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pearson correlation coefficient  (IQVIA Inc)

 
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    IQVIA Inc pearson correlation coefficient
    Correlation between National Tuberculosis Surveillance System case counts and IQVIA ( https://www.iqvia.com ) projected patient counts for isoniazid (A) or pyrazinamide (B) prescriptions, United States, 2006–2019. Horizontal axes show patient counts aggregated by treatment start date, removing patients who had reported resistance to isoniazid or pyrazinamide. Vertical axes show IQVIA projected patient counts aggregated by date drug was dispensed (data are on different scales). Each point represents a month, and all data during 2006–2019 are shown. Solid black lines represent regression fit for a linear model between the 2 databases; dashed gray lines indicate 95% prediction intervals. The <t>Pearson</t> correlation <t>coefficient</t> ( r ) is shown in the lower righthand corner of each plot.
    Pearson Correlation Coefficient, supplied by IQVIA Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/correlation+coefficient/pearson+correlation+coefficient/pmc08962886-25-3-21
    Average 90 stars, based on 1 article reviews
    pearson correlation coefficient - by Bioz Stars, 2026-11
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    Images

    1) Product Images from "Decrease in Tuberculosis Cases during COVID-19 Pandemic as Reflected by Outpatient Pharmacy Data, United States, 2020"

    Article Title: Decrease in Tuberculosis Cases during COVID-19 Pandemic as Reflected by Outpatient Pharmacy Data, United States, 2020

    Journal: Emerging Infectious Diseases

    doi: 10.3201/eid2804.212014

    Correlation between National Tuberculosis Surveillance System case counts and IQVIA ( https://www.iqvia.com ) projected patient counts for isoniazid (A) or pyrazinamide (B) prescriptions, United States, 2006–2019. Horizontal axes show patient counts aggregated by treatment start date, removing patients who had reported resistance to isoniazid or pyrazinamide. Vertical axes show IQVIA projected patient counts aggregated by date drug was dispensed (data are on different scales). Each point represents a month, and all data during 2006–2019 are shown. Solid black lines represent regression fit for a linear model between the 2 databases; dashed gray lines indicate 95% prediction intervals. The Pearson correlation coefficient ( r ) is shown in the lower righthand corner of each plot.
    Figure Legend Snippet: Correlation between National Tuberculosis Surveillance System case counts and IQVIA ( https://www.iqvia.com ) projected patient counts for isoniazid (A) or pyrazinamide (B) prescriptions, United States, 2006–2019. Horizontal axes show patient counts aggregated by treatment start date, removing patients who had reported resistance to isoniazid or pyrazinamide. Vertical axes show IQVIA projected patient counts aggregated by date drug was dispensed (data are on different scales). Each point represents a month, and all data during 2006–2019 are shown. Solid black lines represent regression fit for a linear model between the 2 databases; dashed gray lines indicate 95% prediction intervals. The Pearson correlation coefficient ( r ) is shown in the lower righthand corner of each plot.

    Techniques Used:

    Comparison of National Tuberculosis Surveillance System (NTSS) case counts and IQVIA projected patient counts for isoniazid or pyrazinamide prescriptions, United States, 2020. A, B) Projected patient counts for isoniazid (A) and pyrazinamide (B). Horizontal axis of each plot shows NTSS patient counts aggregated by treatment start date (month), removing patients who had reported resistance. Each point represents a month in 2020. The Pearson correlation coefficient ( r ) is shown in lower righthand corner of each plot. C, D) A linear model fit to the 2006–2019 data with quarter as a covariate to predict 2020 IQVIA projected patient counts for isoniazid (C) or pyrazinamide (D). Black line indicates actual data; gray line indicates expected IQVIA counts with 95% prediction intervals. Note vertical axes are different because of different scales for isoniazid and pyrazinamide in the IQVIA dataset. NTSS, National Tuberculosis Surveillance System.
    Figure Legend Snippet: Comparison of National Tuberculosis Surveillance System (NTSS) case counts and IQVIA projected patient counts for isoniazid or pyrazinamide prescriptions, United States, 2020. A, B) Projected patient counts for isoniazid (A) and pyrazinamide (B). Horizontal axis of each plot shows NTSS patient counts aggregated by treatment start date (month), removing patients who had reported resistance. Each point represents a month in 2020. The Pearson correlation coefficient ( r ) is shown in lower righthand corner of each plot. C, D) A linear model fit to the 2006–2019 data with quarter as a covariate to predict 2020 IQVIA projected patient counts for isoniazid (C) or pyrazinamide (D). Black line indicates actual data; gray line indicates expected IQVIA counts with 95% prediction intervals. Note vertical axes are different because of different scales for isoniazid and pyrazinamide in the IQVIA dataset. NTSS, National Tuberculosis Surveillance System.

    Techniques Used: Comparison

    Related Articles

    Comparison:

    Article Title: Decrease in Tuberculosis Cases during COVID-19 Pandemic as Reflected by Outpatient Pharmacy Data, United States, 2020
    Article Snippet: tested aggregating the data at the month and quarter level. .. We used a Pearson correlation coefficient ( r ) for the aggregated dates available in both datasets to identify the combination (IQVIA database, NTSS aggregation, and month/quarter timeframe) with the strongest correlation (highest r ), which was used in subsequent analyses. .. For visualization, we generated a linear model with IQVIA data



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    Article Title: A quantitative metabolic signature of host response during SARS-CoV-2 infection and recovery

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    Figure Lengend Snippet: Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression coefficient (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,

    Article Snippet: Correlation between Metabo-time and log-transformed CRP values was assessed using Spearman’s rank correlation coefficient.

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    Correlation coefficient (Matel’s and Pearson’s correlation of the observations).

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    Article Title: Evaluating the growth and water use efficiency of chili pepper ‘Tanjung’, ‘Unpad’, and ‘Osaka’ using SFM1 sap flow in agro-environment

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    Figure Lengend Snippet: Correlation coefficient (Matel’s and Pearson’s correlation of the observations).

    Article Snippet: Fig. 5 Correlation coefficient (Matel’s and Pearson’s correlation of the observations).

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