Paul Vaucher · University of Applied Sciences and Arts Western Switzerland

Changes of OR over time can be measured by using logistic regression and comparing two models using likelihood ratio test. First put in your analysis using your baseline value and measure OR by using the coefficient corresponding to exposure at baseline. Then compute the same model but replace the exposure with its value at follow-up. Compare both models using the likelihood ratio test. This will provide you both ORs with 95% CIs and also if the observed ORs are significantly different between both models.

if I could understand the main research question and/or hypothesis you try to obtain from this study, plus general informaiton on what you are calling a "longitudinal study" maybe I could attempt to answer your question about "comparing two odds ratio (one at baseline, another measured at Follow up time)." Without this type of information it is impossible to 'comprehensively" and "effetively" answer your question.

In a longitudinal study we must use relative risk, because we can compute Incidence rate.And then compare two relative risk.

Jul 23, 2012

Remi Laporte · Assistance Publique Hôpitaux de Marseille

Maybe you are talking of case control analysis inside a longitudinal cohort. Although in longitudinal cohorts risk ratio are the main indicators. In the case-control, OR are apropriate and can estimate (if the diseases remain rare). However, in my memory, ln(OR) follow a Normal repartition law, but several statistical online pdf courses describe it. You can study them to foramally calcule the comparaison (if you want a precise p). Otherwise, you get the CI of the OR in every statistical software and you can use it. If not crossing p< 0.05 !

If you consider odds to be independent, you can use the following:

difference of the log odds, δ. The standard error of δ is sqrt(SE(1st odds)+SE(2nd odds)) . Then you can obtain a p-value for the ratio z=δ/SE(δ) from the standard normal.

I am unsure what you are asking. I disagree with Friba. The Odds Ratio is a valid statistic in itsself. If the disease is rare it approximates the rate ratio. The odds ratio lends itself to further statistical manipulation e.g., Logistic regresion etc. If the disease is not rare then I would use the Rate Ratio but most diseases are rare

J

Jul 23, 2012

Heresh Amini · Swiss Tropical and Public Health Institute

First, thank you all for your help!
I have two odds ratios in a longitudinal study, the one related baseline and the other one from follow up time. Now I am going to compare these odds ratio. I don’t know after calculate the two odds ratios over two different time, just I can compare them by providing significantly or not significantly the odds ratios over time or is there any statistical test to compare the odds ratios.

My suggestion is to draw a forest plot and see if there is an overlap between the confidence intervals of the two odds ratios. Thus, by simple visual inspection you can quickly discover whether there is a difference between the two points. If there is overlap, there is no statistically significant difference between the baseline and the later ORs.
Otherwise, if the OR at the baseline is not statisticallly significant, you can be comfortable to describe the later OR by itself. i.e. Its magnitude and direction with tests of statistical significance.

Jul 30, 2012

Paul Vaucher · University of Applied Sciences and Arts Western Switzerland

Changes of OR over time can be measured by using logistic regression and comparing two models using likelihood ratio test. First put in your analysis using your baseline value and measure OR by using the coefficient corresponding to exposure at baseline. Then compute the same model but replace the exposure with its value at follow-up. Compare both models using the likelihood ratio test. This will provide you both ORs with 95% CIs and also if the observed ORs are significantly different between both models.

You really need to have more than one model then you compare them using the likelihood ratio analysis.This will get you onto odd ratio and coenfidence interval. If all models are correlating then you have significant results,If the odd ratio are insignificant in all models then you will be in better position to draw a line by describing the odd ratio down the road of your research.

if I could understand the main research question and/or hypothesis you try to obtain from this study, plus general informaiton on what you are calling a "longitudinal study" maybe I could attempt to answer your question about "comparing two odds ratio (one at baseline, another measured at Follow up time)." Without this type of information it is impossible to 'comprehensively" and "effetively" answer your question.

Dear Hassan, I am surprised there's no reference to Cohen's d. This measure is used to compare outcomes in statistical meta-analysis. See Wolf, "Meta-analysis. Quantative methods for research and synthesis." Beverly Hills, Sage, 1986. (series: Quantative Applications in the Social Sciences, nr 59)
Or just google "meta-analysis odds ratio" : 174.000 hits.

Ah, yesterday I was thinking of OR's of different studies. It's probably my ignorance, but what's the problem in the same study? Why not compare them like one compares anything else? Larger or smaller by so much. When 95% Cls are given, one can see in a glance whether or not the difference is significant on that level.

Thanks Jeffrey for taking the trouble of making clear different samples need other computations than the same samples measured at different times and/or conditions.

## Popular Answers

Paul Vaucher· University of Applied Sciences and Arts Western SwitzerlandOn STATA see help menu for the following commands

xtlogit

est store

lrtest

Eduardo J Simoes· University of Missouriif I could understand the main research question and/or hypothesis you try to obtain from this study, plus general informaiton on what you are calling a "longitudinal study" maybe I could attempt to answer your question about "comparing two odds ratio (one at baseline, another measured at Follow up time)." Without this type of information it is impossible to 'comprehensively" and "effetively" answer your question.

## All Answers (19)

Fariba Ghahramani· Shiraz University of Medical SciencesRemi Laporte· Assistance Publique Hôpitaux de MarseilleSathish chandra Pichika· McMaster Universitydifference of the log odds, δ. The standard error of δ is sqrt(SE(1st odds)+SE(2nd odds)) . Then you can obtain a p-value for the ratio z=δ/SE(δ) from the standard normal.

Jay M Fleisher· Nova Southeastern UniversityJ

Heresh Amini· Swiss Tropical and Public Health InstituteI have two odds ratios in a longitudinal study, the one related baseline and the other one from follow up time. Now I am going to compare these odds ratio. I don’t know after calculate the two odds ratios over two different time, just I can compare them by providing significantly or not significantly the odds ratios over time or is there any statistical test to compare the odds ratios.

Dr. Pallavi Vasantrao Jugale· University of Minnesota Twin CitiesElias Ali Yesuf· Jimma UniversityOtherwise, if the OR at the baseline is not statisticallly significant, you can be comfortable to describe the later OR by itself. i.e. Its magnitude and direction with tests of statistical significance.

Paul Vaucher· University of Applied Sciences and Arts Western SwitzerlandOn STATA see help menu for the following commands

xtlogit

est store

lrtest

Ommari Baaliy MkangaraEduardo J Simoes· University of Missouriif I could understand the main research question and/or hypothesis you try to obtain from this study, plus general informaiton on what you are calling a "longitudinal study" maybe I could attempt to answer your question about "comparing two odds ratio (one at baseline, another measured at Follow up time)." Without this type of information it is impossible to 'comprehensively" and "effetively" answer your question.

Flip Schrameijer· www.architecture-for-autism.orgOr just google "meta-analysis odds ratio" : 174.000 hits.

Flip Schrameijer· www.architecture-for-autism.orgFlip Schrameijer· www.architecture-for-autism.orgHsin-Yi Weng· Purdue Universitylogit=b0+b1E+b2T+b3ExT

Let E be exposure (1: exposed; 0: unexposed); T be index variable for time (0: t0; 1: t1); b0-b3 be regression coefficients.

Thus, OR at t0 is Exp(b1), OR at t1 is Exp(b1+b3), and OR comparing t1 to t0 is Exp(b3). Exp(b3) and its p-value are what you are looking for.

Here's an example how you can set up the data:

E T D COUNT

1 0 0 c1

1 1 0 c2

1 0 1 c3

1 1 1 c4

0 0 0 c5

0 1 0 c6

0 0 1 c7

0 1 1 c8

Hsin-Yi Weng· Purdue UniversityCan you help by adding an answer?