{"id":982,"date":"2020-06-02T08:57:47","date_gmt":"2020-06-02T12:57:47","guid":{"rendered":"http:\/\/pressbooks.library.upei.ca\/montelpare\/?post_type=part&p=982"},"modified":"2020-06-03T06:03:25","modified_gmt":"2020-06-03T10:03:25","slug":"advanced-concepts-for-applied-statistics-in-healthcare","status":"publish","type":"part","link":"https:\/\/pressbooks.library.upei.ca\/montelpare\/part\/advanced-concepts-for-applied-statistics-in-healthcare\/","title":{"raw":"Advanced Concepts for Applied Statistics in Healthcare","rendered":"Advanced Concepts for Applied Statistics in Healthcare"},"content":{"raw":"In this section, the following topics are included:\r\n

Calculating Sample Size and power under different Scenarios<\/h1>\r\n
\r\n

Learner Outcomes<\/p>\r\n\r\n<\/header>\r\n

\r\n
    \r\n \t
  • Describe the importance in establishing a sample to represent the population<\/li>\r\n \t
  • Identify the difference between probabilistic and non-probabilistic sampling strategies.<\/li>\r\n \t
  • Compute sample size under different scenarios using SAS code<\/li>\r\n \t
  • Understand when a given sample size calculation is most appropriate<\/li>\r\n \t
  • Apply the appropriate sampling strategy to a given research design<\/li>\r\n<\/ul>\r\n<\/div>\r\n<\/div>\r\n

    Mixed model analysis<\/h1>\r\n
    \r\n

    Learner Outcomes<\/p>\r\n\r\n<\/header>\r\n

    Understanding repeated measures designs, split-plot factorial models, nested designs and mixed model anovas that incorporate fixed and random effects<\/div>\r\n<\/div>\r\n

    Survival analysis<\/h1>\r\n
    \r\n

    Learner Outcomes<\/p>\r\n\r\n<\/header>\r\n

    \r\n\r\nType your learning objectives here.\r\n
      \r\n \t
    • First<\/li>\r\n \t
    • Second<\/li>\r\n<\/ul>\r\n<\/div>\r\n<\/div>\r\n \r\n

      Computer Simulation and Random Number Generation<\/h1>\r\n
      \r\n

      Learner Outcomes<\/p>\r\n\r\n<\/header>\r\n

      In this chapter we will create new data sets using computer generated random numbers. In this way we can simulate research outcomes without actually\u00a0 performing the research.<\/div>\r\n
      Some basic rules of the exercise are that we must begin by understanding our variables and the parameters that the variables represent. Min max estimates variance, N<\/div>\r\n
      Through computer simulation approaches we can combine logic with combinations and permutations in factorial models to explore wicked problems.<\/div>\r\n<\/div>\r\n \r\n\r\n ","rendered":"

      In this section, the following topics are included:<\/p>\n

      Calculating Sample Size and power under different Scenarios<\/h1>\n
      \n
      \n

      Learner Outcomes<\/p>\n<\/header>\n

      \n
        \n
      • Describe the importance in establishing a sample to represent the population<\/li>\n
      • Identify the difference between probabilistic and non-probabilistic sampling strategies.<\/li>\n
      • Compute sample size under different scenarios using SAS code<\/li>\n
      • Understand when a given sample size calculation is most appropriate<\/li>\n
      • Apply the appropriate sampling strategy to a given research design<\/li>\n<\/ul>\n<\/div>\n<\/div>\n

        Mixed model analysis<\/h1>\n
        \n
        \n

        Learner Outcomes<\/p>\n<\/header>\n

        Understanding repeated measures designs, split-plot factorial models, nested designs and mixed model anovas that incorporate fixed and random effects<\/div>\n<\/div>\n

        Survival analysis<\/h1>\n
        \n
        \n

        Learner Outcomes<\/p>\n<\/header>\n

        \n

        Type your learning objectives here.<\/p>\n

          \n
        • First<\/li>\n
        • Second<\/li>\n<\/ul>\n<\/div>\n<\/div>\n

           <\/p>\n

          Computer Simulation and Random Number Generation<\/h1>\n
          \n
          \n

          Learner Outcomes<\/p>\n<\/header>\n

          In this chapter we will create new data sets using computer generated random numbers. In this way we can simulate research outcomes without actually\u00a0 performing the research.<\/div>\n
          Some basic rules of the exercise are that we must begin by understanding our variables and the parameters that the variables represent. Min max estimates variance, N<\/div>\n
          Through computer simulation approaches we can combine logic with combinations and permutations in factorial models to explore wicked problems.<\/div>\n<\/div>\n

           <\/p>\n

           <\/p>\n","protected":false},"parent":0,"menu_order":7,"template":"","meta":{"pb_part_invisible":false,"pb_part_invisible_string":""},"contributor":[],"license":[],"class_list":["post-982","part","type-part","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pressbooks.library.upei.ca\/montelpare\/wp-json\/pressbooks\/v2\/parts\/982","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.library.upei.ca\/montelpare\/wp-json\/pressbooks\/v2\/parts"}],"about":[{"href":"https:\/\/pressbooks.library.upei.ca\/montelpare\/wp-json\/wp\/v2\/types\/part"}],"version-history":[{"count":3,"href":"https:\/\/pressbooks.library.upei.ca\/montelpare\/wp-json\/pressbooks\/v2\/parts\/982\/revisions"}],"predecessor-version":[{"id":1065,"href":"https:\/\/pressbooks.library.upei.ca\/montelpare\/wp-json\/pressbooks\/v2\/parts\/982\/revisions\/1065"}],"wp:attachment":[{"href":"https:\/\/pressbooks.library.upei.ca\/montelpare\/wp-json\/wp\/v2\/media?parent=982"}],"wp:term":[{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.library.upei.ca\/montelpare\/wp-json\/wp\/v2\/contributor?post=982"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.library.upei.ca\/montelpare\/wp-json\/wp\/v2\/license?post=982"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}