### Clinical Epidemiology made really, really simple.

*ClinEpi Review* is an introductory level course on clinical epidemiology that explains the most commonly encountered terms and concepts in health research and evidence-based medicine. This course was designed for all healthcare professionals seeking to better understand health research and practice evidence-based medicine. *ClinEpi Review* consists of 15 videos on a variety of topics each ranging from 3 to 13 minutes in duration for a total duration of just under 2 hours and 20 minutes.

The video series also includes a downloadable workbook and answer key based on the content of the videos.

$150.00Add to cart

#### Episodes

00 | Introduction (FREE) | 2:41 | Welcome and Introduction to the series. |

01 | Definitions | 6:57 | An explanation of cohorts, prevalence & incidence and how to calculate the latter two. |

02 | Variables | 5:15 | An explanation of independent, dependent & confounding variables and how they relate to one another. |

03 | Observational Studies | 8:59 | An explanation of the features, strengths and limitations of the following observational study designs: case & case series studies, cross-sectional study, ecologic study, cohort study, & case-control study. |

04 | Randomized Controlled Trials | 11:07 | An explanation of the features, strengths & variations (parallel, cross-over, cluster, factorial) of RCTs as well as some of the commonly associated biases. Also, an explanation of methods of data analysis, missing data and the CONSORT Statement. |

05 | Two by Two Tables Tables | 2:59 | An explanation of the two by two table, the foundation on which many calculations in clinical epidemiology are performed including but not limited to relative risk, absolute risk reduction (& increase), relative risk reduction, number needed to treat (& harm), predictive value, sensitivity & specificity. |

06 | All About Risk | 7:17 | An explanation of risk, event rate, absolute risk reduction (& increase), number needed to treat (& harm) & relative risk reduction as well as how to calculate each. |

07 | Risk & Odds | 10:30 | An explanation of risk & odds, specifically relative risk (risk ratio) and odds ratio how to calculate each and their differences. |

08 | Predictive Values, Sensitivity and Specificity | 8:43 | An explanation of positive predictive value, negative predictive value, sensitivity & specificity, their strengths, weaknesses, similarities, differences, clinical applications & how they are each calculated. |

09 | ROC Curves | 6:09 | An explanation of the Receiver Operating Characteristic (ROC) Curve and how it relates to sensitivity & specificity. |

10 | Likelihood Ratios | 11:35 | An explanation of likelihood ratios as diagnostic performance measures, their relationship to sensitivity & specificity and how each is calculated. |

11 | Data & Distribution | 9:50 | An explanation of categorical & quantitative data, data distribution, central tendency (mean, median, mode) range and variability (standard deviation & interquartile range). |

12 | Hypothesis Testing and Errors | 9:13 | An explanation of null and alternate hypotheses, P-value, alpha, beta, type I & II errors & power and how each is related to the others. |

13 | Point Estimates & Confidence intervals | 6:15 | An explanation of point estimates, interval estimates, confidence interval, confidence level, precision and how they relate to each other. |

14 | Which Test to Use | 10:34 | An explanation of parametric and nonparametric statistics, some of the more commonly used biostatistical tests (Chi-square test, McNemar’s test, Student’s T-test, Mann-Whitney U-test, analysis of variance [ANOVA], Kruskall-Wallis, linear regression, Spearman rank correlation), the parameters of each & examples of when each is typically applied. |

15 | Systematic Reviews | 12:54 | An explanation of systematic & non-systematic reviews, their differences as well as biases associated with the former. |

16 | Meta-analyses | 9:46 | An explanation of meta-analyses, how they relate to systematic reviews & heterogeneity in the pooling of results of meta-analyses. |