Real-world Data (RWD) Market by Source (EMR, Claims, Pharmacy, Disease Registries), Application [Market Access, Drug Development & Approvals (Oncology, Neurology), Post Market Surveillance], End User (Pharma, Payers, Providers) - Global Forecast to 2029

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%) in United States was reported at 0.91 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

These are peer-reviewed supplementary materials for the article 'Real-world evidence: state-of-the-art and future perspectives' published in the Journal of Comparative Effectiveness Research.BackgroundAimMethodsStep 1: Selection of TAsStep 2a: Cross-validation of definition of ‘use of routine data in non-experimental settings’ Figure 3: Refinement of the criteria used to define 'use of routine data in non-experimental settings’ for the full assessment of published NICE TAsStep 2b: Full review of 12 Cancer and 67 Non-Cancer TAs published 2022-24ResultsFigure 4: Selection of TAs for reviewFigure 5: Distribution of Cancer (blue) and Non-Cancer (green) TAs submitted to NICE since 2000 (A). Non-Cancer TAs are broken down by specialty (B)Table 1: Results of the cross-validation of the criteria applied to randomly selected Cancer TAsTable 2: Results of the cross-validation of the criteria applied to randomly selected Non-Cancer TAsRecent developments in digital infrastructure, advanced analytical approaches, and regulatory settings have facilitated the broadened use of real-world evidence (RWE) in population health management and evaluation of novel health technologies. RWE has uniquely contributed to improving human health by addressing unmet clinical needs, from assessing the external validity of clinical trial data to discovery of new disease phenotypes. In this perspective, we present exemplars across various health areas that have been impacted by real-world data and RWE, and we provide insights into further opportunities afforded by RWE. By deploying robust methodologies and transparently reporting caveats and limitations, realworld data accessed via secure data environments can support proactive healthcare management and accelerate access to novel interventions in England.

Graph and download economic data for Real Mean Family Income in the United States (MAFAINUSA672N) from 1953 to 2023 about family, average, income, real, and USA.

The real total consumer spending on communication in the Netherlands was forecast to continuously increase between 2024 and 2029 by in total *** billion U.S. dollars (+***** percent). After the fifteenth consecutive increasing year, the real communication-related spending is estimated to reach **** billion U.S. dollars and therefore a new peak in 2029. Notably, the real total consumer spending on communication of was continuously increasing over the past years.Consumer spending, in this case communication-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres roughly to group **, with the exception of information processing equipment (computers) which are here still aggregated into recreation. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real total consumer spending on communication in countries like Luxembourg and Belgium.

United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: Mean data was reported at 41.600 % in May 2018. This records an increase from the previous number of 39.900 % for Apr 2018. United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: Mean data is updated monthly, averaging 38.600 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 46.000 % in Feb 2000 and a record low of 27.200 % in Sep 2011. United States CSI: Personal: Real Income Gains Probability: Next 5 Yrs: Mean data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The question was: What do you think the chances are that your (family) income will increase by more than the rate of inflation in the next five years or so?

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for York County, ME (MWACL23031) from 2009 to 2023 about York County, ME; Portland; ME; adjusted; average; wages; real; and USA.

Real Mean Personal Income in the United States was 63510.00000 2015 CPI-U-RS Adjusted $ in January of 2023, according to the United States Federal Reserve. Historically, Real Mean Personal Income in the United States reached a record high of 63990.00000 in January of 2021 and a record low of 36040.00000 in January of 1981. Trading Economics provides the current actual value, an historical data chart and related indicators for Real Mean Personal Income in the United States - last updated from the United States Federal Reserve on July of 2025.

RMSE value of missing data filling algorithms on different datasets (mean ± std).

Gap filling of real genomics data based on k-means clustering of populations

The observation for 2020 is missing because the U.S. Census Bureau released experimental estimates (https://www.census.gov/programs-surveys/acs/data/experimental-data.html) instead of the standard 1-year data products for the 2020 American Community Survey (ACS). There was no 2020 experimental data provided for the American Community Survey (ACS) 1-year variable S1901_C01_013E.

Mean household income, American Community Survey (ACS) 1-year variable S1901_C01_013E, is adjusted by CPI (https://fred.stlouisfed.org/series/CPIAUCSL) where the price index is re-based to 1999 dollars. Then the series is adjusted for cost of living using regional price parities (RPP) from the U.S. Bureau of Economic Analysis' Real Personal Income for States and Metropolitan Areas (https://fred.stlouisfed.org/release?rid=403&soid=18). Finally to approximate the wage, the series is divided by (52 * 40), which assumes there are 52 weeks in a year and 40 work hours in a week. Note that household income can include additional sources of income beyond wages. See page 83 in the ACS's Subject Definitions (https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2019_ACSSubjectDefinitions.pdf) for more information.

ACS 1-year estimates are not available for all geographic areas. If a county is not included in the 1-year estimates for a given year, the series will not revise or there will be a missing observation. See the Areas Published (https://www.census.gov/programs-surveys/acs/geography-acs/areas-published.html) for more details about the geographies included in the ACS 1-year estimates.

The RPP used to calculate this series is NYNMPRPPALL (https://fred.stlouisfed.org/series/NYNMPRPPALL). If the RPP for the region is zero or missing for a given year, the series will not revise or there will be a missing observation.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for St. Louis city, MO (MWACL29510) from 2009 to 2023 about St. Louis City, MO; St. Louis; adjusted; MO; average; wages; real; and USA.

Real Mean Personal Income in West Census Region was 66780.00000 2015 CPI-U-RS Adjusted $ in January of 2023, according to the United States Federal Reserve. Historically, Real Mean Personal Income in West Census Region reached a record high of 68040.00000 in January of 2021 and a record low of 35259.00000 in January of 1981. Trading Economics provides the current actual value, an historical data chart and related indicators for Real Mean Personal Income in West Census Region - last updated from the United States Federal Reserve on June of 2025.

Overview of attribute information of real heart disease datasets.

Mexico RR: WT: IC: Intermediation except via Internet & Other Elect Means data was reported at 89.207 2013=100 in Mar 2019. This records an increase from the previous number of 88.399 2013=100 for Feb 2019. Mexico RR: WT: IC: Intermediation except via Internet & Other Elect Means data is updated monthly, averaging 86.213 2013=100 from Jan 2008 (Median) to Mar 2019, with 135 observations. The data reached an all-time high of 172.347 2013=100 in Jun 2014 and a record low of 27.586 2013=100 in Mar 2012. Mexico RR: WT: IC: Intermediation except via Internet & Other Elect Means data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.G051: Real Remuneration: Wholesale Trade: 2013=100.

RE of existing and suggested estimators for various values of ρxy, λ and n.

Real interest rates describe the growth in the real value of the interest on a loan or deposit, adjusted for inflation. Nominal interest rates on the other hand show us the raw interest rate, which is unadjusted for inflation. If the inflation rate in a certain country were zero percent, the real and nominal interest rates would be the same number. As inflation reduces the real value of a loan, however, a positive inflation rate will mean that the nominal interest rate is more likely to be greater than the real interest rate. We can see this in the recent inflationary episode which has taken place in the wake of the Coronavirus pandemic, with nominal interest rates rising over the course of 2022, but still lagging far behind the rate of inflation, meaning these rate rises register as smaller increases in the real interest rate.

Mexico RR: WT: IC: Intermediation exc via Internet & Other Electronic Means data was reported at 182.916 2008=100 in Jan 2019. This records a decrease from the previous number of 201.356 2008=100 for Dec 2018. Mexico RR: WT: IC: Intermediation exc via Internet & Other Electronic Means data is updated monthly, averaging 164.611 2008=100 from Jan 2008 (Median) to Jan 2019, with 133 observations. The data reached an all-time high of 300.665 2008=100 in Aug 2015 and a record low of 84.024 2008=100 in Jul 2011. Mexico RR: WT: IC: Intermediation exc via Internet & Other Electronic Means data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.G049: Real Remuneration: Wholesale Trade: 2008=100.

We apply a Genomic Selection Index (GSI) to simulated and real data sets with four traits and numerically we compared its efficiency with that of the phenotypic selection index (PSI) using the ratio of the GSI response over the PSI response. In addition, we used two additional criteria to compare the GSI vs PSI efficiency: the ratio of the average of the top 10% of the predicted values of the net genetic merit from one to the next selection cycle for PSI and GSI and the Technow inequality. Results from the simulated data were confirmed by the real data, indicating that GSI was more efficient than PSI in terms of time and that the means of the top 10% of the net genetic merit predicted by GSI were higher than that predicted by PSI. Thus, we concluded that the proposed GSI is an efficient choice when the purpose of a breeding program is to select genotypes using genomic selection.