class AutoClinicalTrial:
    def __init__(self, dataset):
        self.dataset = dataset
        self.model = self._train_ml_model()

    def _train_ml_model(self):
        # 使用随机森林进行患者分层
        from sklearn.ensemble import RandomForestClassifier
        model = RandomForestClassifier(n_estimators=100)
        model.fit(self.dataset.features, self.dataset.labels)
        return model

    def optimize_design(self):
        # 基于历史数据优化样本量
        sample_size = self._calculate_optimal_size()
        return {"sample_size": sample_size, "strata": self.model.predict_proba}

    def _calculate_optimal_size(self):
        # 结合功效分析与机器学习预测
        power = 0.8
        alpha = 0.05
        # 简化计算公式
        return int((1.96**2 * 0.25) / (power * alpha**2))

-- 从真实世界数据库中构建虚拟对照组
SELECT patient_id, treatment_date, outcome 
FROM real_world_data
WHERE 
    diagnosis_code IN ('J45.90', 'J45.509') AND 
    age BETWEEN 40 AND 65 AND 
    EXTRACT(YEAR FROM treatment_date) BETWEEN 2020 AND 2023
ORDER BY RAND()
LIMIT 1000;

def detect_bias(data):
    # 检测时间趋势偏倚
    time_trend = data.groupby('year')['outcome'].mean().diff()
    if abs(time_trend.max()) > 0.05:
        return "Potential time trend bias detected"
    return "No significant bias"