Power analysis is a key component of planning prospective studies such as clinical trials.However,some journals in biomedical and psychosocial sciences request power analysis for data already collected and analysed be...Power analysis is a key component of planning prospective studies such as clinical trials.However,some journals in biomedical and psychosocial sciences request power analysis for data already collected and analysed before acceptingmanuscripts for publication.Many have raised concerns about the conceptual basis for such post-hoc power analyses.More recently,Zhang et al showed by using simulation studies that such power analyses do not indicate true power for detecting statistical significance since post-hoc power estimates vary in the range of practical interests and can be very different from the true power.On the other hand,journals'request for information about the reliability of statistical findings in a manuscript due to small sample sizes is justified since the sample size plays an important role in the reproducibility of statistical findings.The problem is the wording of the journals'request,as the current power analysis paradigm is not designed to address journals'concerns about the reliability of the statistical findings.In this paper,we propose an alternate formulation of power analysis to provide a conceptually valid approach to the journals'wrongly worded but practically significant concern.展开更多
SUMMARY Linear regression is widely used in biomedical and psychosocial research.A critical assumption that is often overlooked is homoscedasticity.Unlike normality,the other assumption on data distribution,homoscedas...SUMMARY Linear regression is widely used in biomedical and psychosocial research.A critical assumption that is often overlooked is homoscedasticity.Unlike normality,the other assumption on data distribution,homoscedasticity is often taken for granted when fitting linear regression models.However,contrary to popular belief,this assumption actually has a bigger impact on validity of linear regression results than normality.In this report,we use Monte Carlo simulation studies to investigate and compare their effects on validity of inference.展开更多
基金supported by the National Institutes of Health(grant UL1TR001442)of CTSA funding.
文摘Power analysis is a key component of planning prospective studies such as clinical trials.However,some journals in biomedical and psychosocial sciences request power analysis for data already collected and analysed before acceptingmanuscripts for publication.Many have raised concerns about the conceptual basis for such post-hoc power analyses.More recently,Zhang et al showed by using simulation studies that such power analyses do not indicate true power for detecting statistical significance since post-hoc power estimates vary in the range of practical interests and can be very different from the true power.On the other hand,journals'request for information about the reliability of statistical findings in a manuscript due to small sample sizes is justified since the sample size plays an important role in the reproducibility of statistical findings.The problem is the wording of the journals'request,as the current power analysis paradigm is not designed to address journals'concerns about the reliability of the statistical findings.In this paper,we propose an alternate formulation of power analysis to provide a conceptually valid approach to the journals'wrongly worded but practically significant concern.
文摘SUMMARY Linear regression is widely used in biomedical and psychosocial research.A critical assumption that is often overlooked is homoscedasticity.Unlike normality,the other assumption on data distribution,homoscedasticity is often taken for granted when fitting linear regression models.However,contrary to popular belief,this assumption actually has a bigger impact on validity of linear regression results than normality.In this report,we use Monte Carlo simulation studies to investigate and compare their effects on validity of inference.
