1 00:00:00,000 --> 00:00:05,860 Welcome to the Clinician Researcher podcast, where academic clinicians learn the skills 2 00:00:05,860 --> 00:00:11,260 to build their own research program, whether or not they have a mentor. 3 00:00:11,260 --> 00:00:17,340 As clinicians, we spend a decade or more as trainees learning to take care of patients. 4 00:00:17,340 --> 00:00:22,380 When we finally start our careers, we want to build research programs, but then we find 5 00:00:22,380 --> 00:00:27,780 that our years of clinical training did not adequately prepare us to lead our research 6 00:00:27,780 --> 00:00:29,180 program. 7 00:00:29,180 --> 00:00:35,480 Through no fault of our own, we struggle to find mentors, and when we can't, we quit. 8 00:00:35,480 --> 00:00:40,580 However, clinicians hold the keys to the greatest research breakthroughs. 9 00:00:40,580 --> 00:00:46,200 For this reason, the Clinician Researcher podcast exists to give academic clinicians 10 00:00:46,200 --> 00:00:51,800 the tools to build their own research program, whether or not they have a mentor. 11 00:00:51,800 --> 00:01:01,060 Now introducing your host, Toyosi Onwuemene. 12 00:01:01,060 --> 00:01:03,500 Welcome to the Clinician Researcher podcast. 13 00:01:03,500 --> 00:01:05,660 I'm your host, Toyosi Onwuemene. 14 00:01:05,660 --> 00:01:08,180 It is such a pleasure to be with you today. 15 00:01:08,180 --> 00:01:12,220 Thank you for listening and thank you for being here because today's an especially special 16 00:01:12,220 --> 00:01:13,220 episode. 17 00:01:13,220 --> 00:01:18,660 We have a superstar biostatistician here with us, Dr. Maggie Kuchibhatla. 18 00:01:18,660 --> 00:01:23,200 I'm going to allow her to introduce herself in a minute, but I'm so pumped because as 19 00:01:23,200 --> 00:01:27,740 clinician researchers, many times we say we have to work with biostatisticians, but we 20 00:01:27,740 --> 00:01:30,060 don't even know the first thing to do. 21 00:01:30,060 --> 00:01:34,100 And Dr. Cotubatla is going to help to kind of demystify some of that process. 22 00:01:34,100 --> 00:01:38,540 So without further ado, I'm going to invite her to introduce herself. 23 00:01:38,540 --> 00:01:41,540 Maggie, welcome to the show. 24 00:01:41,540 --> 00:01:42,540 Thank you, Tracey. 25 00:01:42,540 --> 00:01:46,380 I don't know about superstar, but I love counting numbers. 26 00:01:46,380 --> 00:01:52,340 I lived that life this year, 30 years at Duke, so, I can't complain. 27 00:01:52,340 --> 00:01:54,060 I love it. 28 00:01:54,060 --> 00:02:02,460 As a statistician, it's a pleasure to help anybody, any researcher with all kinds of 29 00:02:02,460 --> 00:02:05,820 experiences come to the door. 30 00:02:05,820 --> 00:02:15,980 But the one thing that is always helpful and will not be very upsetting for the investigators 31 00:02:15,980 --> 00:02:23,940 would be to have a basic training in research methodology. 32 00:02:23,940 --> 00:02:30,580 So there are a number of places where a young investigator can go get that kind of training. 33 00:02:30,580 --> 00:02:40,940 I mean, for example, one of the easiest places to go is the local universities offer just 34 00:02:40,940 --> 00:02:44,060 a short-term course, like a week-long course. 35 00:02:44,060 --> 00:02:50,820 And NIH offers a summer course on quantitative methods for all the investigators. 36 00:02:50,820 --> 00:02:52,460 And Johns Hopkins has a course. 37 00:02:52,460 --> 00:03:00,500 Even I, as an investigator, for some of the epidemiological stuff, I've been to Johns 38 00:03:00,500 --> 00:03:03,220 Hopkins, where they have a summer program. 39 00:03:03,220 --> 00:03:08,660 I think NIH has provided them with some funding, and they have offered those courses for anybody 40 00:03:08,660 --> 00:03:10,780 from across the country. 41 00:03:10,780 --> 00:03:13,500 And it's a small thing. 42 00:03:13,500 --> 00:03:19,780 And the institutions from where you work from can provide you a little bit of money to go 43 00:03:19,780 --> 00:03:22,540 and get that training. 44 00:03:22,540 --> 00:03:26,780 So I'll now leave it to you now to ask any questions, Toyosi. 45 00:03:26,780 --> 00:03:27,780 Sure. 46 00:03:27,780 --> 00:03:30,620 Maggie, I just want to say thank you for just starting with that. 47 00:03:30,620 --> 00:03:34,100 And something that's so important, and I know we've talked about this before, is that as 48 00:03:34,100 --> 00:03:37,100 clinicians, we get little to no research training. 49 00:03:37,100 --> 00:03:41,660 Yes, we do a little bit of research here and there, but we're not leading the projects. 50 00:03:41,660 --> 00:03:45,140 We're not deciding what's the primary outcome, what's the secondary outcome. 51 00:03:45,140 --> 00:03:46,140 And so you're right. 52 00:03:46,140 --> 00:03:49,980 When we get to our faculty positions and we're like, I now want to do research, and we're 53 00:03:49,980 --> 00:03:52,340 like, well, you've got to talk to the biostatistician. 54 00:03:52,340 --> 00:03:53,860 Sometimes we don't have a clue. 55 00:03:53,860 --> 00:03:58,260 And so I appreciate what you talk about, the importance of getting a little bit of an education, 56 00:03:58,260 --> 00:04:03,380 no matter how small it is, and research methods, so that you can have a conversation. 57 00:04:03,380 --> 00:04:07,700 In a sense, it's just, how can we even start talking when we're not speaking the same language? 58 00:04:07,700 --> 00:04:12,580 And so thank you for sharing that and how important it is for clinicians to get educated 59 00:04:12,580 --> 00:04:16,920 so that they can really contribute to the research conversation. 60 00:04:16,920 --> 00:04:21,360 So the first question I want to ask you, if you don't mind, is can you talk to me about 61 00:04:21,360 --> 00:04:25,860 what is one thing about your role that every clinician researcher should know? 62 00:04:25,860 --> 00:04:27,660 We hear about biostatisticians. 63 00:04:27,660 --> 00:04:31,980 People are always talking about biostatisticians, but what should we know about you that we 64 00:04:31,980 --> 00:04:33,460 may not know? 65 00:04:33,460 --> 00:04:41,940 Well, the one thing that the investigator should know before coming to me is know a 66 00:04:41,940 --> 00:04:48,300 little bit about what their aims are, what their goals are. 67 00:04:48,300 --> 00:04:54,980 But even before they know their aims and goals, they've got to be working on those aspects 68 00:04:54,980 --> 00:04:56,780 before coming to me. 69 00:04:56,780 --> 00:05:03,580 So in that sense, they have to know, for example, what is their study design? 70 00:05:03,580 --> 00:05:07,780 What is it that they, how are they going to answer the questions? 71 00:05:07,780 --> 00:05:13,900 Are they just going to go collect the data that is there in the literature, or in which 72 00:05:13,900 --> 00:05:17,860 case it's just a systematic study? 73 00:05:17,860 --> 00:05:24,980 Or are they going to answer a question by doing a clinical trial? 74 00:05:24,980 --> 00:05:27,720 So they need to know the study design. 75 00:05:27,720 --> 00:05:32,260 So the question is, what kind of study design are they talking about? 76 00:05:32,260 --> 00:05:39,260 Is that a clinical trial, or is it a retrospective study, or a prospective study, or an observational 77 00:05:39,260 --> 00:05:47,100 study, or is that basic science study where you're doing experiments in animals? 78 00:05:47,100 --> 00:05:55,020 Or are they doing, collecting data from running a lot of analyses from the blood? 79 00:05:55,020 --> 00:06:00,740 In other words, it's the omics, proteomics, metabolomics, genetics. 80 00:06:00,740 --> 00:06:04,820 So it's all the blood, all the data that you're getting from the blood. 81 00:06:04,820 --> 00:06:12,500 Now you can have a study where it's a combination of long-term outcomes or long-term outcomes, 82 00:06:12,500 --> 00:06:17,340 short-term outcomes, as well as all these blood work data that you're having. 83 00:06:17,340 --> 00:06:19,180 So it's a combination of all that. 84 00:06:19,180 --> 00:06:25,180 So to know, even to know what you want, you have to know what a study design is. 85 00:06:25,180 --> 00:06:28,180 So the place to go get that is, you can come to us. 86 00:06:28,180 --> 00:06:34,100 We can give you a five-minute list bill, or we can give you a lot of articles to read. 87 00:06:34,100 --> 00:06:40,700 But a good place to start would be to get a short course, because before you're coming 88 00:06:40,700 --> 00:06:45,180 to the statistician, you already know what you want, what questions to be answered. 89 00:06:45,180 --> 00:06:50,940 But how to design the study comes from going to a short course and getting to know what 90 00:06:50,940 --> 00:06:53,860 are all the study designs that are available out there. 91 00:06:53,860 --> 00:06:56,200 How do you ask a question? 92 00:06:56,200 --> 00:06:58,460 What is your outcome? 93 00:06:58,460 --> 00:06:59,700 And what else can you ask? 94 00:06:59,700 --> 00:07:02,580 What is your, what are the groups that you're comparing? 95 00:07:02,580 --> 00:07:04,980 Are you comparing just one group? 96 00:07:04,980 --> 00:07:07,260 Are you comparing several groups? 97 00:07:07,260 --> 00:07:08,700 Then what is your sample size? 98 00:07:08,700 --> 00:07:14,340 Do you have a lot of money from which you can collect the data to answer your questions? 99 00:07:14,340 --> 00:07:17,020 Or is it going to be a very small study? 100 00:07:17,020 --> 00:07:23,820 So you need to know what your sample study size is going to be and things like that. 101 00:07:23,820 --> 00:07:28,980 And then the kind of data that you're collecting, is that quantitative? 102 00:07:28,980 --> 00:07:29,980 Is that qualitative? 103 00:07:29,980 --> 00:07:30,980 Or is that ordinal? 104 00:07:30,980 --> 00:07:33,680 These are all basic stuff. 105 00:07:33,680 --> 00:07:38,540 So it doesn't need, for some statistics can be scary. 106 00:07:38,540 --> 00:07:39,540 It's not. 107 00:07:39,540 --> 00:07:44,100 It's, it can be dumbed down to one, two, three. 108 00:07:44,100 --> 00:07:45,900 Is that a quantitative, continuous one? 109 00:07:45,900 --> 00:07:46,900 Is that ordinal? 110 00:07:46,900 --> 00:07:48,620 Or is that a discrete one? 111 00:07:48,620 --> 00:07:52,900 So they're all, all these can be described to anybody. 112 00:07:52,900 --> 00:07:56,300 These days, even the 10th graders are doing statistics. 113 00:07:56,300 --> 00:07:57,300 They're doing AP statistics. 114 00:07:57,300 --> 00:08:02,180 When my daughter was going to do AP statistics, knowing her background and what she's interested 115 00:08:02,180 --> 00:08:05,420 in, I said, don't do AP statistics yet. 116 00:08:05,420 --> 00:08:08,340 Do statistics before you go to AP statistics. 117 00:08:08,340 --> 00:08:11,300 So stats are being offered at all levels. 118 00:08:11,300 --> 00:08:19,420 And I get calls from all the local schools asking, Karen, can a student just follow you 119 00:08:19,420 --> 00:08:22,660 or just to know what kind of work that you do? 120 00:08:22,660 --> 00:08:27,100 Because the person wants you to, the kid wants you to research, but they don't know where 121 00:08:27,100 --> 00:08:28,100 to start. 122 00:08:28,100 --> 00:08:32,980 So there are many ways to know, offer statistics. 123 00:08:32,980 --> 00:08:36,020 And it starts with high school, for example. 124 00:08:36,020 --> 00:08:42,940 And then if somebody is already in college and is a doctor or trained to be a doctor, 125 00:08:42,940 --> 00:08:49,100 the colleges or the universities and the medical centers offer a lot of training to do research. 126 00:08:49,100 --> 00:08:50,860 So that's the place to start. 127 00:08:50,860 --> 00:08:51,860 Awesome. 128 00:08:51,860 --> 00:08:52,860 Thank you, Maggie. 129 00:08:52,860 --> 00:08:57,140 You know, one of the things I hear you saying is that you've got to know what you want. 130 00:08:57,140 --> 00:08:59,460 Your statistician is not the one to tell you what you want. 131 00:08:59,460 --> 00:09:01,180 You got to figure it out. 132 00:09:01,180 --> 00:09:05,940 What kind of study do you, what kind of study design, how much data do you already have? 133 00:09:05,940 --> 00:09:07,500 Or what data are you collecting? 134 00:09:07,500 --> 00:09:10,100 Do you have money to collect the data prospectively? 135 00:09:10,100 --> 00:09:11,100 Is this retrospective? 136 00:09:11,100 --> 00:09:12,940 You've got to have a plan. 137 00:09:12,940 --> 00:09:15,540 And it sounds like that's where you're able to help the most. 138 00:09:15,540 --> 00:09:19,580 When somebody comes to you with a plan, they know what their primary outcome is. 139 00:09:19,580 --> 00:09:22,980 They know what they're looking for, what they're really, what questions they're seeking to 140 00:09:22,980 --> 00:09:24,740 answer from what data. 141 00:09:24,740 --> 00:09:29,260 And so it sounds like I'm hearing you say, you got to be prepared for these conversations. 142 00:09:29,260 --> 00:09:30,260 Is that fair to say? 143 00:09:30,260 --> 00:09:31,260 Yeah, it is. 144 00:09:31,260 --> 00:09:32,260 It is. 145 00:09:32,260 --> 00:09:36,140 And they turn around, they turn around to do research in a short while when they come 146 00:09:36,140 --> 00:09:39,220 with questions and they've already, they already have their groundwork done. 147 00:09:39,220 --> 00:09:45,620 We can start, though, we can start holding hands now, yes, from ground zero, but then 148 00:09:45,620 --> 00:09:50,060 it will take a longer time to study, to have, to recent goal. 149 00:09:50,060 --> 00:09:51,060 Sure. 150 00:09:51,060 --> 00:09:53,460 It's good to have some kind of training. 151 00:09:53,460 --> 00:09:58,340 And these days, you know, all the medical schools have for one year of research or six 152 00:09:58,340 --> 00:10:00,220 months of research. 153 00:10:00,220 --> 00:10:05,060 And if that's the path one wants to take, take, use that opportunity to be, to do the 154 00:10:05,060 --> 00:10:09,380 research or go to the Institute or go to a medical school where you want to just try 155 00:10:09,380 --> 00:10:10,380 that out. 156 00:10:10,380 --> 00:10:15,100 You could try that out and then get a student to say, no, this is not what you want to do. 157 00:10:15,100 --> 00:10:17,700 You want to be doing clinical, clinical work all the time. 158 00:10:17,700 --> 00:10:18,700 That's fine. 159 00:10:18,700 --> 00:10:23,140 But at least you've bettered your feed by knowing what you need to know to do research. 160 00:10:23,140 --> 00:10:24,140 Awesome. 161 00:10:24,140 --> 00:10:25,140 Thank you, Maggie. 162 00:10:25,140 --> 00:10:26,140 Now, let me ask you a question. 163 00:10:26,140 --> 00:10:28,580 And this is not a question that I thought I would ask you. 164 00:10:28,580 --> 00:10:30,220 It just comes to my mind. 165 00:10:30,220 --> 00:10:33,100 What about the people who say, well, I've had statistics one and two. 166 00:10:33,100 --> 00:10:35,300 I don't need a biostatistician. 167 00:10:35,300 --> 00:10:38,020 What do you say to those people? 168 00:10:38,020 --> 00:10:44,900 So number crunching is different from designing the study. 169 00:10:44,900 --> 00:10:54,340 And one, just let me, let me take a step further behind actually, before you come to a number 170 00:10:54,340 --> 00:10:55,340 crunching. 171 00:10:55,340 --> 00:11:00,180 So the science has to be solid. 172 00:11:00,180 --> 00:11:04,700 One that the science has to be solid, but to get data from that scientific question 173 00:11:04,700 --> 00:11:09,020 that you have, you need to know how to collect the data. 174 00:11:09,020 --> 00:11:13,420 So if you know, if you're not a statistic, statistics is number crunching. 175 00:11:13,420 --> 00:11:15,300 It cannot be just number crunching. 176 00:11:15,300 --> 00:11:18,540 It can be how to collect the data. 177 00:11:18,540 --> 00:11:23,620 You can go wrong in many places by not designing the right study. 178 00:11:23,620 --> 00:11:27,340 So involve a statistician early on. 179 00:11:27,340 --> 00:11:31,300 Engage a statistician early on. 180 00:11:31,300 --> 00:11:33,780 People like me come free. 181 00:11:33,780 --> 00:11:39,540 In some institutes, you've got to pay, but places like Duke, you have a lot of resources, 182 00:11:39,540 --> 00:11:41,020 a lot of places to go to now. 183 00:11:41,020 --> 00:11:46,660 I mean, I agree that 10 years ago, things were different, but 10 years now, there are 184 00:11:46,660 --> 00:11:47,900 a lot of places where you can go. 185 00:11:47,900 --> 00:11:52,500 There are a lot of training grants that offer you places where you can learn how to do research. 186 00:11:52,500 --> 00:11:57,140 But even there, it's good to know where to start. 187 00:11:57,140 --> 00:12:03,500 You already know some things, some basic things about how to do research. 188 00:12:03,500 --> 00:12:04,500 Awesome. 189 00:12:04,500 --> 00:12:05,500 Thank you, Maggie. 190 00:12:05,500 --> 00:12:10,380 So Maggie, what I'm hearing from you is that you don't just finish all the data collection, 191 00:12:10,380 --> 00:12:15,060 finish your study, and then go find a statistician and say, here, crunch my numbers for me. 192 00:12:15,060 --> 00:12:19,580 What you're saying is that a biostatistician is a partner in the research process. 193 00:12:19,580 --> 00:12:23,660 And from the very beginning, where you're even thinking about, how do I design the study 194 00:12:23,660 --> 00:12:29,020 to answer this question, whether it be retrospective data, prospective, how do I design the study 195 00:12:29,020 --> 00:12:30,460 to answer the question? 196 00:12:30,460 --> 00:12:34,620 I'm hearing you say a biostatistician should be a partner with you in figuring out how 197 00:12:34,620 --> 00:12:39,260 to design the study, how to collect the data, and then how to analyze the data. 198 00:12:39,260 --> 00:12:40,260 Yes. 199 00:12:40,260 --> 00:12:41,260 Analyze the data. 200 00:12:41,260 --> 00:12:47,300 And the data that you're collecting also is going to feed into your next set of research. 201 00:12:47,300 --> 00:12:52,220 So you need to have a long-term goal. 202 00:12:52,220 --> 00:12:55,860 So in that sense, have a long-term relationship with your statistician that you're working. 203 00:12:55,860 --> 00:12:56,860 They're like tools. 204 00:12:56,860 --> 00:13:00,820 They're somebody who understands your research. 205 00:13:00,820 --> 00:13:04,180 And it's a training both ways. 206 00:13:04,180 --> 00:13:06,340 I don't know everything about science. 207 00:13:06,340 --> 00:13:07,540 So I value science. 208 00:13:07,540 --> 00:13:13,260 So I'm getting all the knowledge I can from the primary investigators like you. 209 00:13:13,260 --> 00:13:18,260 And I use that knowledge to see, okay, now this is what TOEIC wants. 210 00:13:18,260 --> 00:13:21,300 This is how I need to design a study. 211 00:13:21,300 --> 00:13:24,780 And what are the best ways to collect the data? 212 00:13:24,780 --> 00:13:27,740 And what are the efficient ways to collect the data? 213 00:13:27,740 --> 00:13:32,620 And if the part of money is large, then we go a certain route. 214 00:13:32,620 --> 00:13:39,660 If the part of money is small and we have a limited time, then we just go collect immediate 215 00:13:39,660 --> 00:13:43,100 data or immediate gratification. 216 00:13:43,100 --> 00:13:47,260 So sometimes the grant is down the corner and we don't have enough time to go collect 217 00:13:47,260 --> 00:13:50,100 the data, long-term data. 218 00:13:50,100 --> 00:13:54,620 But you can just go hone in to what you've already done and answer some questions. 219 00:13:54,620 --> 00:14:00,060 A grant based on that, turn that into a proposal and turn that out. 220 00:14:00,060 --> 00:14:01,060 I love it. 221 00:14:01,060 --> 00:14:03,860 So Maggie, one of the things I'm also hearing, and you said this earlier when you talked 222 00:14:03,860 --> 00:14:05,980 about you want to do good science. 223 00:14:05,980 --> 00:14:10,640 You're not just gathering data together to just say, oh, I said something. 224 00:14:10,640 --> 00:14:12,140 You really do want to do good science. 225 00:14:12,140 --> 00:14:14,500 You want to answer a question correctly. 226 00:14:14,500 --> 00:14:16,700 And so it's important to get it right from the beginning. 227 00:14:16,700 --> 00:14:20,500 Get someone who is partnering with you so that at the end of the day, your science is 228 00:14:20,500 --> 00:14:25,720 high quality that will actually be a contribution to the field. 229 00:14:25,720 --> 00:14:30,980 So I'm hearing you talk about just in improving the quality, you're involving a statistician 230 00:14:30,980 --> 00:14:31,980 early. 231 00:14:31,980 --> 00:14:37,080 And it also helps because if you don't have enough resources to answer all the questions 232 00:14:37,080 --> 00:14:41,220 you want to, a statistician can help you focus and say, okay, well, this is the amount of 233 00:14:41,220 --> 00:14:43,500 data this can help you get. 234 00:14:43,500 --> 00:14:47,180 And this is what it will get you to the next step and the next step and the next step after 235 00:14:47,180 --> 00:14:48,180 that. 236 00:14:48,180 --> 00:14:49,660 So I'm hearing you talk about longevity too. 237 00:14:49,660 --> 00:14:52,560 This is not just about one project and you're done. 238 00:14:52,560 --> 00:14:56,580 This is really about answering a series of questions and your statistician partnering 239 00:14:56,580 --> 00:14:58,460 with you to help you do that. 240 00:14:58,460 --> 00:14:59,460 Yes. 241 00:14:59,460 --> 00:15:05,640 So when you write a grant, you also at the end of the grant, you also have to make a 242 00:15:05,640 --> 00:15:11,140 statement on what are the future uses of this grant and where are you going to go as a 243 00:15:11,140 --> 00:15:13,340 researcher at the end of this grant? 244 00:15:13,340 --> 00:15:18,060 Okay, you're giving the institution like NIH is giving us the money. 245 00:15:18,060 --> 00:15:20,460 They like the proposal point. 246 00:15:20,460 --> 00:15:24,980 But if you don't have a plan on what you want to do using the data and using the reserves 247 00:15:24,980 --> 00:15:28,620 from the money that they're funding you with, they're not going to like it. 248 00:15:28,620 --> 00:15:33,100 So you're going to come back and say, okay, once we have all this data and all these things 249 00:15:33,100 --> 00:15:39,140 answered, what are all the paths that you're going to take after you've collected and analyzed 250 00:15:39,140 --> 00:15:40,460 and published your data? 251 00:15:40,460 --> 00:15:43,600 And next set of goals, you want to have that. 252 00:15:43,600 --> 00:15:47,460 So if you don't plan the study, this plan, the study, right? 253 00:15:47,460 --> 00:15:52,540 So you, there's a chain reaction of things that you don't do right. 254 00:15:52,540 --> 00:15:53,920 Thank you for sharing that, Maggie. 255 00:15:53,920 --> 00:15:55,820 So you mentioned grants. 256 00:15:55,820 --> 00:15:57,740 And so can I ask you about that? 257 00:15:57,740 --> 00:16:03,540 In what way can a biostatistician be helpful to an investigator who's writing a grant? 258 00:16:03,540 --> 00:16:09,100 At what point do we get you involved and how do you help us in writing grants? 259 00:16:09,100 --> 00:16:13,660 So once again, I cannot emphasize the importance of science. 260 00:16:13,660 --> 00:16:16,940 So you come with the solid science, you found evidence of this. 261 00:16:16,940 --> 00:16:22,380 Now you want to design a study to answer that, to answer that in some form or in a larger 262 00:16:22,380 --> 00:16:27,420 form, or it's a, it's a conglomeration of lots of variables that are going there. 263 00:16:27,420 --> 00:16:29,840 So you want to answer all those questions. 264 00:16:29,840 --> 00:16:35,140 So now that means again, back to, back to the drawing board, you're going to be needing 265 00:16:35,140 --> 00:16:36,820 to design the study. 266 00:16:36,820 --> 00:16:42,460 So my question would be, when, when do you want to start the study? 267 00:16:42,460 --> 00:16:45,740 So if they say that they want to start the study tomorrow, my question is, what is it 268 00:16:45,740 --> 00:16:47,460 that you have now? 269 00:16:47,460 --> 00:16:52,340 So if they already have the data now and they want to write a grant, my question would be, 270 00:16:52,340 --> 00:16:58,340 well, once you have some data and you already know that some of your questions are answered, 271 00:16:58,340 --> 00:17:05,600 but you want some more to be answered, involve the statistician early on in when you have 272 00:17:05,600 --> 00:17:11,540 your aims, your goals and your future questions to be answered. 273 00:17:11,540 --> 00:17:15,860 When you have all those things written up and you've formed it up, formed it up to some 274 00:17:15,860 --> 00:17:23,740 extent, come see a statistician because then the, the principal investigators and the statisticians 275 00:17:23,740 --> 00:17:30,440 can sit together, put their minds together and design, come up with a design that's best 276 00:17:30,440 --> 00:17:36,820 for what your aims are and what data you have and what data can be collected for the current 277 00:17:36,820 --> 00:17:39,140 study that you have in mind. 278 00:17:39,140 --> 00:17:40,140 Awesome. 279 00:17:40,140 --> 00:17:43,420 And I keep hearing you talk about the importance of that partnership. 280 00:17:43,420 --> 00:17:47,960 So have a plan, come up with your specific aims, and then let's sit together and design 281 00:17:47,960 --> 00:17:51,700 what that study will look like to fit the goals of your study. 282 00:17:51,700 --> 00:17:52,700 Yeah. 283 00:17:52,700 --> 00:17:53,700 Okay. 284 00:17:53,700 --> 00:17:56,180 So now, Maggie, let me ask you this. 285 00:17:56,180 --> 00:18:00,180 You know, you've kind of answered the question about how can clinicians come prepared to 286 00:18:00,180 --> 00:18:01,500 get value from you? 287 00:18:01,500 --> 00:18:05,900 You've talked about get a little bit of an education so we can have a conversation and 288 00:18:05,900 --> 00:18:08,460 then have a clear goal for where you want to go. 289 00:18:08,460 --> 00:18:12,500 I just wonder, is there anything else you want to add to what, how can clinicians best 290 00:18:12,500 --> 00:18:16,100 get value from the experience of working with a biostatistician? 291 00:18:16,100 --> 00:18:21,660 So, so if they're, if they're, if they're from day one, let's say day one, they have 292 00:18:21,660 --> 00:18:26,300 nothing but they have an idea and they want to do something, come to us because we can 293 00:18:26,300 --> 00:18:31,980 design the study at that time and tell you what, what to do several things. 294 00:18:31,980 --> 00:18:37,020 One, to go, we will tell the investigators to go and look into the literature and come 295 00:18:37,020 --> 00:18:39,500 up with it for the questions that they want. 296 00:18:39,500 --> 00:18:42,300 Is there anything in the literature that they have already done? 297 00:18:42,300 --> 00:18:47,220 That are there researchers who've already done that kind of research? 298 00:18:47,220 --> 00:18:50,140 If they have, what are all the results they have? 299 00:18:50,140 --> 00:18:52,980 So that, you know, get an Excel spreadsheet. 300 00:18:52,980 --> 00:18:55,660 I will tell the investigator to get an Excel spreadsheet. 301 00:18:55,660 --> 00:18:56,660 This is the question. 302 00:18:56,660 --> 00:18:59,860 These are all the, these are all the papers that are out there. 303 00:18:59,860 --> 00:19:04,660 In each of these papers, what is it that the, that that particular investigator has looked 304 00:19:04,660 --> 00:19:10,780 at and what is it that I'm going to contribute that they have not contributed to? 305 00:19:10,780 --> 00:19:14,860 Because nobody's going to give us money if something is already looked at several times 306 00:19:14,860 --> 00:19:18,580 and the results and the same results are coming up over and over again. 307 00:19:18,580 --> 00:19:23,860 So you have to, you have to come up with something that is kind of novel, over and above that 308 00:19:23,860 --> 00:19:26,420 is already studied in the literature. 309 00:19:26,420 --> 00:19:33,220 So if, if a part of that particular question that you have in mind, most of it is answered, 310 00:19:33,220 --> 00:19:38,540 but some of it is not answered, then we can help you design a study in addition to what 311 00:19:38,540 --> 00:19:47,900 is already there, how you can either add to what is already there by adding a new design 312 00:19:47,900 --> 00:19:53,020 and going and collecting data for that particular aspect of that aim that you have. 313 00:19:53,020 --> 00:19:57,460 And then move from there to the next phase. 314 00:19:57,460 --> 00:20:01,820 So what I also hear you talking about, Maggie, is that you bring the innovation too. 315 00:20:01,820 --> 00:20:06,700 So you can help people think about how to take what's already present and make it new. 316 00:20:06,700 --> 00:20:10,660 And so in, you know, for clinicians who are trying to write an innovation section of their 317 00:20:10,660 --> 00:20:13,740 grant, you actually can help with that and helping them innovate. 318 00:20:13,740 --> 00:20:16,620 Yes, because innovation is science. 319 00:20:16,620 --> 00:20:18,580 Innovation is also a new statistical methods. 320 00:20:18,580 --> 00:20:25,860 So some of the questions could not be answered, you know, in a very sophisticated way earlier 321 00:20:25,860 --> 00:20:28,380 on from 20 years ago. 322 00:20:28,380 --> 00:20:36,780 But now with the advent of computing power, immense computing powers, especially since 323 00:20:36,780 --> 00:20:42,340 the last 10 years, the immense computing power, the computing, it's not too expensive to run 324 00:20:42,340 --> 00:20:46,660 big numbers, to run big, big models. 325 00:20:46,660 --> 00:20:50,540 Ten years ago, it would take two nights to run a study. 326 00:20:50,540 --> 00:20:56,300 Or we used to, we have to go to a supercomputing center to run some models. 327 00:20:56,300 --> 00:21:02,260 But now with the advent of computers and the cheapness and how cheap they are to run some 328 00:21:02,260 --> 00:21:10,540 of these models, in terms of time, we can advocate newer methodologies that will incorporate 329 00:21:10,540 --> 00:21:15,420 lots and lots of variables from different models to come up with a very complicated 330 00:21:15,420 --> 00:21:16,420 model. 331 00:21:16,420 --> 00:21:17,420 I like it. 332 00:21:17,420 --> 00:21:18,420 Thank you. 333 00:21:18,420 --> 00:21:20,700 Innovation in the statistical methods, which is great. 334 00:21:20,700 --> 00:21:23,540 It's not something I think about, but that's absolutely necessary. 335 00:21:23,540 --> 00:21:24,540 All right. 336 00:21:24,540 --> 00:21:25,540 That's awesome. 337 00:21:25,540 --> 00:21:26,540 Okay. 338 00:21:26,540 --> 00:21:27,540 Let me ask you this. 339 00:21:27,540 --> 00:21:31,540 How, what, what is one life hack that you can share that maybe clinicians don't know 340 00:21:31,540 --> 00:21:35,660 about as what is one life hack that you have to share? 341 00:21:35,660 --> 00:21:43,700 Well, our hearts are very important to us and heart, you know, heart stops, feet all 342 00:21:43,700 --> 00:21:44,700 gone. 343 00:21:44,700 --> 00:21:50,340 So a few years ago, one of the investigators here came to me and said, Hey, we did this 344 00:21:50,340 --> 00:21:59,360 research in our, in our lab and they found this one more car to be very high in these 345 00:21:59,360 --> 00:22:03,860 patients who are going to, whose heart is going to fail. 346 00:22:03,860 --> 00:22:06,220 How do we design a study? 347 00:22:06,220 --> 00:22:09,580 How do we, how, what do we do next? 348 00:22:09,580 --> 00:22:13,540 So we, what we, we want to, we want to say that we have this four hearts out of these 349 00:22:13,540 --> 00:22:21,060 four cars, three hearts had this one marker very high, but this is not enough to get money. 350 00:22:21,060 --> 00:22:23,020 And we will start a design study. 351 00:22:23,020 --> 00:22:27,820 We just put the heart in the solution and we try to see what came out of the, from these 352 00:22:27,820 --> 00:22:30,740 hearts and we tested the solution that was there. 353 00:22:30,740 --> 00:22:36,620 The heart was put in a solution when we looked at the solution and we found this marker in 354 00:22:36,620 --> 00:22:38,540 all three of these cards. 355 00:22:38,540 --> 00:22:42,380 So I said, how, you know, my studies are cheap. 356 00:22:42,380 --> 00:22:45,380 You know, we cannot kill people. 357 00:22:45,380 --> 00:22:47,980 We only can get the hearts from people who die. 358 00:22:47,980 --> 00:22:58,300 So why don't we take the hearts of mice or if you have more money or pigs or rabbits, 359 00:22:58,300 --> 00:22:59,820 any of these. 360 00:22:59,820 --> 00:23:07,720 And we design a study where we put a stressor in the hearts. 361 00:23:07,720 --> 00:23:14,180 So they're going to die or they're going to be near that near death and see what comes 362 00:23:14,180 --> 00:23:21,860 out of their hearts because our animal models eventually do translate into human models. 363 00:23:21,860 --> 00:23:22,860 Right. 364 00:23:22,860 --> 00:23:26,340 It was some for some they are not, but for most part they're, they're pretty, pretty 365 00:23:26,340 --> 00:23:27,340 close. 366 00:23:27,340 --> 00:23:28,340 We are all pretty close. 367 00:23:28,340 --> 00:23:30,380 So why don't you, why don't we do that in the next? 368 00:23:30,380 --> 00:23:32,580 I said, how long will it take? 369 00:23:32,580 --> 00:23:34,860 The grant is due. 370 00:23:34,860 --> 00:23:35,860 This is Brookhaven. 371 00:23:35,860 --> 00:23:41,020 Brookhaven Institute, they were the, the grant is due in like six months. 372 00:23:41,020 --> 00:23:42,020 Can you do a trial? 373 00:23:42,020 --> 00:23:43,820 Can you do studies in the next two to three months? 374 00:23:43,820 --> 00:23:47,100 He said in three months we can get the result that you want. 375 00:23:47,100 --> 00:23:55,620 So they came up, we came up with some, some estimates using mice as an example. 376 00:23:55,620 --> 00:23:59,900 And it translated what we saw in the human, human anecdotal data. 377 00:23:59,900 --> 00:24:01,500 These are anecdotal data. 378 00:24:01,500 --> 00:24:02,500 We use that. 379 00:24:02,500 --> 00:24:05,780 And then we said, okay, mice models are cheaper to run. 380 00:24:05,780 --> 00:24:10,500 So let's see if we can, if we do the same thing in mice, what happens? 381 00:24:10,500 --> 00:24:14,580 So we initially we did three mice and we found some good results. 382 00:24:14,580 --> 00:24:20,260 We then moved on to a slightly larger samples, six mice, and we, the results were consistent 383 00:24:20,260 --> 00:24:23,940 with what we saw in the human hearts. 384 00:24:23,940 --> 00:24:31,820 And we had two cycles of grants that got funded based on those small studies that we designed 385 00:24:31,820 --> 00:24:37,140 in animals and then we went on to get some more grants. 386 00:24:37,140 --> 00:24:38,140 That's really awesome. 387 00:24:38,140 --> 00:24:41,900 So Maggie, I hear you, you keep reiterating the importance of getting your partners by 388 00:24:41,900 --> 00:24:46,140 statisticians involved early because they can help you think about how do you set up 389 00:24:46,140 --> 00:24:50,940 to be ready to submit a grant, to be ready to be successful in grant funding. 390 00:24:50,940 --> 00:24:54,260 But I'm also hearing you, hearing you talk about how much time is needed. 391 00:24:54,260 --> 00:24:59,380 So you were talking about investigators coming to you six months before a grant is due. 392 00:24:59,380 --> 00:25:04,260 Talk about how much time is needed to really prepare a good submission with the help of 393 00:25:04,260 --> 00:25:06,740 the statistician. 394 00:25:06,740 --> 00:25:09,420 So basic science is a different beast. 395 00:25:09,420 --> 00:25:15,540 So sure, this study we were able to do very quickly and there were a lot of resources. 396 00:25:15,540 --> 00:25:20,180 There was a big name, so the investigators have a lot of money and a lot of resources, 397 00:25:20,180 --> 00:25:25,940 so we were able to turn around and do the work and get the data to have to submit to 398 00:25:25,940 --> 00:25:26,940 the grant. 399 00:25:26,940 --> 00:25:29,060 But it's not always the case. 400 00:25:29,060 --> 00:25:33,740 The mice studies, all the mice can die for various reasons. 401 00:25:33,740 --> 00:25:36,140 And so we may not have solid data. 402 00:25:36,140 --> 00:25:38,900 So I would say, come even a year ahead. 403 00:25:38,900 --> 00:25:45,780 If you have some data, then just anecdotally, let's say you found some data anecdotally, 404 00:25:45,780 --> 00:25:50,940 then come to us right away and see how we can set up a study that we can systematically 405 00:25:50,940 --> 00:25:54,620 collect the data and write a grant on that. 406 00:25:54,620 --> 00:25:59,300 Because it's not just one question that you're going to answer from designing the study. 407 00:25:59,300 --> 00:26:01,900 You're going to be answering three or four questions. 408 00:26:01,900 --> 00:26:04,140 The aims typically are... 409 00:26:04,140 --> 00:26:11,820 So an R21 mechanism kind of helps you get data, collect data to write a big old grant. 410 00:26:11,820 --> 00:26:12,820 So it's basically R21. 411 00:26:12,820 --> 00:26:21,220 R03 is basically an R-age mechanism to really collect pilot data and then use that data 412 00:26:21,220 --> 00:26:25,780 then to do kind of an R21 and then move on to a bigger one. 413 00:26:25,780 --> 00:26:33,900 But in the medical schools, there are all these training grants that give you pilot 414 00:26:33,900 --> 00:26:35,520 money. 415 00:26:35,520 --> 00:26:41,060 So some of these small grants, $10,000 grants, can help you set up small studies. 416 00:26:41,060 --> 00:26:48,420 They may not fund your salary, but they can fund small studies, lab studies. 417 00:26:48,420 --> 00:26:49,420 Those are one. 418 00:26:49,420 --> 00:26:50,420 That is lab studies. 419 00:26:50,420 --> 00:26:57,580 But if they're not lab studies, if they involve secondary data, secondary data is already 420 00:26:57,580 --> 00:26:59,220 there. 421 00:26:59,220 --> 00:27:05,500 So this money can be the small study funding from pilot studies internally can be used 422 00:27:05,500 --> 00:27:21,940 on writing up grants, small grants, and also come up with some other decent studies that 423 00:27:21,940 --> 00:27:23,380 can go with that. 424 00:27:23,380 --> 00:27:26,580 So it doesn't have to ask just one question. 425 00:27:26,580 --> 00:27:30,300 You'll have three or four questions that can go with as part of the project. 426 00:27:30,300 --> 00:27:31,460 I love it. 427 00:27:31,460 --> 00:27:36,020 So I hear you talking about how you can even help people really maximize the benefit of 428 00:27:36,020 --> 00:27:37,380 any sample of data. 429 00:27:37,380 --> 00:27:39,780 So it's like here you're collecting this data. 430 00:27:39,780 --> 00:27:40,900 This is what you can get from it. 431 00:27:40,900 --> 00:27:41,900 You can also get this. 432 00:27:41,900 --> 00:27:43,500 You can also get that. 433 00:27:43,500 --> 00:27:48,420 And I also hearing you talk about for everyone to realize that no matter how small the pot 434 00:27:48,420 --> 00:27:52,500 of money you get, you can always do something with it to turn it into the next grant and 435 00:27:52,500 --> 00:27:54,980 the next grant and the next grant after that. 436 00:27:54,980 --> 00:27:55,980 That's awesome. 437 00:27:55,980 --> 00:27:56,980 Awesome. 438 00:27:56,980 --> 00:27:57,980 All right. 439 00:27:57,980 --> 00:28:03,020 Even big data sets, even big data sets, we could do some small studies using the big 440 00:28:03,020 --> 00:28:09,460 national databases, using the internal funding and use that to write bigger grants. 441 00:28:09,460 --> 00:28:10,460 I love it. 442 00:28:10,460 --> 00:28:11,460 I love it. 443 00:28:11,460 --> 00:28:12,460 Thank you. 444 00:28:12,460 --> 00:28:13,460 Thank you. 445 00:28:13,460 --> 00:28:14,460 Okay. 446 00:28:14,460 --> 00:28:17,980 So Maggie, if there is a clinician sitting out there thinking, I want to become a researcher, 447 00:28:17,980 --> 00:28:23,140 I want to work with a biostatistician, but I'm not sure I can, what encouragement do 448 00:28:23,140 --> 00:28:26,540 you have for them in terms of how best to move forward? 449 00:28:26,540 --> 00:28:28,260 Yeah. 450 00:28:28,260 --> 00:28:42,780 So nationally, NIH has started providing mechanisms for quantitative, to provide quantitative 451 00:28:42,780 --> 00:28:44,860 help to the institutes. 452 00:28:44,860 --> 00:28:53,820 So the CTSI is one of, one such grant that many organizations or many academic institutions 453 00:28:53,820 --> 00:29:08,540 write to get money, so that CTSI, it's like a, it's a core that helps investigators within 454 00:29:08,540 --> 00:29:14,100 the institute with all kinds of help they need. 455 00:29:14,100 --> 00:29:23,980 So Duke got two rounds of CTSI grants and as part of that, some of that money is given 456 00:29:23,980 --> 00:29:26,260 to the statisticians. 457 00:29:26,260 --> 00:29:32,180 So a 20 or 25% of a statistician's salary is covered by the CTSI. 458 00:29:32,180 --> 00:29:40,700 So those statisticians, what they do is now help investigators who are starting from ground 459 00:29:40,700 --> 00:29:42,860 zero. 460 00:29:42,860 --> 00:29:48,180 And they can tap into those aspects of any institute that they have. 461 00:29:48,180 --> 00:29:51,540 So practically every institute has some kind of money. 462 00:29:51,540 --> 00:29:55,020 It's the question of how much money that you want or how much effort that you want from 463 00:29:55,020 --> 00:29:56,660 a statistician. 464 00:29:56,660 --> 00:30:01,260 So if an investigator, if a new investigator is getting through the door, we have that 465 00:30:01,260 --> 00:30:02,260 all the time. 466 00:30:02,260 --> 00:30:08,740 We have lots of, lots and lots of investigators coming into our institute. 467 00:30:08,740 --> 00:30:14,380 And the first thing they want to know is what are the research resources available? 468 00:30:14,380 --> 00:30:20,700 So call up, so the thing to do is to call the Biostat department and find out what are 469 00:30:20,700 --> 00:30:22,700 all the resources available. 470 00:30:22,700 --> 00:30:28,620 Or within an institute, for example, Division of Hematology, for example, within the Division 471 00:30:28,620 --> 00:30:34,300 of Hematology, find out who are the, what are all the resources available to do research. 472 00:30:34,300 --> 00:30:43,220 Both basic science, long-term outcomes, outcomes research, what are all the resources available. 473 00:30:43,220 --> 00:30:44,420 So that's the starting point. 474 00:30:44,420 --> 00:30:50,740 If within the department or within the department that you're in, what are the resources available? 475 00:30:50,740 --> 00:30:52,460 And there are multiple resources available. 476 00:30:52,460 --> 00:30:54,700 That's not the only resource available. 477 00:30:54,700 --> 00:31:01,100 If the department tells you that this resource is available only for people who have funding, 478 00:31:01,100 --> 00:31:07,780 then at the School of Medicine level, there are resources available that are available 479 00:31:07,780 --> 00:31:11,300 for any researcher who can start from ground down. 480 00:31:11,300 --> 00:31:12,300 That's awesome. 481 00:31:12,300 --> 00:31:13,300 Thank you, Maggie. 482 00:31:13,300 --> 00:31:17,920 What I'm hearing from you is just that you got to keep pushing for what you need. 483 00:31:17,920 --> 00:31:21,780 Make sure you're looking, especially as you're applying for your first faculty job, making 484 00:31:21,780 --> 00:31:25,760 sure that these resources are already available, how you're going to get access to them. 485 00:31:25,760 --> 00:31:30,140 But even if you come and you don't have access to the resource to get a biostatistician to 486 00:31:30,140 --> 00:31:35,420 work with, look to your department, look to the schools, look to the institutes, especially 487 00:31:35,420 --> 00:31:39,780 if you have a CTSI, and look to see what resources are available. 488 00:31:39,780 --> 00:31:43,700 And I love that because I think one of the things, Maggie, we tell our audience is that 489 00:31:43,700 --> 00:31:45,340 you can't, don't get stuck. 490 00:31:45,340 --> 00:31:49,820 Don't make sure that you're taking ownership and leading your own research, not letting 491 00:31:49,820 --> 00:31:51,100 obstacles stop you. 492 00:31:51,100 --> 00:31:55,100 And so it sounds like really it's that there are resources available, but you do need to 493 00:31:55,100 --> 00:31:56,700 go out and look for them. 494 00:31:56,700 --> 00:31:57,700 Yeah, yeah. 495 00:31:57,700 --> 00:31:58,700 That's right. 496 00:31:58,700 --> 00:32:00,700 I do want to mention one thing though. 497 00:32:00,700 --> 00:32:07,620 So every department, if it's a research institution, every department has a vice chair of research. 498 00:32:07,620 --> 00:32:10,980 Go talk to the vice chair of research and tell them you are interested. 499 00:32:10,980 --> 00:32:16,940 You did some research in college or in high school, college, and in mid school that you 500 00:32:16,940 --> 00:32:18,500 want to pursue some of that research. 501 00:32:18,500 --> 00:32:20,940 How do I go about doing that? 502 00:32:20,940 --> 00:32:25,780 Talk to the vice chair of research and the vice chair of research will be able to help 503 00:32:25,780 --> 00:32:30,500 you put you in touch with somebody who's already doing something along those lines. 504 00:32:30,500 --> 00:32:33,540 So you have a mentoring right there. 505 00:32:33,540 --> 00:32:38,220 And so you get to start working with that particular research with somebody who's already 506 00:32:38,220 --> 00:32:39,540 doing that kind of work. 507 00:32:39,540 --> 00:32:45,620 Or you can start your own search because the department is interested in something that 508 00:32:45,620 --> 00:32:48,420 you just put up with. 509 00:32:48,420 --> 00:32:49,420 I love it. 510 00:32:49,420 --> 00:32:50,420 Thank you, Maggie. 511 00:32:50,420 --> 00:32:53,540 I hear you saying, you know what, there's someone at your institution who cares that 512 00:32:53,540 --> 00:32:54,980 research gets done. 513 00:32:54,980 --> 00:32:58,940 So find them and have them help you because that is their job. 514 00:32:58,940 --> 00:32:59,940 That is so awesome. 515 00:32:59,940 --> 00:33:03,260 Maggie, you have shared such amazing insights. 516 00:33:03,260 --> 00:33:04,260 Thank you so much. 517 00:33:04,260 --> 00:33:09,980 It is rare that we really have access to biostatisticians to help us get the inside story on how to 518 00:33:09,980 --> 00:33:11,980 work well with the biostatistician. 519 00:33:11,980 --> 00:33:15,020 And so I really want to thank you for the insights you've shared today. 520 00:33:15,020 --> 00:33:19,540 And to our audience members, if you have benefited from the things that Maggie has shared, please 521 00:33:19,540 --> 00:33:22,060 share this episode with somebody else who needs to hear it. 522 00:33:22,060 --> 00:33:26,500 Or if you're a mentor and your mentees need to understand this, please share this episode 523 00:33:26,500 --> 00:33:27,500 with them. 524 00:33:27,500 --> 00:33:31,700 So having said that, I want to say Maggie, thank you so much for coming on the show and 525 00:33:31,700 --> 00:33:34,660 sharing your wisdom with our audience. 526 00:33:34,660 --> 00:33:35,660 Thank you, Tiosi. 527 00:33:35,660 --> 00:33:37,780 Thank you for giving me this opportunity. 528 00:33:37,780 --> 00:33:41,260 And so thank you to our audience and we'll see you again next time. 529 00:33:41,260 --> 00:33:44,260 Take care. 530 00:33:44,260 --> 00:33:50,700 Bye. 531 00:33:50,700 --> 00:33:56,060 Thanks for listening to this episode of the Clinician Researcher Podcast, where academic 532 00:33:56,060 --> 00:34:01,500 clinicians learn the skills to build their own research program, whether or not they 533 00:34:01,500 --> 00:34:02,860 have a mentor. 534 00:34:02,860 --> 00:34:08,980 If you found the information in this episode to be helpful, don't keep it all to yourself. 535 00:34:08,980 --> 00:34:10,700 Someone else needs to hear it. 536 00:34:10,700 --> 00:34:14,760 So take a minute right now and share it. 537 00:34:14,760 --> 00:34:20,220 As you share this episode, you become part of our mission to help launch a new generation 538 00:34:20,220 --> 00:34:26,180 of clinician researchers who make transformative discoveries that change the way we do healthcare.