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B.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<title>B 额外数据集 | 医学研究中的生存数据建模</title>
<meta name="author" content="Wang Zhen">
<meta name="description" content="本附录包含若干数据集以及一些分析建议。这些数据集可从出版商的网站下载,其链接在前言中给出。 B.1 慢性活动性肝炎 在 Kirk et al.(1980) 描述的一项临床试验中,44 名慢性活动性肝炎患者被随机分配到泼尼松龙药物治疗组或未治疗的对照组。患者入组后的生存时间(以月为单位)是感兴趣的响应变量。这些数据由 Pocock (1983) 给出,并在表 B.1 中列出。 表 B.1...">
<meta name="generator" content="bookdown 0.38 with bs4_book()">
<meta property="og:title" content="B 额外数据集 | 医学研究中的生存数据建模">
<meta property="og:type" content="book">
<meta property="og:description" content="本附录包含若干数据集以及一些分析建议。这些数据集可从出版商的网站下载,其链接在前言中给出。 B.1 慢性活动性肝炎 在 Kirk et al.(1980) 描述的一项临床试验中,44 名慢性活动性肝炎患者被随机分配到泼尼松龙药物治疗组或未治疗的对照组。患者入组后的生存时间(以月为单位)是感兴趣的响应变量。这些数据由 Pocock (1983) 给出,并在表 B.1 中列出。 表 B.1...">
<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="B 额外数据集 | 医学研究中的生存数据建模">
<meta name="twitter:description" content="本附录包含若干数据集以及一些分析建议。这些数据集可从出版商的网站下载,其链接在前言中给出。 B.1 慢性活动性肝炎 在 Kirk et al.(1980) 描述的一项临床试验中,44 名慢性活动性肝炎患者被随机分配到泼尼松龙药物治疗组或未治疗的对照组。患者入组后的生存时间(以月为单位)是感兴趣的响应变量。这些数据由 Pocock (1983) 给出,并在表 B.1 中列出。 表 B.1...">
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<a href="index.html" title="">医学研究中的生存数据建模</a>
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<nav aria-label="Table of contents"><h2>Table of contents</h2>
<ul class="book-toc list-unstyled">
<li><a class="" href="index.html">前言</a></li>
<li><a class="" href="%E4%BD%9C%E8%80%85%E4%BB%8B%E7%BB%8D.html">作者介绍</a></li>
<li><a class="" href="%E7%9B%AE%E5%BD%95.html">目录</a></li>
<li class="book-part">正文</li>
<li><a class="" href="chap1.html"><span class="header-section-number">1</span> 生存分析</a></li>
<li><a class="" href="chap2.html"><span class="header-section-number">2</span> 一些非参数程序</a></li>
<li><a class="" href="chap3.html"><span class="header-section-number">3</span> Cox 回归模型</a></li>
<li><a class="" href="cox-%E5%9B%9E%E5%BD%92%E6%A8%A1%E5%9E%8B.html">►Cox 回归模型</a></li>
<li><a class="" href="chap4.html"><span class="header-section-number">4</span> Cox 回归模型的模型检查</a></li>
<li><a class="" href="chap5.html"><span class="header-section-number">5</span> 参数回归模型</a></li>
<li><a class="" href="%E5%8F%82%E6%95%B0%E5%9B%9E%E5%BD%92%E6%A8%A1%E5%9E%8B.html">►参数回归模型</a></li>
<li><a class="" href="chap6.html"><span class="header-section-number">6</span> 灵活的参数模型</a></li>
<li><a class="" href="chap7.html"><span class="header-section-number">7</span> 参数模型的模型检查</a></li>
<li><a class="" href="chap8.html"><span class="header-section-number">8</span> 时依变量</a></li>
<li><a class="" href="chap9.html"><span class="header-section-number">9</span> 区间删失生存数据</a></li>
<li><a class="" href="chap10.html"><span class="header-section-number">10</span> 脆弱模型</a></li>
<li><a class="" href="chap11.html"><span class="header-section-number">11</span> 非比例风险和机构的比较</a></li>
<li><a class="" href="chap12.html"><span class="header-section-number">12</span> 竞争风险</a></li>
<li><a class="" href="chap13.html"><span class="header-section-number">13</span> 多次事件和事件史分析</a></li>
<li><a class="" href="chap14.html"><span class="header-section-number">14</span> 相依删失</a></li>
<li><a class="" href="chap15.html"><span class="header-section-number">15</span> 生存研究的样本量要求</a></li>
<li><a class="" href="chap16.html"><span class="header-section-number">16</span> 贝叶斯生存分析</a></li>
<li><a class="" href="chap17.html"><span class="header-section-number">17</span> 使用 R 进行生存分析</a></li>
<li class="book-part">附录</li>
<li><a class="" href="A.html"><span class="header-section-number">A</span> 最大似然估计</a></li>
<li><a class="active" href="B.html"><span class="header-section-number">B</span> 额外数据集</a></li>
<li class="book-part">—</li>
<li><a class="" href="bib.html">参考书目</a></li>
<li><a class="" href="exm.html">示例索引</a></li>
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</header><main class="col-sm-12 col-md-9 col-lg-7" id="content"><div id="B" class="section level1" number="19">
<h1>
<span class="header-section-number">B</span> 额外数据集<a class="anchor" aria-label="anchor" href="#B"><i class="fas fa-link"></i></a>
</h1>
<p>本附录包含若干数据集以及一些分析建议。这些数据集可从出版商的网站下载,其链接在前言中给出。</p>
<div id="secB-1" class="section level2" number="19.1">
<h2>
<span class="header-section-number">B.1</span> 慢性活动性肝炎<a class="anchor" aria-label="anchor" href="#secB-1"><i class="fas fa-link"></i></a>
</h2>
<p>在 Kirk et al.(1980) 描述的一项临床试验中,44 名慢性活动性肝炎患者被随机分配到泼尼松龙药物治疗组或未治疗的对照组。患者入组后的生存时间(以月为单位)是感兴趣的响应变量。这些数据由 Pocock (1983) 给出,并在表 B.1 中列出。</p>
<details><summary><font color="#8B2232">表 B.1</font>
</summary><img src="figure/table%20B.1.png#center" style="width:80.0%"></details><p><br>
根据每个治疗组的生存函数估计总结数据。使用 log-rank 检验和 Wilcoxon 检验比较各组。拟合 Cox 和 Weibull 比例风险模型以确定治疗效应的显著性。根据治疗效应的显著性,比较这些不同分析的结果,以及基于模型的风险比估计和相应的 95% 置信限。获取数据的对数累积风险图,并评论哪种分析方法最合适。</p>
</div>
<div id="secB-2" class="section level2" number="19.2">
<h2>
<span class="header-section-number">B.2</span> 膀胱癌的复发<a class="anchor" aria-label="anchor" href="#secB-2"><i class="fas fa-link"></i></a>
</h2>
<p>由 Veterans Administration Cooperative Urological Research Group 进行的一项关于膀胱癌的安慰剂对照试验中,患有浅表性膀胱肿瘤的患者首先通过经尿道切除术切除了肿瘤。随后,他们被随机分配到安慰剂组或化疗药物噻替哌组。在随机分组时,记录了每个患者的肿瘤初始数量和其中最大肿瘤的直径。Andrews and Herzberg (1985) 中包含的原始数据集给出了最多九次肿瘤复发的时间,但表 B.2 中的数据指的是第一次复发的时间,以月为单位。在随访期结束时仍未复发的患者记录为删失观测。数据集中的变量如下:</p>
<ul>
<li>
<span class="math inline">\(Patient\)</span>:患者编号(1-86)</li>
<li>
<span class="math inline">\(Time\)</span>:生存时间(月)</li>
<li>
<span class="math inline">\(Status\)</span>:患者状态(0 = 删失,1 = 复发)</li>
<li>
<span class="math inline">\(Treat\)</span>:治疗组(1 = 安慰剂,2 = 噻替哌)</li>
<li>
<span class="math inline">\(Init\)</span>:肿瘤初始数量</li>
<li>
<span class="math inline">\(Size\)</span>:最大初始肿瘤直径(厘米)</li>
</ul>
<details><summary><font color="#8B2232">表 B.2</font>
</summary><img src="figure/table%20B.2.png#center" style="width:80.0%"></details><p><br>
使用 Cox 比例风险模型确定复发时间是否依赖于解释变量 <span class="math inline">\(Init\)</span> 和 <span class="math inline">\(Size\)</span>。在调整相关变量后,考察治疗效应的显著性。考察是否有证据表明治疗组和两个解释变量之间存在相互作用。利用模型检查诊断,评论拟合模型的适用性。具体来说,通过拟合一个合适的时依变量,检验关于治疗的比例风险假设。估计治疗效应的风险比,并给出该比值的 95% 置信区间。将该估计与拟合 Weibull 比例风险模型得到的估计进行比较。</p>
</div>
<div id="secB-3" class="section level2" number="19.3">
<h2>
<span class="header-section-number">B.3</span> 黑鸭的生存<a class="anchor" aria-label="anchor" href="#secB-3"><i class="fas fa-link"></i></a>
</h2>
<p>US Fish and Wildlife Service 进行了一项关于黑鸭(学名 Anas rubripes)迁徙和越冬生存情况的研究。在第一年的研究中,研究人员在 New Jersey 的两个地点捕获了 50 只雌性黑鸭,并为它们装上了无线电发射器。这些鸭子是在 1983 年 11 月 8 日至 1983 年 12 月 14 日大约四周时间内捕获的,其中包括 31 只当年孵化的幼鸭(即在前一个繁殖季节出生的鸭子)和 19 只至少一岁大的鸭子。研究人员测量了每只鸭子的体重和翼长。从鸭子被释放之日起,研究人员每天记录每只鸭子的状态,即存活、失踪还是死亡,直到 1984 年 2 月 15 日研究结束。该数据集包含在 Hand et al. (1994) 的著作中,包含以下变量:</p>
<ul>
<li>
<span class="math inline">\(Time\)</span>:生存时间(天)</li>
<li>
<span class="math inline">\(Status\)</span>:鸟的状态( 0 = 存活或失踪,1 = 死亡)</li>
<li>
<span class="math inline">\(Age\)</span>:年龄组(0 = 当年孵化的幼鸭,1 = 年龄大于等于 1 年的鸟)</li>
<li>
<span class="math inline">\(Weight\)</span>:鸟的重量(克)</li>
<li>
<span class="math inline">\(Length\)</span>:翼长(毫米)</li>
</ul>
<p>研究中鸭子的每个变量的值如表 B.3 所示。</p>
<details><summary><font color="#8B2232">表 B.3</font>
</summary><img src="figure/table%20B.3.png#center" style="width:80.0%"></details><p><br>
使用比例风险模型,考察年龄、体重和翼长对生存时间的影响。探究解释变量体重和翼长的系数在每个年龄组的鸭子中是否存在差异。是否有证据表明模型中需要体重和翼长的非线性函数?</p>
</div>
<div id="secB-4" class="section level2" number="19.4">
<h2>
<span class="header-section-number">B.4</span> 骨髓移植<a class="anchor" aria-label="anchor" href="#secB-4"><i class="fas fa-link"></i></a>
</h2>
<p>白血病治疗后,患者通常会接受骨髓移植,以帮助将血细胞恢复到正常水平。这种治疗的一个潜在致命的副作用是移植物抗宿主病 (graft-versus-host disease),即移植细胞攻击宿主细胞。</p>
<p>在 Bagot et al. (1988) 描述的一项研究中,37 名患有急性髓细胞白血病(AML)或急性淋巴细胞白血病(ALL)并已达到完全缓解状态,或是处于慢性粒细胞白血病(CML)慢性期的患者接受了非耗竭异基因骨髓移植 (non-depleted allogeneic bone marrow transplant). 记录了骨髓供体的年龄、供体是否曾怀孕,以及受体的年龄、白血病类型、混合表皮淋巴细胞反应指数,以及受体是否出现移植物抗宿主病。该数据集中的变量如下:</p>
<ul>
<li>
<span class="math inline">\(Patient\)</span>:患者编号(1-37)</li>
<li>
<span class="math inline">\(Time\)</span>:存活天数</li>
<li>
<span class="math inline">\(Status\)</span>:患者状态(0 = 存活,1 = 死亡)</li>
<li>
<span class="math inline">\(Rage\)</span>:患者年龄</li>
<li>
<span class="math inline">\(Dage\)</span>:供体年龄</li>
<li>
<span class="math inline">\(Type\)</span>:白血病类型(1 = AML,2 = ALL,3 = CML)</li>
<li>
<span class="math inline">\(Preg\)</span>:供体妊娠(0 = 否,1 = 是)</li>
<li>
<span class="math inline">\(Index\)</span>:细胞-淋巴细胞反应指数</li>
<li>
<span class="math inline">\(GVHD\)</span>:移植物抗宿主病(0 = 否,1 = 是)</li>
</ul>
<p>Altman (1991) 中也给出了这些数据,如表 B.4 所示。</p>
<details><summary><font color="#8B2232">表 B.4</font>
</summary><img src="figure/table%20B.4.png#center" style="width:80.0%"></details><p><br>
使用 Weibull 加速失效时间模型研究生存时间对预后变量的依赖性。估计并绘制基线生存函数。拟合包含相同解释变量的 loglogistic 和 lognormal 模型,并估计在这些模型下的基线生存函数。将这些估计与从 Cox 比例风险模型拟合得到的基线生存函数估计进行比较。然后,请评论哪种参数模型最合适,并使用模型检查诊断进一步检查该模型的充分性。</p>
</div>
<div id="secB-5" class="section level2" number="19.5">
<h2>
<span class="header-section-number">B.5</span> 慢性肉芽肿病<a class="anchor" aria-label="anchor" href="#secB-5"><i class="fas fa-link"></i></a>
</h2>
<p>慢性肉芽肿病 (chronic granulomatous disease, CGD) 是一组免疫系统的遗传性疾病,这些疾病使患者容易受到反复感染以及慢性炎症的影响,例如牙龈炎、淋巴结肿大或非恶性的肿瘤样肿块(称为肉芽肿)。这些肉芽肿会阻碍食物通过消化系统的通道,并可能抑制尿液从肾脏和膀胱的流动。该病通常使用抗生素进行治疗,但 Genentech 公司开展的一项多中心试验比较了抗病毒药物干扰素与安慰剂的效果。研究人员收集了一系列预后变量的数据,而关注的终点是随机分组后首次出现严重感染的时间。Therneau and Grambsch (2000) 描述的数据集包含以下变量:</p>
<ul>
<li>
<span class="math inline">\(Patient\)</span>:患者编号(1-128)</li>
<li>
<span class="math inline">\(Time\)</span>:第一次感染的时间(天)</li>
<li>
<span class="math inline">\(Status\)</span>:患者状态(0 = 删失,1 = 感染)</li>
<li>
<span class="math inline">\(Center\)</span>:中心(1 = Harvard Medical School,2 = Scripps Institute, California,3 = Copenhagen,4 = National Institutes of Health, Maryland,5 = Los Angeles Children’s Hospital,6 = Mott Children’s Hospital, Michigan,7 = University of Utah,8 = Children’s Hospital of Philadelphia, Pennsylvania,9 = University of Washington,10 = University of Minnesota,11 = University of Zurich,12 = Texas Children’s Hospital,13 = Amsterdam,14 = Mount Sinai Medical Center)</li>
<li>
<span class="math inline">\(Treat\)</span>:治疗组(0 = 安慰剂,1 = 干扰素)</li>
<li>
<span class="math inline">\(Age\)</span>:年龄(年)</li>
<li>
<span class="math inline">\(Sex\)</span>:性别(1 = 男性,2 = 女性)</li>
<li>
<span class="math inline">\(Height\)</span>:身高(厘米)</li>
<li>
<span class="math inline">\(Weight\)</span>:体重(公斤)</li>
<li>
<span class="math inline">\(Pattern\)</span>:遗传模式(1 = X连锁,2 = 常染色体隐性)</li>
<li>
<span class="math inline">\(Cort\)</span>:试验开始时使用皮质类固醇(1 = 使用,2 = 未使用)</li>
<li>
<span class="math inline">\(Anti\)</span>:试验结束时使用抗生素</li>
</ul>
<p>为首次感染的时间确定一个合适的模型,并调查该模型的充分性。是否有任何证据表明不同中心的治疗效应不一致?根据相关的点估计和相应的置信区间总结治疗差异。</p>
<p>现在假设实际的感染时间是区间删失的,并且任何个体的感染状态只能在 90 天、180 天、270 天和 360 天记录。根据生存时间构造一个新的观测,该观测给出了感染或删失发生的区间。对于未感染或在 360 天后发生感染的个体,其观测视为删失。对于这个构造的数据集,使用第 <a href="chap9.html#chap9">9</a> 章描述的方法分析区间删失数据。将治疗效应估计与根据原始数据得到的估计进行比较。</p>
<p>为这些数据拟合具有共享脆弱性的参数模型和 Cox 回归模型,其中中心是随机脆弱效应。比较和对比两组结果并评论中心变异的程度。</p>
<p>确定每个中心在前 180 天内首次感染的患者数。通过为首次感染时间拟合 Cox 回归模型,使用第 <a href="chap11.html#chap11">11</a> 章描述的方法,估计每个中心 180 天风险调整的首次感染率。将中心视为固定或随机的,计算该率的相应区间估计,并评论任何差异的程度。</p>
<p>研究这些数据中相依删失的影响。首先,拟合删失概率的 Weibull 模型,并使用该模型获得与删失成反比的权重。以计数过程格式表示感染时间数据,拟合加权 Cox 回归模型以考虑任何相依删失。将其结果与未加权 Cox 回归分析获得的结果进行比较。</p>
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<nav id="toc" data-toggle="toc" aria-label="On this page"><h2>On this page</h2>
<ul class="nav navbar-nav">
<li><a class="nav-link" href="#B"><span class="header-section-number">B</span> 额外数据集</a></li>
<li><a class="nav-link" href="#secB-1"><span class="header-section-number">B.1</span> 慢性活动性肝炎</a></li>
<li><a class="nav-link" href="#secB-2"><span class="header-section-number">B.2</span> 膀胱癌的复发</a></li>
<li><a class="nav-link" href="#secB-3"><span class="header-section-number">B.3</span> 黑鸭的生存</a></li>
<li><a class="nav-link" href="#secB-4"><span class="header-section-number">B.4</span> 骨髓移植</a></li>
<li><a class="nav-link" href="#secB-5"><span class="header-section-number">B.5</span> 慢性肉芽肿病</a></li>
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<p>"<strong>医学研究中的生存数据建模</strong>" was written by Wang Zhen. It was last built on 2024-04-23.</p>
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