Models for survival and longitudinal data in cancer research.

機(jī)譯：癌癥研究中的生存和縱向數(shù)據(jù)模型。

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In the field of survival analysis, we often encounter the situation where a fraction of the study subjects will never experience an event. Cure models have been formulated to address this issue. We developed a family of cure models, indexed by a Box-Cox type transformation parameter, such that different formulations of cure models can be obtained by varying the index parameter. A profile likelihood approach was used for parameter estimation. Simulation studies were conducted to show unbiasedness. This model was applied to bone marrow transplant data and tonsil cancer data.;Along with survival information, medical studies also often collect longitudinal biomarkers. Joint models have been proposed to analyze these data simultaneously. We developed a non-parametric joint model of longitudinal biomarker and survival data where the longitudinal trajectories are modeled based on penalized B-splines and linked with the risk of failure by the Cox proportional hazard model. This model can accommodate nonlinearity in the longitudinal trajectories with a large degree of flexibility. A Bayesian approach was used for parameter estimation, and the Metropolis-Hastings algorithm was implemented to construct the MCMC chains. This model was applied to prostate cancer data, and a validation set was fit to evaluate the model performance.;Furthermore, we evaluated our joint model in terms of its prognostic power by focusing on the predicted conditional survival probabilities. We proposed absolute distance based measures to assess the predictive accuracy. We carried out simulation studies to evaluate the predictive accuracy of our joint model by comparing it with three alternative approaches. The simulation results showed that our joint model yielded consistently lower average prediction errors, and hence out-performed the other three approaches in terms of its prognostic power.

機(jī)譯：在生存分析領(lǐng)域，我們經(jīng)常會(huì)遇到這樣的情況：一小部分研究對(duì)象永遠(yuǎn)不會(huì)經(jīng)歷任何事件。已經(jīng)制定了固化模型來(lái)解決此問(wèn)題。我們開(kāi)發(fā)了一系列的固化模型，它們通過(guò)Box-Cox類型轉(zhuǎn)換參數(shù)進(jìn)行索引，因此可以通過(guò)更改索引參數(shù)來(lái)獲得不同的固化模型。輪廓似然法用于參數(shù)估計(jì)。進(jìn)行仿真研究以顯示無(wú)偏見(jiàn)。該模型被應(yīng)用于骨髓移植數(shù)據(jù)和扁桃體癌數(shù)據(jù)。除了生存信息，醫(yī)學(xué)研究還經(jīng)常收集縱向生物標(biāo)志物。已經(jīng)提出了聯(lián)合模型來(lái)同時(shí)分析這些數(shù)據(jù)。我們開(kāi)發(fā)了縱向生物標(biāo)志物和生存數(shù)據(jù)的非參數(shù)聯(lián)合模型，其中基于懲罰性B樣條曲線對(duì)縱向軌跡進(jìn)行建模，并通過(guò)Cox比例風(fēng)險(xiǎn)模型將其與失敗風(fēng)險(xiǎn)聯(lián)系起來(lái)。該模型可以高度靈活地適應(yīng)縱向軌跡中的非線性。使用貝葉斯方法進(jìn)行參數(shù)估計(jì)，并采用Metropolis-Hastings算法構(gòu)造MCMC鏈。該模型已應(yīng)用于前列腺癌數(shù)據(jù)，并且驗(yàn)證集適合評(píng)估該模型的性能。此外，我們通過(guò)關(guān)注預(yù)測(cè)的條件生存概率，根據(jù)其預(yù)后能力評(píng)估了聯(lián)合模型。我們提出了基于絕對(duì)距離的措施來(lái)評(píng)估預(yù)測(cè)準(zhǔn)確性。我們進(jìn)行了仿真研究，通過(guò)與三種替代方法進(jìn)行比較來(lái)評(píng)估我們的聯(lián)合模型的預(yù)測(cè)準(zhǔn)確性。仿真結(jié)果表明，我們的聯(lián)合模型產(chǎn)生的一致較低的平均預(yù)測(cè)誤差，因此在預(yù)測(cè)能力方面優(yōu)于其他三種方法。

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Smith, Ning.;
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University of Michigan.;

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授予單位 University of Michigan.;
學(xué)科 Biology Biostatistics.
學(xué)位 Ph.D.
年度 2010
頁(yè)碼 114 p.
總頁(yè)數(shù) 114
原文格式 PDF
正文語(yǔ)種 eng
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Models for survival and longitudinal data in cancer research.

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