Odds Ratio Standard Error Confidence Interval
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Confidence Interval For Relative Risk
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Confidence Interval For Odds Ratio Logistic Regression
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Odds Ratio Confidence Interval P Value Calculator
online Change registration Change address Subscribe to Stata News 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) how to report odds ratios and confidence intervals by mlogit? How are the standard errors and confidence intervals computed for odds 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
only between 0 and 1. Odds and log odds are therefore better suited than probability to some types of calculation. Odds ratio (OR) is related to risk ratio (RR, relative risk): RR = (a / (a+c)) / (b confidence interval for odds ratio in r / (b+d)) When a is small in comparison to c and b is small in confidence interval crosses 0 comparison to d (i.e. relatively small numbers of outcome positive observations or low prevalence) then c can be substituted for a+c and d can relative risk confidence interval calculator be substituted for d+b in the above. With a little rearrangement this gives the odds ratio (cross ratio, approximate relative risk): OR = (a*d)/(b*c). OR can therefore be related to RR by: RR = 1/(BR+(1-BR)/OR) ..where BR is https://www.stata.com/support/faqs/stat/2deltameth.html the baseline (control) response rate; BR can be estimated by b/(b+d) if not known from larger studies. This function uses an exact method to construct confidence limits for the odds ratio of a fourfold table (Martin and Austin, 1991). The Fisher limits complement Fisher's exact test of independence in a fourfold table, for which one and two sided probabilities are provided here. Mid-P values are also given. Please note that this method will take a long time https://www.statsdirect.com/help/exact_tests_on_counts/odds_ratio_ci.htm with large numbers. DATA INPUT: Observed frequencies should be entered as a standard fourfold table: feature present feature absent outcome positive: a b outcome negative: c d sample estimate of the odds ratio = (a*d)/(b*c) Example From Thomas (1971). The following data look at the criminal convictions of twins in an attempt to investigate some of the hereditability of criminality. Monozygotic Dizygotic Convicted: 10 2 Not-convicted: 3 15 To analyse these data in StatsDirect select Odds Ratio Confidence Interval from the Exact Tests section of the analysis menu. Choose the default 95% two sided confidence interval. For this example: Confidence limits with 2.5% lower tail area and 2.5% upper tail area two sided: Observed odds ratio = 25 Conditional maximum likelihood estimate of odds ratio = 21.305318 Exact Fisher 95% confidence interval = 2.753383 to 301.462338 Exact Fisher one sided P = 0.0005, two sided P = 0.0005 Exact mid-P 95% confidence interval = 3.379906 to 207.270568 Exact mid-P one sided P = 0.0002, two sided P = 0.0005 Here we can say with 95% confidence that one of a pair of identical twins who has a criminal conviction is between 2.75 and 301.5 times more likely than non-identical twins to have a convicted twin. P values confidence intervals Copyright © 2000-2016 StatsDirect Lim
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more http://stats.stackexchange.com/questions/10375/how-to-calculate-standard-error-of-odds-ratios about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top How to calculate Standard Error of confidence interval Odds Ratios? up vote 9 down vote favorite 3 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 confidence interval for the SE and 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♦ 37.6k6125243 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 computation
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