribute to improved treatment and could even serve as a prognostic factor along with medical parameters. This paper presents a review of quality of life outcomes in patients with breast cancer according to previous descriptive findings. This is a bibliographic review of the literature covering publications that appeared in English language biomedical journals between 1987 and 2008. The search strategy included a combination of the key words quality of life and breast cancer in the titles of published articles. The major findings are summarized and presented under different headings: evaluation of health-related quality of life i) at the time of diagnosis, ii) during treatment, and iii) after the completion of treatment. Breast cancer patients receiving chemotherapy might experience several side-effects and symptoms that have a negative effect on their quality of life. Also adjuvant hormonal therapies were found to have a similar negative impact on quality of life. Psychological distress-anxiety and depression were found to be common among breast cancer patients. Symptoms-pain, fatigue, and insomnia were among the most common symptoms reported. There was quite an extensive body of literature on quality of life in breast cancer patients. These papers have made a considerable contribution to improving breast cancer care.

Quality of life outcomes in patients with breast cancer.

Health-related quality of life is now considered an important endpoint in cancer clinical trials. It has been shown that assessing quality of life in cancer patients could contribute to improved treatment and could even serve as a prognostic factor along with medical parameters. This paper presents a review of quality of life outcomes in patients with breast cancer according to previous descriptive findings. This is a bibliographic review of the literature covering publications that appeared in English language biomedical journals between 1987 and 2008. The search strategy included a combination of the key words quality of life and breast cancer in the titles of published articles. The major findings are summarized and presented under different headings: evaluation of health-related quality of life i) at the time of diagnosis, ii) during treatment, and iii) after the completion of treatment. Breast cancer patients receiving chemotherapy might experience several side-effects and symptoms that have a negative effect on their quality of life. Also adjuvant hormonal therapies were found to have a similar negative impact on quality of life. Psychological distress-anxiety and depression were found to be common among breast cancer patients. Symptoms-pain, fatigue, and insomnia were among the most common symptoms reported. There was quite an extensive body of literature on quality of life in breast cancer patients. These papers have made a considerable contribution to improving breast cancer care.

Calibrating clinically significant effects in survival and response endpoints in cancer clinical trials.

6130 Background: Clinically significant effect sizes have been the focus of much research for assessing patient-reported outcomes (PROs). Methods such as the empirical rule effect size or ½ standard deviation (SD) method are useful for efficient design and improved interpretation of clinical studies. While the concept of clinical significance is ubiquitous in PRO research, it is less standardized in clinical trials involving tumor response and survival endpoints. Methods: We applied the ½ SD rule to survival and response endpoints to define clinically significant effect sizes. We present clinically significant effect sizes for expected mean and median survival using the exponential distribution and response rate using the binomial distribution. We apply the method to published studies. Results: The clinically significant effect size for survival endpoints is 50% of the expected mean survival or roughly 72% of the expected median survival. A small effect size (1/5 SD) is 20% or roughly 29% of the expected mean or median survival, respectively. For example, a recent study of lapatinib alone vs in combination with trastuzumab reported a median survival difference of 12.6 weeks (39 vs 51.6 weeks) with a p-value of 0.11. This translates into an effect size of 0.22 SD, which calibrates to a small effect. Further, Booth et al. (JCO, 2008) summarized results for 321 randomized clinical trials. Applying our method indicates that survival effect sizes ranged from very small (0.01 SD) to very large (1.5 SD). The clinically significant effect size for response endpoints is a function of the expected response rate. For a 50% expected response rate, the clinically significant effect size is 25% and a small effect size is 10%. Conclusions: This method allows for more ready interpretation of the clinical significance of survival and response studies. It allows for direct cross study comparison even if the endpoints are different. It also facilitates study design as it builds clinical significance into the study directly. Cancer clinical trials seem to be designed with smaller effect sizes for survival than for tumor response and PRO endpoints.