Frailty Cox
Frailty Cox 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?
Frailty 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 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 Frailty Cox.
Panel assignments (form fields in the program):
- Sure sutunu:
sure_yil - Olay sutunu (0/1):
olay - Ozellik Sutunlari:
['yas_dikim'] - Kume sutunu:
mahalle_id
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/93_frailty_cox_neighborhood.xlsx
🎬 Scenario
For neighborhood-clustered tree survival, we use frailty Cox, which adds a shared neighborhood-level random effect (“frailty”).
⚙️ Variable Selection
- Duration: duration_year | Event: event | Covariate: age_planting | Cluster (frailty): neighborhood_id
Data Preview (First 5 Rows)
| tree_id | neighborhood_id | duration_year | event | age_planting |
|---|---|---|---|---|
| 1.0 | 1.0 | 5.03 | 0.0 | 12.0 |
| 2.0 | 1.0 | 1.23 | 1.0 | 4.0 |
| 3.0 | 1.0 | 4.89 | 1.0 | 1.0 |
| 4.0 | 1.0 | 11.06 | 1.0 | 10.0 |
| 5.0 | 1.0 | 2.71 | 1.0 | 7.0 |
n = 120 · Columns: tree_id, neighborhood_id, duration_year, event, age_planting
📈 MerQur Output
💬 Interpretation
Street trees are clustered within neighborhoods; trees in the same neighborhood share unmeasured common factors (microclimate, maintenance regime, pollution, infrastructure) and carry similar death risk. Frailty Cox adds a shared “frailty” (random effect) for each neighborhood to model this clustering — the mixed-model version of survival analysis. Concordance 0.55. Accounting for neighborhood-level hidden differences gives both correct standard errors and information on “how much heterogeneity exists in tree survival among neighborhoods” — showing which neighborhoods are tree-friendly/hostile. It is the correct method for clustered urban-tree survival data (neighborhoods, streets).
⚠ 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.
▶ Frailty Cox — video walkthrough
This analysis is demonstrated on a sample dataset from Anaesthesiology (the steps are identical across disciplines). The link jumps straight to 9:54. 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.