请给出儿童过敏纵向数据处理的具体过程及代码

儿童过敏的纵向数据处理过程如下：

1. 数据清洗：

import pandas as pd

# 读取数据
data = pd.read_csv("allergy_data.csv")

# 筛选需要的列
data = data[["id", "time", "age", "allergic_status"]]

# 去除缺失值
data.dropna(inplace=True)

# 处理异常值
data = data[data["age"] >= 0]

2. 数据整合：

from functools import reduce

# 将不同时间点的数据整合成一张表格
dfs = []
for t in range(1, 7):
    df = data[data["time"] == t]
    df = df.rename(columns={"allergic_status": f"allergic_status_{t}"})
    df = df.drop(columns=["time"])
    dfs.append(df)

merged_data = reduce(lambda left, right: pd.merge(left, right, on=["id", "age"]), dfs)

3. 数据分析：

import scipy.stats as stats

# 描述性统计
merged_data.describe()

# 方差分析
stats.f_oneway(merged_data["allergic_status_1"], merged_data["allergic_status_2"], merged_data["allergic_status_3"], merged_data["allergic_status_4"], merged_data["allergic_status_5"], merged_data["allergic_status_6"])

# 回归分析
import statsmodels.api as sm

X = merged_data[["age", "allergic_status_1", "allergic_status_2", "allergic_status_3", "allergic_status_4", "allergic_status_5"]]
Y = merged_data["allergic_status_6"]
X = sm.add_constant(X)
model = sm.OLS(Y, X).fit()
model.summary()

4. 模型建立：

from lifelines import CoxPHFitter

# 生存分析
cph = CoxPHFitter()
cph.fit(merged_data, duration_col="time", event_col="allergic_status_6")
cph.print_summary()

# 混合效应模型
import statsmodels.formula.api as smf

mixed_model = smf.mixedlm("allergic_status_6 ~ age + allergic_status_1 + allergic_status_2 + allergic_status_3 + allergic_status_4 + allergic_status_5", merged_data, groups=merged_data["id"])
mixed_model_fit = mixed_model.fit()
mixed_model_fit.summary()

5. 结果解释：

根据分析结果得出结论并提出建议，如加强儿童的预防措施、提高家长的健康意识等。