• Mashup Score: 0
    Data Analysis Techniques for Sedentary Behavior (Pt2) - Data Analysis for SB Mini Series | Physical Activity Researcher - 8 month(s) ago

    About the Miniseries: This podcast miniseries, “Data Analysis for Sedentary Behaviour”, hosted by Dr. Olli Tikkanen, aims to deliver a comprehensive understanding of sedentary behavior data analysis. Across six informative episodes, Dr. Tikkanen brings clarity to complex concepts, explores advanced analytic techniques, and introduces software tools that aid in the examination and interpretation of sedentary behavior and physical activity data. This miniseries is designed for a wide audience, from novices in the field to seasoned researchers seeking new insights.   About the Episode Topic: In episode two of the series, Dr. Tikkanen dives deep into the key techniques for analyzing sedentary behavior data: descriptive statistics and time series analysis. These approaches represent foundational elements of data analysis, offering researchers powerful tools to better understand and interpret their data. Descriptive statistics, a method used for summarizing and organizing data, offers a prel

    Source: paresearcher.podbean.com
    Categories: General Medicine News, Latest Headlines
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    • Profile photo of PA_Researcher
      PA_Researcher

      📈 Episode 2 delves into techniques for sedentary behavior data analysis, from descriptive statistics to time series analysis. Gain insights into evolving patterns and trends. https://t.co/V2caAnqAb2 #Statistics 🧵 3/7

  • Mashup Score: 2
    Data Analysis Techniques for Sedentary Behavior (Pt2) - Data Analysis for SB Mini Series | Physical Activity Researcher - 8 month(s) ago

    About the Miniseries: This podcast miniseries, “Data Analysis for Sedentary Behaviour”, hosted by Dr. Olli Tikkanen, aims to deliver a comprehensive understanding of sedentary behavior data analysis. Across six informative episodes, Dr. Tikkanen brings clarity to complex concepts, explores advanced analytic techniques, and introduces software tools that aid in the examination and interpretation of sedentary behavior and physical activity data. This miniseries is designed for a wide audience, from novices in the field to seasoned researchers seeking new insights.   About the Episode Topic: In episode two of the series, Dr. Tikkanen dives deep into the key techniques for analyzing sedentary behavior data: descriptive statistics and time series analysis. These approaches represent foundational elements of data analysis, offering researchers powerful tools to better understand and interpret their data. Descriptive statistics, a method used for summarizing and organizing data, offers a prel

    Source: paresearcher.podbean.com
    Categories: General Medicine News, Latest Headlines
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    • Profile photo of PA_Researcher
      PA_Researcher

      📈 Episode 2 delves into techniques for sedentary behavior data analysis, from descriptive statistics to time series analysis. https://t.co/V2caAnqAb2 #Statistics 🧵 3/7

  • Mashup Score: 5
    Timing and Reporting of Secondary Overall Survival End Points for Phase III Trials in Advanced/Metastatic Disease - 8 month(s) ago

    Recent therapeutic advances have led to improved patient survival in many cancer settings. Although prolongation of survival remains the ultimate goal of cancer treatment, the availability of effective salvage therapies could make definitive phase III trials with primary overall survival (OS) end points difficult to complete in a timely manner. Therefore, to accelerate development of new therapies, many phase III trials of new cancer therapies are now designed with intermediate primary end points (eg, progression-free survival in the metastatic setting) with OS designated as a secondary end point. We review recently published phase III trials and assess contemporary practices for designing and reporting OS as a secondary end point. We then provide design and reporting recommendations for trials with OS as a secondary end point to safeguard OS data integrity and optimize access to the OS data for patient, clinician, and public-health stakeholders.

    Source: ascopubs.org
    Categories: Hem/Onc News and Journals, Latest Headlines
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      ASCO_pubs

      🤔 #JCO #CommentsAndControversies: Timing reporting of overall survival analyses should follow protocol, interim OS results should be reported with primary analysis 👉 https://t.co/7XCbWGT4jA #clinicaltrials #statistics https://t.co/WZKqzfPRDu

  • Mashup Score: 0
    Why estimands are needed to define treatment effects in clinical trials - BMC Medicine - 9 month(s) ago

    Background The estimand for a clinical trial is a precise definition of the treatment effect to be estimated. Traditionally, estimates of treatment effects are based on either an ITT analysis or a per-protocol analysis. However, there are important clinical questions which are not addressed by either of these analyses. For example, consider a trial where patients take a rescue medication. The ITT analysis includes data after use of rescue, while the per-protocol analysis excludes these patients altogether. Neither of these analyses addresses the important question of what the treatment effect would have been if patients did not take rescue medication. Main text Trial estimands provide a broader perspective compared to the limitations of ITT and per-protocol analysis. Trial treatment effects depend on how events occurring after treatment initiation such as use of alternative medication or discontinuation of the intervention are included in the definition. These events can be accounted f

    Source: bmcmedicine.biomedcentral.com
    Categories: General Medicine Journals and Societies, Latest Headlines
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      BMCMedicine

      Why estimands are needed to define treatment effects in #clinicaltrials? https://t.co/aJmdMWRNCS Independent statistical experts led by Oliver N. Keene guide us on #statistics. Of interest to those who don't know their #ITT from their #per-protocol analysis. #medtwitter https://t.co/vPQVIhSEwb

  • Mashup Score: 486
    Download Statistics And Machine Learning In Python - 9 month(s) ago

    In this book we will go back to mathematics and study statistics, and how to calculate important numbers based on data sets. We will also learn how to use various Python modules to get the answers we need.

    Source: pyoflife.com
    Categories: Cardiologists1, Latest Headlines
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    • Profile photo of Ritmonegro
      Ritmonegro

      RT @KirkDBorne: Download this FREE 388-page PDF ebook >> #Statistics and #MachineLearning in #Python: https://t.co/ycxvQLz3rj via @Parajuli…

  • Mashup Score: 0
    Lack of Treatment Efficacy From Statistically Nonsignificant RCT Results - 10 month(s) ago

    This cross-sectional study of statistically nonsignificant results for clinical trial primary outcomes published in leading medical journals in 2021 estimates the strength of support the findings provide for the null vs alternative hypothesis, and reports that many demonstrate conclusive evidence of…

    Source: jamanetwork.com
    Categories: Cardiologists, Latest Headlines
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    • Profile photo of hvanspall
      hvanspall

      The #LikelihoodRatio, typically used for diagnostic tests, could be used estimate treatment #effect in neutral #RCTs: - no treatment effect in majority of neutral RCTs - large likelihood of no effect in 1/2 neutral RCTs #ResearchMethods #Statistics https://t.co/eY70kubAeH

  • Mashup Score: 6
    Cancer Mortality Rates - SEER Cancer Statistics - 12 month(s) ago

    Definition and explanation of cancer mortality rate.

    Source: SEER
    Categories: Hem/Onc News and Journals, Latest Headlines
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    • Profile photo of NCICancerStats
      NCICancerStats

      A cancer mortality rate is the number of deaths, with cancer as the underlying cause of death, occurring in a specified population during a year. Here's our formula: mortality rate = (cancer deaths/population) x 100,000 https://t.co/VJBbZ0td1Z #statistics #CancerResearch https://t.co/3SBWptynLH

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