Manisha Desai's guest appearances

Manisha Desai

Women in Data Science

The Center for the Study of Genocide and Human Rights

The Data Pulse

Francesca Dominici

Andreessen Horowitz

Daphne Koller

John Evans

Laura Simms

Patricia Smith

Micheline Aharonian Marcom

Janet George

Kyle Swanson

Newsha Ajami

Khatchig Mouradian

Yusef Komunyakaa

Karen Hao

Carlos Bustamante

Chiara Sabatti

OwlTail

Working with humans poses significant challenges to acquiring robust and complete data, but also remarkable opportunity, as I learn in today's episode with Professor Manish Desai of Stanford University. We discuss inferring causality from longitudinal data, clinical trial and observational study considerations, and the intersection of statistics and medicine at large. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary--- Support this podcast: https://podcasters.spotify.com/pod/show/the-data-pulse/support

Working with humans poses significant challenges to acquiring robust and complete data, but also remarkable opportunity, as I learn in today's episode with Professor Manish Desai of Stanford University. We discuss inferring causality from longitudinal data, clinical trial and observational study considerations, and the intersection of statistics and medicine at large. Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: <a href="https://anchor.fm/dashboard/episode/egftra/metadata/bit.ly/data-pulse-glossary" rel="nofollow" target="_blank">bit.ly/datapulse-glossary</a>--- Support this podcast: <a href="https://podcasters.spotify.com/pod/show/the-data-pulse/support" rel="payment nofollow" target="_blank">https://podcasters.spotify.com/pod/show/the-data-pulse/support</a>

Clinical trials and longitudinal studies with Manisha Desai (Stanford University)

Manisha Desai is a professor of medicine (research) and of biomedical data science, and director of the Quantitative Sciences Unit at Stanford University. She is an expert in the design and analysis of clinical trials and epidemiologic studies across multiple diseases, including COVID-19. In this podcast, she provides some insights into the challenges and progress of COVID-19 clinical trials.When COVID happened, Manisha knew her team’s expertise in clinical trials would allow them to get up to speed quickly to study this new disease. So, she augmented her team with additional data science experts to focus on trials to identify safe and effective drugs to help prevent hospitalization and disease progression.“This whole period has been so anxiety-inducing for so many of us, it's actually been nice to focus that energy and anxiety towards work that could be part of the solution to some of the crisis,” she says. In studies so far, they have learned that there's a real disparity in outcomes by ethnicity and race and they are trying to determine if that’s due to genetic, cultural, or environmental factors. They are also launching trials to figure out questions about immunity, seroprevalence and incidents. Clinical trials are the gold standard for identifying safe and effective therapies. Since these trials require a lot of time and rigor, the findings have high internal validity. In a typical clinical trial, there is a sequential plan which starts by designing the study, setting up a data and safety monitoring board, database capture, and analysis plan. It's a totally different in a pandemic when you want to launch all of these events quickly, so developing the analysis plan and designing the study don't happen sequentially. “You're talking to the FDA and the IRB at the same time that you're developing the protocol, just as the ideas are sort of coming together. Everyone needs the study launched, we're all sheltering in place and solutions need to be found, so everyone is rushing to do this,” she says.She cautions that we need to be careful. “These protocols are in place for the safety of the patients, and the integrity of the data and the study. The worst thing would be spending all these resources, having patients contribute to the trial, and then at the end of the day having inconclusive evidence,” she says. People are anxious about finding answers, they want to publish and disseminate their findings and be the first to solve the problem. And as data scientists, it’s critical to ensure the rigor, that the study is designed well, that the limitations are understood and that there's an appropriate interpretation of the findings. All of the misinformation that has been going on during this pandemic can contribute to further loss of lives.Collaboration on COVID-19 research across groups is essential because we need to pool data. When you combine data across institutions and countries, we need a level of certainty about who is collecting what, how often, on whom, under which policy. This will really impact how and if you even can pool the data, and how you can interpret it. She says one of the most important foundations to the success of the research is ensuring that the data has integrity. People spend so much time spent thinking about the models but we need to get the data management process right before applying the models. There is not currently one centralized, robust data repository in the US that makes it easy for researchers to study questions when they arise. Manisha says people are trying to get these registries up and going quickly, and it's not easy because we ascertain data differently across different sites and countries. This is a very worthwhile investment to put time into thinking about how to collect data in standardized forms. Manisha has seen an enormous response to solving the COVID crisis. She is optimistic that we will identify safe and effective treatments and a vaccine on the horizon. “We’ve learned that the FDA and regulatory agencies can be quite responsive. And maybe even in a non-COVID world, we can learn about how to gain efficiency and have some work arounds when necessary, and get rid of things that might be unnecessary. There will be many lessons learned, not just for COVID, but there will be other pandemics, and we will be able to learn from this experience.”RELATED LINKSConnect with Manisha Desai on LinkedInFind out more about Manisha on her Stanford ProfileRead more about Stanford School of MedicineConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

Manisha Desai is a professor of medicine (research) and of biomedical data science, and director of the Quantitative Sciences Unit at Stanford University. She is an expert in the design and analysis of clinical trials and epidemiologic studies across multiple diseases, including COVID-19. In this podcast, she provides some insights into the challenges and progress of COVID-19 clinical trials.When COVID happened, Manisha knew her team’s expertise in clinical trials would allow them to get up to speed quickly to study this new disease. So, she augmented her team with additional data science experts to focus on trials to identify safe and effective drugs to help prevent hospitalization and disease progression.“This whole period has been so anxiety-inducing for so many of us, it's actually been nice to focus that energy and anxiety towards work that could be part of the solution to some of the crisis,” she says. In studies so far, they have learned that there's a real disparity in outcomes by ethnicity and race and they are trying to determine if that’s due to genetic, cultural, or environmental factors. They are also launching trials to figure out questions about immunity, seroprevalence and incidents. Clinical trials are the gold standard for identifying safe and effective therapies. Since these trials require a lot of time and rigor, the findings have high internal validity. In a typical clinical trial, there is a sequential plan which starts by designing the study, setting up a data and safety monitoring board, database capture, and analysis plan. It's a totally different in a pandemic when you want to launch all of these events quickly, so developing the analysis plan and designing the study don't happen sequentially. “You're talking to the FDA and the IRB at the same time that you're developing the protocol, just as the ideas are sort of coming together. Everyone needs the study launched, we're all sheltering in place and solutions need to be found, so everyone is rushing to do this,” she says.She cautions that we need to be careful. “These protocols are in place for the safety of the patients, and the integrity of the data and the study. The worst thing would be spending all these resources, having patients contribute to the trial, and then at the end of the day having inconclusive evidence,” she says. People are anxious about finding answers, they want to publish and disseminate their findings and be the first to solve the problem. And as data scientists, it’s critical to ensure the rigor, that the study is designed well, that the limitations are understood and that there's an appropriate interpretation of the findings. All of the misinformation that has been going on during this pandemic can contribute to further loss of lives.Collaboration on COVID-19 research across groups is essential because we need to pool data. When you combine data across institutions and countries, we need a level of certainty about who is collecting what, how often, on whom, under which policy. This will really impact how and if you even can pool the data, and how you can interpret it. She says one of the most important foundations to the success of the research is ensuring that the data has integrity. People spend so much time spent thinking about the models but we need to get the data management process right before applying the models. There is not currently one centralized, robust data repository in the US that makes it easy for researchers to study questions when they arise. Manisha says people are trying to get these registries up and going quickly, and it's not easy because we ascertain data differently across different sites and countries. This is a very worthwhile investment to put time into thinking about how to collect data in standardized forms. Manisha has seen an enormous response to solving the COVID crisis. She is optimistic that we will identify safe and effective treatments and a vaccine on the horizon. “We’ve learned that the FDA and regulatory agencies can be quite responsive. And maybe even in a non-COVID world, we can learn about how to gain efficiency and have some work arounds when necessary, and get rid of things that might be unnecessary. There will be many lessons learned, not just for COVID, but there will be other pandemics, and we will be able to learn from this experience.”RELATED LINKSConnect with Manisha Desai on <a href="https://www.linkedin.com/in/manisha-desai-a6610aa5/" rel="nofollow" target="_blank">LinkedIn</a> Find out more about Manisha on her <a href="https://profiles.stanford.edu/manisha-desai" rel="nofollow" target="_blank">Stanford Profile</a> Read more about <a href="http://med.stanford.edu/" rel="nofollow" target="_blank">Stanford School of Medicine</a> Connect with Margot Gerritsen on <a href="https://twitter.com/margootjeg" rel="nofollow" target="_blank">Twitter (@margootjeg)</a> and <a href="https://www.linkedin.com/in/margot-gerritsen-2b01084/" rel="nofollow" target="_blank">LinkedIn</a> Find out more about Margot on her <a href="https://profiles.stanford.edu/margot-gerritsen" rel="nofollow" target="_blank">Stanford Profile</a> Find out more about Margot on her <a href="https://gerritsn.people.stanford.edu/" rel="nofollow" target="_blank">personal website</a>

Manisha Desai | The Importance of Data Integrity in COVID-19 Clinical Trials

Manisha Desai - Gender and the Politics of Possibilities: Rethinking Globalization (September 22, 2008)