Q (haiku): 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"
}

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

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

◀ 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)

◀ 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
{"result":[{"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_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_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"}]}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_attribute_meta"
}

◀ 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)."}]}

◀ result
{"columns":[{"name":"attr_id","type":"String"},{"name":"display_name","type":"String"},{"name":"description","type":"String"},{"name":"datatype","type":"String"},{"name":"patient_attribute","type":"Int32"},{"name":"priority","type":"String"},{"name":"cancer_study_id","type":"Int64"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT attribute_name
FROM clinical_data_derived
WHERE cancer_study_identifier = 'nbl_target_2018_pub'
ORDER BY attribute_name
",
 "max_rows": 100
}

◀ result
{"rows":[{"attribute_name":"AGE"},{"attribute_name":"AGE_IN_DAYS"},{"attribute_name":"ANALYSIS_COHORT"},{"attribute_name":"CANCER_TYPE"},{"attribute_name":"CANCER_TYPE_DETAILED"},{"attribute_name":"CNS_SITE_OF_RELAPSE"},{"attribute_name":"DIAGNOSIS"},{"attribute_name":"EFSCENS"},{"attribute_name":"EFS_TIME"},{"attribute_name":"ETHNICITY"},{"attribute_name":"FIRST_EVENT"},{"attribute_name":"FRACTION_GENOME_ALTERED"},{"attribute_name":"GRADE"},{"attribute_name":"ICDO"},{"attribute_name":"ICDO_SNOMED_DESCRIPTION"},{"attribute_name":"INSS_STAGE"},{"attribute_name":"LAST_FOLLOWUP_YEAR"},{"attribute_name":"MKI"},{"attribute_name":"MUTATION_COUNT"},{"attribute_name":"MYCN"},{"attribute_name":"ONCOTREE_CODE"},{"attribute_name":"OS_DAYS"},{"attribute_name":"OS_MONTHS"},{"attribute_name":"OS_STATUS"},{"attribute_name":"PERCENTAGE_NECROSIS"},{"attribute_name":"PERCENTAGE_TUMOR_PURITY"},{"attribute_name":"PERCENT_TUMOR_CELLS_RELAPSE"},{"attribute_name":"PERCENT_TUMOR_VS_STROMA"},{"attribute_name":"PLOIDY"},{"attribute_name":"PROTOCOL"},{"attribute_name":"RACE"},{"attribute_name":"RELAPSE_PERCENT_NECROSIS"},{"attribute_name":"RELAPSE_PERCENT_STROMA"},{"attribute_name":"RISK_GROUP"},{"attribute_name":"SAMPLE_COUNT"},{"attribute_name":"SCENS"},{"attribute_name":"SEX"},{"attribute_name":"SNOMED"},{"attribute_name":"SOMATIC_STATUS"},{"attribute_name":"STIME"},{"attribute_name":"TMB_NONSYNONYMOUS"},{"attribute_name":"TUMOR_PLOIDY"},{"attribute_name":"TUMOR_SAMPLE_HISTOLOGY"},{"attribute_name":"TUMOR_TISSUE_SITE"},{"attribute_name":"YEAR_OF_DIAGNOSIS"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    toFloat64OrNull(MIN(attribute_value)) AS min_age,
    toFloat64OrNull(MAX(attribute_value)) AS max_age,
    countIf(toFloat64OrNull(attribute_value) = 18) AS at_18,
    count() AS total_age_records
FROM clinical_data_derived
WHERE cancer_study_identifier = 'nbl_target_2018_pub' AND attribute_name = 'AGE'
",
 "max_rows": 10
}

◀ result
{"rows":[{"min_age":0,"max_age":9,"at_18":1,"total_age_records":1076}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH patient_age AS (
    SELECT DISTINCT
        patient_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'AGE') AS age_at_dx
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'nbl_target_2018_pub' AND attribute_name = 'AGE'
    GROUP BY patient_unique_id
),
patient_survival AS (
    SELECT DISTINCT
        patient_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'OS_MONTHS') AS os_months,
        maxIf(attribute_value, attribute_name = 'OS_STATUS') AS os_status
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'nbl_target_2018_pub' AND attribute_name IN ('OS_MONTHS', 'OS_STATUS')
    GROUP BY patient_unique_id
)
SELECT
    CASE WHEN pa.age_at_dx <= 4 THEN '≤4 years' ELSE '>4 years' END AS age_group,
    count(DISTINCT ps.patient_unique_id) AS n_patients,
    countIf(startsWith(ps.os_status, '1')) AS n_events_deceased,
    countIf(startsWith(ps.os_status, '0')) AS n_censored_living,
    round(min(ps.os_months), 2) AS min_followup_months,
    round(max(ps.os_months), 2) AS max_followup_months,
    round(avg(ps.os_months), 2) AS mean_os_months
FROM patient_survival ps
LEFT JOIN patient_age pa ON ps.patient_unique_id = pa.patient_unique_id
WHERE ps.os_months IS NOT NULL AND ps.os_status != '' AND pa.age_at_dx IS NOT NULL
GROUP BY age_group
ORDER BY age_group
",
 "max_rows": 10
}

◀ result
{"rows":[{"age_group":">4 years","n_patients":250,"n_events_deceased":121,"n_censored_living":129,"min_followup_months":1,"max_followup_months":183,"mean_os_months":55.88},{"age_group":"≤4 years","n_patients":822,"n_events_deceased":276,"n_censored_living":546,"min_followup_months":1,"max_followup_months":183,"mean_os_months":58.21}]}

▶ resolve_and_route
{
 "studyIds": [
  "nbl_target_2018_pub"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"nbl_target_2018_pub","name":"Pediatric Neuroblastoma (TARGET, 2018)","sampleCount":1089,"studyViewUrl":"https://www.cbioportal.org/study?id=nbl_target_2018_pub","metadata":{"clinicalAttributeIds":["AGE","AGE_IN_DAYS","ANALYSIS_COHORT","CANCER_TYPE","CANCER_TYPE_DETAILED","CNS_SITE_OF_RELAPSE","DIAGNOSIS","EFSCENS","EFS_TIME","ETHNICITY","FIRST_EVENT","FRACTION_GENOME_ALTERED","GRADE","ICDO","ICDO_SNOMED_DESCRIPTION","INSS_STAGE","LAST_FOLLOWUP_YEAR","MKI","MUTATION_COUNT","MYCN","ONCOTREE_CODE","OS_DAYS","OS_MONTHS","OS_STATUS","PERCENTAGE_NECROSIS","PERCENTAGE_TUMOR_PURITY","PERCENT_TUMOR_CELLS_RELAPSE","PERCENT_TUMOR_VS_STROMA","PLOIDY","PROTOCOL","RACE","RELAPSE_PERCENT_NECROSIS","RELAPSE_PERCENT_STROMA","RISK_GROUP","SAMPLE_COUNT","SCENS","SEX","SNOMED","SOMATIC_STATUS","STIME","TMB_NONSYNONYMOUS","TUMOR_PLOIDY","TUMOR_SAMPLE_HISTOLOGY","TUMOR_TISSUE_SITE","YEAR_OF_DIAGNOSIS"],"molecularProfileIds":["nbl_target_2018_pub_gistic","nbl_target_2018_pub_mrna","nbl_target_2018_pub_mrna_median_Zscores","nbl_target_2018_pub_mrna_median_all_sample_Zscores","nbl_target_2018_pub_mutations","nbl_target_2018_pub_rna_seq_mrna","nbl_target_2018_pub_rna_seq_mrna_median_Zscores","nbl_target_2018_pub_rna_seq_mrna_median_all_sample_Zscores"],"heatmapProfileIds":["nbl_target_2018_pub_mrna_median_Zscores","nbl_target_2018_pub_mrna_median_all_sample_Zscores","nbl_target_2018_pub_rna_seq_mrna_median_Zscores","nbl_target_2018_pub_rna_seq_mrna_median_all_sample_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna"]}}]}}

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

▶ navigate_to_group_comparison
{
 "studyIds": [
  "nbl_target_2018_pub"
 ],
 "groups": [
  {
   "name": "Age ≤4 years",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "AGE",
      "values": [
       {
        "end": 2
       },
       {
        "start": 3,
        "end": 3
       },
       {
        "start": 4,
        "end": 4
       }
      ]
     }
    ]
   }
  },
  {
   "name": "Age >4 years",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "AGE",
      "values": [
       {
        "start": 5
       }
      ]
     }
    ]
   }
  }
 ],
 "tab": "survival"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6ab5d0bdc2115c492d884e1f","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6ab5d0bdc2115c492d884e1f","data":{"description":"Group comparison (2 custom groups)","studies":["nbl_target_2018_pub"],"totalGroups":2,"groups":[{"name":"Age ≤4 years","sampleCount":836},{"name":"Age >4 years","sampleCount":126}],"studyViewUrl":"https://www.cbioportal.org/study?id=nbl_target_2018_pub","groupUrls":[{"groupName":"Age ≤4 years","url":"https://www.cbioportal.org/study?id=nbl_target_2018_pub#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22AGE%22%2C%22values%22%3A%5B%7B%22end%22%3A2%7D%2C%7B%22start%22%3A3%2C%22end%22%3A3%7D%2C%7B%22start%22%3A4%2C%22end%22%3A4%7D%5D%7D%5D%7D"},{"groupName":"Age >4 years","url":"https://www.cbioportal.org/study?id=nbl_target_2018_pub#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22AGE%22%2C%22values%22%3A%5B%7B%22start%22%3A5%7D%5D%7D%5D%7D"}],"pageDescription":"Kaplan-Meier survival analysis comparing Age ≤4 years (836) vs Age >4 years (126)."}}

═ answer (success)
---

## Survival Comparison: Pediatric Neuroblastoma (TARGET, 2018)

**Study:** [Pediatric Neuroblastoma (TARGET, 2018)](https://www.cbioportal.org/study?id=nbl_target_2018_pub) (1,089 samples)

### Study Population Summary

| Age Group | N Patients | OS Events (Deaths) | Censored (Living) | Follow-up Range |
|-----------|------------|-------------------|-------------------|-----------------|
| **≤4 years** | 822 | 276 | 546 | 1–183 months |
| **>4 years** | 250 | 121 | 129 | 1–183 months |

### Key Findings

**Important:** I cannot compute a p-value or median overall survival from the database — survival analysis requires **Kaplan-Meier estimation**, which accounts for censoring (patients still living have not yet had the event). The counts above show that:

- **Younger patients (≤4 years):** 33.6% experienced a death event during follow-up
- **Older patients (>4 years):** 48.4% experienced a death event during follow-up

The older cohort has a **higher event rate**, suggesting worse survival, but the statistical significance requires a **log-rank test**, which cBioPortal computes.

### View Kaplan-Meier Curves and Log-Rank Test

**[Open Group Comparison: Survival Analysis →](https://www.cbioportal.org/comparison/survival?comparisonId=6ab5d0bdc2115c492d884e1f)**

This page displays:
- **Kaplan-Meier curves** for each age group (median OS, if reached)
- **Log-rank test p-value** (statistical significance of the survival difference)
- Confidence intervals and risk tables

The cBioPortal Group Comparison page is the appropriate tool for this analysis and will give you the exact median survival times and p-value.