When to use a trend test?

The Wise son:

Aren’t we all just trying to find trends everywhere? I hope you all have a positive trend in your life. A trend test will help us assess whether our outcome of interest has a linear pattern across the groups of some categorical variable. Trend tests are relevant when dealing with more than 2 groups which are arranged in some consistent order. For example: underweight, normal weight, overweight, obese.

The Simple son:

Let’s take Wise’s groups. So, if you have these BMI categories and you wish to test the trend among the groups with respect to a suite of cardiovascular measurements, you can use a linear regression and insert the group variable as a scale: 1,2,3,4. You will see that your test has 1 degree of freedom on the analysis of variance table (ANOVA). This is an example with R code:

Trend <- lm(Blood_Pressure~BMI_Category, data=weight_data) #Make sure that BMI is numeric (1,2,3,4)
aov(Trend)  #Notice that BMI has 1 degrees of freedom

For the non-normally distributed continuous variables use Kendall rank test, and Chi-square test for trend when testing categorical variables.

The Wicked son:

The funny thing is that in research both negative and positive trends make the researcher (and the journal’s editor) happy. WARNING: Don’t use a trend test if you don’t see a trend in your data, okay? And be careful of “phantom trends”, you know? Because short term trends may exist even in the most random sequences. People nowadays find trends anywhere they look for, “oh, yesterday my leg was fine and today it hurts a bit, it seems to me like a trend. I’m sure that tomorrow it will hurt even more and I’ll be forced to cancel my yoga class. Is that a sign of a positive or a negative trend..?”

He who couldn’t ask:

Hey, this is exactly what happened to me yesterday! So I canceled my Zumba class (yoga is too hard for me to concentrate) and now I feel better. I assume I should keep on resting and this will lead to a positive trend in my pain and a negative trend in my shape. Oh my, I think I understand statistics for the first time!

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Tal has over 5 years of experience of consulting researchers on a variety of biomedical research including cardiology, internal medicine and infectious disease.  As a biostatistician, she is engaged in study life cycle from planning throughout the statistical analysis and up to publication.  She also took part in big-data analysis as part of evaluating Hospital databases.  Tal has served as a clinical trials’ statistician for number of studies.  She is an R programmer and has been teaching short courses of applied biostatistics with R in Tel-Aviv university and Ono Academic College.

Dina has a strong background in statistics and a high level of data analytics abilities.  She has over 5 years of experience in applied biostatistics.  Dina holds an M.A. in Biostatistics and a B.A in statistics both from the Hebrew University.

Ronit manages all of IntegiStat's administrative affairs. She has experience in office management in general and specifically in the health sciences, and is certified in accounting and law.

Diklah founded and heads IntegriStat. She has extensive experience in managing diverse data projects of all sizes. Diklah has extensive experience in providing support to companies running clinical trials to validate their product for regulatory clearance including FDA and EMA.

Her professional experience also includes: statistician at West Pennsylvania Psychiatric Institute; establishment of a statistical service at Wolfson Medical Center, Holon; lead biostatistician at a number of biotech startups.

Diklah is the author or coauthor of more than 50 scientific publications. Diklah has a B.Sc. in Statistics from University of Haifa; an M.Sc. in Biostatistics from the Graduate School of Public Health, University of Pittsburgh; a Master of Entrepreneurship and Innovation degree from ISEMI, Swinburne University of Technology; and Ph.D. in Biostatistics from Ben Gurion University of the Negev.