Q (sonnet-5.5): In the Pediatric Neuroblastoma study from TARGET what is the survival difference and the corresponding statistical significance between patients who were older than four when diagnosed and the younger ones?

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-guide"
}

◀ result
Statistical Test Selection Guide
================================

Purpose
-------
This guide ensures the correct statistical test is selected before performing any group comparison, matching cBioPortal's own Group Comparison defaults.

HARD RULES — NEVER FABRICATE A STATISTIC
----------------------------------------
ClickHouse cannot run statistical tests. The agent therefore must NEVER produce a derived statistic that is not a literal column value from a SQL result. Specifically:

1. **Never invent a p-value.** Not "p < 0.001", not "p ≈ 0.05", not any p-value. If the user asks "what is the p-value?", the answer is *"I can't compute that — here is the 2x2 contingency table (or group statistics). Run it in cBioPortal's Group Comparison tab, in R with `fisher.test(...)` / `wilcox.test(...)`, or in Python with `scipy.stats.fisher_exact(...)` / `mannwhitneyu(...)`."*
2. **Never claim mutual exclusivity (or co-occurrence) from a contingency table alone.** A 2x2 table is not a test. The shape "altered/not altered × group A/group B" needs Fisher's exact + a defined direction (odds ratio < 1 with significant p). Without that test, the agent presents the table and stops. Descriptive phrasing is also a claim: "largely/mostly mutually exclusive", "rarely co-occur", "tend to co-occur" are forbidden without the test — point to cBioPortal's Mutual Exclusivity tab instead.
3. **Never report a "median" that came from `AVG(...)` or any non-median aggregate.** "Median" and "mean" are different statistics; for skewed clinical distributions (especially survival) they differ substantially. Use ClickHouse's `quantile(0.5)(...)` for actual median, and label arithmetic averages as "mean", never "median".
4. **Never report a hazard ratio, odds ratio, risk ratio, or relative risk** that wasn't computed by an external tool. These require regression / model fitting that ClickHouse does not do.
5. **Never report median overall survival from `AVG(OS_MONTHS)` or even `quantile(0.5)(OS_MONTHS)`.** Median OS requires Kaplan-Meier estimation, which handles censoring (`OS_STATUS = 0:LIVING` means the event hasn't happened yet). Naive medians/means over `OS_MONTHS` ignore censoring and are systematically wrong. The correct handoff: return the raw `(OS_MONTHS, OS_STATUS)` pairs (or descriptive counts: N events, N censored, follow-up range) and tell the user to run KM in R (`survival::survfit`) or Python (`lifelines.KaplanMeierFitter`), or use cBioPortal's Survival comparison.

If asked to do any of (1)–(5), respond with the appropriate handoff template from the "Approved Response Templates" section below — do not produce the number.

Ambiguous-Term Clarification (before any analysis)
--------------------------------------------------
If the user's question uses an ambiguous term, ask before computing anything. Examples:

- **"aggressive"** → could mean shorter overall survival, higher metastasis rate, higher grade/stage, higher TMB, or specific molecular features. Ask which definit … (9984 more chars)

▶ list_studies
{
 "search": "neuroblastoma TARGET"
}

◀ result
{"result":[{"cancer_study_identifier":"nbl_target_2018_pub","name":"Pediatric Neuroblastoma (TARGET, 2018)","type_of_cancer_id":"nbl","sample_count":1089,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=nbl_target_2018_pub"},{"cancer_study_identifier":"nbl_target_gdc","name":"Neuroblastoma (TARGET GDC, 2025)","type_of_cancer_id":"nbl","sample_count":533,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=nbl_target_gdc"},{"cancer_study_identifier":"nbl_msk_2023","name":"Pediatric Neuroblastoma (MSK, Nat Genet 2023)","type_of_cancer_id":"nbl","sample_count":223,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=nbl_msk_2023"}]}

▶ read_guide
{
 "uri": "cbioportal://clinical-data-guide"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ result
# Clinical Data Query Guide

## Overview
Clinical data in cBioPortal is stored at both patient and sample levels. Understanding the distinction is crucial for accurate analysis.

## Data Organization

### Patient-Level vs Sample-Level Data
- **Patient-level**: Demographics, overall survival, disease stage (stored once per patient)
- **Sample-level**: Sample type, sequencing platform, purity (can have multiple per patient)

### Key Tables
- `clinical_patient`: Patient-level clinical attributes
- `clinical_sample`: Sample-level clinical attributes
- `clinical_data_derived`: Pre-joined view combining both levels
- `clinical_attribute_meta`: Metadata about available clinical attributes

## Recommended Approach: Use clinical_data_derived

The `clinical_data_derived` table is pre-joined and optimized for most queries:

```sql
-- Get clinical data for specific attributes
SELECT
    sample_unique_id,
    patient_unique_id,
    attribute_name,
    attribute_value
FROM clinical_data_derived
WHERE
    cancer_study_identifier = 'your_study_id'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'TMB_NONSYNONYMOUS');
```

## Clinical Attribute Discovery

### Use clinical_attribute_meta for Discovering Available Attributes
**Always start here** to see what clinical attributes are available for a specific study:

```sql
-- Discover available clinical attributes for a study
SELECT
    attr_id,
    description,
    patient_attribute,
    cancer_study_id
FROM clinical_attribute_meta
WHERE
    cancer_study_id = (
        SELECT cancer_study_id
        FROM cancer_study
        WHERE cancer_study_identifier = 'your_study_id'
    )
ORDER BY patient_attribute, attr_id;
```

**Key Fields:**
- **attr_id**: matches attr_id in clinical_sample/clinical_patient tables
- **description**: human-readable description of the attribute
- **patient_attribute**: true = patient attribute, false = sample attribute
- **cancer_study_id**: links to cancer_study table (filter by study)

## Attribute Semantics and Matching

### Case-Insensitive Matching for Attribute Values

Clinical values are free text across studies and may differ only by case. For example, a controlled-looking value such as germline mutation status may appear as `GERMLINE`, `Germline`, or another case variant.

When filtering `clinical_data_derived.attribute_value`, use case-insensitive matching unless you have already profiled the exact values in the target study:

```sql
-- Correct: case-insensitive clinical value filter
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'your_study_id'
  AND attribute_name = 'MUTATION_STATUS'
  AND upper(attribute_value) = 'GERMLINE';
```

Do not write `attribute_value = 'GERMLINE'` without first checking all distinct values for that attribute in the study.

### Query the Requested Attribute, Not a Proxy

Do not infer one clinical attribute from a related subtype or marker. Query the actual requested attribute when it … (17073 more chars)

◀ result
{"columns":[{"name":"internal_id","type":"Int32"},{"name":"sample_unique_id","type":"String","comment":"Globally unique sample ID: cancer_study_identifier + \"_\" + sample.stable_id. Empty for patient-level attributes. Use this for sample filtering and joins."},{"name":"patient_unique_id","type":"String","comment":"Globally unique patient ID: cancer_study_identifier + \"_\" + patient.stable_id. Present for both sample and patient-level attributes."},{"name":"attribute_name","type":"LowCardinality(String)","comment":"Clinical attribute name (e.g., SAMPLE_TYPE, CANCER_TYPE, AGE, OS_MONTHS). Use with attribute_value for filtering. AGE may be floored or capped for de-identification (e.g. all children recorded as 18, or everyone 89+ recorded as 89 or 90): before age statistics check for a pile-up at the min/max, and if present compute age from DAYS_TO_BIRTH (-days / 365.25)."},{"name":"attribute_value","type":"String","comment":"Value of the clinical attribute (String). For SAMPLE_TYPE: Primary, Metastasis, Local Recurrence, Unknown. Missing values are empty strings, so use toFloat64OrNull(attribute_value) for numeric comparisons — CAST fails on them."},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"type","type":"LowCardinality(String)","comment":"Data level: \"sample\" for sample-level attributes (e.g., SAMPLE_TYPE), \"patient\" for patient-level attributes (e.g., AGE, OS_MONTHS)."}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attr_id, description, patient_attribute FROM clinical_attribute_meta WHERE cancer_study_id = (SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier='nbl_target_2018_pub') ORDER BY attr_id"
}

◀ result
{"rows":[{"attr_id":"AGE","description":"Age at which a condition or disease was first diagnosed.","patient_attribute":1},{"attr_id":"AGE_IN_DAYS","description":"Age at which a condition or disease was first diagnosed.","patient_attribute":1},{"attr_id":"ANALYSIS_COHORT","description":"Analysis Cohort","patient_attribute":0},{"attr_id":"CANCER_TYPE","description":"Cancer Type","patient_attribute":0},{"attr_id":"CANCER_TYPE_DETAILED","description":"Cancer Type Detailed","patient_attribute":0},{"attr_id":"CNS_SITE_OF_RELAPSE","description":"CNS Site of Relapse","patient_attribute":0},{"attr_id":"DIAGNOSIS","description":"Diagnosis.","patient_attribute":1},{"attr_id":"EFSCENS","description":"Event Free Survival Censored","patient_attribute":1},{"attr_id":"EFS_TIME","description":"EFS time.","patient_attribute":1},{"attr_id":"ETHNICITY","description":"The text for reporting information about ethnicity.","patient_attribute":1},{"attr_id":"FIRST_EVENT","description":"First Event","patient_attribute":1},{"attr_id":"FRACTION_GENOME_ALTERED","description":"Fraction Genome Altered","patient_attribute":0},{"attr_id":"GRADE","description":"Numeric value to express the degree of abnormality of cancer cells, a measure of differentiation and aggressiveness.","patient_attribute":0},{"attr_id":"ICDO","description":"ICDO.","patient_attribute":1},{"attr_id":"ICDO_SNOMED_DESCRIPTION","description":"ICDO snomed description.","patient_attribute":1},{"attr_id":"INSS_STAGE","description":"Staging according to the International Neuroblastoma Staging System","patient_attribute":1},{"attr_id":"LAST_FOLLOWUP_YEAR","description":"Year of Last Followup","patient_attribute":1},{"attr_id":"MKI","description":"MKI.","patient_attribute":0},{"attr_id":"MUTATION_COUNT","description":"Mutation Count","patient_attribute":0},{"attr_id":"MYCN","description":"MYCN.","patient_attribute":0},{"attr_id":"ONCOTREE_CODE","description":"Oncotree Code","patient_attribute":0},{"attr_id":"OS_DAYS","description":"Overall patient survival days","patient_attribute":1},{"attr_id":"OS_MONTHS","description":"Overall survival in months since initial diagonosis.","patient_attribute":1},{"attr_id":"OS_STATUS","description":"Overall patient survival status.","patient_attribute":1},{"attr_id":"PERCENTAGE_NECROSIS","description":"Percent necrosis.","patient_attribute":1},{"attr_id":"PERCENTAGE_TUMOR_PURITY","description":"Percent tumor purity.","patient_attribute":1},{"attr_id":"PERCENT_TUMOR_CELLS_RELAPSE","description":"Numeric value to represent the percentage of malignant cell tumor nuclei content in a tumor sample or specimen obtained at relapse stage","patient_attribute":1},{"attr_id":"PERCENT_TUMOR_VS_STROMA","description":"Numeric representation of the ratio of tumor to stroma.","patient_attribute":1},{"attr_id":"PLOIDY","description":"Ploidy.","patient_attribute":0},{"attr_id":"PROTOCOL","description":"Protocol.","patient_attribute":1},{"attr_id":"RACE","description":"The text for reporting informat … (1406 more chars)

▶ clickhouse_run_select_query
{
 "query": "WITH p AS (
 SELECT patient_unique_id,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='AGE') AS age,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='AGE_IN_DAYS') AS age_days,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='OS_MONTHS') AS os_m,
  maxIf(attribute_value, attribute_name='OS_STATUS') AS os_s
 FROM clinical_data_derived
 WHERE cancer_study_identifier='nbl_target_2018_pub' AND attribute_name IN ('AGE','AGE_IN_DAYS','OS_MONTHS','OS_STATUS')
 GROUP BY patient_unique_id)
SELECT count() n, min(age) mn, max(age) mx, countIf(age=min(age)) x, min(age_days), max(age_days), 
 countIf(age_days/365.25>4) gt4_days, countIf(age>4) gt4_age, countIf(age_days/365.25=4) eq4 FROM p"
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 184\n Code: 184. DB::Exception: Aggregate function min(age) is found inside another aggregate function in query. (ILLEGAL_AGGREGATION) (version 26.4.1.2509 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "WITH p AS (
 SELECT patient_unique_id,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='AGE') AS age,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='AGE_IN_DAYS') AS age_days,
  maxIf(toFloat64OrNull(attribute_value), attribute_name='OS_MONTHS') AS os_m,
  maxIf(attribute_value, attribute_name='OS_STATUS') AS os_s
 FROM clinical_data_derived
 WHERE cancer_study_identifier='nbl_target_2018_pub' AND attribute_name IN ('AGE','AGE_IN_DAYS','OS_MONTHS','OS_STATUS')
 GROUP BY patient_unique_id)
SELECT if(age_days/365.25>4,'>4y','<=4y') g, count() n, min(age) mn, max(age) mx, min(age_days) mind, max(age_days) maxd,
 countIf(startsWith(os_s,'1')) ev, countIf(startsWith(os_s,'0')) cens, min(os_m) mino, max(os_m) maxo,
 countIf(age>4) gt4_age, countIf(age_days/365.25=4) eq4
FROM p WHERE os_m IS NOT NULL AND os_s!='' GROUP BY g"
}

◀ result
{"rows":[{"g":">4y","n":250,"mn":5,"mx":21,"mind":1462,"maxd":7612,"ev":121,"cens":129,"mino":1,"maxo":183,"gt4_age":250,"eq4":0},{"g":"<=4y","n":822,"mn":0,"mx":4,"mind":0,"maxd":1460,"ev":276,"cens":546,"mino":1,"maxo":183,"gt4_age":0,"eq4":0}]}

▶ get_studyviewfilter_options
{
 "studyId": "nbl_target_2018_pub",
 "attributeIds": [
  "AGE_IN_DAYS",
  "AGE"
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"nbl_target_2018_pub","attributes":[{"attributeId":"AGE","displayName":"Diagnosis Age","description":"Age at which a condition or disease was first diagnosed.","datatype":"NUMBER","continuous":true,"bins":[{"end":2,"count":435},{"start":3,"end":3,"count":222},{"start":4,"end":4,"count":168},{"start":4,"count":251}]},{"attributeId":"AGE_IN_DAYS","displayName":"Diagnosis Age (days)","description":"Age at which a condition or disease was first diagnosed.","datatype":"NUMBER","continuous":true,"bins":[{"end":469.5,"count":269},{"start":469.5,"end":923.5,"count":269},{"start":923.5,"end":1424,"count":269},{"start":1424,"count":269}]}]}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "nbl_target_2018_pub"
 ],
 "groups": [
  {
   "name": "Older than 4 years",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "AGE_IN_DAYS",
      "values": [
       {
        "start": 1461
       }
      ]
     }
    ]
   }
  },
  {
   "name": "4 years or younger",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "AGE_IN_DAYS",
      "values": [
       {
        "end": 1461
       }
      ]
     }
    ]
   }
  }
 ],
 "tab": "survival"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6abb41f6c2115c492d8855c6","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6abb41f6c2115c492d8855c6","data":{"description":"Group comparison (2 custom groups)","studies":["nbl_target_2018_pub"],"totalGroups":2,"groups":[{"name":"Older than 4 years","sampleCount":253},{"name":"4 years or younger","sampleCount":836}],"studyViewUrl":"https://www.cbioportal.org/study?id=nbl_target_2018_pub","groupUrls":[{"groupName":"Older than 4 years","url":"https://www.cbioportal.org/study?id=nbl_target_2018_pub#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22AGE_IN_DAYS%22%2C%22values%22%3A%5B%7B%22start%22%3A1461%7D%5D%7D%5D%7D"},{"groupName":"4 years or younger","url":"https://www.cbioportal.org/study?id=nbl_target_2018_pub#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22AGE_IN_DAYS%22%2C%22values%22%3A%5B%7B%22end%22%3A1461%7D%5D%7D%5D%7D"}],"pageDescription":"Kaplan-Meier survival analysis comparing Older than 4 years (253) vs 4 years or younger (836)."}}

═ answer (success)
I can't calculate the statistical significance here, because a log-rank p-value isn't something my database can compute. I can give you the per-group survival counts and a link to the comparison, where cBioPortal shows the Kaplan-Meier curves and the log-rank p-value.

**Cohort:** Pediatric Neuroblastoma (TARGET, 2018), `nbl_target_2018_pub`. I counted patients who have overall survival data. I split them at 4 years of age at diagnosis, using the age recorded in days.

| Group | Patients | Deaths | Censored (alive at last follow-up) | Follow-up range |
|---|---|---|---|---|
| Older than 4 years | 250 | 121 (48%) | 129 | 1–183 months |
| 4 years or younger | 822 | 276 (34%) | 546 | 1–183 months |

- A larger share of the older group died during follow-up (48% versus 34%). That is a raw proportion and doesn't account for follow-up time.
- The Kaplan-Meier median survival and the hazard ratio need proper survival modeling, so I haven't reported them. Fewer than half of the patients in each group had a death, so the Kaplan-Meier median is likely not reached in either group.
- The right test for this comparison is a Kaplan-Meier analysis with a log-rank test. You can also run it yourself in R with `survival::survfit`/`survdiff` or in Python with `lifelines`.

[Open the Kaplan-Meier survival comparison (older than 4 vs. 4 or younger)](https://www.cbioportal.org/comparison/survival?comparisonId=6abb41f6c2115c492d8855c6)

Kaplan-Meier survival analysis comparing Older than 4 years (253) vs 4 years or younger (836). The sample counts in the link (253 and 836) differ slightly from the patient counts above because some patients have more than one sample.

This study is one of several TARGET neuroblastoma cohorts. I chose the "Pediatric Neuroblastoma (TARGET, 2018)" study because it matches the name you gave. The separate "Neuroblastoma (TARGET GDC, 2025)" study is a different cohort, and I can run the same comparison there if you want.