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Discover why overly safe playgrounds might contribute to childhood obesity and how functionality acts as the third factor explaining this surprising correlation. Question What is a plausible conclusion in this situation? It was observed that wherever playgrounds were more safe, kids gained more weight. A. Safety makes kids overweight B. The higher the weight of …

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Discover the top HTA agencies embracing real-world evidence (Canada, Australia, England) for regulatory decisions and improved healthcare outcomes. Question Select the most open health technology assessment agencies to using Real world evidence A. Canada B. Germany C. Australia D. England E. France Answer A. Canada C. Australia D. England Explanation The health technology assessment (HTA) …

Read More about Data Science with Real World Data in Pharma: Which Health Technology Assessment Agencies Are Leading in Real-World Evidence Adoption?

Discover which therapy (Yescarta, Evrysdi, Luxturna, Kymriah, Zolgensma, Polivy, Tecartus, or Rozlytrek) leverages real-world evidence most effectively for HTA approval across countries. Question Select the therapy with the best impact of real world evidence in most for the countries’ HTA recommendations A. Yescarta B. Evrysdi C. Luxturna D. Kymriah E. Zolgensma F. Polivy G. Tecartus …

Read More about Data Science with Real World Data in Pharma: Which Therapy Demonstrates the Strongest Real-World Evidence Impact on HTA Recommendations Globally?

Discover why none of the listed therapies (Tecartus, Rozlytrek, Zolgensma, etc.) achieved full agreement between regulatory approval and HTA reimbursement decisions. Question Select the therapies where Regulators and Health technology assessment bodies agreed on the decision A. Tecartus B. Rozlytrek C. Polivy D. Zolgensma E. Yescarta F. Kymriah G. Luxturna H. Evrysdi I. None Answer …

Read More about Data Science with Real World Data in Pharma: Which Therapies Achieved Alignment Between Regulators and Health Technology Assessment Bodies?

Discover how real-world evidence (RWE) addresses long-term treatment effects, fills evidence gaps, and enhances decision-making in health technology assessments (HTA). Question Select the focus of real world evidence in recent health technology assessment dossiers A. Effectiveness of the treatment B. Effectiveness of the comparator C. Long term treatment effect Answer C. Long term treatment effect …

Read More about Data Science with Real World Data in Pharma: What Is the Primary Focus of Real-World Evidence in Recent Health Technology Assessments?

Discover which recent FDA drug approvals, including Prograf for lung transplants, utilized real-world evidence (RWE) to demonstrate efficacy and safety. Question Select recent FDA approvals based on real world evidence A. Prograf B. Zolgensma C. Crenezumab Answer A. Prograf Explanation Explanation of FDA Approvals Based on Real-World Evidence (RWE) Prograf (tacrolimus) Approval Context: In July …

Read More about Data Science with Real World Data in Pharma: Which Recent FDA Approvals Leveraged Real-World Evidence in Drug Development?

Discover which prostate cancer patient groups (metastatic, progressing to metastatic) show the greatest benefit from novel therapies like NXP800 and next-gen hormone inhibitors, based on 2025 clinical trial data. Question Based on the study’s findings, which patients may benefit the most from the development of new treatments for prostate cancer? (Select all that apply) A. …

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Discover the meaning of a hazard ratio (HR) < 1 in treatment comparisons. Learn why it indicates a better outcome for the new therapy and how to interpret survival analysis results. Question What does a hazard ratio less than 1 indicate when comparing two treatments? (Assuming the standard treatment is the denominator and the new …

Read More about Data Science with Real World Data in Pharma: What Does a Hazard Ratio Less Than 1 Mean in Clinical Trial Comparisons?

Learn why hazard ratio confidence intervals crossing 1 indicate non-significant survival differences in clinical trials. Essential for Pharma Data Science certification exam prep. Question What does it mean if confidence intervals for hazard ratios cross 1? A. The survival difference is significant B. The new treatment is definitely better C. The new treatment is definitely …

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Discover why metastatic cancer patients aren’t always older than in-situ cases. Explore age-related patterns in metastasis, survival outcomes, and tumor biology across breast, colorectal, and bladder cancers. Question Metastatic patients are older than patients with an in-situ disease. A. Yes, but only by a little bit B. Yes, by a large difference. C. No, that’s …

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Learn the correct method to calculate follow-up time in oncology trials using diagnosis dates, death dates, and follow-up intervals, with evidence-based explanations for data science certification exams. Question How do you calculate the follow-up time? A. The difference between the death date and the diagnosis date (neoplasm or metastatic). B. The difference between the latest …

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Discover what the “R” in FINER criteria represents and how Feasible, Interesting, Novel, Ethical, and Relevant principles shape impactful research questions. Question According to Hulley, a research question should be formulated keeping in mind the FINER (feasible, interesting, novel, ethical, and rxxx). What does the R stands for: A. Rigorous B. Respectable C. Redundant D. …

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Discover why potential reduction in generalizability is a critical limitation of using matching in observational studies, impacting data science and pharmaceutical research validity.   Question Select a limitation of using Matching A. Potential reduction in generalizability B. No separation of study design and analysis Answer A. Potential reduction in generalizability Explanation While the separation of …

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Discover expert strategies for addressing unmeasured smoking confounding in occupational health research when data is lacking. Learn why traditional adjustment fails and advanced alternatives succeed. Question In a study of risk of death in a occupational cohort of miners, smoking is considered a potential confounder, however information on smoking is lacking. Which method can be …

Read More about Data Science with Real World Data in Pharma: What Are the Best Methods to Handle Missing Smoking Data in Occupational Cohort Studies?

Discover why prospective cohort studies are less prone to bias than retrospective designs in epidemiological research, with detailed explanations of bias risks and methodological strengths. Question Select the study design that is less prone to bias A. Prospective cohort B. Retrospective cohort Answer A. Prospective cohort Explanation Prospective cohort studies are less prone to bias …

Read More about Data Science with Real World Data in Pharma: Which Study Design is Less Prone to Bias: Prospective vs. Retrospective Cohort Studies?

Discover why cohort studies are more efficient for investigating rare exposures like asbestos or radiation, requiring smaller sample sizes compared to case-control designs. Question Select the study design that will require smaller sample size when the exposure is rare; for example an occupational exposure (e.g. asbestos, radiation, and pesticides). A. Case-control B. Cohort Answer B. …

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Confounding bias occurs when a third variable distorts the exposure-outcome relationship. Learn how to identify it with real-world examples critical for data science and pharma research. Question What is an example of confounding bias? A. Different interviewers administering the same survey differently B. Using self-reported questionnaires that inaccurately record dietary intake C. Including both healthy …

Read More about Data Science with Real World Data in Pharma: What is an Example of Confounding Bias in Epidemiological Studies?

Discover why over-representation of health-conscious volunteers in a lifestyle study exemplifies selection bias, with detailed explanations and examples for exam preparation. Question An example of selection bias in a study would be A. Random errors in data entry B. Over-representation of volunteers who are health-conscious in a lifestyle study C. Participants mistakenly recalling their past …

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Discover which data sources (Medicare claims, EMRs, or cancer registries) include prostate-specific antigen (PSA) test results independently for evaluating treatment efficacy. Question You are interested in evaluating whether a specific treatment can lower levels of serum prostate-specific antigen, a biomarker often used in prostate cancer. Which data sources are likely to contain results for this …

Read More about Data Science with Real World Data in Pharma: Which Data Sources Contain PSA Test Results Without Linking to Other Data?

Explore the key limitations of electronic health record (EHR) data, including unstructured formats, incomplete patient records, lack of standardization, and challenges in clinical research. Learn more about EHR challenges in healthcare. Question What are some limitations of electronic health record data? A. The vast majority of data are in unstructured form (eg physician notes) B. …

Read More about Data Science with Real World Data in Pharma: What Are the Limitations of Electronic Health Record (EHR) Data?

Discover the key limitations of administrative claims data, including issues like lack of clinical details, coding inconsistencies, and missing over-the-counter medication data. Essential for the Data Science with Real World Data in Pharma certification exam. Question What are some limitations of administrative claims data? A. Poor capture of costly procedures (eg surgery) B. Little information …

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Discover which checklist is not part of the quality assessment tools for real-world evidence in the pharmaceutical industry. Learn about key checklists like GRACE, STROBE, and NICE QuEENS. Question One of the key concerns about real world evidence is its quality. Select one option that IS NOT a Quality checklists that can be used for …

Read More about Data Science with Real World Data in Pharma: Which Checklist is NOT Used for Real-World Evidence Quality Assessment in Pharma?

Discover the key limitations of Randomized Controlled Trials (RCTs), including inefficiency in detecting rare outcomes, limited generalizability, high costs, and more. Learn why RCTs may not always be the ideal research design. Question Select limitations of Randomized Controlled Trials A. Inefficiency of detection of rare or delayed outcomes B. Shorter-duration follow-up than observational studies C. …

Read More about Data Science with Real World Data in Pharma: What Are the Limitations of Randomized Controlled Trials (RCTs)?

Discover why evidence from single-arm trials is considered less biased than case studies in clinical research, with insights into study design limitations and comparative reliability. Question What evidence has less risk of bias? A. Evidence from single arm trial B. Case study Answer A. Evidence from single arm trial Explanation Single-arm trials (Option A) generally …

Read More about Data Science with Real World Data in Pharma: Which Evidence Has Less Risk of Bias?

Discover how observational studies in pharma leverage real-world data and RCTs as secondary sources for robust research outcomes. Expert insights on data integration explained. Question An observational study can use both Real World Data sources and randomized control trial as secondary data use A. True B. False Answer A. True Explanation While observational studies typically …

Read More about Data Science with Real World Data in Pharma: Can Observational Studies Use Both Real-World Data and Randomized Trials as Secondary Sources?