Do multiple outcome measures require p-value adjustment?

BACKGROUND: Readers may question the interpretation of findings in clinical trials when multiple outcome measures are used without adjustment of the p-value. This question arises because of the increased risk of Type I errors (findings of false "significance") when multiple simultaneous hypotheses are tested at set p-values. The primary aim of this study was to estimate the need to make appropriate p-value adjustments in clinical trials to compensate for a possible increased risk in committing Type I errors when multiple outcome measures are used. DISCUSSION: The classicists believe that the chance of finding at least one test statistically significant due to chance and incorrectly declaring a difference increases as the number of comparisons increases. The rationalists have the following objections to that theory: 1) P-value adjustments are calculated based on how many tests are to be considered, and that number has been defined arbitrarily and variably; 2) P-value adjustments reduce the chance of making type I errors, but they increase the chance of making type II errors or needing to increase the sample size. SUMMARY: Readers should balance a study's statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures might want to either select a primary outcome measure or use a global assessment measure, rather than adjusting the p-value

Significance testing as perverse probabilistic reasoning

Truth claims in the medical literature rely heavily on statistical significance testing. Unfortunately, most physicians misunderstand the underlying probabilistic logic of significance tests and consequently often misinterpret their results. This near-universal misunderstanding is highlighted by means of a simple quiz which we administered to 246 physicians at two major academic hospitals, on which the proportion of incorrect responses exceeded 90%. A solid understanding of the fundamental concepts of probability theory is becoming essential to the rational interpretation of medical information. This essay provides a technically sound review of these concepts that is accessible to a medical audience. We also briefly review the debate in the cognitive sciences regarding physicians' aptitude for probabilistic inference

The Role of Social Work in Residential Aged Care Facilities: Evaluation of a Pilot Program in Australia

In some international settings, social workers are employed within aged care settings. However, in Australia, social workers rarely work in residential aged care facilities. In an innovative program, an Australian health network employed a social worker in an aged residential care facility from 2010 to 2011. In this research we examine and evaluate this program. Qualitative semi-structured interviews with nine key stakeholders and data extraction from medical records were conducted. Data from medical records and interview transcripts were coded and themes extracted using thematic analysis. Thematic analysis identified five key themes reflecting the roles performed by the social worker. These were: (1) The importance of having an independent third party, (2) The provision of emotional support to residents, carers and families during the transition period, (3) The importance of role clarity, (4) The provision of family-centered care, and (5) Social work responses to potential difficulties which were preventative rather than reactive. The move into residential aged care can be an overwhelming, and in some cases, traumatic transition for residents and families. Results identified that timely and expert social work intervention can improve the transition process through the provision of counselling to effectively manage grief, loss, and psychosocial issues

Fuzzy p-value in testing fuzzy hypotheses with crisp data

Testing hypotheses, Monotone likelihood ratio, Fuzzy hypothesis, Fuzzy p-value, Fuzzy significance level,