confidence_interval#
- OddsRatioResult.confidence_interval(confidence_level=0.95, alternative='two-sided')[source]#
- Confidence interval for the odds ratio. - Parameters:
- confidence_level: float
- Desired confidence level for the confidence interval. The value must be given as a fraction between 0 and 1. Default is 0.95 (meaning 95%). 
- alternative{‘two-sided’, ‘less’, ‘greater’}, optional
- The alternative hypothesis of the hypothesis test to which the confidence interval corresponds. That is, suppose the null hypothesis is that the true odds ratio equals - ORand the confidence interval is- (low, high). Then the following options for alternative are available (default is ‘two-sided’):- ‘two-sided’: the true odds ratio is not equal to - OR. There is evidence against the null hypothesis at the chosen confidence_level if- high < ORor- low > OR.
- ‘less’: the true odds ratio is less than - OR. The- lowend of the confidence interval is 0, and there is evidence against the null hypothesis at the chosen confidence_level if- high < OR.
- ‘greater’: the true odds ratio is greater than - OR. The- highend of the confidence interval is- np.inf, and there is evidence against the null hypothesis at the chosen confidence_level if- low > OR.
 
 
- Returns:
- ciConfidenceIntervalinstance
- The confidence interval, represented as an object with attributes - lowand- high.
 
- ci
 - Notes - When kind is - 'conditional', the limits of the confidence interval are the conditional “exact confidence limits” as described by Fisher [1]. The conditional odds ratio and confidence interval are also discussed in Section 4.1.2 of the text by Sahai and Khurshid [2].- When kind is - 'sample', the confidence interval is computed under the assumption that the logarithm of the odds ratio is normally distributed with standard error given by:- se = sqrt(1/a + 1/b + 1/c + 1/d) - where - a,- b,- cand- dare the elements of the contingency table. (See, for example, [2], section 3.1.3.2, or [3], section 2.3.3).- References [1]- R. A. Fisher (1935), The logic of inductive inference, Journal of the Royal Statistical Society, Vol. 98, No. 1, pp. 39-82. [2] (1,2)- H. Sahai and A. Khurshid (1996), Statistics in Epidemiology: Methods, Techniques, and Applications, CRC Press LLC, Boca Raton, Florida. [3]- Alan Agresti, An Introduction to Categorical Data Analysis (second edition), Wiley, Hoboken, NJ, USA (2007).