Sample size and power estimation for studies with health related quality of life outcomes: a comparison of four methods using the SF-36

We describe and compare four different methods for estimating sample size and power, when the primary outcome of the study is a Health Related Quality of Life (HRQoL) measure. These methods are: 1. assuming a Normal distribution and comparing two means; 2. using a non-parametric method; 3. Whitehead's method based on the proportional odds model; 4. the bootstrap. We illustrate the various methods, using data from the SF-36. For simplicity this paper deals with studies designed to compare the effectiveness (or superiority) of a new treatment compared to a standard treatment at a single point in time. The results show that if the HRQoL outcome has a limited number of discrete values (< 7) and/or the expected proportion of cases at the boundaries is high (scoring 0 or 100), then we would recommend using Whitehead's method (Method 3). Alternatively, if the HRQoL outcome has a large number of distinct values and the proportion at the boundaries is low, then we would recommend using Method 1. If a pilot or historical dataset is readily available (to estimate the shape of the distribution) then bootstrap simulation (Method 4) based on this data will provide a more accurate and reliable sample size estimate than conventional methods (Methods 1, 2, or 3). In the absence of a reliable pilot set, bootstrapping is not appropriate and conventional methods of sample size estimation or simulation will need to be used. Fortunately, with the increasing use of HRQoL outcomes in research, historical datasets are becoming more readily available. Strictly speaking, our results and conclusions only apply to the SF-36 outcome measure. Further empirical work is required to see whether these results hold true for other HRQoL outcomes. However, the SF-36 has many features in common with other HRQoL outcomes: multi-dimensional, ordinal or discrete response categories with upper and lower bounds, and skewed distributions, so therefore, we believe these results and conclusions using the SF-36 will be appropriate for other HRQoL measures

What is the relationship between the minimally important difference and health state utility values? The case of the SF-6D

BACKGROUND: The SF-6D is a new single summary preference-based measure of health derived from the SF-36. Empirical work is required to determine what is the smallest change in SF-6D scores that can be regarded as important and meaningful for health professionals, patients and other stakeholders. OBJECTIVES: To use anchor-based methods to determine the minimally important difference (MID) for the SF-6D for various datasets. METHODS: All responders to the original SF-36 questionnaire can be assigned an SF-6D score provided the 11 items used in the SF-6D have been completed. The SF-6D can be regarded as a continuous outcome scored on a 0.29 to 1.00 scale, with 1.00 indicating "full health". Anchor-based methods examine the relationship between an health-related quality of life (HRQoL) measure and an independent measure (or anchor) to elucidate the meaning of a particular degree of change. One anchor-based approach uses an estimate of the MID, the difference in the QoL scale corresponding to a self-reported small but important change on a global scale. Patients were followed for a period of time, then asked, using question 2 of the SF-36 as our global rating scale, (which is not part of the SF-6D), if there general health is much better (5), somewhat better (4), stayed the same (3), somewhat worse (2) or much worse (1) compared to the last time they were assessed. We considered patients whose global rating score was 4 or 2 as having experienced some change equivalent to the MID. In patients who reported a worsening of health (global change of 1 or 2) the sign of the change in the SF-6D score was reversed (i.e. multiplied by minus one). The MID was then taken as the mean change on the SF-6D scale of the patients who scored (2 or 4). RESULTS: This paper describes the MID for the SF-6D from seven longitudinal studies that had previously used the SF-36. CONCLUSIONS: From the seven reviewed studies (with nine patient groups) the MID for the SF-6D ranged from 0.010 to 0.048, with a weighted mean estimate of 0.033 (95% CI: 0.029 to 0.037). The corresponding Standardised Response Means (SRMs) ranged from 0.11 to 0.48, with a mean of 0.30 and were mainly in the "small to moderate" range using Cohen's criteria, supporting the MID results. Using the half-standard deviation (of change) approach the mean effect size was 0.051 (range 0.033 to 0.066). Further empirical work is required to see whether or not this holds true for other patient groups and populations

The Lick AGN Monitoring Project 2011: Reverberation Mapping of Markarian 50

The Lick AGN Monitoring Project 2011 observing campaign was carried out over the course of 11 weeks in spring 2011. Here we present the first results from this program, a measurement of the broad-line reverberation lag in the Seyfert 1 galaxy Mrk 50. Combining our data with supplemental observations obtained prior to the start of the main observing campaign, our data set covers a total duration of 4.5 months. During this time, Mrk 50 was highly variable, exhibiting a maximum variability amplitude of a factor of ~4 in the U-band continuum and a factor of ~2 in the Hβ line. Using standard cross-correlation techniques, we find that Hβ and Hγ lag the V-band continuum by τ_(cen) = 10.64^(+0.82)_(–0.93) and 8.43^(+1.30)_(–1.28) days, respectively, while the lag of He II λ4686 is unresolved. The Hβ line exhibits a symmetric velocity-resolved reverberation signature with shorter lags in the high-velocity wings than in the line core, consistent with an origin in a broad-line region (BLR) dominated by orbital motion rather than infall or outflow. Assuming a virial normalization factor of f = 5.25, the virial estimate of the black hole mass is (3.2 ± 0.5) × 10^7 M_☉. These observations demonstrate that Mrk 50 is among the most promising nearby active galaxies for detailed investigations of BLR structure and dynamics

G-Bikes: Gettysburg Bike Share

The focus of this paper was to asses Gettysburg as possible location to implement a bike share program and ultimately to propose a framework for a successful program. We evaluated bike share programs across North America and created a list of criteria of successful programs. The second part of our data collection included a Google Forms survey which targeted three demographics, students, locals and tourists. We targeted our focus groups by posting on Facebook pages frequented by each demographic, as well as administering the survey in person with smart phones in Lincoln Square in Gettysburg. Our survey generated 134 responses, 86 of which were students, 27 locals, and 21 tourists. Our research showed that, demographically, successful programs occur in areas with high traffic from college students and tourists, as well as support from the local population. On the technical side, successful programs have 10-30 bikes per 10,000 residents with bike stations that range from 1-2 miles apart, averaging 4-8 trips per day, per bike. Our survey showed that a bike share program in Gettysburg would receive heavy support from our three demographics. It also showed that the largest concern from each demographic was bike related travel during the winter months which is consistent with the other programs we studied. Based on our research, we propose that the G-Bikes program should have 5 stations located at the top five intended locations of visitation, Gettysburg Town Center, Gettysburg College, Little Round Top, The Observation Tower, and on Steinwehr Avenue near the National Cemetery. Based off the overall population we recommend that the program start with a minimum of 20 bikes. We also recommend that the bike models follow the oBike specs from European bike share programs to maximize user convenience and minimize the threat of theft and vandalism. Through our study we determined Gettysburg\u27s unique niche as a small college town and tourist hub to be a possible location to implement a successful bike share program that implements many of the similar characteristics of other tourist destinations we studied

Cohort profile of the UK Biobank: diagnosis and characteristics of cerebrovascular disease

Purpose: The UK Biobank is a large-scale biomedical resource, containing sociodemographic and medical information, including data on a previous diagnosis of stroke or transient ischaemic attack (TIA). We described these participants and their medication usage. Participants: We identified participants who either self-reported or were identified from a nurse-led interview, having suffered a stroke or a TIA and compared them against participants without stroke ort TIA. We assessed their risk factor burden (sex, age, deprivation, waist to hip ratio (WHR), hypertension, smoking, alcohol intake, diabetes, physical exercise and oral contraception use (oral contraceptive pill, OCP)) and medication usage. Findings: to date We studied 502 650 people (54.41% women), 6669 (1.23%) participants self-reported a stroke. The nurse-led interview identified 7669 (1.53%) people with stroke and 1781 (0.35%) with TIA. Hypertension, smoking, higher WHR, lower alcohol consumption and diabetes were all more common in people with cerebrovascular disease (p&lt;0.0001 for each). Women with cerebrovascular disease were less likely to have taken the OCP (p=0.0002). People with cerebrovascular disease did more exercise (p=0.03). Antithrombotic medication was taken by 81% of people with stroke (both self-report and nurse-led responders) and 89% with TIA. For self-reported stroke, 63% were taking antithrombotic and cholesterol medications, 54% taking antithrombotic and antihypertensive medications and 46% taking all 3. For the nurse-led interview and TIA, these figures were 65%, 54% and 46%, and 70%, 53% and 45%, respectively. Future plans: The UK Biobank provides a large, generalisable and contemporary data source in a young population. The characterisation of the UK Biobank cohort with cerebrovascular disease will form the basis for ongoing research using this data source