Relative value assessment: characterizing the benefit of oncology therapies through diverse survival metrics from a US perspective.
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ABSTRACT:
SUBMITTER: Macaulay R
PROVIDER: S-EPMC6430994 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Patients with certain cancers are treated with curative intent, but for others the results are less favorable and different therapeutic approaches are needed. Early data suggest that new therapies, which modulate immune responses to cancers, may have potential for long-term survival in a proportion of cases. Therefore, it is timely to consider whether metrics generally used to describe the medical value of therapies for patients with common solid tumors remain appropriate for therapies with curative potential. Literature reviews were conducted to define how various stakeholders describe cure in oncology and to identify the endpoints used in clinical trials for selected solid tumors. The results showed that "cure" is described using various terms that can be divided broadly into lack of disease progression, eradication of cancerous cells, and survival. The review of trial endpoints showed frequent use of median overall survival (OS) and progression- and response-related endpoints. Because these endpoints were mainly described in the context of chemotherapies that are not generally curative, they may not adequately capture outcomes of new therapeutic modalities with potential for long-term survival. More appropriate endpoints may include mean OS, cure fraction, and OS rate at landmark time points.
| S-EPMC4486603 | biostudies-literature
Project description:Pancreatic cancer remains the fourth leading cause of cancer deaths in the United States with a dismal prognosis and a 5-year survival of less than 5% across all stages.(1) In 2014, there were approximately 46,420 new cases of pancreatic cancer with only 9% of patients having localized disease.(2) Given that the vast majority of patients present with advanced disease, much of the focus for drug development has been in the metastatic setting, which is evident with the advent of two combination chemotherapy regimens for this indication. Although conventional cytotoxic chemotherapy remains the standard of care, an ongoing search for novel therapeutic approaches continues. We will highlight several new approaches here, with a particular emphasis on immunotherapeutic strategies. We will also introduce concepts regarding the potential economic effects associated with the development and implementation of new treatments in pancreatic cancer.
| S-EPMC4964785 | biostudies-literature
Project description:Relative potency plays an important role in toxicology. Estimates of relative potency are used to rank chemicals by their effects, to calculate equivalent doses of test chemicals compared to a standard, and to weight contributions of constituent chemicals when evaluating mixtures. Typically relative potency is characterized by a constant dilution factor, even when non-similar dose-response curves indicate that constancy is inappropriate. Improperly regarding relative potency as constant may distort conclusions and potentially mislead investigators or policymakers. We consider a more general approach that allows relative potency to vary as a function of dose, response, or response quantile. Distinct functions can be defined, each generalizing different but equivalent descriptions of constant relative potency. When two chemicals have identical response limits, these functions all carry fundamentally equivalent information; otherwise, relative potency as a function of response quantile is distinct and embodies a modified definition of relative potency. Which definition is preferable depends on whether one views any differences in response limits as intrinsic to the chemicals or as extrinsic, arising from idiosyncrasies of data sources. We illustrate these ideas with constructed examples and real data. Relative potency functions offer a unified and principled description of relative potency for non-similar dose-response curves.
| S-EPMC3134169 | biostudies-literature
Project description:PurposeTo assess the diagnostic weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers (csPCa).Materials and methodsWe used a prospective database of 262 patients who underwent T2-weighted, diffusion-weighted, and dynamic contrast-enhanced (DCE) imaging before prostatectomy. For each lesion, two independent readers (R1, R2) prospectively defined nine features: shape, volume (V_Max), signal abnormality on each pulse sequence, number of pulse sequences with a marked (S_Max) and non-visible (S_Min) abnormality, likelihood of extracapsular extension (ECE) and PSA density (dPSA). Overall likelihood of malignancy was assessed using a 5-level Likert score. Features were evaluated using the area under the receiver operating characteristic curve (AUC). csPCa was defined as Gleason ≥7 cancer (csPCa-A), Gleason ≥7(4+3) cancer (csPCa-B) or Gleason ≥7 cancer with histological extraprostatic extension (csPCa-C).ResultsFor csPCa-A, the Signal1 model (S_Max+S_Min) provided the best combination of signal-related variables, for both readers. The performance was improved by adding V_Max, ECE and/or dPSA, but not shape. All models performed better with DCE findings than without. When moving from csPCa-A to csPCa-B and csPCa-C definitions, the added value of V_Max, dPSA and ECE increased as compared to signal-related variables, and the added value of DCE decreased. For R1, the best models were Signal1+ECE+dPSA (AUC = 0,805 [95%CI:0,757-0,866]), Signal1+V_Max+dPSA (AUC = 0.823 [95%CI:0.760-0.893]) and Signal1+ECE+dPSA [AUC = 0.840 (95%CI:0.774-0.907)] for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0.844 [95%CI:0.806-0.877, p = 0.11], 0.841 [95%CI:0.799-0.876, p = 0.52]) and 0.849 [95%CI:0.811-0.884, p = 0.49], respectively. For R2, the best models were Signal1+V_Max+dPSA (AUC = 0,790 [95%CI:0,731-0,857]), Signal1+V_Max (AUC = 0.813 [95%CI:0.746-0.882]) and Signal1+ECE+V_Max (AUC = 0.843 [95%CI: 0.781-0.907]) for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0. 829 [95%CI:0.791-0.868, p = 0.13], 0.790 [95%CI:0.742-0.841, p = 0.12]) and 0.808 [95%CI:0.764-0.845, p = 0.006]), respectively.ConclusionCombination of simple variables can match the Likert score's results. The optimal combination depends on the definition of csPCa.
| S-EPMC5466299 | biostudies-literature
Project description:PurposeBoth the superstructures of virtual discourse in radiation oncology and the entities occupying influential positions in the social media landscape of radiation oncology remain poorly characterized.Methods and materialsNodeXL Pro was used to prospectively sample all tweets with the hashtag #radonc every 8 to 10 days during the course of 1 year (December 4, 2018, to November 29, 2019). Twitter handles were grouped into conversational clusters using the Clauset-Newman-Moore community detection algorithm. For each sample period, the top 10 #radonc Twitter influencers, defined using betweenness centrality, were categorized. Influencers were scored in each sample period according to their top 10 influence rank and summarized with descriptive statistics. Linear regression assessed for characteristics that predicted higher influence scores among top influencers.ResultsIn the study, 684,000 tweets were sampled over 38 periods. #radonc tweets took on the crowd superstructure of a hub-and-spoke broadcast network formed when prominent individuals are widely repeated by many audience members. Professional societies were the most influential category of Twitter handles with an average influence score of 7.63 out of 10 (standard deviation [SD] = 1.94). When industry handles were present among top 10 influencers, they exhibited the second highest average influence scores (6.75, SD = 1.06), followed by individuals with scores of 5.28 (SD = 0.43). The categories of influencers were stable during the course of 1 year. The role of attending physician, radiation oncology specialty, male sex, academic practice, and US-based handles in North America were predictors of higher influence score.ConclusionsTwitter influencers in radiation oncology represent a diverse group of people and organizations, but male academic radiation oncologists based in North America occupy particularly influential positions in virtual communities broadly characterized as "hub and spoke" broadcast networks. Periodic network-based analyses of the social media discourse in radiation oncology are warranted to maintain an awareness of the handles that are influencing discussions on Twitter and ensure that social media utilization continues to contribute to the field of radiation oncology in a meaningful way.
| S-EPMC9184867 | biostudies-literature
Project description:ImportanceThe American Society of Clinical Oncology (ASCO) and the European Society for Medical Oncology (ESMO) have independently published value frameworks. To date, whether the clinical benefit scoring algorithms from these framework were intended to measure absolute or relative survival benefit remains unclear.ObjectiveTo empirically examine the measurement characteristics of these frameworks by comparing their survival efficacy components (ASCO clinical benefit score [CBS] and ESMO preliminary magnitude of clinical benefit grade [PMCBG]) with established measures of absolute (median survival difference and restricted mean survival time [RMST] difference) and relative (hazard ratios [HRs]) survival benefit.Data sourcesThe US Food and Drug Administration (FDA)'s Hematology and Oncology Approvals and Safety Notifications database was retrospectively reviewed to identify phase 3 randomized controlled trials (RCTs) cited for clinical efficacy evidence in oncology drug approvals from January 1, 2006, through December 31, 2017.Study selectionTwo reviewers searched the database for initial trials cited for approval. Phase 3 trials with overall survival, progression-free survival, and/or time to progression as their primary or coprimary end points were included. Notifications for noncancer indications or presenting label changes and trials that did not report HRs for the required end points and/or did not publish survival curves with number-at-risk data were excluded. Of 269 notifications initially identified, 107 met the selection criteria.Data extraction and synthesisSensitivity analyses were conducted by calculating the scores using (1) the framework-defined end point, including tail-of-curve bonus points (ASCO) or long-term plateau adjustments (ESMO) (framework-defined end point plus tail-of-curve bonus), (2) overall survival data only, and (3) progression-free survival data only. For primary and sensitivity analyses, Spearman correlation coefficients were calculated to examine the relationships between (1) ASCO-CBS or ESMO-PMCBG and RMST difference, (2) ASCO-CBS or ESMO-PMCBG and median survival difference, and (3) ASCO-CBS or ESMO-PMCBG and HR. Data were analyzed from January 7 through April 30, 2018.Main outcomes and measuresIn the primary analysis, ASCO-CBSs and ESMO-PMCBGs were calculated for the included trials using the framework-defined end point.ResultsCompared with measures of absolute survival benefit, ESMO-PMCBGs showed low to moderate correlations with RMST difference (ρ = 0.44) and moderate to high correlations with median survival difference (ρ = 0.64). ASCO-CBSs showed low to moderate correlations with both measures of absolute benefit (ρ = 0.43 for RMST difference; ρ = 0.44 for median survival). Compared with a relative measure of survival (HRs), ESMO-PMCBGs showed a low correlation (ρ = 0.47) and ASCO-CBSs showed a higher correlation (ρ = 0.76).Conclusions and relevanceNeither framework consistently performed as an absolute measure of survival benefit. The incorporation of a direct measure of absolute clinical benefit, such as RMST difference, into the survival efficacy components of their algorithms should be considered.
| S-EPMC6537770 | biostudies-literature
Project description:Colorectal cancer (CRC) has high incidence and is associated with high case fatality. In France, the 5-year survival, pooled across all cancer stages at diagnosis, ranges from 57% in men to 60% in women. About one third of patients diagnosed with CRC will develop a metachronous recurrence during the following years. It is of paramount importance to accurately identify factors associated with the increased risk of progression and death, in order to develop effective follow-up and treatment strategies. However, to accurately assess the role of patients’ specific characteristics in the progression of cancer several methodological challenges need to be overcome.
One difficulty, common to prognostic studies of cancer, concerns the need to separate the effects of prognostic factors on different clinical endpoints, such as disease recurrence vs recurrence-free death. Another difficulty, encountered in prognostic studies, is that the cause of death is not available or not accurately coded. Yet, some patients are likely to die of causes not related to the disease of primary interest, especially in cancers with longer survival and in those that affect older subjects. Until recently, the existing statistical methodology was not able to simultaneously, deal with both difficulties, i.e. to account for (i) possibly different effects of prognostic factors on death vs recurrence, and (ii) unknown causes of death. However, this challenge has been addressed by the recent development of the Markov relative survival model (MRS) , which extends the Markov multi-state model to incorporate relative survival modelling. Simulations demonstrate that MRS is able to accurately estimate different effects of prognostic factors on the risk of each of several events, including separate effects on disease-specific vs other causes of death. To date, the MRS had not been applied in clinical or epidemiological studies.
The aim of this study was to assess the potential advantages of the new multi-state relative survival model (MRS), proposed by Huszti et al. (2012), in a prognostic cancer study. To this end, we compared the MRS results with those obtained with two more conventional analyses, based on Cox’s proportional hazards model, and the multi-state Markov model proposed by Alioum and Commenges (2001). The three models were applied to explore the impact of prognostic factors on cancer-specific mortality and recurrence, in a large population-based French registry of colorectal cancer, with up to 25 years of follow-up.
| 2224546 | ecrin-mdr-crc
Project description:BackgroundStandard parametric survival models are commonly used to estimate long-term survival in oncology health technology assessments; however, they can inadequately represent the complex pattern of hazard functions or underlying mechanism of action (MoA) of immuno-oncology (IO) treatments.ObjectiveThe aim of this study was to explore methods for extrapolating overall survival (OS) and provide insights on model selection in the context of the underlying MoA of IO treatments.MethodsStandard parametric, flexible parametric, cure, parametric mixture and landmark models were applied to data from ATLANTIC (NCT02087423; data cut-off [DCO] 3 June 2016). The goodness of fit of each model was compared using the observed survival and hazard functions, together with the plausibility of corresponding model extrapolation beyond the trial period. Extrapolations were compared with updated data from ATLANTIC (DCO 7 November 2017) for validation.ResultsA close fit to the observed OS was seen with all models; however, projections beyond the trial period differed. Estimated mean OS differed substantially across models. The cure models provided the best fit for the new DCO.ConclusionsStandard parametric models fitted to the initial ATLANTIC DCO generally underestimated longer-term OS, compared with the later DCO. Cure, parametric mixture and response-based landmark models predicted that larger proportions of patients with metastatic non-small cell lung cancer receiving IO treatments may experience long-term survival, which was more in keeping with the observed data. Further research using more mature OS data for IO treatments is needed.
| S-EPMC6830404 | biostudies-literature
Project description:Purpose: Gastric cancer is mainly treated by gastrectomy, the results of which were unsatisfactory without any adjuvant treatments. This study aimed to examine the performance of radiotherapy, chemotherapy, and chemoradiotherapy after surgery in order to acquire the optimal adjuvant treatment. Method: Embase and PubMed were retrieved to conduct a systematic research. Hazard ratios (HR) of overall survival (OS) and progression-free survival (PFS) as outcomes were calculated by synthesizing direct and indirect evidence to evaluate the efficacy of three treatments against surgery alone. The P-score ranking was utilized to rank the therapies. Consistency was assessed by heat plot. Begg's test was performed to evaluate publication bias. Results: A total of 35 randomized controlled studies (RCTs) with 8973 patients were included in our network meta-analysis (NMA). As for efficacy outcomes, OS and PFS of 1, 2, 3, and 5 years, all revealed chemoradiotherapy (CRT) as the best of three adjuvant therapies. Meanwhile, P-score ranking results also displayed that CRT was the optimal regimen. Additionally, radiotherapy (RT) and chemotherapy (CT) were two alternative options following CRT since RT performed well in short-term survival while CT could improve the long-term survival. Conclusion: CRT was the most recommended therapy to accompany surgery according to our results. However, no analysis about the safety of these three treatments was mentioned in our study. Further studies including safety outcomes were required to draw a more comprehensive conclusion.
| S-EPMC6119769 | biostudies-literature
Project description:BackgroundStructured reporting and standardized criteria are increasingly recognized as means of improving both radiological and clinical practice by allowing for better content and clarity. Our aim was to examine oncologists' opinions and expectations concerning the radiologist's report to identify general needs in daily practice and ways to improve interdisciplinary communication.MethodsA 19-question survey was sent to 230 oncologists from three different countries (France, Romania, Switzerland) identified on the online web pages of different hospitals and private clinics. The survey was sent by electronic mail with an online survey program (Google Forms®). All recipients were informed of the purpose of the study. The data were collected by the online survey program and analysed through filtering the results and cross-tabulation.ResultsA total of 52 responses were received (response rate of 22.6%). The majority of the respondents (46/52, 88%) preferred the structured report, which follows a predefined template. Most of the respondents (40/52, 77%) used RECIST 1.1 or iRECIST in tumour assessment. Nearly half of the oncologists (21/52, 40%) measured 1-3 cases per week. On a 10-point Likert scale, 34/52 (65%) oncologists rated their overall level of satisfaction with radiologists' service between 7 and 10. In contrast, 12/52 (19%) oncologists rated the radiologists' service between 1 and 4. Moreover, 42/52 (80%) oncologists acknowledged that reports created by a radiologist with a subspecialty in oncologic imaging were superior to those created by a general radiologist.ConclusionStructured reports in oncologic patients and the use of RECIST criteria are preferred by oncologists in their daily clinical practice, which signals the need for radiologists also to implement such reports to facilitate communication. Furthermore, most of the oncologists we interviewed recognized the added value provided by radiologists specializing in oncologic imaging. Because this subspecialty is present in only a few countries, generally in large clinics, further training might become a challenge; nevertheless, intensive efforts should be made to enhance expertise in cancer imaging.
| S-EPMC8620527 | biostudies-literature