Question

Scenario 2: Does vitamin D help prevent catching a cold?
A woman under a blanket and blowing her nose.
Could taking vitamin D help prevent catching a cold?
You read a blog post that claims vitamin D could help prevent people from catching a cold. You ask for volunteers at work to help you test this statement. One hundred people volunteer to take 1000 IU of vitamin D per day for 180 days and track the number of colds they get during that time. These people make up Group 1. One hundred other people volunteer to not take any vitamin D for 180 days, but to still track the number of colds they get during that time. These people make up Group 2. You do not ask any of the volunteers any questions about their health or what they do outside of work. The experiment runs the same length of time for both groups, and everyone starts on the same day. You rely on the volunteers to report if they had a cold. The results are shown in the table below.
A 3 column table with 1 row. Column 1 is unlabeled with entry Average number of colds. Column 2 is labeled Group 1 with entry 1.5. Column 3 is labeled Group 2 with entry 1.5.
Read the Vitamin D experiment. What variables would you need to control in order to make the experiment reliable?

Answer 1

The experiment as run cannot reliably test whether vitamin D prevents colds because many other factors could differ between the two groups and explain the identical averages. To make the experiment reliable you must control or account for the following variables (with a short note on how/why for each):

Study design basics
- Randomization of participants into treatment and control groups (prevents selection bias).
- Placebo control and double-blinding (participants and those assessing outcomes shouldn’t know who gets vitamin D to avoid expectation/placebo effects).
- Clear, pre-specified case definition of a “cold” (symptoms, minimum duration, or clinician diagnosis) and standardized reporting procedures.

Vitamin D–specific variables
- Baseline vitamin D status (measure serum 25(OH)D) — people with deficiency may respond differently than those already replete.
- Dose and formulation of vitamin D and verification of adherence (pill counts, serum level checks).

Participant characteristics (potential confounders)
- Age, sex, pregnancy status.
- Pre-existing health conditions (chronic lung disease, diabetes, immunosuppression, etc.).
- Medication and supplement use (esp. other immune-modifying drugs or vitamin/mineral supplements).
- Baseline frequency of colds (prior history) so groups are comparable.

Exposure and lifestyle
- Occupation and exposure risk (healthcare workers, teachers, amount of interpersonal contact).
- Household composition (children, other adults).
- Travel during study period.
- Sleep, stress, exercise, smoking, alcohol — all affect infection risk.
- Hygiene practices (handwashing) and preventive behaviors (mask use).
- Sunlight exposure and diet (affect endogenous vitamin D and immunity).

Timing and environment
- Seasonality and local viral circulation (run across same season(s) or adjust for season).
- Workplace and home environment (crowding, ventilation).

Measurement and follow-up
- Objective outcome measures where possible (physician confirmation, virologic testing).
- Consistent follow-up length and timing for all participants.
- Sufficient sample size and pre-specified statistical analysis plan (power calculation).

Data collection and analysis
- Collect these variables at baseline and, where relevant, during follow-up so you can adjust for residual confounding.
- Plan intention-to-treat analysis and check per-protocol secondary analyses.

Putting it together: a reliable test would be a randomized, placebo-controlled, double-blind trial that measures baseline and follow-up 25(OH)D, monitors adherence, uses a clear cold definition (ideally confirmed clinically or virologically), and collects the demographic/exposure/lifestyle covariates so you can ensure groups are comparable or adjust statistically.