ON POWER AND SAMPLE-SIZE FOR STUDYING FEATURES OF THE RELATIVE ODDS OF DISEASE
ON POWER AND SAMPLE-SIZE FOR STUDYING FEATURES OF THE RELATIVE ODDS OF DISEASE
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DOI：
10.1093/oxfordjournals.aje.a115530
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发表时间：
1990-03-01
影响因子：
5
通讯作者：
GAIL, MH
中科院分区：
文献类型：
--
作者：
LUBIN, JH;GAIL, MH
在流行病学研究的设计中，样本大小和统计能力的估计是必不可少的成分。一旦疾病和暴露之间的关联被证实，通常需要进一步的研究来调查暴露、其他协变量和疾病风险之间关系的特殊特征。作者提出了一种一般公式来计算病例对照和队列研究的样本量和功率，以调查优势比中更复杂的模式，例如区分线性趋势的两种不同斜率，区分两种可能的剂量-反应关系，或区分两种重要暴露或一种暴露因素调整另一种暴露因素的联合效应的不同模型。这种对暴露-反应关系的物种研究可能有助于研究人员区分合理的生物学模型，并可能导致更现实的模型来计算归因风险和终生疾病风险。样本量公式适用于室内氡暴露和肺癌的研究，并表明流行病学研究对于解决某些问题可能不可行。例如，如果从地下矿工的研究中得出的风险估计实际上不适用于家庭暴露，并且将家庭暴露于氡的风险梯度高估了2倍，那么就需要大量的受试者来检测差异。此外，如果吸烟和氡暴露之间的真正相互作用小于乘法，那么只有最大的调查才有足够的力量来拒绝加法。对于测试无暴露效应的简单情况，当暴露是二分或连续时，这些方法产生众所周知的公式。
Estimates of samples size and statistical power are essential ingredients in the design of epidemiologic studies. Once an association between disease and exposure has been demonstrated, additional studies are often needed to investigate special features of the relation between exposure, other covariates, and risk of disease. The authors present a general formulation to compute sample size and power for case-control and cohort studies to investigate more complex patterns in the odds ratios, such as to distinguish between two different slopes of linear trend, to distinguish between two possible dose-response relations, or to distinguish different models for the joint effects of two important exposures or of one exposure factor adjusting for another. Such species studies of exposure-response relations may help investigators to distinguish between plausible biologic models and may lead to more realistic models for calculating attributable risk and lifetime disease risk. The sample size formulae are applied to studies of indoor radon exposure and lung cancer and suggest that epidemiologic studies may not be feasible for addressing some issues. For example, if the risk estimates from underground miners'' studies are, in truth, not applicable to home exposures and overestimate the gradient of risk from home exposure to radon by, for example, a factor of 2, then enormously large numbers of subjects would be required to detect the difference. Furthermore, if the true interaction between smoking and radon exposure is less than multiplicative, only the largest investigations will have sufficient power to reject additivity. For the simple case of testing for no exposure effect, when exposure is either dichotomous or continuous, these methods yield well-known formulae.