Time-Dependent Cox
Time-Dependent Cox is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Education Sciences sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.
🎯 What is it for?
Time-Dependent Cox automatically performs all required assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + chart. Automatic interpretation to the APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Education Sciences domain
- To produce APA 7-compatible result tables for academic publications
- Hypothesis testing and decision-making processes
- Undergraduate / master’s / PhD theses after the appropriate method has been selected
📐 Assumptions
- Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
- Independent observations — Observations must come from individuals independent of one another
- Sufficient sample size — The minimum n requirement for the analysis must be met
- Outlier check — Outliers must be detected and evaluated
If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.
🛠 How to do it in MerQur
Load the data. Select the sample file from File → Open. MerQur auto-detects column types.
Select the analysis. From the left side select Time-Dependent Cox.
Panel assignments (form fields in the program):
- id_col:
ogrenci_id - start_col:
baslangic - stop_col:
bitis - event_col:
olay - Covariates:
['GPA'] - penalizer:
0.0
Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).
▶ Run — click the button. Results are produced automatically as a table + chart.
📄 Export to Word. APA 7-formatted report with italic statistical symbols.
📊 Sample Dataset — Education Sciences
ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.
🎬 Example File
This analysis is demonstrated on the following example dataset for Education Sciences:
Egitim_Bilimleri/90_tvcox_GPA_dropout.xlsx
🎬 Scenario
Suppose a student’s GPA changes from term to term, and we believe their current GPA, not just their starting GPA, drives dropout risk. Our data are in counting-process form with 464 records, each giving a start and end term interval, the GPA that held during that interval, and an event flag for whether dropout occurred at its end, with student_id linking a student’s successive intervals. We use a Time-Dependent Cox model, which lets GPA vary over follow-up so the hazard at any moment reflects the student’s GPA at that moment.
⚙️ Variable Selection
- Start time: start
- Stop time: end
- Event indicator (1=dropout): event
- Time-varying covariate: GPA
- Subject id: student_id
Data Preview (First 5 Rows)
| student_id | start | end | GPA | event |
|---|---|---|---|---|
| 1.0 | 0.0 | 1.0 | 2.72 | 0.0 |
| 1.0 | 1.0 | 2.0 | 2.92 | 0.0 |
| 1.0 | 2.0 | 3.0 | 2.99 | 0.0 |
| 1.0 | 3.0 | 4.0 | 3.18 | 0.0 |
| 1.0 | 4.0 | 5.0 | 3.32 | 0.0 |
n = 464 · Columns: student_id, start, end, GPA, event
📈 MerQur Output
💬 Interpretation
A student’s GPA changes from term to term; standard Cox assumes it constant, time-dependent Cox uses the current value in each time interval (start-stop format). In this model the effect of current GPA on dropout risk is non-significant (HR = 1.16, p = 0.65) — in this data the momentary GPA change does not predict dropout timing. A non-significant result is information too. Time-dependent Cox is the correct method when “the current, continuously monitored condition matters, not just the baseline”; it is the standard for modelling time-varying factors (GPA, absenteeism, support) in longitudinal educational monitoring.
⚠ Common Mistakes
- Misidentifying the data type (e.g., loading a categorical variable as numeric)
- Skipping assumption checks and going straight to the p-value
- Failing to report effect size — APA 7 requires both p and effect size
- Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
- Not switching to a non-parametric alternative when n is insufficient
📹 Video Walkthrough
Watch the video below for an end-to-end walkthrough of this analysis on a Education Sciences file.
▶ Time-Dependent Cox — video walkthrough
This analysis is demonstrated on a sample dataset from Landscape Architecture (the steps are identical across disciplines). The link jumps straight to 7:12. Narration is in Turkish.
🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic
📚 If You Used This Analysis, Cite MerQur
If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
For BibTeX, RIS, EndNote and the English citation form: all citation formats →
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.