professor madeleine king

Please check you selected the correct society from the list and entered the user name and password you use to log in to your society website. The group who organised Read for the Globe met in 2018 when they began the Shakespeare Studies MA, which is taught jointly by King’s and the Globe. The AUC summary measure, approximated with the trapezoid method, is calculated for the ith subject as. AUC summary statistics were consistently superior to AUC summary measures in terms of precision and bias. – Professor Farah Karim-Cooper, Head of Higher Education & Research for Shakespeare’s Globe and Globe Professor of Shakespeare Studies at King’s College London. When the data were MNAR, the bias of the mixed model estimates was low for most scenarios, except for the temporary change, unequal drop-out case, which had a 29% underestimation. As a sensitivity analysis, a threshold missingness mechanism was also used: for MAR, if yij− 1 < threshold, then delete yij with a given probability; for MNAR, if yij < threshold, then delete yij with a given probability. To discover more #ContinuingToServe stories, visit our webpage and follow @ServiceAtKings on Twitter and Instagram. To make the missing data as realistic as possible, we increased the proportion of missingness with each time point. Precision, as measured by the 95% CI widths was comparable for all patterns, although the mixed models had slightly smaller CI widths and the complete case analysis consistently had the largest CI widths, due to smaller sample size. . As a barrister she spent ten years fighting for her clients in English courtrooms. Because the within-arm trajectories differ substantially by attrition group, these data are unlikely to be MCAR. Lean Library can solve it. (, Neoptolemos, J. P., Stocken, D. D., Friess, H., Bassi, C., Dunn, J. For each of the three data patterns, we simulated data that were complete, MCAR, MAR, and MNAR. AUC has not been investigated using simulated data informed by PRO questionnaires, although some have considered single PRO data sets (Curran et al., 2000; D. L. Fairclough, 1997; Qian et al., 2000). 100%. This is to be expected based on the central limit theorem but is mentioned to underscore the validity of parametric approaches. The email address and/or password entered does not match our records, please check and try again. We simulated data for a randomized, two group, longitudinal design of five time points, using a repeated-measures (means) mixed model: where Yij is the outcome for the ith subject at the jth time i = 1, . As well as raising vital funds for the Globe, the readathon brought people together in an act of creation, despite the volunteer readers being in different places and time zones. Study 1: The Australian Ovarian Cancer Study is a population based study that recruited women aged 18 to 79 years with ovarian cancer from hospitals and registries (Price et al., 2013). Objective The aim of this study was to determine the optimal patient-reported outcome measure (PROM) for assessing symptom benefit in trials of palliative chemotherapy for women with symptomatic ovarian cancer. Summary measures AUC with ad hoc approaches to missing data were compared with mixed model AUC summary statistics. In contrast to summary measures, summary statistics summarize group values not individuals. The objective was to compare overall QoL between the two treatment groups, thus the choice of AUC is appropriate. QoL in the renal cell carcinoma trial, stratified by dropout time and treatment group. . Randall Division of Cell & Molecular Biophysics. . Table A1. Madeleine grew up in Paris, … Because groups can be compared using the difference in mean AUC with a t test (or similar, including linear regression if covariates are to be included), the advantage of this approach is its simplicity. When they finished the final play on Monday evening, the group described feeling emotional that the readathon was over. She is involved in building collaborative research efforts in quality of life research in the Australian cancer community. Figure 2. For example, bias with LOCF ranged from −24% to 8% for MCAR data, and −24% to 16% for the MAR case. Our research was informed and motivated by two studies. This article focuses on AUC as a summary. We address the aims of our study by varying the trajectories, as well as the type and rates of missing data as shown in Table 1. In all, 197 patients had QoL assessed at four time points: baseline, 2, 8, and 17 weeks. Madeleine King, Australian Chair in Cancer Quality of Life, Psycho‐Oncology Cooperative Research Group, School of Psychology, University of Sydney NSW. If AUC is used as a longitudinal summary when data are not MCAR, it should be estimated using maximum likelihood (such as a mixed model) using summary statistics rather than from individuals’ summary measures to minimize bias in treatment effect estimation. While some have argued that the compound symmetric correlation structure assumed by this model is unrealistically simple (Fitzmaurice et al., 2011), others have argued that this is a reasonable assumption in the context of RCTs (Frison & Pocock, 1992). Estimates of Difference in QoL AUC (Treat − Control) for Renal Carcinoma RCT Data. Strand To help raise much-needed funds and awareness of the theatre’s plight, a group of former King’s students brought together hundreds of people from around the world to read a selection of their favourite Shakespeare plays over 48 hours. Study 2: The second study is a multicenter randomized Phase III trial comparing two treatments in advanced renal cell carcinoma patients (D. F. Fairclough, 2010). Professor Nicholson is an internationally renowned scientist leading the Australian National Phenome Centre. Professor Maddy Parsons. The summary measure AUC was calculated from the raw data, with each patient’s five PRO measurements summarized into one value. Melanie Bell is Associate Professor of Biostatistics at the Mel & Enid Zuckerman College of Public Health at the University of Arizona. A review of published randomized controlled trials in major medical journals, International Journal of Sport Nutrition and Exercise Metabolism. The t test tested the null hypothesis H0:μAUC0=μAUC1, where μAUC0 is estimated by AUC¯0=1/n∑i=1nAUCi, for example, for the control group. The initiative was a huge success and over the UK’s Spring Bank Holiday weekend (from Saturday 23 to Monday 25 May) 200 volunteers read 16 plays, all of which are available to view on the Read for the Globe YouTube channel. ., m, and Yij− 1 is the j − 1th time for the ith subject. This product could help you, Accessing resources off campus can be a challenge. This is simply the area of each trapezoid formed by connecting consecutive Y values and is a weighted linear combination of individual measurements with the average PRO level of each pair of adjacent assessments weighted by the duration of the time period between those two assessments, summed over m − 1 time periods. Despite these warnings, LOCF is still often used (Fielding et al., 2008; Wood et al., 2004). Specifically, for MAR, P(Mij=1)=1/(1+e−θj), where θj=yij−1/y¯j−1log(pj/1−pj), and y¯j−1 = the mean of y at the j − 1th time point. London United Kingdom, The Read for the Globe co-founders: Sophie Allin, Madeleine Saidenberg, Alex Mason, Regine Vital, Jamie McGeown, Catriona Bolt, Olivia Perrett, Katherine Corner, Elise Lamsdale and Ben Kingcox, The Read for the Globe co-founders during their Shakespeare Studies MA, Professor Stephen Greenblatt as Edmund in King Lear, Kevin McNally introduces Read for the Globe Part 3, Dr Valerie Clayman Pye as Imogen in Cymbeline, Chukwudi Iwuji reading a monologue from Hamlet, The cast of King Lear, including Emily Carding, Professor Stephen Greenblatt, Professor Ramie Targoff and Dr Miranda Fay Thomas. . . Data missing not at random (MNAR) are those where missingness depends on the value of the missing data itself, even when taking observed data into account. By the fourth assessment, only 43% of the surviving patients had complete QoL data, which was 35% of all patients. Although some researchers have advocated for keeping PRO analysis simple (Cox et al., 1992), it is not clear how this can be accomplished when data are missing, as PRO data often are, because they are often suspected of missing non-randomly (D. F. Fairclough, 2010). The mixed model estimates showed negligible bias for both MCAR and MAR data: less than 1% for all patterns and drop-out rates. We aimed to examine the bias of these two methods and their sensitivity to assumptions about missing data and patterns of change. A., Rock, E. P., Dawisha, S., . To mimic ceiling effects commonly found in PRO data, we used a truncated normal distribution. Level of Difficulty. Although summary measures are equivalent to summary statistics (for certain models, such as those shown here), when no data are missing, a complete data set in longitudinal studies is rare, so missingness must be considered in any valid analysis. She was then educated in South Africa, England, Switzerland and France. Complete case analyses yielded unbiased results for MCAR data, as expected. Using the model in Equation 1, the estimated mean at any time j = 1, . Note. Some people even created fan art inspired by the performances. Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Only monotone (dropout) missing patterns were simulated. All simulations and analyses were performed in SAS v9.2. Graham Huggan is Professor and Chair of Commonwealth and Postcolonial Literatures at the University of Leeds, UK. Prepared by Dr Melanie Bell and Professor Madeleine King The objective of this paper is to outline important issues in the statistical analysis of HRQoL as an outcome measure in randomised controlled trials (RCT), and in the reporting and interpretation of HRQoL results. In contrast, for the temporary decline pattern, it produced overestimates. Both of these highly cited papers used individual summary measures, but neither explained how they handled missing data in their computations. The most dramatic and not unexpected result occurs with the complete case analysis, with an estimated treatment difference of 0.56 as compared with the mixed model estimate of −20.8. Why an Edmonton professor barely received any credit for his role in helping discover insulin. To reenergise the cast, she started a poll on Twitter asking the audience to vote for their favourite bromance in the play which provoked a competition among the volunteer readers. Sustained effects over time might best be assessed by AUC or the mean over time (these are similar when times between assessments are equal; Curran et al., 2000). Table 4. Find the perfect Sweden Royal Family stock photos and editorial news pictures from Getty Images. SAGE Publications Inc, unless otherwise noted. Table 2. The ten friends decided to host a 48-hour readathon of The Bard’s greatest hits, such as Hamlet, King Lear and Macbeth, as well as some of the lesser known but still much-loved plays, including Cymbeline and Pericles. Table 3. Sensitivity of results to the method of missing data creation. The complete case analysis of AUC summary measures consistently had the lowest precision (due to reduced sample size), and the bias with this approach doubled when the rate of missing data differed between groups. Beta values varied by trajectory, and are given in Table 1. . . Little and Rubin (1987) defined three types of missingness. Georgina Luscombe, Medical Statistician, Faculty of Medicine, University of Sydney NSW. Dawson (1994) used simulation to investigate various summary measures including AUC but did not compare the commonly used “naïve” summary measures approach with summary statistics. She is involved in clinical trials research, particularly with regard to missing data and patient reported outcomes. The results given in Table A1 are nearly identical to the original results in the article. Rates at each time point for the MCAR and MNAR data were similar and are not shown. With regard to PRO trajectories over time, we note that the extrapolation technique appears to stand out in contrast to the other methods (Figure 2). MCAR missing data were created by random deletion. View or download all content the institution has subscribed to. Perhaps researchers use simple imputation methods because of a desire to follow the intention to treat principle and are not aware that likelihood methods can be consistent with this principle (Molenberghs et al., 2004). Summary measures (or individual’s raw data summaries) are an approach to simplifying longitudinal data by reducing an individual’s data to a single value, such as the maximum, the slope over time, or the area under the curve (AUC). We also present the bias divided by the estimated standard error (SE) that represents how far off the t statistic for the test of difference in AUC is from the t statistic computed from a non-biased estimate. It has been scaled to a range of 0 to 100, with higher values indicating better QoL. ., n = 200, j = 1, . .Bonomi, P. (, Cheng, K. K., Leung, S. F., Liang, R. H., Tai, J. W., Yeung, R. M., Thompson, D. R. (, Cox, D. R., Fitzpatrick, R., Fletcher, A. E., Gore, S. M., Spiegelhalter, D. J., Jones, D. R. (, Curran, D., Aaronson, N., Standaert, B., Molenberghs, G., Therasse, P., Ramirez, A., . The bias resulting from the summary measure approach was considerable under some conditions, and importantly, the size and direction of bias was unpredictable, varying with data pattern, missingness, and imputation method. The possible range of QoL is 0 to 100, with higher values indicating better QoL. Missing data can cause biased estimates of treatment effect and change over time, particularly if patients with missing data have poorer health than those whose data are complete. He was born in Madras, British India on 8 September 1856, the eldest son of the Deputy Surgeon-General Henry King, also the Principal of the Medical School in Madras.. This lecture series is FREE however, tickets are essential. Her early career focused on evolutionary and population genetics of algae and fish, and subsequently corals. QoL = quality of life.

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