Cespedes Feliciano Elizabeth M, Prentice Ross L, Aragaki Aaron K, Neuhouser Marian L, Banack Hailey R, Kroenke Candyce H, Ho Gloria Y F, Zaslavsky Oleg, Strickler Howard D, Cheng Ting-Yuan David, Chlebowski Rowan T, Saquib Nazmus, Nassir Rami, Anderson Garnet, Caan Bette J
Division of Research, Oakland, Kaiser Permanente Northern California, CA.
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA.
Int J Cancer. 2017 Dec 1;141(11):2281-2290. doi: 10.1002/ijc.30931. Epub 2017 Aug 31.
Often, studies modeling an exposure's influence on time to disease-specific death from study enrollment are incorrectly interpreted as if based on time to death from disease diagnosis. We studied 151,996 postmenopausal women without breast or colorectal cancer in the Women's Health Initiative with weight and height measured at enrollment (1993-1998). Using Cox regression models, we contrast hazard ratios (HR) from two time-scales and corresponding study subpopulations: time to cancer death after enrollment among all women and time to cancer death after diagnosis among only cancer survivors. Median follow-up from enrollment to diagnosis/censoring was 13 years for both breast (7,633 cases) and colorectal cancer (2,290 cases). Median follow-up from diagnosis to death/censoring was 7 years for breast and 5 years for colorectal cancer. In analyses of time from enrollment to death, body mass index (BMI) ≥ 35 kg/m versus 18.5-<25 kg/m was associated with higher rates of cancer mortality: HR = 1.99; 95% CI: 1.54, 2.56 for breast cancer (p trend <0.001) and HR = 1.40; 95% CI: 1.04, 1.88 for colorectal cancer (p trend = 0.05). However, in analyses of time from diagnosis to cancer death, trends indicated no significant association (for BMI ≥ 35 kg/m , HR = 1.25; 95% CI: 0.94, 1.67 for breast [p trend = 0.33] and HR = 1.18; 95% CI: 0.84, 1.86 for colorectal cancer [p trend = 0.39]). We conclude that a risk factor that increases disease incidence will increase disease-specific mortality. Yet, its influence on postdiagnosis survival can vary, and requires consideration of additional design and analysis issues such as selection bias. Quantitative tools allow joint modeling to compare an exposure's influence on time from enrollment to disease incidence and time from diagnosis to death.
通常，那些模拟从研究入组开始暴露因素对特定疾病死亡时间影响的研究，常被错误地解读为基于疾病诊断后的死亡时间。我们对女性健康倡议（Women's Health Initiative）中151,996名无乳腺癌或结直肠癌的绝经后女性进行了研究，这些女性在入组时（1993 - 1998年）测量了体重和身高。使用Cox回归模型，我们对比了两个时间尺度以及相应研究亚组的风险比（HR）：所有女性入组后至癌症死亡的时间，以及仅癌症幸存者诊断后至癌症死亡的时间。从入组到诊断/截尾的中位随访时间，乳腺癌（7,633例）和结直肠癌（2,290例）均为13年。从诊断到死亡/截尾的中位随访时间，乳腺癌为7年，结直肠癌为5年。在从入组到死亡时间的分析中，体重指数（BMI）≥35 kg/m²与18.5 - <25 kg/m²相比，癌症死亡率更高：乳腺癌的HR = 1.99；95% CI：1.54, 2.56（p趋势<0.001），结直肠癌的HR = 1.40；95% CI：1.04, 1.88（p趋势 = 0.05）。然而，在从诊断到癌症死亡时间的分析中，趋势表明无显著关联（对于BMI≥35 kg/m²，乳腺癌的HR = 1.25；95% CI：0.94, 1.67（p趋势 = 0.33），结直肠癌的HR = 1.18；95% CI：0.84, 1.86（p趋势 = 0.39））。我们得出结论，增加疾病发病率的风险因素会增加特定疾病的死亡率。然而，其对诊断后生存的影响可能不同，需要考虑额外的设计和分析问题，如选择偏倚。定量工具允许联合建模，以比较暴露因素对从入组到疾病发病时间以及从诊断到死亡时间的影响。
