I'm new to using R. Most of my past experience is with the CDC's EpiINFO package.
I've been involved in numerous medical studies wherein we generate tables showing both the crude odds ratio and an adjusted odds ratio for 3 to 10 negative outcomes, comparing a control group and two or more other groups. Typically, I would already have a table with a row for each outcome and a set of columns for each group. In each row, I would have the total of all members of each group, the number who experienced the negative outcome, the number that did not have the negative outcome. From these I can calculate the unadjusted odds ratio, even in Excel.
How do I use R to caclulate an adjusted odds ratio( and 95% confidence intervals) adjusting for age, race, state of residence, etc?
Ideally, I'd like a function that would produce an output in a single line that I could cut and paste into my Excel sheet that would show these eight values:
Crude OR, Lower 95% CI, Upper 95% CI, p, Adjusted OR, Lower 95% CI, Upper 95% , p
Does such a function or package exist?
Thanks!
Related
I have a table which has been generated in one of the publication and I am trying to do similar analysis for another study. please help me out to find CI by non-parametric percentile bootstrap method.
here is the table
I want to know how the ci for 1-RR is calculated.
here is the statistical explanation for it:
We used the Kaplan-Meier estimator18 to construct
cumulative incidence curves and to estimate the risk for
each outcome. The risks were compared via ratios and
differences. We estimated the risk ratio for each outcome
using only matched pairs in which both individuals were
still at risk 7 days after receipt of the third vaccine dose in
those vaccinated. We analysed outcomes in the full
population and in subgroups defined by strata of age,
sex, and number of comorbidities. 95% CIs were
calculated using the nonparametric percentile bootstrap
method with 1000 repetitions. The effectiveness of the
third dose was estimated as 1 – risk ratio. As a sensitivity
analysis, vaccine effectiveness was also estimated as
1 – incidence rate ratio derived from a Poisson regression
using the same dataset, with no further adjustment.
Analyses were done using R software (version 4.0.4)."
How to calculate CI.
I am conducting a propensity score match analysis on the outcome of two different new cancer treatments where the outcome is binary (cancer-free or not cancer free). Following successful matching I get my paired 2x2 contingency table for my outcome between my matched pairs which looks like below;
**Treatment 1**
Not-Cancer Free Cancer Free
**Treatment 2** Not-Cancer Free 50 39
Cancer Free 53 60
I'd like to compare the outcomes to figure out if one treatment is better than the other by comparing odds ratios of being cancer free. I've been advised to conduct a McNemar's test due to the matched nature of the data which I do and get a p-value of 0.17 (non-significant). However, I've also been advised that instead of simply using the odds ratio normally used for such 2x2 tables (B/C --> 39/53 = 0.78 OR) that I should calculate the odds ratio and 95% confidence intervals using the methods shown in Agresti Alan, Min Yongyi. Effects and non‐effects of paired identical observations in comparing proportions with binary matched‐pairs data. Statistics in medicine. 2004 Jan 15;23(1):65-75. as it accounts for the matched nature of the data.
Unfortunately after reading this paper numerous times (especially it's odds ratio section) I can't figure out what the equations given for the odds ratio and 95% CI calculations are that they are referring to but know that they must be in there somewhere as other papers have cited this paper when referring to their odds ratios but don't share their methodology making it difficult to traceback.
If anyone has read this paper or has experience with odds ratios for matched binary data, can you please let me know how I can go about to get matched pair odds ratios. Thank you incredibly much in advance!
You can use McNemar exact test for the paired data. A point they are making in the paper and what the exact test uses are the off-diagonal elements (b,c) in the calculations. You can use exact2x2 package (https://cran.r-project.org/web/packages/exact2x2/exact2x2.pdf) to get the test results with 95%CI:
library(exact2x2)
# Set up your data as matrix
x<-matrix(c(50,53,39,60),2,2)
mcnemar.exact(x)
Gives:
Exact McNemar test (with central confidence intervals)
data: x
b = 39, c = 53, p-value = 0.175
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.4738071 1.1339142
sample estimates:
odds ratio
0.7358491
I want to perform Mann-Kendall test at 99% and 90% confidence interval (CI). When running the lines below the analysis will be based on a 95% CI. How to change the code to perform it on 99 and 90% CI?
vec = c(1,2,3,4,5,6,7,8,9,10)
MannKendall(vec)
I cannot comment yet, but I have a question, what do you mean when you say that you need to perform the analysis on a 99 and 95% CI. Do you want to know if your value is significant at the 99 and 90% significance level?
If you just need to know if your score is significant at 99 and 90% significance then r2evans was right, the alpha or significance level is just an arbitrary threshold that you use to define how small your probability should be for you to assume that there "is no effect" or in this case that there is independence between the observations. More importantly, the calculation of the p-value is independent of the confidence level you select, so if you want to know if your result is significant at different confidence levels just compare your p-value at those levels.
I checked how the function works and did not see any indication that the alpha level selected is going to affect the results. if you check the source code of MannKendall(x) (by typing MannKendall without parenthesis or anything) you can see that is just Kendall(1:length(x), x). The function Kendall calculates a statistic tau, that "measures the strength of monotonic association between the vectors x and y", then it returns a p-value by calculating how likely your observed tau is under the assumption that there is no relation between length(x) and x. In other words, how likely it is that you obtain that tau just by chance, as you can see this is not dependent on the confidence level at all, the confidence level only matters at the end when you are deciding how small the probability of your tau should be for you to assume that it cannot have been obtained just by chance.
In R I am having a problem obtaining exact confidence intervals for the rate ratio as calculated by the poisson.test function. This function uses the method in binom.test to calculate the confidence limits between two rates e.g.
poisson.test(x = c(10000,20000), T = c(15000,15000), r = 1, conf.level = 0.95)
This works fine when x (events) is lower than T (observations). However, at very high x (events) and relatively low T (observations), the variance of the Poisson distribution will approach infinity. This is not accounted for in the poisson.test function, as the binom.test method has no such limits. Consequently, with a progressively higher event rates, the confidence interval of both the individual rates and the ratios of these rates become progressively narrower, while they should asymptotically widen.
Would anybody know an alternative way to test the ratio of two very high rates using the Uniformly Most Powerful method and obtain their correct confidence limits?
I want to obtain the the limits that determine the significance of autocorrelation coefficients and partial autocorrelation coefficients, but I don't know how to do it.
I obtained the Partial autocorrelogram using this function pacf(data). I want that R print me the values indicated in the figure.
The limits that determine the significance of autocorrelation coefficients are: +/- of (exp(2*1.96/√(N-3)-1)/(exp(2*1.96/√(N-3)+1).
Here N is the length of the time series, and I used the 95% confidence level.
The correlation values that correspond to the m % confidence intervals chosen for the test are given by 0 ± i/√N where:
N is the length of the time series
i is the number of standard deviations we expect m % of the correlations to lie within under the null hypothesis that there is zero autocorrelation.
Since the observed correlations are assumed to be normally distributed:
i=2 for a 95% confidence level (acf's default),
i=3 for a 99% confidence level,
and so on as dictated by the properties of a Gaussian distribution
Figure A1, Page 1011 here provides a nice example of how the above principle applies in practice.
After investigating acf and pacf functions and library psychometric with its CIz and CIr functions I found this simple code to do the task:
Compute confidence interval for z Fisher:
ciz = c(-1,1)*(-qnorm((1-alpha)/2)/sqrt(N-3))
here alpha is the confidence level (typically 0.95). N - number of observations.
Compute confidence interval for R:
cir = (exp(2*ciz)-1)/(exp(2*ciz)+1