How To Calculate Standard Error Of Odds Ratios
Contents |
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 about hiring odds ratio confidence interval crosses 1 developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _
Risk Ratio Confidence Interval
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; how to calculate odds ratio in excel 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 Odds Ratios? up vote odds ratio confidence interval p value calculator 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 the SE and OR confidence
Confidence Interval Crosses 0
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♦ 37.5k6125243 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(metafor) yi <- log(c(.85,
on statistics Stata Journal Stata Press Stat/Transfer Gift Shop Purchase Order Stata Request a quote Purchasing FAQs Bookstore Stata Press books Books on Stata relative risk confidence interval calculator Books on statistics Stat/Transfer Stata Journal Gift Shop Training NetCourses Classroom and how to report odds ratios and confidence intervals web On-site Video tutorials Third-party courses Support Updates Documentation Installation Guide FAQs Register Stata Technical services Policy Contact
Confidence Interval For Odds Ratio Logistic Regression
Publications Bookstore Stata Journal Stata News Conferences and meetings Stata Conference Upcoming meetings Proceedings Email alerts Statalist The Stata Blog Web resources Author Support Program Installation Qualification Tool Disciplines Company StataCorp http://stats.stackexchange.com/questions/10375/how-to-calculate-standard-error-of-odds-ratios Contact us Hours of operation Announcements Customer service Register Stata 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, https://www.stata.com/support/faqs/stat/2deltameth.html 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 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 univar
Health Search databasePMCAll DatabasesAssemblyBioProjectBioSampleBioSystemsBooksClinVarCloneConserved DomainsdbGaPdbVarESTGeneGenomeGEO DataSetsGEO ProfilesGSSGTRHomoloGeneMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedPubMed HealthSNPSparcleSRAStructureTaxonomyToolKitToolKitAllToolKitBookToolKitBookghUniGeneSearch termSearch Advanced Journal list Help https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1127651/ Journal ListBMJv.320(7247); 2000 May 27PMC1127651 BMJ. 2000 May 27; 320(7247): 1468. http://handbook.cochrane.org/chapter_7/7_7_7_3_obtaining_standard_errors_from_confidence_intervals_and.htm PMCID: PMC1127651Statistics NotesThe odds ratioJ Martin Bland, professor of medical statisticsa and Douglas G Altman, professor of statistics in medicinebaDepartment of Public Health Sciences, St George's Hospital Medical School, London SW17 0RE, bICRF Medical Statistics Group, Centre for Statistics in Medicine, confidence interval Institute of Health Sciences, Oxford OX3 7LFCorrespondence to: Professor BlandAuthor information ► Copyright and License information ►Copyright © 2000, British Medical JournalThis article has been cited by other articles in PMC.In recent years odds ratios have become widely used in medical reports—almost certainly some will appear in today's BMJ. There are three reasons ratio confidence interval for this. Firstly, they provide an estimate (with confidence interval) for the relationship between two binary (“yes or no”) variables. Secondly, they enable us to examine the effects of other variables on that relationship, using logistic regression. Thirdly, they have a special and very convenient interpretation in case-control studies (dealt with in a future note).The odds are a way of representing probability, especially familiar for betting. For example, the odds that a single throw of a die will produce a six are 1 to 5, or 1/5. The odds is the ratio of the probability that the event of interest occurs to the probability that it does not. This is often estimated by the ratio of the number of times that the event of interest occurs to the number of times that it does not. The table shows data from a cross sectional study showing the prevalence of hay fever and eczema in 11 year
ratio measures are performed on the natural log scale (see Chapter 9, Section 9.2.7). For a ratio measure, such as a risk ratio, odds ratio or hazard ratio (which we will denote generically as RR here), first calculate lower limit = ln(lower confidence limit given for RR) upper limit = ln(upper confidence limit given for RR) intervention effect estimate = lnRR Then the formulae in Section 7.7.7.2 can be used. Note that the standard error refers to the log of the ratio measure. When using the generic inverse variance method in RevMan, the data should be entered on the natural log scale, that is as lnRR and the standard error of lnRR, as calculated here (see Chapter 9, Section 9.4.3).