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Smarter Real World Evidence

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A Flexible Client-Centered Dashboard Can Help Data Tell a Better Story

If analyzed correctly, compelling data can serve business needs and reveal commercial opportunities. The KMK Real World Evidence Dashboard is a customized client-centered approach to help biopharmaceutical companies reach intuitive conclusions faster, save significant time on re-analysis, and improve recall and comprehension with a clean graphical approach to sharing the story inherent in the data.

Real World Evidence (RWE) Analysis Allows Us to Tell a Product’s Value Story Using Real World Data

The data collected from insurers, patients, and physicians, in addition to facilities’ information, can allow one to accurately predict post- market medication behaviors and provide for better allocation of resources. By studying real world data, we can gain the many insights needed to improve decision-making, increase commercial success, and affect patients’ lives.
However, the decisionmaking process is never a direct route. It involves numerous detours along the way requiring feasibility tests and multiple comparisons to maximize the benefits of Real World Evidence, thereby ensuring the best decisions are made. As analysts in support of our biopharmaceutical clients, our mission is to convert the raw data into several huge and complex story lines. The first priority is to narrate the story efficiently, precisely, and vividly.
Traditionally, the story has been recorded in a static book with illustrations to interpret the contents. When using Real World Evidence, the analysis has been delivered in a spreadsheet with several static charts. As technology has evolved, various unconventional methods of presenting data have improved the story, with dynamic and interactive functions. Now there are Numerous visualization tools on the market, such as Tableau, Power BI, Spotfire®, and Shiny, and all fulfill different demonstration requirements.
Our choice for our Real World Evidence analysis and delivery tool is R/R Shiny based on its strengths in building models, designing algorithms, and developing useful visualizations. As an innovative technology solution built on top of the Shiny platform, the KMK RWE Dashboard offers a more complete communication tool, not only for its flexibility, interactivity, and visualization, but for its capability to accommodate different types of real world data.

What Are The Strengths of KMK RWE Dashboard

The KMK RWE Dashboard improves project flexibility with its main feature—the automatic cohort summarizing tool. With the support of the dashboard, the programmer no longer needs to manually settle for bunches of aggregate data and spreadsheets generated from different cohorts. The requestor is able to flexibly assemble their desired criteria from a drop-down menu. The dashboard then implements the computing in the back-end and reports the summaries of desired cohorts. This flexibility encourages the stakeholders to explore as many cohorts, with different criteria combinations as possible, to increase the robustness of the study.

The KMK RWE Dashboard significantly increases efficiency and lessens the workload with the support provided from predefined filters such as age, gender, index year, table layout or other demographics or common clinical characteristics. This “predefined” filter does not mean it’s an “uncontrollable” filter; rather, the predefined filter actually provides more flexibility for the dashboard in providing selection on all spans of the variables, instead of setting a specific number of categories in the criteria. The project duration is reduced by eliminating the back-andforth testing on different demographic norms. The project efficiency is increased by skipping the common-characteristics settings, allowing the user to delve directly into core business questions.

Visualization is important not only for data presentation but also for data exploration. KMK RWE Dashboard with its visualization features, is well-designed to meet both objectives. In more traditional reporting, demographic characteristics are reported by a few categories or simple calculations such as mean and standard deviation. The KMK RWE Dashboard now provides comprehensive distributions and various statistical plots for exploration. Furthermore, with the support of plots, the dashboard can demonstrate such various features as medical patterns, patient journeys, and medical burdens which were not delivered in traditional tables due to structure limitations. This visualization tool thus provides the capacity to profoundly analyze panel and spatial data, allowing analysts to design more scenarios than before. The more insights the stakeholders gain from the analysis, the closer they are to achieving the desired commercial success.

KMK Real World Evidence Dashboard Examples

Let’s take a detailed look. The KMK RWE Dashboard can be customized to meet a client’s unique business needs. The following examples shows how the KMK RWE Dashboard functions, and how different criteria selections and user assumptions can change the story.

Figure 0:  Study design The front page of this interactive dashboard is the study design, including an introduction of the study scope and a visualized timeline to demonstrate the design.

Figure 1: The control panel of the dashboard

The main control panel contains 4 parts: 1) criterion selection, 2) filters selection, 3) attrition table, and 4) statistics result. The first two parts allow the user to select 1) inclusive/exclusive criterion, and 2) further filters such as age range, gender and index year. With the selection input from the user, the control panel will revise the attrition table located below the selection area. The attrition table now shows the raw number of patients, which is the number of patients with a specific disease diagnosis during the identification period. Last but not least, the three light blue boxes show the statistics results and will be automatically updated according to the final cohort.

Figure 2.1: The control panel with criterion and filter selection

Figure 2.2: The attrition table with age and gender filters

Figure 2.3: The attrition table with different inclusive/ exclusive criterion order

After submitting criteria and filter selection, The attrition table criteria and filter selection, the attrition table section generates patient flow for the determined cohort. The attrition will follow this order: 1) index patient, 2) filters selection, 3) criterion selection, which is shown in Figure 2.1. In the attrition table, the filter selections are set to be under the index patient and follow the order of 1) age, 2) gender and 3) index year. In Figure 2.2, you can see the user applied “age” and “gender” filters. Compare to Figure 2.1, the attrition table automatically adds another row for the “gender” filter below “age restriction.” In addition, the order of criterion will reflect the corresponding inclusion/exclusion criteria selection. For example, in Figure 2.3, the user chose the same inclusive/exclusive criterion as in Figure 2.1 but in a different order.

The attrition table reflects the change and automatically re-orders the criteria. A dynamic attrition table allows user to test different inclusive/exclusive criterion composition and arrangement; therefore, helping the user have a better understanding of the patient structure. Last, after determining the cohort, the final patient number will be shown at the top left and the statistics results will be generated for these patients in the three light blue boxes.

Figure 3.1: Demographic characteristics – age and gender

Figure 3.2: Demographic characteristics – region

The first visualized tab is demographic characteristics, which contains, but is not limited to, two subtabs – 1) age & gender distribution, and 2) region. The first subtab provides 3 different charts, bar, ring, and value box, to demonstrate age and gender statistical results; the second one is a map visualization for regional statistics. In our visualization platform, the user can flexibly choose demonstration methods shown in our sample dashboard or ask for added customized visualization charts and layouts according to their project requirements. For example, the map visualization in Figure 3.2 is shown at the state level, but it could be divided into city level with the provided data.

Figure 4.1: Clinical characteristics – charlson comorbidity

Figure 4.2: Clinical characteristics – resource utilization

Next tab shows the clinical characteristics, which includes 1) Charlson comorbidity, and 2) resource utilization. We provide four sample charts for Charlson comorbidity, which could be analyzed by seventeen sub-categories, gender, age and a continuous index. For the resource utilization subtab, it contains the analyses of inpatient visit, emergency, office visit, and other visits. The analysis provides patient counts and box plot with mean, median, and quantile visiting times.

Figure 5.1: Incidence rate (5 years truncation)

Figure 5.2: Incidence rate (8 years truncation)

The last tab in the dashboard is the tab of incidence rate with exposure in years. In the top of this tab, the user is able to choose the years to show. The maximum length to choose will adjust with the change of the index year and study period in the provided study design. By setting up the years to show, the total number of new patients will be truncated with the selected years. For example, in Figure 5.1, with 5 years incidence rate, the total number of new patients is 900 which is the summation from index date until 5 years after index date. In Figure 5.2, with 8 years incidence rate, the total number of new patients is 1,008. The user can choose the years to show that fits the study.

After selecting years to show, the dashboard will generate the corresponding incidence table, line plot and a donut plot. The incidence table provides six statistical results, sample size, number of new patients, exposure in years, incidence rate, lower (95%) confidence interval, and higher (95%) confidence interval. It is worth noting that the exposure in years is one of the methods to analyze incidence rate. The tab of incidence rate could also be replaced with the simple incidence rate which is calculated by number of new patients and the sample size.

A Feasibility Test for Post-Market Monitoring


In Real World Evidence, feasibility research always needs back-and-forth testing on different combinations of diagnosis claims, treatment claims, and demographic restrictions, while juggling the retrieval of data from disparate sources. Every time the criteria changes for a new experiment, it creates a domino effect. By adding or modifying a specific variable, a huge number of aggregate data and tables are generated from variable testing within a project. This not only consumes a tremendous amount of time to convert each number into the report, but causes data management issues.

For example, take post-market monitoring, a common process in Real World Evidence. The feasibility test for a monitoring project usually adopts, but is not limited to the following procedures to determine the study design: 1) find the target patients, 2) address the monitoring features, 3) set up the monitoring period. The KMK interactive RWE Dashboard has a high level of flexibility to test different outcomes without any coding. By deploying the KMK interactive RWE Dashboard, the requestor can test different combinations of criteria, fine tune the desired demographics and clinical characteristics, select monitoring features of interest, and adjust the monitoring period with just a few clicks. The dashboard then generates correlated attrition tables, descriptive analysis, and charts for the selected term automatically with all these steps taking place within a few minutes. Meanwhile, only one cohort table is generated and no excel file needs to be established, thus saving both the stakeholders and programmers time by eliminating the back-andforth testing and report generation.


The evolution of Real World Data expands our perspectives to drive analytical solutions. With its built-in flexibility, interactivity, and visualization, the KMK RWE Dashboard provides a more accurate, efficient analytics solution to support our clients in achieving commercial excellence.

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