###### Interpretation of Genetic Association Studies: Markers with Replicated Highly Significant Odds Ratios May Be Poor Classifiers

Recent successful discoveries of potentially causal single nucleotide polymorphisms (SNPs) for complex diseases hold great promise, and commercialization of genomics in personalized medicine has already begun. The hope is that genetic testing will benefit patients and their families, and encourage positive lifestyle changes and guide clinical decisions. However, for many complex diseases, it is arguable whether the era of genomics in personalized medicine is here yet. We focus on the clinical validity of genetic testing with an emphasis on two popular statistical methods for evaluating markers. The two methods, logistic regression and receiver operating characteristic (ROC) curve analysis, are applied to our age-related macular degeneration dataset. By using an additive model of the CFH, LOC387715, and C2 variants, the odds ratios are 2.9, 3.4, and 0.4, with p-values of 10−13, 10−13, and 10−3, respectively. The area under the ROC curve (AUC) is 0.79, but assuming prevalences of 15%, 5.5%, and 1.5%

###### Alternative Confidence interval for Odds Ratio $\hat{p}\over{1-\hat{p}}$ from Logistic Regression? - Cross Validated

I have a question about confidence interval calculations for the

**odds ratio**$\hat{p}\over{1-\hat{p}}$ from a logistic regression model (perhaps obtained from the method of Generalized Estimating Equations, but thats of secondary importance). Its best asked after a bit of background and an example:. Lets assume we have a simple logistic regression model with a single binary independent variable ($X={0,1} $ ). For $X=1$, we can estimate the log odds by:. $\log({\hat{p}\over{1-\hat{p}}})=\hat{\beta}_0+\hat{\beta}_1$. To obtain a 95% confidence interval (CI) for the**odds ratio**, textbooks that Ive consulted indicate that you calculate the 95% CI for the odds-ratio, ${\hat{p}\over{1-\hat{p}}}$, by:. Lower Bound= $e^{[\hat{\beta}_0+\hat{\beta}_1-1.96Var(\hat{\beta}_0+\hat{\beta}_1)]}$. Upper Bound= $e^{[\hat{\beta}_0+\hat{\beta}_1+1.96Var(\hat{\beta}_0+\hat{\beta}_1)]}$. What I am wondering is if it is appropriate to use the the Delta Method to obtain the variance of the**odds ratio**...###### Odds Ratio and Relative Risks :: SAS/STAT(R) 13.1 Users Guide

The value of the

**odds ratio**can be any nonnegative number. When the row and column variables are independent, the true value of the**odds ratio**equals 1. An**odds ratio**greater than 1 indicates that the odds of a positive response are higher in row 1 than in row 2. An**odds ratio**less than 1 indicates that the odds of positive response are higher in row 2. The strength of association increases with the deviation from 1. For details, see Stokes, Davis, and Koch (2000); Agresti (2007). PROC SURVEYFREQ constructs confidence limits for the**odds ratio**by using the log transform. The ...###### Epidemiology - Odds Ratio (OR) | Bean Around The World

PLEASE WHATS THE FORMULA TO GET THE ODDS IF WE HAVE THE RISK COS I DONT KNOW HOW YOU. This is a basic introduction to interpreting odds ratios, confidence Efficacy of drugs: 3 examples to get you to truly understand Number. Definition The

**Odds Ratio**is a measure of association which compares the Formulae OR = (odds of disease in exposed) / (odds of disease in the Great analogy, really helped me to understand odds ration in a simple. I will use this in my teachings. Try out an online to check your math when you first get started. Specifically, the OR measures the ratio of the odds that an event or result will occur to the odds of the event not happening. Olli Raitakari December 2, at 9: Read sporting odds as the likelihood that one team, athlete, or horse, will win. Odds ratios https://www.facebook.com/BeGambleAware/photos/a.1069882653087425.1073741831.1022075201201504/1482759451799741/?type=3 one of a category of statistics clinicians often use to make treatment decisions. The ...###### Can you add up different genes odds ratios to get a general odds ratio? - Cross Validated

If you are using a generalized linear model to obtain

**odds ratio**estimates, assuming that there are no interactions between the genes, then you can simply multiply the odds ratios for the two present genes to get the OR for disease.. From your example, assuming the presence of A does not affect how much B contributes to the risk of disease and vice versa, then the OR is 0.8*1.2=0.96.. If the presence of one gene affects how another gene contributes to the disease (ie. there are interactions) then you would also need to multiply the ORs of the interactions.. Edit: Just noticed that these ORs are obtained from 3 different studies. In this case, no it would not be appropriate to combine the ORs in the way I described above. This is because there is no way for you to find out if there are interactions or not.. ...###### Theres Nothing Odd about the Odds Ratio: Interpreting Binary Logistic Regression - Statistics Solutions

As we covered above, one of these predictors is binary and the other is continuous. This means we have to interpret the two a little bit differently. First is the binary score: gender. We first look at the p value. It is below .05, telling us that it is significant, and we can safely interpret the

**odds ratio**. To interpret this result, we have to know what a 0 (low) and a 1 (high) correspond to, and our researcher recalls that she coded this as 0 = female, and 1 = male. She finds this to be a good thing because when the**odds ratio**is greater than 1, it describes a positive relationship. The positive relationship means that as gender increases, the odds of being eaten increases. Based on our coding, an increase in gender means a gender of 1 instead of 0 - in other words, being male. This can be interpreted to mean that being in the (1) group, or being male, puts you at 5 times greater odds of being eaten.. If the**odds ratio**for gender had been below 1, she would have been in trouble, as an ...###### IJERPH | Free Full-Text | Computing Power and Sample Size for Informational Odds Ratio

The informational

**odds ratio**(IOR) measures the post-exposure odds divided by the pre-exposure odds (i.e., information gained after knowing exposure status). A desirable property of an adjusted ratio estimate is collapsibility, wherein the combined crude ratio will not change after adjusting for a variable that is not a confounder. Adjusted traditional odds ratios (TORs) are not collapsible. In contrast, Mantel-Haenszel adjusted IORs, analogous to relative risks (RRs) generally are collapsible. IORs are a useful measure of disease association in case-referent studies, especially when the disease is common in the exposed and/or unexposed groups. This paper outlines how to compute power and sample size in the simple case of unadjusted IORs.###### rs3918242 - SNPedia

rs3918242 is one of two SNPs in the MMP9 gene associated with increased risk for myocardial infarction, but not coronary artery disease. The relatively weak odds ratios for carriers of one rs3918242(T) allele was 1.14 (p=0.06), and for (T;T) homozygotes, 1.33 (p=0.19). However, this study of 5,000+ Caucasian patients with heart disease also indicated that a composite genotype (i.e. unphased haplotype) of the two MMP9 SNPs (rs3918242 and rs17576, respectively) yielded a significant

**odds ratio**of 1.25 (CI: 1.07 - 1.48, p=0.007) for the (C;T)/(A;G) combination compared to (C;C)/(G;G) combination genotypes.[PMID 17893005 ...###### Odds Ratio Proc Logistic Question - SAS Support Communities

I am running several proc logistic models, utilizing the

**odds ratio**estimates. Although SAS will produce the point estimate, and 95% wald confidence###### Formats for p-values and odds ratios in SAS » SAS博客列表

For the statistical programmer, the question is not why is SAS displaying funny-looking numbers, but what do these numbers tell me about my model and my data? For a p-value, small numbers indicate statistical significance. This is usually good. It means that an effect is significant or that there is evidence to reject some null hypothesis. For an

**odds ratio**, medium-sized numbers appear most often. For example, an**odds ratio**of 10 (or, equivalently, 1/10) might enable you to conclude that patients in the experimental group are 10 times more likely to survive than patients in the placebo group. Seeing very large numbers (like 104) or very tiny number (10-4) often indicates that something is wrong. Perhaps your model is misspecified, or perhaps the data is degenerate in some way and does not provide any evidence for or against a statistical hypothesis. As an example, lets try to understand why the**odds ratio**is so extreme for the example in this article. The model is trying to predict ...###### Geeglm odds ratio geepack r example

What is an

**odds ratio**? Odds ratios are used to compare the relative odds of the occurrence of the outcome of interest This example illustrates a few important###### ExploringDataBlog: Computing Odds Ratios in R

Including alpha = 0.05 in the parameter list fixes the default value for alpha at 0.05, which yields the 95% confidence intervals for the computed

**odds ratio**, based on the Wald approximation described above. An important practical point is that these intervals become infinitely wide if any of the four numbers Nij are equal to zero; also, note that in this case, the computed**odds ratio**is either zero or infinite. Finally, it is worth noting that if the numbers Nij are large enough, the procedure just described can encounter numerical overflow problems (i.e., the products in either the numerator or the denominator become too large to be represented in machine arithmetic). If this is a possibility, a better alternative is to regroup the computations as follows ...###### Odds ratio interpretation | Statistics Help @ Talk Stats Forum

This comes from SAS

**odds ratio**out put. The second column is the**odds ratio**SAS prints out (for example .615 for race_p 0 vs 1). One in the DV is being...###### absolute risk, relative risk, and odds ratio | Term Papers Writer

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The covariates obtainable for all databases had been normally forced to enter the product, supplied they reached at the least 5% prevalence amongst controls. Other covariates had been bundled, delivered they have been substantially (P0.ten for removal). Secondly, we utilised a random outcomes meta-analytic model29 30 to estimate a summary

**odds ratio**(and ninety five% self-confidence interval) throughout databases for current use of each unique NSAID (supplied that a point estimate was available from a minimum of two databases), as opposed with past utilization of any NSAID. Heterogeneity concerning databases specific odds ratios was assessed by Cochrans Q and Higgins I2 figures.31 ...###### Risk and Odds · Part One

Relative Risk and Odds Ratios are both methods of comparing the likelihood of an outcome occurring between two groups. The difference, and particularly the concept of odds ratios, are commonly confused. Relative risk tends be much more intuitive than odds ratios. Imagine a trial has been performed, where group A was exposed group:. ...

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The covariates accessible for all databases had been usually compelled to enter the design, presented they arrived at a minimum of 5% prevalence between controls. Other covariates ended up provided, delivered they ended up drastically (P0.ten for elimination). Secondly, we used a random results meta-analytic model29 30 to estimate a summary

**odds ratio**(and ninety five% self-confidence interval) across databases for existing use of each and every specific NSAID (provided that some extent estimate was obtainable from not less than two databases), when compared with earlier usage of any NSAID. Heterogeneity involving databases specific odds ratios was assessed by Cochrans Q and Higgins I2 figures. ...###### Odds ratio of 1.00 while the IV is statistically significant | Statistics Help @ Talk Stats Forum

I apologize in advance for the stupid question. I am new to statistics.
I got an

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7) OR=Odds Ratio. The odds of a change in the dependent variable (vote) given a one unit change in age is 1.03. Note: if the OR is ,1, odds increase; if OR ,1, odds decrease; if OR =1, odds are unchanged by this variable. In this example, the odds of voting increases with a one unit increase in age ...

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Hello, I am using SNP Test to do association testing and generate relevant statistics. One of my SNPs appeared strongly associated, p=1.09*10-6; OR = 1.16, 95% CI 0.99 -1.35.. I was just wondering what is being used to calculate the OR CI since it crosses 1, but the p-value appears highly significant.. Can anyone explain this to me?. ...

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###### Odds Ratio and Attributable Risk (Retrospective) - StatsDirect

Odds ratio (OR) = (a*d)/(b*c). Estimate of population exposure (Px) = c/(c+d). Estimate of population attributable risk% = 100*(Px*(OR-1))/(1+(Px*(OR-1))). In retrospective studies you select subjects by outcome and look back to see if they have a characteristic factor such as a risk factor or a protection factor for a disease. The

**odds ratio**((a/c)/(b/d)) looks at the likelihood of an outcome in relation to a characteristic factor. In epidemiological terms, the**odds ratio**is used as a point estimate of the relative risk in retrospective studies.**Odds ratio**is the key statistic for most case-control studies.. In prospective studies, Attributable risk or risk difference is used to quantify risk in the exposed group that is attributable to the exposure. In retrospective studies, attributable risk can not be calculated directly but population attributable risk can be estimated. Population attributable risk estimates the proportion of disease in the study population that is attributable to the ...###### Odds Ratio and Attributable Risk (Retrospective) - StatsDirect

Odds ratio (OR) = (a*d)/(b*c). Estimate of population exposure (Px) = c/(c+d). Estimate of population attributable risk% = 100*(Px*(OR-1))/(1+(Px*(OR-1))). In retrospective studies you select subjects by outcome and look back to see if they have a characteristic factor such as a risk factor or a protection factor for a disease. The

**odds ratio**((a/c)/(b/d)) looks at the likelihood of an outcome in relation to a characteristic factor. In epidemiological terms, the**odds ratio**is used as a point estimate of the relative risk in retrospective studies.**Odds ratio**is the key statistic for most case-control studies.. In prospective studies, Attributable risk or risk difference is used to quantify risk in the exposed group that is attributable to the exposure. In retrospective studies, attributable risk can not be calculated directly but population attributable risk can be estimated. Population attributable risk estimates the proportion of disease in the study population that is attributable to the ...###### Drive Traffic To Website targeting the keyword Betting Odds Horse Racing - TRAFFICGUN

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**odds ratio**1.30, 95% confidence interval 1.02-1.66) after full adjustment. In addition, in females, a significant linear trend in HbA1c level**odds ratio**by increased snoring frequency was apparent (p trend=0.019 in model 3). In contrast, no significant association between snoring frequency and HbA1c level was identified in males. No significant association between snoring frequency and HOMA-IR was detected in either gender ...###### Tom Prophet Hsiung » Why the Odds Ratio Can Be Used as an Estimate for Relative Risk in a Case-Control Study

The latter term is the

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Approximate confidence intervals are given for the odds ratios derived from the covariates.. Bootstrap estimates A bootstrap procedure may be used to cross-validate confidence intervals calculated for odds ratios derived from fitted logistic models (Efron and Tibshirani, 1997; Gong, 1986). The bootstrap confidence intervals used here are the bias-corrected type.. The mechanism that StatsDirect uses is to draw a specified number of random samples (with replacement, i.e. some observations are drawn once only, others more than once and some not at all) from your data. These re-samples are fed back into the logistic regression and bootstrap estimates of confidence intervals for the model parameters are made by examining the model parameters calculated at each cycle of the process. The bias statistic shows how much each mean model parameter from the bootstrap distribution deviates from observed model parameters.. Classification and ROC curve The confidence interval given with the likelihood ...

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19, P = 0.003). It is a vintage example of confounding, Because the altered

**odds ratio**differed from the crude**odds ratio**(0.7) due to the fact zidovudine use was a lot more likely among the equally case people and controls after exposure characterized by one or more of the 4 possibility components inside the design. These hazard components have been much more commonplace between case clients than among the controls, indicating the case patients had far more major publicity in comparison to the controls; for this reason, the crude**odds ratio**for zidovudine use was seriously confounded ...###### The best Side of case study help

19, P = 0.003). This is the common illustration of confounding, For the reason that modified

**odds ratio**differed from the crude**odds ratio**(0.7) because zidovudine use was far more very likely amid both equally case sufferers and controls immediately after publicity characterised by one or more with the four possibility variables from the model. These hazard elements ended up much more prevalent amid case people than amid controls, indicating which the case patients experienced additional significant publicity as opposed to controls; thats why, the crude**odds ratio**for zidovudine use was severely confounded ...###### English language

Parity: is 4,968,750,001 an odd number?An integer is an odd number if it is not evenly divisible by 2, that is, it yields a remainder of 1 when divided by 2. 4,968,750,001 is odd since its not divisible by 2: remainder = 1 when divided by 2. An integer is odd if its last digit is: 1, 3, 5, 7, or 9. Odd = {2 × k + 1, where k ∈ Z} Operating rules: The following rules apply when operating with even and odd numbers: Adding: even + even = even; even + odd = odd; odd + odd = even. Subtracting: even - even = even; even - odd = odd; odd - odd = even. Multiplying: even × even = even; even × odd = even; odd × odd = odd. More operations of this kind: 4,968,750,000 even, odd? 4,968,750,002 even, odd?

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19, P = 0.003). This can be a basic illustration of confounding, since the altered

**odds ratio**differed in the crude**odds ratio**(0.7) because zidovudine use was browse around here more very likely amongst both equally case sufferers and controls after publicity characterized by a number of of your 4 danger things while in the model. These chance variables were extra common amid case people than among the controls, indicating the case clients had far more severe exposure in comparison to the controls; as a result, the crude**odds ratio**for zidovudine use was seriously confounded ...###### R] glm: getting the confidence interval for an Odds Ratio, when using predict()

Case solved. Thanks a lot Peter! Dominic C. -----Message dorigine----- De : peter dalgaard [mailto:pdalgd at gmail.com] Envoyé : 20 mars 2012 07:57 À : Dominic Comtois Cc : r-help at r-project.org help Objet : Re: [R] glm: getting the confidence interval for an

**Odds Ratio**, when using predict() [Oops, forgot cc. to list] On Mar 20, 2012, at 04:40 , Dominic Comtois wrote: , I apologize for the errors in the previous code. Here is a reworked example. It works, but I suspect problems in the se calculation. I changed, from the 1st prediction to the 2nd only one covariate, so that the ORs CI should be equal to the exponentiated variables coefficient and ci. And we get something different: Yep. Classical rookie mistake: Forgot to take sqrt() in the se. I then get , se ,- sqrt(contr %*% V %*% t(contr)) , , # display the CI , exp(contr %*% coef(model) + qnorm(c(.025,.50,.975))*se) [1] 0.655531 1.686115 4.336918 , , # the point estimate is ok, as verified with , exp(model$coefficients[3]) x2cat2 ...###### R] Plot of odds ratios obtained from a logistic model

Hi all! I am trying to develop a plot a figure in which I would like to show the odds ratios obtained from a logistic model. I have tried with the dotplot option but no success. Could you help me? Is there any option when modelling the logistic model in R? Thank you in advance ...

###### Whats the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes

Logistic regression is used frequently in cohort studies and clinical trials. When the incidence of an outcome of interest is common in the study population (|10%), the adjusted

**odds ratio**derived from the logistic regression can no longer approximate the risk ratio. The more frequent the outcome …###### Journal: Journal of the American College of Nutrition / Publication Year: 2019 / Source: 2019 v.38 no.5 / Subject: odds ratio /...

This meta-analysis aimed to summarize the association between anthocyanin consumption and the risk of colorectal cancer. All relative articles were located on online databases, including PubMed, Embase, and the Cochrane Library as of June 11, 2018. Risk ratios (RRs) or

**odds ratio**and their 95% confidence intervals (CIs) were calculated through the STATA 12.0 software package. A total of seven stud ...###### Journal: Journal of the American College of Nutrition / Source: 2019 v.38 no.5 / Subject: odds ratio - PubAg Search Results

This meta-analysis aimed to summarize the association between anthocyanin consumption and the risk of colorectal cancer. All relative articles were located on online databases, including PubMed, Embase, and the Cochrane Library as of June 11, 2018. Risk ratios (RRs) or

**odds ratio**and their 95% confidence intervals (CIs) were calculated through the STATA 12.0 software package. A total of seven stud ...###### Plus it

Validity of responses to film questions-To evaluate the validity of adolescents recollection of films they had seen, we re-contacted 49 adolescents who had participated in a longitudinal study in which they reported each month the films they had seen in the past week. Adolescents had excellent recognition of the films they reported seeing during the previous year, identifying films correctly 88% of the time. In addition, the adolescents rarely reported seeing false film titles with false actors (3.0%) or false film titles with real actors (2.7%).. Statistical analysis-We used the χ2 test or analysis of variance to evaluate the association between trying smoking and each of the confounding variables. We used logistic regression to determine the crude odds ratios, adjusted odds ratios, and 95% confidence intervals. Firstly, we used a crude model in which exposure to smoking in films was entered as four categories that corresponded to fourths of exposure in the student population. Next, we added ...

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###### ChB3-CDFOR2LRR: Convert CDF Odds Ratio to Logged Relative Risks in PwrGSD: Power in a Group Sequential Design

Given the values of the baseline hazard and

**odds ratio**of the CDF at a grid of time points find the corresponding logged risk ratio.###### The Estimation of the Odds Ratio by Petos Method<...

TY - JOUR. T1 - The Estimation of the

**Odds Ratio**by Petos Method. AU - Scott, William F. PY - 2004. Y1 - 2004. M3 - Article. VL - 29. SP - 45. EP - 48. JO - Mathematical Sciences. JF - Mathematical Sciences. SN - 0312-3685. ER - ...###### combinatorics - What is the probability that a 1 was sent, given that 1 was received? - Mathematics Stack Exchange

Lets start out simply:. The odds of a 0 being sent are 0.3. The odds of a 1 being sent are 0.4. The odds of a 2 being sent are 0.3.. The odds of a 1 being received given that a 0 was sent are 0.2. Out of the total sample space, thats odds of 0.3 * 0.2 = 0.06.. Likewise, odds of a 2 being sent and a 1 being received (out of total sample space) is 0.3 * 0.1 = 0.03.. Total odds (out of total sample space) of a 1 being received when something else was sent is then 0.03 + 0.06 = 0.09.. Odds of a 1 being sent and received is 0.4 * 0.7 = 0.28. (Note: Given that a 1 was sent, odds of a 0 being received are 0.2. Given that a 1 was sent, odds of a 2 being received are 0.1. Thus, given that a 1 was sent, odds of a 1 being received are 0.7.). So given that a 1 was received, the odds that it was actually a 1 that was sent are 0.28 / (0.28 + 0.09) = 0.28 / 0.37 = 28/37 $\approx$ 0.757.. ...

###### HomVEE - Child First - Effects Shown in Research & Outcome Measure Details

For a uniform approach, HomVEE calculates mean differences as the program group mean minus the comparison group mean. In some studies, authors did this calculation in the opposite direction.. The

**odds ratio**is the odds of an event occurring in one group divided by the odds of an event occurring in another group. If the odds of an event occurring in group A equals 3 and the odds of an event occurring in group B equals 2, the**odds ratio**equals 1.5. In other words, there is a 1.5 times greater likelihood the event will occur in group A than in group B.. ...###### HomVEE - Parents as Teachers (PAT)<sup>®</sup> - Effects Shown in Research & Outcome...

For a uniform approach, HomVEE calculates mean differences as the program group mean minus the comparison group mean. In some studies, authors did this calculation in the opposite direction.. The

**odds ratio**is the odds of an event occurring in one group divided by the odds of an event occurring in another group. If the odds of an event occurring in group A equals 3 and the odds of an event occurring in group B equals 2, the**odds ratio**equals 1.5. In other words, there is a 1.5 times greater likelihood the event will occur in group A than in group B.. ...###### Quantitative Bias Analysis for Epidemiologic Data

List of 4 ## $ obs.data : num [1:2, 1:2] 215 668 1449 4296 ## ..- attr(*, dimnames)=List of 2 ## .. ..$ : chr [1:2] BC+ BC- ## .. ..$ : chr [1:2] Smoke+ Smoke- ## $ obs.measures: num [1:2, 1:3] 0.965 0.954 0.852 0.809 1.093 ... ## ..- attr(*, dimnames)=List of 2 ## .. ..$ : chr [1:2] Observed Relative Risk: Observed Odds Ratio: ## .. ..$ : chr [1:3] 2.5% 97.5% ## $ adj.measures: num [1:4, 1:3] 0.943 0.925 0.936 0.916 0.882 ... ## ..- attr(*, dimnames)=List of 2 ## .. ..$ : chr [1:4] Relative Risk -- systematic error:

**Odds Ratio**-- systematic error: Relative Risk -- systematic and random error:**Odds Ratio**-- systematic and random error: ## .. ..$ : chr [1:3] Median 2.5th percentile 97.5th percentile ## $ sim.df :data.frame: 50000 obs. of 12 variables: ## ..$ seca : num [1:50000] 0.772 0.897 0.802 0.921 0.935 ... ## ..$ seexp : num [1:50000] 0.772 0.897 0.802 0.921 0.935 ... ## ..$ spca : num [1:50000] 0.919 0.94 0.912 0.943 0.965 ... ## ..$ spexp : ...###### 2008.01479] Interaction between two exposures: determining odds ratios and confidence intervals for risk estimates

Abstract: In epidemiological research, it is common to investigate the interaction between risk factors for an outcome such as a disease and hence to estimate the risk associated with being exposed for either or both of two risk factors under investigation. Interactions can be estimated both on the additive and multiplicative scale using the same regression model. We here present a review for calculating interaction and estimating the risk and confidence interval of two exposures using a single regression model and the relationship between measures, particularly the standard error for the combined exposure risk group ...

###### the odds 1 out - Yahoo Search Results

A 1 in 500 chance of winning, or probability of winning, is entered into this calculator as 1 to 500 Odds are for winning. You may also see odds reported simply as chance of winning as 500:1. This most likely means 500 to 1 Odds are against winning which is exactly the same as 1 to 500 Odds are for winning ...

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Results of epidemiological studies need to be expressed in understandable terms if they are to be of practical use to clinicians and policy makers. Case-control studies are often used to study adverse effects of treatment; odds ratios from these are used to express the magnitude of adverse effects, but are not intuitively understandable estimates of risk. A more understandable and informative means of expressing the risk of adverse events in case-control studies is the number of patients needed to be treated for one additional patient to be harmed. This is calculated from the

**odds ratio**and the unexposed event ratemdash;that is, the rate of occurrence of the adverse event of interest in people not exposed to the treatment. ...###### Our Daily Bread http://odb.org Living Beyond The Odds David C. McCasland August 21, 2013 at 01:00AM Many of us make daily...

Our Daily Bread http://odb.org Living Beyond The Odds David C. McCasland August 21, 2013 at 01:00AM Many of us make daily decisions based on the odds. If theres a 20 percent chance of rain, we may ignore it. If theres a 90 percent chance, well take an umbrella. The greater the odds, the more our behavior is affected because we want to choose wisely and be successful ...

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###### 北京市18~65岁居民高胆固醇血症流行现状与影响因素分析 - 中华流行病学杂志

结果. 17 662例有效样本中，血清胆固醇均值为（4.69±0.95）mmol/L，高胆固醇血症患病率为6.26%，边缘值升高率为21.34%。男性高胆固醇血症患病率为6.33%，女性为6.20%，差异无统计学意义（Z=1.64，P=0.10）；城区居民患病率为6.73%，高于郊区居民的5.59%（Z=-7.27，P,0.01）。高胆固醇血症患病率随年龄增加呈上升趋势（趋势检验χ2=308.85，P,0.01），其中男性（χ2=81.65，P,0.01）、女性（χ2=318.04，P,0.01）、城区居民（χ2=201.77，P,0.01）、郊区居民（χ2=114.65，P,0.01）均呈相同趋势。多因素logistic回归分析显示，高年龄组、女性（OR=1.23，95%CI：1.04~1.45）、超重（OR=1.56，95%CI：1.34~1.81）、肥胖（OR=1.82，95%CI：1.54~2.16）、每天吸烟（OR=1.24，95%CI：1.03~1.50）、每天饮酒（OR=1.40，95%CI：1.12~1.75）、牛羊肉摄入每周,1次（OR=1.19，95%CI：1.02~1.39）是高胆固醇血症的危险因素。 ...

###### Converting probability to odds

Having problems converting some probabilities into odds. Need to turn these probabilities into odds as 1 in X chance
0.192
0.01408
0.00128
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0.00000256
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