Calculate Standard Error From Odds Ratio Confidence Interval
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Odds Ratio Confidence Interval In R
vote 9 down vote favorite 2 I have two datasets from genome-wide association studies. The only information available is the odds ratio and the p-value for the first data set. For the second data set I have the Odds Ratio, p-value and allele frequencies (AFD= disease, AFC= controls) (e.g: 0.321). I'm trying to do a meta-analysis of these data but I don't have the effect size parameter to perform this. Is there a possibility to calculate the SE and odds ratio confidence interval logistic regression OR confidence intervals for each of these data only using the info that is provided?? Thank you in advance example: Data available: Study SNP ID P OR Allele AFD AFC 1 rs12345 0.023 0.85 2 rs12345 0.014 0.91 C 0.32 0.25 With these data can I calculate the SE and CI95% OR ? Thanks meta-analysis genetics share|improve this question edited May 6 '11 at 13:45 chl♦ 37.4k6124243 asked May 5 '11 at 22:18 Bernabé Bustos Becerra 4814 add a comment| 1 Answer 1 active oldest votes up vote 15 down vote accepted You can calculate/approximate the standard errors via the p-values. First, convert the two-sided p-values into one-sided p-values by dividing them by 2. So you get $p = .0115$ and $p = .007$. Then convert these p-values to the corresponding z-values. For $p = .0115$, this is $z = -2.273$ and for $p = .007$, this is $z = -2.457$ (they are negative, since the odds ratios are below 1). These z-values are actually the test statistics calculated by taking the log of the odds ratios divided by the corresponding standard errors (i.e., $z = log(OR) / SE$). So, it follows that $SE = log(OR) / z$, which yields $SE = 0.071$ for the first and $SE = .038$ for the second study. Now you have everything to do a meta-analysis. I'll illustrate how you can do the computations with R, using the metafor package: library(meta
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Odds Ratio Confidence Interval Excel
Classroom and web On-site Video tutorials Third-party courses Support Updates Documentation Installation Guide FAQs odds ratio and confidence interval interpretation Register Stata Technical services Policy Contact Publications Bookstore Stata Journal Stata News Conferences and meetings Stata Conference Upcoming meetings Proceedings Email relative risk confidence interval calculator alerts Statalist The Stata Blog Web resources Author Support Program Installation Qualification Tool Disciplines Company StataCorp Contact us Hours of operation Announcements Customer service Register Stata online Change registration Change address Subscribe to Stata News http://stats.stackexchange.com/questions/10375/how-to-calculate-standard-error-of-odds-ratios Subscribe to email alerts International resellers Careers Our sites Statalist The Stata Blog Stata Press Stata Journal Advanced search Site index Purchase Products Training Support Company >> Home >> Resources & support >> FAQs >> Standard errors, confidence intervals, and significance tests How are the standard errors and confidence intervals computed for relative-risk ratios (RRRs) by mlogit? How are the standard errors and confidence intervals computed for odds https://www.stata.com/support/faqs/stat/2deltameth.html ratios (ORs) by logistic? How are the standard errors and confidence intervals computed for incidence-rate ratios (IRRs) by poisson and nbreg? How are the standard errors and confidence intervals computed for hazard ratios (HRs) by stcox and streg? Title Standard errors, confidence intervals, and significance tests for ORs, HRs, IRRs, and RRRs Authors William Sribney, StataCorp Vince Wiggins, StataCorp Someone asked: How does Stata get the standard errors of the odds ratios reported by logistic and why do the reported confidence intervals not agree with a 95% confidence bound on the reported odds ratio using these standard errors? Likewise, why does the reported significance test of the odds ratio not agree with either a test of the odds ratio against 0 or a test against 1 using the reported standard error? Standard Errors The odds ratios (ORs), hazard ratios (HRs), incidence-rate ratios (IRRs), and relative-risk ratios (RRRs) are all just univariate transformations of the estimated betas for the logistic, survival, and multinomial logistic models. Using the odds ratio as an example, for any coefficient b we have ORb = exp(b) When ORs (or HRs, or IRRs, or RRRs) are reported, Stata uses the delta rule to derive an estimate of the standard error of ORb. For the simple expressio
Date Mon, 26 Sep 2005 16:45:05 -0500 On Monday morning Kate asked about the standard error of the odds ratio: I need to calculate the standard error http://www.stata.com/statalist/archive/2005-09/msg00829.html of the odds ratio and use the "cc" command which reports the https://select-statistics.co.uk/calculators/confidence-interval-calculator-odds-ratio/ 95% confidence interval but does not report the standard error itself. I would appreciate it very much if anyone could let me know if there is a way or an option to specify that the standard error of the odds ratio be reported in the output as well. confidence interval The epitab command -cc- reports, by default, an exact confidence interval for the odds ratio, so there is in fact no standard error when the interval is computed this way. One thing Kate might want to do is to use the Woolf confidence interval. One can obtain the upper and lower bounds of the interval using the option -woolf- on odds ratio confidence -tabodds-. A standard error is reported if we use the command -logistic- to obtain the Woolf confidence interval. Here is an example that shows what I mean by all of this: clear webuse downs expand pop cc case exposed cc case exposed, wo logistic case exposed Now, you may notice that the interval is not symmetric so one doesn't just add and subtract 1.96*se to get the confidence interval. It is in confidence interval of the log of the odds ratio that is symmteric. Here is how the above interval is calculated (I'm using the option -coef- on -logistic- to show the actual coefficients of the model as opposed to the odds ratio here): logistic case exposed, coef display "or = " exp(_b[exposed]) display "lb = " exp(_b[exposed]-invnorm(0.975)*_se[exposed]) display "ub = " exp(_b[exposed]+invnorm(0.975)*_se[exposed]) So what is the standard error that -logistic- reports then? The standard error of the odds ratio is display "se(OR) = " exp(_b[exposed])*_se[exposed] (see [R] logistic, page 67 in version 9 manuals). --May mmb@stata.com * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://
Leisure Agriculture Logistics Finance & Insurance Retail Charities Education Environment Healthcare Legal Market Research Public Sector Services Advice Analysis Data Collection Data Mining Design Innovation & Research Modelling Prediction Qualitative Analysis Reporting Review Surveys & Sampling Testing Training Visualisation Resources FAQs Glossary Calculators Downloads Videos Contact us Odds ratio - Confidence Interval Calculators Use this calculator to determine a confidence interval for your odds ratio. An odds ratio is a measure of association between the presence or absence of two properties. For example, it could provide a measure of association between customers who are either older or younger than 25 and either have or have not claimed on their car insurance, in order to determine whether age is associated with the propensity to claim. The value of the odds ratio tells you how much more likely someone under 25 might be to make a claim, for example, and the associated confidence interval indicates the degree of uncertainty associated with that ratio. Calculator Contingency table Property B Presence Absence Property A Presence Absence What confidence level do you need? Typical choices are 90%, 95%, or 99% % The confidence level indicates the probability that the confidence interval will contain the true odds ratio. Your odds ratio is 14.04 The odds ratio quantifies the odds of property A being present given property B is present compared to if property B were absent. Your confidence interval is (3.33 , 59.3) This is the range of values in which we estimate the odds ratio to lie given our level of confidence. Alternative Scenarios With a confidence level of % % % Your confidence interval would be (4.19 , 47.04) (3.33 , 59.3) (2.11 , 93.25) Worked Example In 1950, the Medical Research Council conducted a case-control study of smoking and lung cancer (Doll and Hill 1950). 649 male cancer patients were included (the cases), 647 of whom were reported to be smokers. 649 men without cancer were also included (controls), 622 of whom were reported to be smokers. The odds ratio of lung cancer for smokers compared with non-smokers can be calculated as (647*27)/(2*622) = 14.04, i.e., the odds of lung cancer in smokers is estimated to be 14 times the odds of lung cancer in non-smokers. We would like to know how reliable this estimate is? The 95% confidence interval for this odds ratio is between 3.33 and 59.3. The interval is rathe