Coefficients (standard errors) of regressing log waist circumference (meter) upon log weight (kg) and log height (meter).

<p>Coefficients (standard errors) of regressing log waist circumference (meter) upon log weight (kg) and log height (meter).</p

Correlation coefficients between ABSI and anthropometric indicators.

<p>P>0.01; all other P<0.01</p><p>ABSI (A): ABSI as proposed by Krakauer and Krakauer (2012), see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085421#pone.0085421.e002" target="_blank">equation (1)</a></p><p>ABSI (I): ABSI with locally derived exponents, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085421#pone.0085421.e007" target="_blank">equation (3)</a></p

Weight status in 2000.

<p>Weight status in 2000.</p

Odds ratios (OR) estimated from logistic regression analysis and area under curve (AUC) estimated from receiver operating characteristic analysis of incident hypertension in relation to anthropometric indices in age-and-gender standardized z-scores (n = 3788).

<p>ABSI (A): ABSI as proposed by Krakauer and Krakauer (2012), see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085421#pone.0085421.e002" target="_blank">equation (1)</a></p><p>ABSI (I): ABSI with locally derived exponents, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085421#pone.0085421.e007" target="_blank">equation (3)</a></p

Descriptive summary of the participants in 2000.

<p>ABSI (A): ABSI as proposed by Krakauer and Krakauer (2012), see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085421#pone.0085421.e002" target="_blank">equation (1)</a></p><p>ABSI (I): ABSI with locally derived exponents, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085421#pone.0085421.e007" target="_blank">equation (3)</a></p

Sample size determination for fold-increase endpoints defined by paired interval-censored data

<p>Medical studies often define binary end-points by comparing the ratio of a pair of measurements at baseline and end-of-study to a clinically meaningful cut-off. For example, vaccine trials may define a response as at least a four-fold increase in antibody titers from baseline to end-of-study. Accordingly, sample size is determined based on comparisons of proportions. Since the pair of measurements is quantitative, modeling the bivariate cumulative distribution function to estimate the proportion gives more precise results than using dichotomization of data. This is known as the distributional approach to the analysis of proportions. However, this can be complicated by interval-censoring. For example, due to the nature of some laboratory measurement methods, antibody titers are interval-censored. We derive a sample size formula based on the distributional approach for paired interval-censored data. We compare the sample size requirement in detecting an intervention effect using the distributional approach to a conventional approach of dichotomization. Some practical guidance on applying the sample size formula is given.</p

Measurement properties of the Functional Assessment of Cancer Therapy-Breast may depend upon the education levels of patients

<p><b><i>Background</i></b>: Education level may vary in older cancer patients. This study aims to compare the measurement properties of the Functional Assessment of Cancer Therapy-Breast (FACT-B) when self-administered by breast cancer patients with various education levels.</p> <p><b><i>Methods</i></b>: An observational study of 244 Singaporean breast cancer patients who self-administered the instrument. FACT-General and FACT-B scores were assessed for the discriminatory ability, responsiveness to change and test–retest reliability. We hypothesized that patients with better performance status would result in higher FACT-General and FACT-B scores. Regression models were constructed, and relative precisions were also examined.</p> <p><b><i>Results</i></b>: The mean baseline FACT-General and FACT-B scores monotonically decreased with performance status in the higher but not lower educated group (relative precision = 1.13 and 1.08, respectively). Stronger responsiveness to deterioration in quality of life were found in the higher (each <i>p</i> < 0.001) than lower educated group (<i>p</i> = 0.146 and 0.245). Larger intra-class correlation coefficient of the FACT scores (each <i>p</i> < 0.05) and smaller variability in the Bland-Altman plots indicated better test–retest reliability in higher educated group.</p> <p><b><i>Conclusion</i></b>: Measurement properties may be reduced when the instrument is self-administered by lower educated cancer patients.</p

DS_10.1177_0272989X18756890 – Supplemental material for Mean Rank, Equipercentile, and Regression Mapping of World Health Organization Quality of Life Brief (WHOQOL-BREF) to EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L) Utilities

<p>Supplemental material, DS_10.1177_0272989X18756890 for Mean Rank, Equipercentile, and Regression Mapping of World Health Organization Quality of Life Brief (WHOQOL-BREF) to EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L) Utilities by Hwee Lin Wee, Khung Keong Yeo, Kok Joon Chong, Eric Yin Hao Khoo and Yin Bun Cheung in Medical Decision Making</p

Using best-worst scaling choice experiments to elicit the most important domains of health for health-related quality of life in Singapore

<div><p>Health-related quality of life (HRQOL) instruments are sometimes used without explicit understanding of which HRQOL domains are important to a given population. In this study, we sought to elicit an importance hierarchy among 27 HRQOL domains (derived from the general population) via a best-worst scaling survey of the population in Singapore, and to determine whether these domains were consistently valued across gender, age, ethnicity, and presence of chronic illnesses. We conducted a community-based study that sampled participants with quotas for gender, ethnicity, age, presence of chronic illness, and interview language. For the best-worst scaling exercise, we constructed comparison sets according to a balanced incomplete block design resulting in 13 sets of questions, each with nine choice tasks. Each task involved three HRQOL domains from which participants identified the most and least important domain. We performed a standard analysis of best-worst object scaling design (Case 1) using simple summary statistics; 603 residents participated in the survey. The three most important domains of health were: “the ability to take care of self without help from others” (best-worst score (BWS): 636), “healing and resistance to illness” (BWS: 461), and “having good relationships with family, friends, and others” (BWS: 373). The 10 top-ranked domains included physical, mental, and social health. The three least important domains were: “having a satisfying sex life” (BWS: -803), “having normal physical appearance” (BWS: -461), and “interacting with others (talking, shared activities, etc.)” (BWS: -444). Generally, top-ranked domains were consistently valued across gender, age, ethnicity, and presence of chronic illness. We conclude that the 10 top-ranked domains reflect physical, mental, and social dimensions of well-being suggesting that the sampled population’s views on health are consistent with the World Health Organization’s definition of health, “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”.</p></div

Importance ranks (index values, IV) and consistency of ranks (coefficient of variation, CV) by ethnicity and presence of chronic conditions.

<p>Importance ranks (index values, IV) and consistency of ranks (coefficient of variation, CV) by ethnicity and presence of chronic conditions.</p