scholarly journals Generating realistic simulated administrative health data for population-based drug safety and effectiveness research and training

2019 ◽  
Author(s):  
Olawale Ayilara ◽  
Robert W Platt ◽  
Matthew Dahl ◽  
Janie Coulombe ◽  
Pablo Gonzalez Ginestet ◽  
...  
Keyword(s):  
Drug Use ◽  
Prescription Drug ◽  
Drug Safety ◽  
Real World ◽  
Simulated Data ◽  
Data Access ◽  
Data Repository ◽  
Real World Data ◽  
The Real ◽  
Prescription Drug Use

Abstract Background: Administrative health records (AHRs), which are generated primarily for management and billing purposes, are now widely used in drug safety and comparative effectiveness studies. The development of analytic methods for multi-site studies can benefit from the availability of simulated data, which do not require ethical approvals and data access permissions. We simulated AHRs using both the Observational Medical Dataset Simulator II (OSIM2) proposed by the Observational Medical Outcomes Partnership, and a modified OSIM (ModOSIM) method developed by the Canadian Network for Observational Drug Effect Studies (CNODES). Our objective was to compare the simulated data to real-world AHR data to assess the representativeness of the simulated data.Methods: The real-world data comprised prescription drug records for all individuals with healthcare coverage at any point in a 10-year period (2008 – 2017) from the Manitoba Population Research Data Repository (MPRDR) in the province of Manitoba, Canada. OSIM2 and ModOSIM, which are empirical simulation models for longitudinal patient data, were used to simulate AHRs. The data were described using frequencies and percentages. We estimated agreement of prescription drug use measures in MPRDR, OSIM2 and ModOSIM using the concordance coefficient.Results: The MPRDR cohort included 169,586,633 drug records and 1,395 drug types for 1,604,734 individuals. Data for 50,000 individuals were simulated using OSIM2 and ModOSIM. Sex and age group distributions were similar in the real-world and simulated data. There were significant differences in the total number of drug records and number of unique drugs for OSIM2 and ModOSIM when compared with MPRDR; the median number of unique drugs in MPRDR, OSIM2 and ModOSIM was 9.0, 6.0 and 10.0, respectively. For average number of days of drug use, concordance was 16% (95% confidence interval [CI]: 12% – 19%) for MPRDR and OSIM2 and 88% (95% CI: 87%-90%) for MPRDR and ModOSIM.Conclusions: ModOSIM data were more similar to MPRDR than OSIM2 data on many measures of prescription drug use. Simulated AHRs that are consistent with those found in real-world settings can be generated using ModOSIM; these simulated data will benefit methodological studies and data analyst training.

scholarly journals Generating realistic simulated administrative health data for population-based drug safety and effectiveness research and training

2019 ◽  
Author(s):  
Olawale Ayilara ◽  
Robert W Platt ◽  
Matthew Dahl ◽  
Janie Coulombe ◽  
Pablo Gonzalez Ginestet ◽  
...  
Keyword(s):  
Drug Use ◽  
Prescription Drug ◽  
Drug Safety ◽  
Real World ◽  
Simulated Data ◽  
Data Access ◽  
Data Repository ◽  
Real World Data ◽  
The Real ◽  
Prescription Drug Use

Abstract Background: Administrative health records (AHRs), which are generated primarily for management and billing purposes, are now widely used in drug safety and comparative effectiveness studies. The development of analytic methods for multi-site studies can benefit from the availability of simulated data, which do not require ethical approvals and data access permissions. We simulated AHRs using both the Observational Medical Dataset Simulator II (OSIM2) proposed by the Observational Medical Outcomes Partnership, and a modified OSIM (ModOSIM) method developed by the Canadian Network for Observational Drug Effect Studies (CNODES). Our objective was to compare the simulated data to real-world AHR data to assess the representativeness of the simulated data.Methods: The real-world data comprised prescription drug records for all individuals with healthcare coverage at any point in a 10-year period (2008 – 2017) from the Manitoba Population Research Data Repository (MPRDR) in the province of Manitoba, Canada. OSIM2 and ModOSIM, which are empirical simulation models for longitudinal patient data, were used to simulate AHRs. The data were described using frequencies and percentages. We estimated agreement of prescription drug use measures in MPRDR, OSIM2 and ModOSIM using the concordance coefficient.Results: The MPRDR cohort included 169,586,633 drug records and 1,395 drug types for 1,604,734 individuals. Data for 50,000 individuals were simulated using OSIM2 and ModOSIM. Sex and age group distributions were similar in the real-world and simulated data. There were significant differences in the total number of drug records and number of unique drugs for OSIM2 and ModOSIM when compared with MPRDR; the median number of unique drugs in MPRDR, OSIM2 and ModOSIM was 9.0, 6.0 and 10.0, respectively. For average number of days of drug use, concordance was 16% (95% confidence interval [CI]: 12% – 19%) for MPRDR and OSIM2 and 88% (95% CI: 87%-90%) for MPRDR and ModOSIM.Conclusions: ModOSIM data were more similar to MPRDR than OSIM2 data on many measures of prescription drug use. Simulated AHRs that are consistent with those found in real-world settings can be generated using ModOSIM; these simulated data will benefit methodological studies and data analyst training.

scholarly journals Validity of social media for assessing treatment patterns in oncology patients: a case study in melanoma

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 416-422
Author(s):  
Laura McDonald ◽  
Varun Behl ◽  
Vijayarakhavan Sundar ◽  
Faisal Mehmud ◽  
Bill Malcolm ◽  
...  
Keyword(s):  
Social Media ◽  
Confidence Intervals ◽  
Real World ◽  
Unmet Need ◽  
Data Access ◽  
Treatment Patterns ◽  
Patient Treatment ◽  
Real World Data ◽  
The Real ◽  
Third Line

Abstract There is a need to understand how patients are managed in the real world to better understand disease burden and unmet need. Traditional approaches to gather these data include the use of electronic medical record (EMR) or claims databases; however, in many cases data access policies prevent rapid insight gathering. Social media may provide a potential source of real-world data to assess treatment patterns, but the limitations and biases of doing so have not yet been evaluated. Here, we assessed whether patient treatment patterns extracted from publicly available patient forums compare to results from more traditional EMR and claims databases. We observed that the 95% confidence intervals of proportions of treatments received at first, second, and third line for advanced/metastatic melanoma generated from unstructured social media data overlapped with 95% confidence intervals from proportions obtained from 1 or more traditional EMR/Claims databases. Social media may offer a valid data option to understand treatment patterns in the real world.

Recreational Prescription Drug Use Among College Students

NASPA Journal ◽  
2006 ◽  
Vol 43 (1) ◽  
Author(s):  
Ethan A Kolek
Keyword(s):  
Drug Use ◽  
Prescription Drug ◽  
Undergraduate Students ◽  
Prescription Drugs ◽  
Drug Users ◽  
Research University ◽  
Future Research ◽  
Negative Consequences ◽  
Prescription Drug Use ◽  
Other Substances

The purpose of this study was to explore recreational prescription drug use among undergraduate students. Although anecdotal accounts on this subject abound, empirical research is extremely limited. Data from a survey of a random sample of 734 students at a large public research university in the Northeast were examined. Results indicate that a substantial proportion of students reported having used prescription drugs for recreational purposes in the year prior to survey administration. Recreational prescription drug use was positively associated with the use of other substances including alcohol. Recreational prescription drug users were also more likely than other drug users to report negative consequences as a result of their drug use. Implications for future research and for student affairs are discussed.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

scholarly journals Trastuzumab-based palliative chemotherapy for HER2-positive gastric cancer: a single-center real-world data

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tae-Hwan Kim ◽  
Hun Do Cho ◽  
Yong Won Choi ◽  
Hyun Woo Lee ◽  
Seok Yun Kang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Real World ◽  
Group Performance ◽  
Eastern Cooperative Oncology Group ◽  
Conversion Surgery ◽  
Single Center ◽  
Her2 Positive ◽  
Real World Data ◽  
World Data ◽  
The Real

Abstract Background Since the results of the ToGA trial were published, trastuzumab-based chemotherapy has been used as the standard first-line treatment for HER2-positive recurrent or primary metastatic gastric cancer (RPMGC). However, the real-world data has been rarely reported. Therefore, we investigated the outcomes of trastuzumab-based chemotherapy in a single center. Methods This study analyzed the real-world data of 47 patients with HER2-positive RPMGC treated with trastuzumab-based chemotherapy in a single institution. Results With the median follow-up duration of 18.8 months in survivors, the median overall survival (OS) and progression-free survival were 12.8 and 6.9 months, respectively, and the overall response rate was 64%. Eastern Cooperative Oncology Group performance status 2 and massive amount of ascites were independent poor prognostic factors for OS, while surgical resection before or after chemotherapy was associated with favorable OS, in multivariate analysis. In addition, 5 patients who underwent conversion surgery after chemotherapy demonstrated an encouraging median OS of 30.8 months, all with R0 resection. Conclusions Trastuzumab-based chemotherapy in patients with HER2-positive RPMGC in the real world demonstrated outcomes almost comparable to those of the ToGA trial. Moreover, conversion surgery can be actively considered in fit patients with a favorable response after trastuzumab-based chemotherapy.

scholarly journals Exploring the use of cannabis as a substitute for prescription drugs in a convenience sample

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Sinikka L. Kvamme ◽  
Michael M. Pedersen ◽  
Kristine Rømer Thomsen ◽  
Birgitte Thylstrup
Keyword(s):  
Side Effects ◽  
Drug Use ◽  
Prescription Drug ◽  
Prescription Drugs ◽  
Online Survey ◽  
Convenience Sample ◽  
Patient Organizations ◽  
Public Media ◽  
Prescription Drug Use ◽  
The Impact

Abstract Background The use of cannabis as medicine (CaM) both prescribed and non-prescribed has increased markedly in the last decade, mirrored in a global shift in cannabis policy towards a more permissive stance. There is some evidence that cannabis functions as a substitute for prescription drugs, particularly opioids; however, more knowledge is needed on the motives of substitution users, their patterns of use, and perceived effects of substitution use. Aims To explore who substitutes prescription drugs with cannabis, the type of prescription drugs substituted and the type of cannabis used, and the impact that substitution with cannabis has on prescription drug use as well as the motives for substitution in terms of experienced effects and side effects. Methods A self-selected convenience sample was recruited through social media, public media, and patient organizations to take part in an anonymous online survey. Inclusion criteria were 18 years or older and use of cannabis (prescribed or non-prescribed) with a medical purpose. Results The final sample included 2.841 respondents of which the majority (91%) used non-prescribed cannabis, and more than half (54.6%) had used CaM with the purpose of replacing a prescribed drug. Compared to non-substitution users, substitution users were more likely to be women and to use CaM in the treatment of chronic pain and other somatic conditions. Pain medication (67.2%), antidepressants (24.5%), and arthritis medication (20.7%) were the most common types of drugs replaced with CaM. Among substitution users, 38.1% reported termination of prescription drug use, and 45.9% a substantial decrease in prescription drug use. The most frequent type of cannabis used as a substitute was CBD-oil (65.2%), followed by ‘hash, pot or skunk’ (36.6%). More than half (65.8%) found CaM much more effective compared to prescription drugs, and 85.5% that the side effects associated with prescription drug use were much worse compared to use of CaM. Conclusion CaM is frequently used as a substitute for prescription drugs, particularly opioids. More research is needed on the long-term consequences of use of CaM, including the impact from low and high THC cannabis products on specific somatic and mental health conditions.

scholarly journals Improving the performance of a radio-frequency localization system in adverse outdoor applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Marcelo N. de Sousa ◽  
Ricardo Sant’Ana ◽  
Rigel P. Fernandes ◽  
Julio Cesar Duarte ◽  
José A. Apolinário ◽  
...  
Keyword(s):  
Random Forest ◽  
Ray Tracing ◽  
Real World ◽  
Practical Implication ◽  
Real Life ◽  
Simulated Data ◽  
Real Data ◽  
Gradient Boosting ◽  
Real World Data ◽  
Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

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