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7 results on '"Jogarah, Vidoushee"'

1. Early versus delayed weight-bearing following operatively treated ankle fracture (WAX): a non-inferiority, multicentre, randomised controlled trial

Author

McAndrew, Andrew, Jacobs, Neal, Forder, Justin, Hester, Thomas, Cross, Charlotte, Bateman, Tony, Kieffer, Will, Barton, Tristan, Walter, Richard, Savva, Nick, Marsland, Daniel, Rose, Barry, Beech, Zine, Koc, Togay, Armstead, Bethany, Ollivere, Ben, Diamond, Owen, Teoh, Kar, Magill, Paul, Mangwani, Jitendra, Hodgson, Paul, Ray, Robbie, Dhinsa, Baljinder, Majeed, Haroon, Wong-Chung, John, Young, Jonathan, Lagare, Agnes, Soogumbur, Akash, Morozova, Albina, Hunt, Alexander, Adamson, Amanda, Dempster, Angie, McCormack, Ann, Arif, Azra, Vye, Charlotte, Dojode, Chetan, Brown, Chloe, Haines, Christina, To, Christopher, Brennan, Ciaran, Winson, Dan, McGough, Elizabeth, Jessup-Dunton, Ellen, Bintcliffe, Fiona, Thompson, Fiona, Omogra, Gabriel, Scott, Georgia, Samuel, Helen, Fraig, Hossam, Burokiene, Ina, Odysseos-Beaumont, Isabel, Rand, James, Edkins, Janet, Barrett-Lee, Joe, McFall, John, Wahed, Karim, Herbert, Kate, Death, Kelly, Beddard, Laura, Dupley, Leanne, Bryce, Leeann, Wright, Lianne, Bailey, Lucy, Maling, Lucy, Raad, Marjan, Morris, Matt, Williams, Matthew, Labidi, May, Holmes, Natalie, Staines, Nikki, Matthews, Paul A, McCormac, Philip, Easow, Rashmi, Matthews, Scott, Kapoor, Smriti, Harris, Sophie, Wagland, Susan, Cobb, Timothy, White, Tracey, Bretherton, Christopher Patrick, Achten, Juul, Jogarah, Vidoushee, Petrou, Stavros, Peckham, Nicholas, Achana, Felix, Appelbe, Duncan, Kearney, Rebecca, Claireux, Harry, Bell, Philip, and Griffin, Xavier L

Published
2024
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2. Regularised B-splines projected Gaussian Process priors to estimate time-trends of age-specific COVID-19 deaths related to vaccine roll-out

Author

Monod, Mélodie, Blenkinsop, Alexandra, Brizzi, Andrea, Chen, Yu, Perello, Carlos Cardoso Correia, Jogarah, Vidoushee, Wang, Yuanrong, Flaxman, Seth, Bhatt, Samir, and Ratmann, Oliver

Subjects
Statistics - Applications
Abstract

The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 attributable deaths over time in the United States. The model is specified by a novel non-parametric spatial approach, a low-rank Gaussian Process (GP) projected by regularised B-splines. We show that this projection defines a new GP with attractive smoothness and computational efficiency properties, derive its kernel function, and discuss the penalty terms induced by the projected GP. Simulation analyses and benchmark results show that the spatial approach performs better than standard B-splines and Bayesian P-splines and equivalently well as a standard GP, for considerably lower runtimes. The B-splines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesian regularising priors. We apply the model to weekly, age-stratified COVID-19 attributable deaths reported by the US Centers for Disease Control, which are subject to censoring and reporting biases. Using the B-splines projected GP, we can estimate longitudinal trends in COVID-19 associated deaths across the US by 1-year age bands. These estimates are instrumental to calculate age-specific mortality rates, describe variation in age-specific deaths across the US, and for fitting epidemic models. Here, we couple the model with age-specific vaccination rates to show that lower vaccination rates in younger adults aged 18-64 are associated with significantly stronger resurgences in COVID-19 deaths, especially in Florida and Texas. These results underscore the critical importance of medically able individuals of all ages to be vaccinated against COVID-19 in order to limit fatal outcomes.

Published
2021

3. Early versus delayed weight-bearing following operatively treated ankle fracture (WAX): a non-inferiority, multicentre, randomised controlled trial

Author

Bretherton, Christopher Patrick, primary, Achten, Juul, additional, Jogarah, Vidoushee, additional, Petrou, Stavros, additional, Peckham, Nicholas, additional, Achana, Felix, additional, Appelbe, Duncan, additional, Kearney, Rebecca, additional, Claireux, Harry, additional, Bell, Philip, additional, Griffin, Xavier L, additional, McAndrew, Andrew, additional, Jacobs, Neal, additional, Forder, Justin, additional, Hester, Thomas, additional, Cross, Charlotte, additional, Bateman, Tony, additional, Kieffer, Will, additional, Barton, Tristan, additional, Walter, Richard, additional, Savva, Nick, additional, Marsland, Daniel, additional, Rose, Barry, additional, Beech, Zine, additional, Koc, Togay, additional, Armstead, Bethany, additional, Ollivere, Ben, additional, Diamond, Owen, additional, Teoh, Kar, additional, Magill, Paul, additional, Mangwani, Jitendra, additional, Hodgson, Paul, additional, Ray, Robbie, additional, Dhinsa, Baljinder, additional, Majeed, Haroon, additional, Wong-Chung, John, additional, Young, Jonathan, additional, Lagare, Agnes, additional, Soogumbur, Akash, additional, Morozova, Albina, additional, Hunt, Alexander, additional, Adamson, Amanda, additional, Dempster, Angie, additional, McCormack, Ann, additional, Arif, Azra, additional, Vye, Charlotte, additional, Dojode, Chetan, additional, Brown, Chloe, additional, Haines, Christina, additional, To, Christopher, additional, Brennan, Ciaran, additional, Winson, Dan, additional, McGough, Elizabeth, additional, Jessup-Dunton, Ellen, additional, Bintcliffe, Fiona, additional, Thompson, Fiona, additional, Omogra, Gabriel, additional, Scott, Georgia, additional, Samuel, Helen, additional, Fraig, Hossam, additional, Burokiene, Ina, additional, Odysseos-Beaumont, Isabel, additional, Rand, James, additional, Edkins, Janet, additional, Barrett-Lee, Joe, additional, McFall, John, additional, Wahed, Karim, additional, Herbert, Kate, additional, Death, Kelly, additional, Beddard, Laura, additional, Dupley, Leanne, additional, Bryce, Leeann, additional, Wright, Lianne, additional, Bailey, Lucy, additional, Maling, Lucy, additional, Raad, Marjan, additional, Morris, Matt, additional, Williams, Matthew, additional, Labidi, May, additional, Holmes, Natalie, additional, Staines, Nikki, additional, Matthews, Paul A, additional, McCormac, Philip, additional, Easow, Rashmi, additional, Matthews, Scott, additional, Kapoor, Smriti, additional, Harris, Sophie, additional, Wagland, Susan, additional, Cobb, Timothy, additional, and White, Tracey, additional

Published
2024
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5. Regularised B-splines Projected Gaussian Process Priors to Estimate Time-trends in Age-specific COVID-19 Deaths

Author

Monod, Mélodie, Blenkinsop, Alexandra, Brizzi, Andrea, Chen, Yu, Cardoso Correia Perello, Carlos, Jogarah, Vidoushee, Wang, Yuanrong, Flaxman, Seth, Bhatt, Samir, Ratmann, Oliver, Monod, Mélodie, Blenkinsop, Alexandra, Brizzi, Andrea, Chen, Yu, Cardoso Correia Perello, Carlos, Jogarah, Vidoushee, Wang, Yuanrong, Flaxman, Seth, Bhatt, Samir, and Ratmann, Oliver

Abstract

The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 attributable deaths over time in the United States. The model is specified by a novel non-parametric spatial approach over time and age, a low-rank Gaussian Process (GP) projected by regularised B-splines. We show that this projection defines a GP with attractive smoothness and computational efficiency properties, derive its kernel function, and discuss the penalty terms induced by the projected GP. Simulation analyses and benchmark results show that the B-splines projected GP may perform better than standard B-splines and Bayesian P-splines, and equivalently well as a standard GP at considerably lower runtimes. We apply the model to weekly, age-stratified COVID-19 attributable deaths reported by the US Centers for Disease Control, which are subject to censoring and reporting biases. Using the B-splines projected GP, we can estimate longitudinal trends in COVID-19 associated deaths across the US by 1-year age bands. These estimates are instrumental to calculate age-specific mortality rates, describe variation in age-specific deaths across the US, and for fitting epidemic models. Here, we couple the model with age-specific vaccination rates to show that vaccination rates were significantly associated with the magnitude of resurgences in COVID-19 deaths during the summer 2021. With counterfactual analyses, we quantify the avoided COVID-19 deaths under lower vaccination rates and avoidable COVID-19 deaths under higher vaccination rates. The B-splines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesian regularising priors.

Published
2023

7. Protocol for open-label randomized clinical trial of intensive surveillance versus standard postoperative follow-up in patients undergoing surgical resection for oesophageal and gastric cancer.

Author

Markar, Sheraz R., Guazzelli, Alice, Taylor, Amy, Jones, Laura Lloyd, Dutton, Susan, Jogarah, Vidoushee, Brittain, Clare, Maynard, Nick, Cromwell, David, Landeiro, Filipa, Underwood, Tim, Lagergren, Jesper, Gleeson, Fergus, Moss, Alan, and Crosby, Tom

Subjects
ESOPHAGEAL cancer, GASTRECTOMY, SURGICAL excision, STOMACH cancer
Published
2023
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