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ebm:effect_estimation

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ebm:effect_estimation [2020/07/20 01:32] – [Confidence intervals] dhawannebm:effect_estimation [2020/07/20 01:40] (current) – [Odds ratio] dhawann
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 ===== Odds ratio ===== ===== Odds ratio =====
  
-  * Better for regression analysis, but relative ratio is more intuitive.+  * Odds ration makes regression analysis easier, but relative ratio is more intuitive.
   * Odds are defined as $$\frac{p}{1-p}$$ if $$p$$ is the probability of an event   * Odds are defined as $$\frac{p}{1-p}$$ if $$p$$ is the probability of an event
-    * if the probability is 50% then it's $$ which is 50-50.+    * if the probability is 50% then it's $$\frac{0.5}{1-0.5}$$ which is 50-50 or even odds. 
  
 ===== Relative ratio ===== ===== Relative ratio =====
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   * People use the term effect size to mean standardized effect size.   * People use the term effect size to mean standardized effect size.
-  * The standardized effect size, called Cohen¡¯s d, is the actual effect size +  * The standardized effect size, called Cohen's d, is the actual effect size described above (such as a mean number) divided by the standard deviation (the measure of variability).
- +
-described above (such as a mean number) divided by the standard deviation (the measure of variability). +
   * Cohen's d makes a number between 0 to 1 or higher   * Cohen's d makes a number between 0 to 1 or higher
   * 0.4 or lower is small effect size   * 0.4 or lower is small effect size
   * 0.4 to 0.7 is medium effect size   * 0.4 to 0.7 is medium effect size
   * greater than 0.7 is large effect size   * greater than 0.7 is large effect size
-  * Nonetheless, Cohen¡¯s d is especially useful in research using continuous measures of outcome (such as psychiatric rating scales) and is commonly employed in experimental psychology research+  * Nonetheless, Cohen's d is especially useful in research using continuous measures of outcome (such as psychiatric rating scales) and is commonly employed in experimental psychology research.
  
 ===== Number Needed to Treat and Number Needed to Harm ===== ===== Number Needed to Treat and Number Needed to Harm =====
  
-==== Forumula ====+==== Formula ====
  
   * Number needed to treat or harm is 1 divided by the absolute risk reduction or risk increase.   * Number needed to treat or harm is 1 divided by the absolute risk reduction or risk increase.
-  * example: If 50% of people responded to a drug and 30% responded to placebo the +  * example: If 50% of people responded to a drug and 30% responded to placebo the absolute risk reduction would be 20%. The number needed to treat would be 1/0.2 which is 5.
- +
-absolute risk reduction would be 20%. The number needed to treat would be 1/0.2 which is 5.+
  
 ==== NNT ==== ==== NNT ====
ebm/effect_estimation.1595208753.txt.gz · Last modified: 2020/07/20 01:32 by dhawann