Evaluation of two health status measures in adults with growth hormone deficiency

Objective: To evaluate the psychometric properties of two health status measures for adults with growth hormone deficiency (GHD): Nottingham Health Profile (NHP) and Short Form Health Survey (SF-36). Design: (1) A cross-sectional survey of adults with treated or untreated GHD, to assess reliability and validity of the questionnaires. (2) A randomised, placebo-controlled study of 3 months’ GH withdrawal from GH-treated adults, to assess the questionnaires’ sensitivity to change. Patients: (1) Cross-sectional survey of 157 patients with severe GHD (peak GH Measurements: The NHP and SF-36 were used once in the cross-sectional survey, but twice in the GH-withdrawal study, at baseline and end-point (after 3 months). Results: (1) Cross-sectional survey. Both questionnaires had high internal consistency reliability with subscale Cronbach’s alphas of > 0.73 (NHP) and > 0.78 (SF 36). Calculation of a NHP Total score, occasionally reported in the literature, was shown to be inadvisable. Overall, patients with GHD were found to have significantly worse perceived functioning than the UK general population in SF 36 subscales of General Health, Pain, Social Functioning, Role-Emotional, Role-Physical, and Vitality. Whilst neither questionnaire found significant differences between GH-treated and non-GH-treated patients, there were correlations with duration of GH treatment (p Conclusions: The SF-36 is a better measure than the NHP of health status of people with GHD, owing to its greater discriminatory power with ability to detect lesser degrees of disability. It also has superior sensitivity to some sub-group differences and superior sensitivity to change than the NHP. The SF-36 is highly acceptable to respondents, and has very good internal consistency reliability. The SF-36 is recommended to measure the health status of adults with GHD

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants