審數析

評數析線之正、韌、可復。

用時

• 審同仁析示前乃用
• 驗 ML 線產部署前乃用
• 審析報為規或業決乃用
• 估析支其述論乎乃用
• 於規境行二析者審乃用

入

• 必要：析碼（文、本、線定）
• 必要：析出（果、表、圖、模指）
• 可選：原數或數典
• 可選：析計或程（先註或臨）
• 可選：目眾與決境

法

第一步：察數質

評析前先審入數：

## Data Quality Assessment

### Completeness
- [ ] Missing data quantified (% by column and by row)
- [ ] Missing data mechanism considered (MCAR, MAR, MNAR)
- [ ] Imputation method appropriate (if used) or complete-case analysis justified

### Consistency
- [ ] Data types match expectations (dates are dates, numbers are numbers)
- [ ] Value ranges are plausible (no negative ages, future dates in historical data)
- [ ] Categorical variables have expected levels (no misspellings, consistent coding)
- [ ] Units are consistent across records

### Uniqueness
- [ ] Duplicate records identified and handled
- [ ] Primary keys are unique where expected
- [ ] Join operations produce expected row counts (no fan-out or drop)

### Timeliness
- [ ] Data vintage appropriate for the analysis question
- [ ] Temporal coverage matches the study period
- [ ] No look-ahead bias in time-series data

### Provenance
- [ ] Data source documented
- [ ] Extraction date/version recorded
- [ ] Any transformations between source and analysis input documented

得： 數質患已書附其於果之影 敗則： 若數不可審，自碼察其質（何察與化已施）

第二步：察其假

各統法或所用之模：

## Assumption Check Results
| Analysis Step | Method | Assumption | Checked? | Result |
|---------------|--------|------------|----------|--------|
| Primary model | Linear regression | Normality of residuals | Yes | Q-Q plot shows mild deviation — acceptable for n>100 |
| Primary model | Linear regression | Homoscedasticity | No | Not checked — recommend adding Breusch-Pagan test |

得： 各統法之諸假明察或承 敗則： 若假違，察著者是否處之（韌法、化、敏析）

第三步：察數漏

數漏發於訓集外之信影模時，致過樂之性：

常漏形：

• 目漏：直編目變之特（如「treat_outcome」用以預「treat_success」）
• 時漏：用未來信預過去（自預測時不可得之數算之特）
• 訓試污：分前以全數合之預處（縮、補、特擇）
• 群漏：相關察（同患、同器）分於訓試集
• 特工漏：聚算於全數而非於訓折內

## Leakage Assessment
| Check | Status | Evidence |
|-------|--------|----------|
| Target leakage | Clear | No features derived from target |
| Temporal leakage | CONCERN | Feature X uses 30-day forward average |
| Train-test contamination | Clear | StandardScaler fit on train only |
| Group leakage | CONCERN | Patient IDs not used for stratified split |

得： 諸常漏形已察附 clear/concern 之態 敗則： 若得漏，估其影由再行而無漏特（若可），或標待析者察

第四步：驗模性

為預模：

• 為患宜之指（非獨準——慮精、召、F1、AUC、RMSE、MAE）
• 交驗或留之策已述且宜
• 訓對試/驗集之性已較（過擬之察）
• 基線較已供（樸模、隨機、前法）
• 性指之信區或標誤
• 性於相關子群評（公、邊例）

為推/釋模：

• 模合統已報（R²、AIC、BIC、偏差）
• 系釋正（向、量、義）
• 多共線已察（VIF < 5–10）
• 影察已識（Cook 距、leverage）
• 若試多規格，模較已行

得： 模驗合用境（預對推） 敗則： 若試集之性疑近訓性，標可漏

第五步：察可復

## Reproducibility Checklist
| Item | Status | Notes |
|------|--------|-------|
| Code runs without errors | [Yes/No] | Tested on [environment description] |
| Random seeds set | [Yes/No] | Line [N] in [file] |
| Dependencies documented | [Yes/No] | requirements.txt / renv.lock present |
| Data loading reproducible | [Yes/No] | Path is [relative/absolute/URL] |
| Results match reported values | [Yes/No] | Verified: Table 1 ✓, Figure 2 ✗ (minor discrepancy) |
| Environment documented | [Yes/No] | Python 3.11 / R 4.5.0 specified |

得： 可復由再行析（或自碼察若數不可得）已驗 敗則： 若果不精復，定差於浮點容內或示患

第六步：書其審

## Data Analysis Review

### Overall Assessment
[1-2 sentences: Is the analysis sound? Does it support the conclusions?]

### Data Quality
[Summary of data quality findings, impact on results]

### Methodological Concerns
1. **[Title]**: [Description, location in code/report, suggestion]
2. ...

### Strengths
1. [What was done well]
2. ...

### Reproducibility
[Tier assessment: Gold/Silver/Bronze/Opaque with justification]

### Recommendations
- [ ] [Specific action items for the analyst]

得： 審供可行之反附碼所/報段之具引 敗則： 若時限，先處數質與漏察而後格患

驗

• 數質已察過全、恆、唯、時、源
• 各所用法之統假已察
• 數漏已系察
• 模性以宜指與基線驗
• 可復已評（碼行、果合）
• 反具體，引碼行或報段
• 調建設且協

陷

• 獨審其碼：析計與結論等於施而要
• 忽數質：劣數上之繁模生信誤之答
• 以繁假正：95% 準之隨森或有數漏；簡 t 試或為正徑
• 不行其碼：若可，行碼以驗可復。讀碼不足
• 見樹失林：勿陷碼格而失基析誤

參

• review-research — 廣研法與稿審
• validate-statistical-output — 雙程驗法
• generate-statistical-tables — 公示備之統表
• review-software-architecture — 碼構與設審