Preparing Time Variables for Trend Analysis
Learn how to format, validate, and aggregate date and time variables correctly — the essential first step before any time series or trend analysis.
You'll learn
How to format, validate, and aggregate time variables correctly before running any trend or time series analysis.
Use this when
Your dataset has a date or time column and you want to analyse trends or temporal patterns.
What Makes a Valid Time Variable
A time variable is any column that encodes when an observation occurred — admission date, collection year, visit month. For trend and time series analysis, the time variable must be (1) consistently formatted, (2) unambiguous, and (3) at the right granularity for your research question.
⚠️ Common data problems with time variables
Time columns in clinical databases are notoriously inconsistent: mixed date formats (DD/MM/YYYY vs MM/DD/YYYY), text entries like "January 2022," encoded years as 2-digit numbers, and empty cells that represent "unknown" rather than a specific time. All of these must be resolved before analysis.
Choosing the Right Time Granularity
Granularity — whether you use day, month, quarter, or year — determines what patterns you can detect. Too fine-grained and you see noise; too coarse and you miss variation.
| Granularity | Best for | Minimum data requirement |
|---|---|---|
| Daily | Emergency department visits, ICU census | 1–2 years of daily data |
| Weekly | Infectious disease surveillance, outpatient volume | 2+ years of weekly data |
| Monthly | Hospital admissions, mortality trends, lab values | 3–5 years of monthly data |
| Quarterly | Administrative data, quality indicators | 5+ years |
| Yearly | Population-level epidemiology, long-term trends | 10+ years for reliable trend |
Aggregation: Count, Sum, or Mean?
Once you have chosen granularity, you must decide how to aggregate observations within each time unit. The choice depends on what your outcome represents.
- ●Count: use when each row is an event (admission, death, diagnosis) — aggregate by counting rows per time unit
- ●Sum: use when rows have a numeric measure you want the total of (bed-days, doses administered)
- ●Mean: use when rows have a numeric measure you want the average of (lab value, length of stay)
- ●Rate per 1,000 (or per population): use when comparing across sites or time periods with different denominator sizes
⚠️ Aggregating counts when you need rates
Raw counts increase as your database grows (more patients enrolled each year). If hospital volume grew from 5,000 to 8,000 admissions over five years, mortality counts will rise even if the mortality rate is stable. Always normalise by denominator when comparing across time periods with different volumes.
Missing Time Points
Gaps in your time series — months or years with no data — must be handled explicitly. Time series models assume regular spacing. Even one missing period will break autocorrelation-based analyses.
- ●Identify gaps before analysis: sort by time and check for missing periods
- ●For short gaps (1–2 periods): impute with interpolation or the period average
- ●For long gaps (data collection stopped, then restarted): consider analysing the two segments separately
- ●Always document gaps in your Methods section
Practice with your own dataset
Run a time series trend analysis.
Required variables
- • A date or time variable
- • At least one numeric value variable
- 1.Ensure your dataset has a date or year variable and a numeric outcome
- 2.Go to Analyze → Time Series
- 3.Select the time variable and value variable
- 4.Choose aggregation method (count, sum, or mean) and frequency (monthly, yearly)
- 5.Review the trend decomposition and forecast output
Read next
Interpreting Trends: Real Change vs Random Variation
Learn how to distinguish genuine trends from random fluctuation in longitudinal data, and how to describe trends correctly in your results section.