scholarly journals The Value of Real-World Data in Understanding Prostate Cancer Risk and Improving Clinical Care: Examples from Swedish Registries

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 875
Author(s):  
Kerri Beckmann ◽  
Hans Garmo ◽  
Ingela Franck Lissbrant ◽  
Pär Stattin
Keyword(s):  
Prostate Cancer ◽  
Real World ◽  
Clinical Care ◽  
Prostate Cancer Risk ◽  
Real World Data ◽  
Level Of Evidence ◽  
World Data ◽  
Cancer Data ◽  
Using Data ◽  
Randomised Controlled

Real-world data (RWD), that is, data from sources other than controlled clinical trials, play an increasingly important role in medical research. The development of quality clinical registers, increasing access to administrative data sources, growing computing power and data linkage capacities have contributed to greater availability of RWD. Evidence derived from RWD increases our understanding of prostate cancer (PCa) aetiology, natural history and effective management. While randomised controlled trials offer the best level of evidence for establishing the efficacy of medical interventions and making causal inferences, studies using RWD offer complementary evidence about the effectiveness, long-term outcomes and safety of interventions in real-world settings. RWD provide the only means of addressing questions about risk factors and exposures that cannot be “controlled”, or when assessing rare outcomes. This review provides examples of the value of RWD for generating evidence about PCa, focusing on studies using data from a quality clinical register, namely the National Prostate Cancer Register (NPCR) Sweden, with longitudinal data on advanced PCa in Patient-overview Prostate Cancer (PPC) and data linkages to other sources in Prostate Cancer data Base Sweden (PCBaSe).

Efficacy of abiraterone acetate in castration‐resistant metastatic prostate cancer: A real‐world data analysis

2020 ◽  
Author(s):  
Abdilkerim Oyman ◽  
Mustafa Başak ◽  
Melike Özçelik ◽  
Deniz Tataroğlu Özyükseler ◽  
Selver Işık ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Data Analysis ◽  
Real World ◽  
Metastatic Prostate Cancer ◽  
Abiraterone Acetate ◽  
Real World Data ◽  
World Data ◽  
Castration Resistant

scholarly journals Real-World Data Analysis of Confirmatory Biopsy Utilization in Suspected Prostate Cancer Patients

2018 ◽  
Vol 21 ◽  
pp. S161
Author(s):  
J Scott ◽  
R Concepcion ◽  
D Garofalo ◽  
S Verma-Kurvari ◽  
B Xu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Data Analysis ◽  
Cancer Patients ◽  
Real World ◽  
Real World Data ◽  
World Data

scholarly journals PCN263 - A REAL-WORLD DATA STUDY REGARDING HOSPITAL RESOURCES USE AND COSTS ASSOCIATED WITH PROSTATE CANCER IN SPAIN.

2018 ◽  
Vol 21 ◽  
pp. S58-S59
Author(s):  
M. Solozabal ◽  
A. De Prado ◽  
L. Planellas ◽  
Baltasar-Sanchez Á ◽  
A. Carreño-Serra ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Hospital Resources

The rate of tumor growth, g, as a biomarker for overall survival (OS) in prostate cancer (PC) in clinical trials as well as in real-world data from the Veterans Administration Medical Centers (VAMCs).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 5074-5074
Author(s):  
Harshraj Leuva ◽  
Mengxi Zhou ◽  
Julia Wilkerson ◽  
Keith Sigel ◽  
Ta-Chueh Hsu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Trial ◽  
Overall Survival ◽  
Clinical Trials ◽  
Tumor Growth ◽  
Real World ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Real World Data ◽  
World Data

5074 Background: Novel assessments of efficacy are needed to improve determination of treatment outcomes in clinical trials and in real-world settings. Methods: Cancer treatments usually lead to concurrent regression and growth of the drug-sensitive and drug-resistant fractions of a tumor, respectively. We have exploited novel methods of analysis that assess these two simultaneous processes and have estimated rates of tumor growth ( g) and regression ( d) in over 30,000 patients (pts) with diverse tumors. Results: In prostate cancer (PC) we have analyzed both clinical trial and real-world data from Veterans. Using clinical trial data from 6819 pts enrolled in 15 treatment arms we have established separately and by combining all the data that g correlates highly (p<0.0001) with overall survival (OS) – slower g associated with better OS. In PC, abiraterone (ABI) and docetaxel (DOC) are superior to placebo, prednisone and mitoxantrone. ABI (median g =0.0017) is superior to DOC ( g=0.0021) in first line (p=0.0013); and ABI in 2nd line ( g=0.0034) is inferior to ABI in 1st line ( g=0.0017; p<0.0001). Finally, using combined clinical trial data as a benchmark we could assess the efficacy of novel therapies in as few as 30-40 patients. Amongst 7457 Veterans, the median g on a taxane ( g=0.0022) was similar to that from clinical trials ( g=0.0012). Although only 258 Veterans received cabazitaxel (CAB), g values for CAB ( g=0.0018) and DOC ( g=0.0023) were indistinguishable (p=0.3) consistent with their identical mechanism of action. Finally, outcomes with DOC in African American (AA) ( g=0.00212) and Caucasian ( g=0.00205) Veterans were indistinguishable (p=0.9) and comparable across all VAMCs. Conclusions: The rate of tumor growth, g, is an excellent biomarker for OS both in clinical trials and in real-world settings. g allows comparisons between trials and for large trial data sets to be used as benchmarks of efficacy. Real-world outcomes in the VAMCs are similar to those in clinical trials. In the egalitarian VAMCs DOC efficacy in PC is comparable in AA and Caucasian Veterans -- indicating inferior outcomes reported in AAs are likely due to differential health care access, not differences in biology.

scholarly journals Developing a machine learning environmental allergy prediction model from real world data through a novel decentralized mobile study platform.

2020 ◽  
Author(s):  
Chethan Sarabu ◽  
Sandra Steyaert ◽  
Nirav Shah
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Predictive Models ◽  
Clinical Care ◽  
Sensor Data ◽  
Real World Data ◽  
World Data ◽  
Demographic Groups ◽  
Prediction And Prevention ◽  
Wide Range

Environmental allergies cause significant morbidity across a wide range of demographic groups. This morbidity could be mitigated through individualized predictive models capable of guiding personalized preventive measures. We developed a predictive model by integrating smartphone sensor data with symptom diaries maintained by patients. The machine learning model was found to be highly predictive, with an accuracy of 0.801. Such models based on real-world data can guide clinical care for patients and providers, reduce the economic burden of uncontrolled allergies, and set the stage for subsequent research pursuing allergy prediction and prevention. Moreover, this study offers proof-of-principle regarding the feasibility of building clinically useful predictive models from 'messy,' participant derived real-world data.

scholarly journals Protocol for data extraction: how real-world data have been used in the National Institute for Health and Care Excellence appraisals of cancer therapy

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e055985
Author(s):  
Jiyeon Kang ◽  
John Cairns
Keyword(s):  
Real World ◽  
Data Extraction ◽  
Systematic Research ◽  
London School ◽  
Real World Data ◽  
World Data ◽  
Data Extraction Tool ◽  
Potential Benefits ◽  
Critical Components ◽  
Randomised Controlled

IntroductionDue to the limitations of relying on randomised controlled trials, the potential benefits of real-world data (RWD) in enriching evidence for health technology assessment (HTA) are highlighted. Despite increased interest in RWD, there is limited systematic research investigating how RWD have been used in HTA. The main purpose of this protocol is to extract relevant data from National Institute for Health and Care Excellence (NICE) appraisals in a transparent and reproducible manner in order to determine how NICE has incorporated a broader range of evidence in the appraisal of oncology medicines.Methods and analysisThe appraisals issued between January 2011 and May 2021 are included following inclusion criteria. The data extraction tool newly developed for this research includes the critical components of economic evaluation. The information is extracted from identified appraisals in accordance with extraction rules. The data extraction tool will be validated by a second researcher independently. The extracted data will be analysed quantitatively to investigate to what extent RWD have been used in appraisals. This is the first protocol to enable data to be extracted comprehensively and systematically in order to review the use of RWD.Ethics and disseminationThis study is approved by the Ethics Committee of the London School of Hygiene and Tropical Medicine on 14 November 2019 (17315). Results will be published in peer-reviewed journals.

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