Richard L Kravitz, N. Duan, Edmund J. Niedzinski, M. Hay, SASKIA K. Subramanian, THOMAS S. Weisner
{"title":"n(1)次试验发生了什么?业内人士的观点和对未来的展望。","authors":"Richard L Kravitz, N. Duan, Edmund J. Niedzinski, M. Hay, SASKIA K. Subramanian, THOMAS S. Weisner","doi":"10.1111/j.1468-0009.2008.00533.x","DOIUrl":null,"url":null,"abstract":"CONTEXT\nWhen feasible, randomized, blinded single-patient (n-of-1) trials are uniquely capable of establishing the best treatment in an individual patient. Despite early enthusiasm, by the turn of the twenty-first century, few academic centers were conducting n-of-1 trials on a regular basis.\n\n\nMETHODS\nThe authors reviewed the literature and conducted in-depth telephone interviews with leaders in the n-of-1 trial movement.\n\n\nFINDINGS\nN-of-1 trials can improve care by increasing therapeutic precision. However, they have not been widely adopted, in part because physicians do not sufficiently value the reduction in uncertainty they yield weighed against the inconvenience they impose. Limited evidence suggests that patients may be receptive to n-of-1 trials once they understand the benefits.\n\n\nCONCLUSIONS\nN-of-1 trials offer a unique opportunity to individualize clinical care and enrich clinical research. While ongoing changes in drug discovery, manufacture, and marketing may ultimately spur pharmaceutical makers and health care payers to support n-of-1 trials, at present the most promising resuscitation strategy is stripping n-of-1 trials to their essentials and marketing them directly to patients. In order to optimize statistical inference from these trials, empirical Bayes methods can be used to combine individual patient data with aggregate data from comparable patients.","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"25 1","pages":"533-55"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"82","resultStr":"{\"title\":\"What ever happened to N-of-1 trials? Insiders' perspectives and a look to the future.\",\"authors\":\"Richard L Kravitz, N. Duan, Edmund J. Niedzinski, M. Hay, SASKIA K. Subramanian, THOMAS S. Weisner\",\"doi\":\"10.1111/j.1468-0009.2008.00533.x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"CONTEXT\\nWhen feasible, randomized, blinded single-patient (n-of-1) trials are uniquely capable of establishing the best treatment in an individual patient. Despite early enthusiasm, by the turn of the twenty-first century, few academic centers were conducting n-of-1 trials on a regular basis.\\n\\n\\nMETHODS\\nThe authors reviewed the literature and conducted in-depth telephone interviews with leaders in the n-of-1 trial movement.\\n\\n\\nFINDINGS\\nN-of-1 trials can improve care by increasing therapeutic precision. However, they have not been widely adopted, in part because physicians do not sufficiently value the reduction in uncertainty they yield weighed against the inconvenience they impose. Limited evidence suggests that patients may be receptive to n-of-1 trials once they understand the benefits.\\n\\n\\nCONCLUSIONS\\nN-of-1 trials offer a unique opportunity to individualize clinical care and enrich clinical research. While ongoing changes in drug discovery, manufacture, and marketing may ultimately spur pharmaceutical makers and health care payers to support n-of-1 trials, at present the most promising resuscitation strategy is stripping n-of-1 trials to their essentials and marketing them directly to patients. In order to optimize statistical inference from these trials, empirical Bayes methods can be used to combine individual patient data with aggregate data from comparable patients.\",\"PeriodicalId\":78777,\"journal\":{\"name\":\"The Milbank Memorial Fund quarterly\",\"volume\":\"25 1\",\"pages\":\"533-55\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"82\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Milbank Memorial Fund quarterly\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/j.1468-0009.2008.00533.x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Milbank Memorial Fund quarterly","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/j.1468-0009.2008.00533.x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
What ever happened to N-of-1 trials? Insiders' perspectives and a look to the future.
CONTEXT
When feasible, randomized, blinded single-patient (n-of-1) trials are uniquely capable of establishing the best treatment in an individual patient. Despite early enthusiasm, by the turn of the twenty-first century, few academic centers were conducting n-of-1 trials on a regular basis.
METHODS
The authors reviewed the literature and conducted in-depth telephone interviews with leaders in the n-of-1 trial movement.
FINDINGS
N-of-1 trials can improve care by increasing therapeutic precision. However, they have not been widely adopted, in part because physicians do not sufficiently value the reduction in uncertainty they yield weighed against the inconvenience they impose. Limited evidence suggests that patients may be receptive to n-of-1 trials once they understand the benefits.
CONCLUSIONS
N-of-1 trials offer a unique opportunity to individualize clinical care and enrich clinical research. While ongoing changes in drug discovery, manufacture, and marketing may ultimately spur pharmaceutical makers and health care payers to support n-of-1 trials, at present the most promising resuscitation strategy is stripping n-of-1 trials to their essentials and marketing them directly to patients. In order to optimize statistical inference from these trials, empirical Bayes methods can be used to combine individual patient data with aggregate data from comparable patients.