R codes for longitudinal healthspan data analysis

R codes for assessing longitudinal health span data ( frailty Index). These codes are for
0. Preparation steps for calculating Total Scores (mean scores) and converting measurement dates to measurement IDs
1. Repeated Measures Correlation: To assess the correlation between life-expectancy (Remaining Lifespan) and total score ( considering the Intra-dependency of the data set).
2. Mixed Model (mouse as Random effect, treatment and time as fixed effects): Now, to assess the statistical difference between the slopes of regression lines, we applied a mixed model to the combined data set of AKG and control and compared the slopes ( considering the Intra-dependency of the data set collected throughout the study).
3. Mann-Kendall Trend Test : To assess the possible monotonic trend for each frailty phenotype with aging (time).

本R代码用于评估纵向健康跨度数据（衰弱指数，frailty Index），具体包含以下内容：
0. 总得分（平均得分）计算与测量日期转换为测量ID的预处理步骤
1. 重复测量相关性分析（Repeated Measures Correlation）：用于评估预期寿命（剩余寿命，Remaining Lifespan）与总得分之间的相关性，分析过程考虑数据集的内部依赖性
2. 混合效应模型（Mixed Model）：以小鼠作为随机效应，处理方式与时间作为固定效应。为评估回归直线斜率间的统计学差异，本研究将该模型应用于AKG与对照组的合并数据集，并对各组斜率进行比较，分析过程考虑全研究周期内采集数据集的内部依赖性
3. 曼-肯德尔趋势检验（Mann-Kendall Trend Test）：用于评估每种衰弱表型随衰老（时间推移）的潜在单调趋势