Guide · Protocols
Baseline first: measure before you change anything
Every protocol started without a baseline is a result you never get back. How to sequence measurement around an intervention.
Every protocol you start without a baseline is a result you never get back. Whatever happens next, you will not be able to say whether the supplement, the diet, or the training block did it, because you will not know where you started or how much your numbers wobble on their own.
The wobble is larger than most people assume, and it is measured.
In the European Biological Variation Study, healthy adults were sampled weekly for ten weeks with nothing changing. Within one person, triglycerides varied by about 20%, cholesterol by about 5%. Run the standard arithmetic that combines that biology with assay error and two triglyceride readings must differ by roughly half before the change is statistically believable; cholesterol needs about 15%. One draw is not a fact about you; it is one sample from your distribution.
People usually begin interventions right after an alarming result. That is precisely when improvement is guaranteed for free: an unusually high reading is, by construction, likely to be followed by one closer to your personal average, treatment or no treatment. The statistical name is regression to the mean, and it has flattered ineffective protocols for as long as people have measured themselves once. The known fixes are unglamorous: repeat the baseline, average it, and compare like conditions.
Baselines also sharpen risk itself. Long-term cohort work shows that associations computed from a single baseline measurement understate the true relationship with usual levels by about a third within a decade, because one-off values blur the signal. Formal single-patient trial methodology reaches the same verdict from the other direction: without a baseline phase, no observed change can be attributed to the treatment.
Clinical practice already works this way; statin response, for instance, is defined as percent reduction from baseline. The sequence costs nothing but order: measure, then change one thing, then measure again. Every number you collect before the intervention is leverage afterward.
- 01Aarsand AK, et al. The EuBIVAS: within- and between-subject biological variation data for lipids, glucose, and related analytes. Clinical Chemistry, 2018.
- 02Fraser CG. Reference change values: the way forward in monitoring. Annals of Clinical Biochemistry, 2009.
- 03Barnett AG, van der Pols JC, Dobson AJ. Regression to the mean: what it is and how to deal with it. International Journal of Epidemiology, 2005.
- 04Clarke R, et al. Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. American Journal of Epidemiology, 1999.
- 05Duan N, Kravitz RL, Schmid CH. Single-patient (n-of-1) trials: a pragmatic clinical decision methodology. Journal of Clinical Epidemiology, 2013.
