Forecasting age-specific breast cancer mortality using functional data models
Forecasting age-specific breast cancer mortality using functional data models
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DOI：
10.1002/sim.2306
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发表时间：
2007-01-30
影响因子：
2
通讯作者：
Gertig, Dorota M.
中科院分区：
文献类型：
--
作者：
Erbas, Bircan;Hyndman, Rob J.;Gertig, Dorota M.
对未来特定年龄的发病率和死亡率的准确估计对于将资源分配给乳腺癌控制方案和评估筛查方案至关重要。这项研究的目的是应用功能数据分析技术来建模特定年龄的乳腺癌死亡率时间趋势，并使用状态空间方法预测整个特定年龄的死亡率函数。我们使用澳大利亚从1921年到2001年的5个年龄组(45岁到85岁以上)未调整的乳腺癌死亡率。我们使用函数数据分析技术，其中死亡率和发病率被建模为随年龄的曲线，作为随时间变化的函数协变量。使用非参数平滑方法对数据进行平滑，然后进行分解(使用主成分分析)以估计代表函数曲线的基函数。拟合系数的周期效应被预测，然后乘以基函数，得到具有预测区间的预测死亡率曲线。为了预测，我们采用了状态空间方法和自动建模框架来选择指数平滑方法。总体而言，从1960年到90年代末，澳大利亚的乳腺癌死亡率保持相对稳定，但在过去几年里有所下降。一组四个基函数将平均积分平方预测误差降至最低，并解释了平均死亡率曲线周围99.3%的变化。20年的预测显示，这一数字将继续下降，但速度会放缓，并在2010年后趋于稳定。预测显示，所有年龄段的女性都出现了下降，其中老年女性的下降幅度最大。建议的方法有可能纳入重要的协变量，如激素替代疗法和干预措施，以代表乳房X光检查。这对于评估筛查对乳腺癌死亡率和发病率的影响特别有用。版权所有(C)2005 John Wiley&Sons，Ltd.
Accurate estimates of future age-specific incidence and mortality are critical for allocation of resources to breast cancer control programmes and evaluation of screening programmes. The purpose of this study is to apply functional data analysis techniques to model age-specific breast cancer mortality time trends, and forecast entire age-specific mortality functions using a state-space approach. We use annual unadjusted breast cancer mortality rates in Australia, from 1921 to 2001 in 5 year age groups (45 to 85+).We use functional data analysis techniques where mortality and incidence are modelled as curves with age as a functional covariate varying by time. Data are smoothed using non-parametric smoothing methods then decomposed (using principal components analysis) to estimate basis functions that represent the functional curve. Period effects from the fitted coefficients are forecast then multiplied by the basis functions, resulting in a forecast mortality curve with prediction intervals. To forecast, we adopt a state-space approach and an automatic modelling framework for selecting among exponential smoothing methods.Overall, breast cancer mortality rates in Australia remained relatively stable from 1960 to the late 1990s, but have declined over the last few years. A set of four basis functions minimized the mean integrated squared forecasting error and account for 99.3 per cent of variation around the mean mortality curve. Twenty year forecasts suggest a continuing decline, but at a slower rate, and stabilizing beyond 2010. Forecasts show a decline in all age groups with the greatest decline in older women.The proposed methods have the potential to incorporate important covariates such as hormone replacement therapy and interventions to represent mammographic screening. This would be particularly useful for evaluating the impact of screening on mortality and incidence from breast cancer. Copyright (c) 2005 John Wiley & Sons, Ltd.