SEAMAN AND JAEGER (1990) contend that presumptuous use of parametric statistical methods to test hypotheses can lead ecologists astray; I wholeheartedly concur. As they point out, the usual argument against nonparametric alternatives is that they lack power-there is a high probability of failing to reject a false null hypothesis (type II error). To increase the power of a particular test (lower probability of type II error), however, one must generally increase probability of type I error, the probability of rejecting a true null hypothesis (Conover, 1980). Seaman and Jaeger (1990) suggest that the view that nonparametric tests are less powerful than parametric alternatives is often untrue and rests in many instances on assumptions about the shape of an underlying distribution that are either incorrect or untestable. In addition, even in situations in which nonparametric tests are somewhat less powerful than parametric ones, there may still be a good reason for ecologists to favor the former-relative costs of the two kinds of errors (Connor and Simberloff, 1986; Toft and Shea, 1983). It is important to distinguish between statistical hypotheses and scientific hypotheses (Boen, 1989; Connor and Simberloff, 1986). Scientific hypotheses are about phenomena in nature. Statistical hypotheses are about properties of populations based on samples. Thus, a statistical hypothesis can be a quantified application of a scientific hypothesis to a specific set of data. Rejection of one or more statistical hypotheses would constitute one piece of evidence to be weighed in deciding whether to reject a scientific hypothesis. A related distinction is between global and local hypotheses (Dolby, 1982). A global hypothesis applies to all of nature, while a local one applies to particular systems. A specific statistical hypothesis is local unless global populations have been sampled. Error classification was developed specifically for testing statistical hypotheses, but the terms are used metaphorically, and appropriately, for global, scientific hypotheses. In either case, if the null hypothesis is that some process or phenomenon has no effect, then a type I error consists in concluding that the process does have an effect when, in fact, it does not. A type II error for the same case would be concluding that the process has no effect when it actually does. Finally, a distinction must be made between classical statistical hypothesis-testing and decision theory (Kyburg, 1974). Testing statistical hypotheses is often an aid to inferring whether a scientific hypothesis is true or false, as noted above. It does not explicitly take account of costs of errors. By contrast, in some settings a particular statistical hypothesis may be tested over and over again, and the test results acted on each time. For example, one may test samples from batches of computer chips, then save or discard entire batches based on test results. This problem has motivated the development of decision theory, which is a theory of acting rationally (with respect to expected losses and gains) in the face of uncertainty, rather than a theory of inference about nature. If statistics is to be used for this purpose, costs attending various kinds of errors must be explicitly assessed. Below I argue that costs of errors must also be considered when statistical hypotheses are tested as part of testing scientific hypotheses. Perusing the medical literature nowadays, one can easily conclude that type II error is the greater scourge and entails the larger costs (e.g., Freiman et al., 1978; Marks et al., 1988). Bourne (1987) went so far as to title a paper, "No Statistically Significant Difference?: So What?" His particular hypothetical example was a test of whether a particular treatment of a disease is efficacious, and he betrayed his underlying reasoning at the outset (p. 40):

Hypotheses, errors, and statistical assumptions

https://www.jrheum.org/content/jrheum/33/11/2365.full.pdf

Remitting seronegative symmetrical synovitis with pitting edema syndrome: followup for neoplasia.

Objective: To study health-related quality of life (HRQoL) in arthritis rehabilitation performed by multidisciplinary teams in patients with chronic inflammatory arthritis. Predictors of change in health-related quality of life and the proportion of patients with clinical improvement were investigated. Design: Multicentre prospective observational study in 4 European countries. Methods: HRQoL was measured with the European Quality 5 Dimensions (EQ-5D) and the Short Form 36 Health Survey (SF-36) in 731 patients who underwent multidisciplinary rehabilitation. Potential predictors were physical functioning (Health Assessment Questionnaire (HAQ)), self-efficacy (Arthritis Self Efficacy Scale (ASES)), psychological health (Hopkins Symptom Check List (HSCL-25)), pain/fatigue (numeric rating scales (NRS)), age, sex, diagnosis, comorbidity, education, clinical setting and change of medication during rehabilitation. Analysis of covariance (ANCOVA) was used to assess for potential predictors and interactions. The minimal important differences for HRQoL were analysed. Results: Reporting worse function (b 0.05, p = 0.01), less psychological well-being (b 0.09, p = 0.000), and experiencing more pain (b 0.03, p = 0.000) or fatigue (b 0.02, p = 0.000) at admission predicted improved HRQoL. Change in medication during rehabilitation (b 0.08, p = 0.013) was associated with greater improvement in HRQoL. These EQ-5D findings were supported by SF-36 findings. Positive minimal important differences were noted in 46% (EQ-5D) and 23-47% (SF-36 subscales) of the patients. Conclusion: Patients with more severe symptoms experienced the largest gain in HRQoL post-intervention. The results of this study are of value for selecting the right patients for rheumatological team rehabilitation. (Less)

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Which patients improve the most from arthritis rehabilitation?: Results from patients with inflammatory arthritis in northern Europe, the STAR-ETIC collaboration

The aim of this thesis was to study outcome and evaluation of rheumatological multidisciplinary team rehabilitation programmes in patients with chronic inflammatory arthritis (CIA); rheumatoid arthritis (RA), ankylosing spondylitis, psoriatic arthritis and other spondyloarthritides, and to analyse health care utilization over the last decade in patients with RA. 
In Study I the outcome of an 18 days outpatient multidisciplinary team rehabilitation programme in 174 patients with CIA was analysed. The patients were evaluated pre- and post the rehabilitation programme and 4 and 12 months thereafter. Health related quality of life (HRQoL), general health and aerobic capacity was significantly improved at the end of the rehabilitation programme and after 12 months. 
In Study II 731 patients with CIA participated in different team rehabilitation programmes in four European countries and were evaluated pre- and post rehabilitation. Through analysis of covariance we studied which patients improved the most by team rehabilitation. Females experiencing more pain, fatigue and lower psychosocial wellbeing improved most in HRQoL. HRQoL for half of the patients improved according to analysis of Minimal Important Difference. 
In Study III we analysed the validity and responsiveness of 15 standardized outcome measures used to evaluate outcomes from multidisciplinary team rehabilitation in 216 patients with CIA. According to our linking of the outcome measures to the International Classification of Functioning, Disability and Health (ICF) most ICF components were reasonably well covered except environmental aspects.Further, out of three outcome measures used to evaluate HRQoL, the Euroqol-5Dimensions (EQ-5D) performed as well as the more extensive short form 36 health survey (SF-36) and Nottingham health profile (NHP). Aerobic capacity did not correlate to other measures of observed physical functioning . It was highly responsive to change. 
In Study IV we wanted to study health care utilization in patients with RA over time. By using the Skane Health Care Register we identified 3977 persons who had been diagnosed with RA when consulting health care during 1998-2001. Two referents from the general population per RA patient were matched for age, sex, and area of residence. The annual mean number of hospitalizations and outpatient clinic visits 2001-2010 and the annual ratio (RA cohort/referents) were analysed. 
The overall inpatient and outpatient health care utilization was found to decrease in RA patients as compared to the general population. 
To conclude, multidisciplinary team rehabilitation is beneficial for patients with CIA with regards to HRQoL, general health and aerobic capacity, both short and long term. Further, patients with more severe disease consequences benefit most from rheumatological team rehabilitation. 
When evaluating HRQoL in rheumatological team rehabilitation the commonly used questionnaires EQ-5D, NHP and SF-36, showed important differences in construct validity and responsiveness and are thus not interchangeable. 
Improving aerobic capacity is an important aspect of team rehabilitation not covered by other outcome measures on physical function and thus important to evaluate. 
Patients with RA have utilized less health care during the last decade which might be an effect of changing treatment strategies.

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Team rehabilitation and health care utilization in chronic inflammatory arthritis patients

3. Information on permissions/orders of reprints rheumatology and related fields. Silverman featuring research articles on clinical subjects from scientists working in is a monthly international serial edited by Earl D.

https://www.jrheum.org/content/jrheum/33/11/2366.full.pdf

Moses Le

Papers

Parametric and nonparametric analyses of the same data.