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Start Over Author "Wang, Hai Lin" Journal bone marrow transplantation
5 results on '"Wang, Hai Lin"'

3. Correction to: Impact of depth of clinical response on outcomes of acute myeloid leukemia patients in first complete remission who undergo allogeneic hematopoietic cell transplantation

Author

Percival, Mary-Elizabeth, primary, Wang, Hai-Lin, additional, Zhang, Mei-Jie, additional, Saber, Wael, additional, de Lima, Marcos, additional, Litzow, Mark, additional, Kebriaei, Partow, additional, Abdel-Azim, Hisham, additional, Adekola, Kehinde, additional, Aljurf, Mahmoud, additional, Bacher, Ulrike, additional, Badawy, Sherif M., additional, Beitinjaneh, Amer, additional, Bejanyan, Nelli, additional, Bhatt, Vijaya, additional, Byrne, Michael, additional, Cahn, Jean-Yves, additional, Castillo, Paul, additional, Chao, Nelson, additional, Chhabra, Saurabh, additional, Copelan, Edward, additional, Cutler, Corey, additional, DeFilipp, Zachariah, additional, Dias, Ajoy, additional, Diaz, Miguel Angel, additional, Estey, Elihu, additional, Farhadfar, Nosha, additional, Frangoul, Haydar A., additional, Freytes, César O., additional, Gale, Robert Peter, additional, Ganguly, Siddhartha, additional, Gowda, Lohith, additional, Grunwald, Michael, additional, Hossain, Nasheed, additional, Kamble, Rammurti T., additional, Kanakry, Christopher G., additional, Kansagra, Ankit, additional, Kharfan-Dabaja, Mohamed A., additional, Krem, Maxwell, additional, Lazarus, Hillard M., additional, Lee, Jong Wook, additional, Liesveld, Jane L., additional, Lin, Richard, additional, Liu, Hongtao, additional, McGuirk, Joseph, additional, Munker, Reinhold, additional, Murthy, Hemant S., additional, Nathan, Sunita, additional, Nishihori, Taiga, additional, Olsson, Richard F., additional, Palmisiano, Neil, additional, Passweg, Jakob R., additional, Prestidge, Tim, additional, Ringdén, Olov, additional, Rizzieri, David A., additional, Rybka, Witold B., additional, Savoie, Mary Lynn, additional, Schultz, Kirk R., additional, Seo, Sachiko, additional, Sharma, Akshay, additional, Solh, Melhem, additional, Strair, Roger, additional, van der Poel, Marjolein, additional, Verdonck, Leo F., additional, Yared, Jean A., additional, Weisdorf, Daniel, additional, and Sandmaier, Brenda M., additional

Published
2021
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4. Impact of depth of clinical response on outcomes of acute myeloid leukemia patients in first complete remission who undergo allogeneic hematopoietic cell transplantation

Author

Percival, Mary-Elizabeth, primary, Wang, Hai-Lin, additional, Zhang, Mei-Jie, additional, Saber, Wael, additional, de Lima, Marcos, additional, Litzow, Mark, additional, Kebriaei, Partow, additional, Abdel-Azim, Hisham, additional, Adekola, Kehinde, additional, Aljurf, Mahmoud, additional, Bacher, Ulrike, additional, Badawy, Sherif M., additional, Beitinjaneh, Amer, additional, Bejanyan, Nelli, additional, Bhatt, Vijaya, additional, Byrne, Michael, additional, Cahn, Jean-Yves, additional, Castillo, Paul, additional, Chao, Nelson, additional, Chhabra, Saurabh, additional, Copelan, Edward, additional, Cutler, Corey, additional, DeFilipp, Zachariah, additional, Dias, Ajoy, additional, Diaz, Miguel Angel, additional, Estey, Elihu, additional, Farhadfar, Nosha, additional, Frangoul, Haydar A., additional, Freytes, César O., additional, Gale, Robert Peter, additional, Ganguly, Siddhartha, additional, Gowda, Lohith, additional, Grunwald, Michael, additional, Hossain, Nasheed, additional, Kamble, Rammurti T., additional, Kanakry, Christopher G., additional, Kansagra, Ankit, additional, Kharfan-Dabaja, Mohamed A., additional, Krem, Maxwell, additional, Lazarus, Hillard M., additional, Lee, Jong Wook, additional, Liesveld, Jane L., additional, Lin, Richard, additional, Liu, Hongtao, additional, McGuirk, Joseph, additional, Munker, Reinhold, additional, Murthy, Hemant S., additional, Nathan, Sunita, additional, Nishihori, Taiga, additional, Olsson, Richard F., additional, Palmisiano, Neil, additional, Passweg, Jakob R., additional, Prestidge, Tim, additional, Ringdén, Olov, additional, Rizzieri, David A., additional, Rybka, Witold B., additional, Savoie, Mary Lynn, additional, Schultz, Kirk R., additional, Seo, Sachiko, additional, Sharma, Akshay, additional, Solh, Melhem, additional, Strair, Roger, additional, van der Poel, Marjolein, additional, Verdonck, Leo F., additional, Yared, Jean A., additional, Weisdorf, Daniel, additional, and Sandmaier, Brenda M., additional

Published
2021
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5. A SAS macro for estimating direct adjusted survival functions for time-to-event data with or without left truncation

Author

Hu, Zhen-Huan, Wang, Hai-Lin, Gale, Robert Peter, and Zhang, Mei-Jie

Abstract

Series Editors Introduction: Since we published our last article on adjusted survival curves many readers wrote asking how to calculate these for left-truncated, right-censored data and what confidence intervals and p-value should be included when comparing adjusted curves. Responding, we developed a SAS macro to address these questions. The macro, %adjsurvlt() is available online, reduces computing time and is easy to use. (If you are having difficulty ask any 5 year old with an iPad.) Some terms we use such as do-loops, Monte Carlo simulations and confidence bands rather than intervals may be foreign to many readers but do not be put off. The message should be clear and the macro useful. Many readers will want to share this article with their bio-statistician. We realize %adjsurvlt() is not a catchy name for our macro and are hosting a competition for suggestions. The winner will receive a Keuffel and Esser slide rule. We are standing by to announce the lucky reader. Abstract: There are several statistical programmes to compute direct adjusted survival estimates from results of the Cox proportional hazards model. However, when used to analyze observational databases with large sample sizes or highly stratified treatment groups such as in registry-related datasets, these programmes are inefficient or unable to generate confidence bands and simultaneous pvalues. Also, these programmes do not consider potential left-truncation in retrospectively collected data. To address these deficiencies we developed a new SAS macro %adjsurvlt() able to produce direct adjusted survival estimates based on a stratified Cox model. The macro has improved computational performance and is able to handle left-truncated and right-censored time-to-eventdata. Several mechanisms were implemented to improve computational efficiency including choosing matrix operations over do-loops and reducing dimensions of co-variate matrices. Compared to the latest SAS macro, %adjsurvlt() used <0.1% computational time to process a dataset with 100 treatment cohorts and a sample size of 20,000 and showed similar computational efficiency when analyzing left-truncated and right-censored data. We illustrate use of %adjsurvlt() to compare retrospectively collected survival data of 2 transplant cohorts.

Published
2022
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