I did and educational intervention to type 2 DM (clinical trial for 60 patients, 30 in intervention arm and 30 in control arm)
I want to do regression analysis to a dependent variable, improvement in HbA1c (improvement was abnormally distributed) with a number of independent variables (such as improvement in diabetes self-management, adherence, age, sex, educational level, duration of Diabetes)...
So which regression analysis can fit to my above data?
Note: log transformation convert abnormally distributed HbA1c to normal distribution
Please look at the following below links which may help you in your analysis:
http://personal.cb.cityu.edu.hk/msawan/teaching/FB8916/FB8916Ch1.pdf
http://web.nchu.edu.tw/~numerical/course1012/ra/Applied_Regression_Analysis_A_Research_Tool.pdf
http://statlab.stat.yale.edu/workshops/IntroRegression/StatLab-IntroRegressionFa08.pdf
http://home.iitk.ac.in/~shalab/regression/Chapter2-Regression-SimpleLinearRegressionAnalysis.pdf
Thanks
This depends on the data. One could try linear regression, but you have to carefully check the fit and the assumptions. This is model building and needs some basic statistical skills. See e.g.
https://cran.r-project.org/doc/contrib/Faraway-PRA.pdf
Normally, in a clinical study, you should have planned that BEFORE you do the study. In hope the study was randomized as well. Otherwise the results might be confounded and then its just a observation study.
I didn't understand well, but if the dependent variable is categorical, you should try logistic regression.
Thank you for answers,
prof Nicoleta, the dependent variable is continuous not categorical, however I can shift the result to categorical, but the question here is 30 patient sufficient for doing regression for 6 independent variables with 1 dependent variable?
In theory, a sample of 30 observations is enough, but never in practice! My opinion.
Dear, can you give me a paper that confirm a sample of 30 is enough?
Because I already did a clinical trial of 2 arms (each arm is 30 patient) so I can't increase my sample size
Hi, Ehab,
Your question asks "what is the best regression analysis?".
This modern, exact, non-parametric alternative to regression analysis always provides the most accurate model, by definition:
- https://odajournal.com/2016/09/19/novometric-analysis-with-ordered-class-variables-the-optimal-alternative-to-linear-regression-analysis/
This methodology does NOT require large samples to achieve satisfactory (90%) statistical power:
- https://odajournal.com/2020/02/08/statistical-power-analysis-in-oda-cta-and-novometrics-invited/
And, while this approach always identifies the most accurate model which is possible for your data and hypothesis, it does NOT over-fit. That is, (1) legacy methods identify a less accurate model when an effect in reality exists; and (2) the legacy methods all find effects in random data while the modern approach does not find effects in random data:
- https://odajournal.com/2019/03/21/some-machine-learning-algorithms-find-relationships-between-variables-when-none-exist-cta-doesnt/
- https://odajournal.com/2019/04/11/effect-of-sample-size-on-discovery-of-relationships-in-random-data-by-classification-algorithms/
- https://odajournal.com/2019/04/22/multi-layer-perceptron-neural-net-model-identifies-effect-in-random-data/
- https://odajournal.com/2019/05/16/logistic-discriminant-analysis-and-structural-equation-modeling-both-identify-effects-in-random-data/
Dear all
If my sample is 35, and the data (some of independent variables are abnormal), can I do multiple linear regression (since my data is more than 30)? which assumption should I meet to do multiple regression in such a case (the dependent variable is normal)
In summary how can I use linear regression and avoid non-linear regression?
Your last comment stated that you wanted to "avoid non-linear regression".
Why?
If one has nonlinear trends in their data, then avoiding a nonlinear model is a type of error known as model misspecification. Sometimes a non-linear model is just what is needed! Here's how to tell when a non-linear model is appropriate (note that distributional assumptions speak to the validity of the p-value).
- https://odajournal.com/2019/01/19/when-to-evaluate-a-nonlinear-model/
linear regression is probably the first type you should consider. with special options available for linear regression.
You can find the minimum sample size for regression: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229345
See the section on mutivar. longitudinal models:
https://www.google.com/search?q=Spaghetti+plot&oq=Spaghetti+plot&aqs=chrome..69i57.8655j0j1&sourceid=chrome&ie=UTF-8
Davidian Notes
https://www4.stat.ncsu.edu/~davidian/st732/notes.html
Multivar. Long. Models:
https://www.researchgate.net/post/What_statistical_test_is_appropriate_for_pre_and_post_trails_with_1_before_treatment_value_and_4_after_treatment_values_assessed_weekly
Best D. Booth
Multiple linear regression. In interpretation, however, look at exponentiated regression coefficients.... Sample size concerns though with many predictors??
https://statisticsbyjim.com/regression/choosing-regression-analysis/
Cordial saludo, eso depende de la base de datos que tengas y cual sea el ajuste de los datos, debes modelar cada uno de ellos para revisar realmente la afinidad de las variables independientes respecto a la dependiente, puede ensayar con una logística a ver como le va. éxitos
Dear Ehab Mudher Mikhael ,
Hi,
You can try NTSYS-pc in this special case to get linear regression with confident ellipse curve.
Best,
Saeed
Dear Ehab,
Also, I refer you to the article link below
Article Gathering, Identification, Medicinal Utilization and Domesti...
Best,
Saeed
Linear or logistic regression depends on the type of outcome. If your outcome is quantitative without categorization then multiple linear regression is quite reasonable to do. Assumptions should be checked before any analysis, pl don't forget that too.
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