Cox Proportional-Hazards Regression
Cox Proportional-Hazards Regression is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Architecture, Planning & Design 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?
Cox Proportional-Hazards Regression 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 Architecture, Planning & Design 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 Cox Proportional-Hazards Regression.
Panel assignments (form fields in the program):
- Sure sutunu:
sure_yil - Olay sutunu (0/1):
olay - Kovaryans(lar):
['yas_yil', 'stres_skoru', 'bakim_var', 'nem_pct']
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 — Architecture, Planning & Design
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 Architecture, Planning & Design:
Peyzaj_Mimarligi/87_cox_death_hazard.xlsx
🎬 Scenario
We relate street-tree death risk to environmental variables and obtain hazard ratios (HR); Cox regression is appropriate.
⚙️ Variable Selection
- Duration: duration_year | Event: event | Covariates: age_year, stress_score, moisture_pct
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Proportional-hazards assumption test (Schoenfeld): per-covariate chi-square/p; significant = PH violated, consider a time-varying effect.
Data Preview (First 5 Rows)
| tree_id | age_year | stress_score | maintenance_present | moisture_pct | duration_year | event |
|---|---|---|---|---|---|---|
| 1.0 | 24.0 | 48.1 | 1.0 | 33.9 | 2.67 | 0.0 |
| 2.0 | 44.0 | 27.0 | 0.0 | 54.1 | 0.73 | 0.0 |
| 3.0 | 35.0 | 52.7 | 1.0 | 76.8 | 0.5 | 0.0 |
| 4.0 | 12.0 | 20.3 | 1.0 | 75.7 | 3.16 | 1.0 |
| 5.0 | 49.0 | 43.2 | 1.0 | 31.4 | 0.5 | 1.0 |
n = 250 · Columns: tree_id, age_year, stress_score, maintenance_present, moisture_pct, duration_year, event
📈 MerQur Output
💬 Interpretation
We modelled street trees’ death risk from environmental/physiological variables (age, stress score, moisture) with Cox regression. In this model the stress score is borderline non-significant (HR = 1.009, p = 0.106), concordance 0.57 indicating modest discrimination. This is informative too: tree death is partly explained by these variables, with other factors (root damage, infrastructure conflict, vandalism) also at play. Cox regression is the gold- standard survival model relating “time-to-event” to several predictors; it gives hazard ratios (HR). Relating urban tree death risk to environmental conditions is valuable for resistant species selection and planting-site improvement. >> ADVANCED PARAMETERS (optional in the form — what they do): – Proportional-hazards assumption test (Schoenfeld): per-covariate chi-square/p; significant = PH violated, consider a time-varying effect.
⚠ 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 Architecture, Planning & Design file.
▶ Cox Proportional-Hazards Regression — video walkthrough
This section is part of the Sağkalım Analizi video (6 analyses in one video). The link below jumps straight to 1:42, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · ⚙ 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.