Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
J Clin Psychopharmacol. 2010 Feb;30(1):72-5. doi: 10.1097/JCP.0b013e3181c9fdd3.
Because long-term monitoring of morbidity or wellness is often impractical, survival analysis is widely used in experimental therapeutics. In long-term applications, the method assumes that time-to-recurrence or time-to-intervention is a surrogate of long-term clinical status during sustained treatment. Because this plausible assumption has not been tested empirically, we evaluated prospectively and systematically acquired data from consenting adults with major affective disorders treated with lithium for an average of 5.4 years to compare, within-subjects, estimated long-term morbidity versus the first-wellness interval after clinical remission of a treated index episode. These measures were inversely correlated in all 450 subjects with mood disorder (rs = -0.65) and in those with bipolar I (n = 259; rs = -0.58), II (n = 150; rs = -0.65), or recurrent unipolar depression (n = 50; rs = -0.66). The wellness interval also predicted episodes per year (rs = -0.58, all P < 0.0001). These associations were sustained in multiple-regression modeling, in which a bipolar diagnosis (but not sex, age, or measures of preintake morbidity) was associated with greater long-term morbidity than in major depression. These findings provide moderate support for a basic assumption underlying the use of survival analysis for psychopharmacology research and suggest that clinical outcomes might be predictable from initial wellness intervals.
由于长期监测发病率或健康状况通常不切实际,生存分析在实验治疗学中被广泛应用。在长期应用中,该方法假设复发时间或干预时间是持续治疗期间长期临床状况的替代指标。由于这一合理的假设尚未经过实证检验,我们前瞻性地评估并系统地获取了 450 名同意接受锂治疗的成年重性情感障碍患者的平均 5.4 年的持续数据,以比较治疗指数发作缓解后个体的估计长期发病率与首次健康间隔。在所有 450 名心境障碍患者(rs = -0.65)和双相 I 型(n = 259;rs = -0.58)、II 型(n = 150;rs = -0.65)或复发性单相抑郁(n = 50;rs = -0.66)患者中,这两种方法都存在显著相关性。健康间隔也可以预测每年的发作次数(rs = -0.58,均 P < 0.0001)。在多元回归模型中,这些关联仍然存在,双相诊断(而非性别、年龄或治疗前发病率的衡量标准)与更大的长期发病率相关,而与重性抑郁症相关。这些发现为生存分析在精神药理学研究中的基本假设提供了一定的支持,并表明临床结局可能可以从初始健康间隔中预测。