Standard inference procedures for regression analysis make assumptions that are rarely satisfied in practice. Adjustments must be made to insure the validity of statistical inference. These adjustments, known for many years, are used routinely by some health researchers but not by others. We review some of these methods and give an example of their use in a health services study for a continuous and a count outcome. For the continuous outcome, we describe re-transformation using the smear factor, accounting for missing cases via multiple imputation and attrition weights and improving results with bootstrap methods. For the count outcome, we describe zero inflated Poisson and negative binomial models and the two-part model to account for overabundance of zero values. Recent advances in computing and software development have produced user-friendly computer programs that enable the data analyst to improve prediction and inference based on regression analysis.

中文翻译：

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改进偏斜连续或计数响应的回归分析的方法。

用于回归分析的标准推理程序做出的假设在实践中很少满足。必须进行调整以确保统计推断的有效性。这些调节方法已经众所周知多年了，一些健康研究人员通常会使用它们，而其他一些研究人员则没有。我们回顾了其中的一些方法，并举例说明了它们在卫生服务研究中用于连续和计数结果的用途。对于连续结果，我们使用涂抹因子来描述重新转换，通过多次插补和损耗权重解决丢失的案例，并使用自举方法改善结果。对于计数结果，我们描述零膨胀的Poisson模型和负二项式模型以及两部分模型，以解决零值的过剩问题。