Question

during regression analysis what are the relationships and difference between p-values, crude odds ratio(OR), and adjusted odds ratio (AOA) ?what we know from their values ( when their values greater or less than 1 or equal to 1) . this quation is based on biology or epidemiology concept . thanks and please ni plagiarism and type the answers.

Answer 1

What is P - value?

In statistical significance testing, the p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. If the p-value is less than 0.05 or 0.01, corresponding respectively to a 5% or 1% chance of rejecting the null hypothesis when it is true.

Suppose that the experimental results show the coin turning up heads 14 times out of 20 total flips
► null hypothesis (H0): fair coin;
► observation O: 14 heads out of 20 flips; and
► p-value of observation O given H0 = Prob(≥ 14 heads or ≥ 14 tails) = 0.115.

The calculated p-value exceeds 0.05, so the observation is consistent with the null hypothesis - that the observed result of 14 heads out of 20 flips can be ascribed to chance alone - as it falls within the range of what would happen 95% of the time were this in fact the case. In our example, we fail to reject the null hypothesis at the 5% level. Although the coin did not fall evenly, the deviation from expected outcome is small enough to be reported as being "not statistically significant at the 5% level".

How Do I Interpret the P-Values in Linear Regression Analysis?

The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (< 0.05) indicates that you can reject the null hypothesis. In other words, a predictor that has a low p-value is likely to be a meaningful addition to your model because changes in the predictor's value are related to changes in the response variable.

Conversely, a larger (insignificant) p-value suggests that changes in the predictor are not associated with changes in the response.

In the output below, we can see that the predictor variables of South and North are significant because both of their p-values are 0.000. However, the p-value for East (0.092) is greater than the common alpha level of 0.05, which indicates that it is not statistically significant.

Odd Ration

Odds ratio or Crude Odds ratio are obtained when you are considering the effect of only one predictor variable ie your equation consists of only one independent variable. However when you include more variables in the analysis (confounder variables for the said relationship) you get what is called and Adjusted Odds Ratio(AOR) , which takes into account the effect due to all the additional variables included in the analysis.

Logic behind using odds ratio is to check if the factor we consider is a risk factor or protective factor.

Try to explain it with an example: You want to check is mother age is a risk factor for child mortality so your dependent (main) variable is child mortality (yes or no) and independent variable is mother's age (18, 20,....).

In that case if you use binary logistic for these two variables it will give you a odds ratio (OR). Then value of the OR shows you either it is risk or protective factor.

In addition if you add another variable like income status as independent variable with previous one the your OR converted to income adjusted OR or adjusted OR. You can add many more independent variables in your analysis based on previous literatures.