Chinese Journal of Tissue Engineering Research ›› 2013, Vol. 17 ›› Issue (35): 6228-6233.doi: 10.3969/j.issn.2095-4344.2013.35.002
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Jia Hai-ying, Lu Ming, Wang Xiao-ping, Zheng Rui, Zhang Jing, Ma Hua-song
Received:
2012-10-03
Revised:
2012-10-24
Online:
2013-08-27
Published:
2013-08-27
Contact:
Ma Hua-song, Chief physician, Master’s supervisor, Medical Examination Center, the 306th Hospital of PLA, Beijing 100101, China
luming8788@yahoo.com.cn
About author:
Jia Hai-ying★, Master, Medical Examination Center, the 306th Hospital of PLA, Beijing 100101, China
luming8788@yahoo.com.cn
CLC Number:
Jia Hai-ying, Lu Ming, Wang Xiao-ping, Zheng Rui, Zhang Jing, Ma Hua-song. Total knee arthroplasty: Goodness-of-fit analysis of intraoperative knee society score of Peking Union Medical College[J]. Chinese Journal of Tissue Engineering Research, 2013, 17(35): 6228-6233.
2.3 组间差异的协方差分析 患者治疗前、治疗后3,6个月随访组的数据间的差异采用协方差分析,以患者和评估者作为2个因素,相应的基线值作为协变量。相应的基线值作为协变量。Hochberg递增法许多情况都可能产生多重性:例如多个终点/主要变量,治疗的多重比较,及不同时间点多次测定和中期分析等。存在多重性时,检验主要假设的次数增加,产生Ⅰ类错误的机会就会变大。分析数据时可能有必要对5类错误进行控制和调节[14-15]。首先,最好能避免或减少多重性的产生,如从多个主要变量中鉴别出关键的主要变量;对反复测定则采用一个综合测量指标如“曲线下面积”[16]。多重比较的常用统计方法有Bonferroni方法、Holm法和Hochberg方法。Bonferroni方法是一个保守的方法,对于成对比较,它调节P值以控制总的Ⅰ类误差率。Hochberg方法比另两种方法更有效,它只需控制最大的P值小于显著性水平。多个终点的α调节用Bonferroni方法和Hochberg方法[17]。用来控制各治疗组间比较的Ⅰ类错误。 2.4 对KSS of PUMC量表进行Mentel-Hanszel分层分析 对于KSS of PUMC联合量表的评估,患者状况根据以下标准被分为“良好”、“一般”或“差”。改善定义:基线值为3-5级的,评估下降2级以上,或基线时为2组的下降至1级。恶化定义为基线值为1-3级的,评估上升2级及2级以上,或基线时为4级的上升至5级。各中心进行了基于Cochran-Mantel-Haenszel检验(对分类变量计算频数分布,产生从一维到n维的频数表和列联表;对于二维表,可进行c2检验,对于三维表,可作Mentel-Hanszel分层分析)[18-20]。用中心校正的变化的分类分析。采用Fisher精确检验分析因治疗失败而脱落的发生率,由于Fisher检验是在每列和每行的合计数都是常数的假定下的精确检验,而对于一般的研究:如病例对照(仅病例组和对照组的人数是常数,即:仅每行的合计数是常数),横断面抽样调查(样本总数是常数),所以对于不满足每列和每行的合计数都是常数假定的资料而言,Fisher检验也是一种近似检验[21-22]。 文章通过应用主成分分析法把相关变量转换成另一组不相关的变量,新的变量按照方差依次递减的顺序排列。这样获得包括骨关节炎患者的主观症状,患病时间,关节功能以及由此产生的患者自我健康状况评估结果,4项内容的“测量指标”,得出的权重分别为:“主观症状”的权重为0.466 8、“患病时间”的权重为0.160 3、“膝关节功能”的权重为0.277 6、“亚健康状况的自我评价”的权重为0.095 3。进一步通过统计检验加以证明。设定该量表的评分标准为5分制。通过对疼痛、关节活动度、肌力、伸膝迟滞、内外翻角度以及健康状况的分析结果显示,行变量和列变量之间,第一个维度与第二个维度的列宽分布相似,间距相等。Rasch分析结果表明,该量表以100分制评分最好。 2.5 统计结果分析 样本的大小是根据用标准疗效评估假设患者临床症状的表现率为35%,而误差系数为+0.20来设定。α为0.05是已足够能比较出3组患者的组间的差异。见表3。"
表3显示出新建联合量表KSS of PUMC与HSS、疼痛目测类比评分以及WOMAC之间的关系:KSS of PUMC对骨关节炎患者的症状和体征评估表现与患者关节炎疼痛目测类比评分中疼痛程度呈相关分布,也就是说,症状重的患者疼痛程度较重,自我健康状况评分也较低。治疗后6个月随访患者总体评分较治疗前有明显改善,治疗后3,6个月随访,患者KSS of POMC评分均明显高于治疗前(P < 0.05),与HSS评分类似。治疗后3个月时会有一定下降,但治疗后6个月时会呈上升状态,虽然疗效的显著性会有变化,但对总体的检验结果无影响,同时也体现出本量表的敏感性。治疗后3,6个月随访时KSS of POMC评分结果平均提高在1级以上,疼痛目测类评分较术前降低> 30%。 2.6 主要观察指标分析结果 WOMAC骨关节炎指数结果,包括疼痛程度、功能状况、关节僵硬程度[23-24]。在治疗前和治疗后随访,KSS of PUMC和HSS与WOMAC相比较,差异有显著性意义(P < 0.05)。在所有评分点,KSS of PUMC评估治疗前和治疗后随访组效果与HSS评估结果相当。在3,6个月的评估中,随访6个月时KSS of PUMC评估结果优于治疗前。在两个时间的WOMAC疼痛评估项及生理功能评估项中,治疗后随访6个月时显著优于治疗前 (P < 0.05)。治疗后3个月随访时WOMAC疼痛评估项和疼痛目测类比评分与治疗前比较均有明显改善,差异有显著性意义(P < 0.05)。 最终获得量表共4个维度22个评估项目。并应用该评估表进行拟和优度的分析,证明该量表的拟和优度良好,也说明该量表的科学性[25]。将应用该量表所获得的测量结果与WOMAC、疼痛目测类比评分、HSS相比较,KSS of PUMC对骨关节炎患者症状和体征的评估表现与患者关节炎疼痛目测类比评分中疼痛程度呈正相关关系;KSS of PUMC治疗前组和治疗后随访组效与HSS评估结果相当。KSS of PUMC评分在治疗后随访6个月评估结果优于治疗前;WOMAC疼痛评估项及生理功能评估项中,治疗后随访6个月结果亦显著优于治疗前组(P < 0.05)。患者治疗后功能改善程度体现在KSS of PUMC评分结果数值的提高,治疗后患者膝关节功能恢复越好,KSS of PUMC评分结果越高,这也正是KSS of PUMC量表的价值体现。"
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