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Sample study · survival design

Time to Recidivism After Release

Research information

Statement of the problem

This study examines how quickly released prisoners are re-arrested and which baseline factors — including a randomized financial-aid intervention — influence the timing of re-arrest.

Objectives

To describe the weeks_to_arrest and arrested indicator among the released prisoners.
To estimate survival curves for the financial_aid vs no-aid groups (Kaplan-Meier).
To compare recidivism-free time between the financial_aid groups (log-rank).
To model how age and prior offenses predict recidivism time (Cox regression).

Dataset

recidivism.csv432 rows × 7 cols

Column	Kind	Distinct
weeks_to_arrest	numeric	49
arrested	numeric	2
financial_aid	numeric	2
age	numeric	28
race	numeric	2
wexp	numeric	2
prio	numeric	17

Feasibility & estimateFree

✓ Feasibledesign: survival · via heuristic

Detected a survival design with 7 analyzable variable(s) and 3 analytical question(s). Recommended 5 statistical test(s). Estimated cost: 47 credits.

Recommended analysis

To describe the weeks_to_arrest and arrested indicator among the released prisoners.

Descriptive Statistics(weeks_to_arrest)

Frequency & Percentage Distribution(arrested)

To estimate survival curves for the financial_aid vs no-aid groups (Kaplan-Meier).

Kaplan–Meier Survival Estimate(weeks_to_arrest, arrested, financial_aid)

Log-Rank Test(weeks_to_arrest, arrested, financial_aid)

Cox Proportional Hazards Regression(weeks_to_arrest, arrested, financial_aid)

To model how age and prior offenses predict recidivism time (Cox regression).

Kaplan–Meier Survival Estimate(weeks_to_arrest, arrested)

Cox Proportional Hazards Regression(weeks_to_arrest, arrested, age, prio)

Dataset Complexity12

Statistical Tests13

AI Interpretation9

Visualizations8

Report Generation5

Total to run47 credits

Estimated runtime: ~55-110 seconds

ResultsComputed

7 tests47 credits

Descriptive Statistics (weeks_to_arrest)

n	max	min	std	mean	mode	range	median	variable	variance
432	52	1	12.662	45.854	52	51	52	weeks_to_arrest	160.334

Frequency & Percentage Distribution (arrested)

arrested · 073.6%

arrested · 126.4%

Kaplan–Meier Survival Estimate (weeks_to_arrest, arrested, financial_aid)

N432

n	group	events	censored	median survival	restricted mean
216	0	66	150	—	0.859
216	1	48	168	—	0.889

Observed 114 event(s) in 432 subject(s).

Log-Rank Test (weeks_to_arrest, arrested, financial_aid)

n.s.

χ²3.838df1N432p.050

n	group	events
216	0	66
216	1	48

The result is not statistically significant (p = 0.0501 >= 0.05).

Cox Proportional Hazards Regression (weeks_to_arrest, arrested, financial_aid)

n.s.

N432p.050

Concordance0.546

medium

coef	p value	hr ci low	predictor	hr ci high	hazard ratio
-0.369	0.052	0.477	financial_aid	1.003	0.691

The result is not statistically significant (p = 0.0501 >= 0.05).

Kaplan–Meier Survival Estimate (weeks_to_arrest, arrested)

N432

n	group	events	censored	median survival	restricted mean
432	overall	114	318	—	0.884

Observed 114 event(s) in 432 subject(s).

Cox Proportional Hazards Regression (weeks_to_arrest, arrested, age, prio)

significant

N432p< .001

Concordance0.633

medium

coef	p value	hr ci low	predictor	hr ci high	hazard ratio
-0.069	0.001	0.896	age	0.972	0.933
0.095	0.000	1.042	prio	1.159	1.099

The result is statistically significant (p = 0.0000 < 0.05).

Proportional-hazards assumption may be violated; hazard ratios should be interpreted cautiously.

Report

Publication-ready report

Tests used · Results · Discussion — generated from the computed statistics.

Numeric-claim check passed · 27 numbers verified · prose: llm:gemini

Open report ↗Download DOCX Download PDF

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