Nicole L De La Mata1, Mi-Young Ahn2, Nagalingeswaran Kumarasamy3, Penh Sun Ly4, Oon Tek Ng5, Kinh Van Nguyen6, Tuti Parwati Merati7, Thuy Thanh Pham8, Man Po Lee9, Nicolas Durier10, Matthew G Law11. 1. The Kirby Institute, UNSW Australia, Wallace Wurth Building, Sydney, NSW 2052, Australia. Electronic address: ndelamata@kirby.unsw.edu.au. 2. Department of Internal Medicine and AIDS Research Institute, Yonsei University College of Medicine, Severance Hospital, Seoul, South Korea. 3. Chennai Antiviral Research and Treatment Clinical Research Site (CART CRS), YRGCARE Medical Centre, VHS, Chennai, India. 4. National Center for HIV/AIDS, Dermatology & STDs, Phnom Penh, Cambodia. 5. Department of Infectious Diseases, Tan Tock Seng Hospital, Tan Tock Seng, Singapore. 6. National Hospital for Tropical Diseases, Hanoi, Vietnam. 7. Department of Internal Medicine, Udayana University, Sanglah Hospital, Bali, Indonesia. 8. Bach Mai Hospital, Hanoi, Vietnam. 9. Department of Medicine, Queen Elizabeth Hospital, Hong Kong, China. 10. TREAT Asia, amfAR-The Foundation for AIDS Research, Bangkok, Thailand. 11. The Kirby Institute, UNSW Australia, Wallace Wurth Building, Sydney, NSW 2052, Australia.
Abstract
OBJECTIVES: To compare two human immunodeficiency virus (HIV) cohorts to determine whether a pseudo-random sample can represent the entire study population. STUDY DESIGN AND SETTING: HIV-positive patients receiving care at eight sites in seven Asian countries. The TREAT Asia HIV Observational database (TAHOD) pseudo-randomly selected a patient sample, while TREAT Asia HIV Observational database-Low Intensity Transfer (TAHOD-LITE) included all patients. We compared patient demographics, CD4 count, and HIV viral load testing for each cohort. Risk factors associated with CD4 count response, HIV viral load suppression (<400 copies/mL), and survival were determined for each cohort. RESULTS: There were 2,318 TAHOD patients and 14,714 TAHOD-LITE patients. Patient demographics, CD4 count, and HIV viral load testing rates were broadly similar between the cohorts. CD4 count response and all-cause mortality were consistent among the cohorts with similar risk factors. HIV viral load response appeared to be superior in TAHOD and many risk factors differed, possibly due to viral load being tested on a subset of patients. CONCLUSION: Our study gives the first empirical evidence that analysis of risk factors for completely ascertained end points from our pseudo-randomly selected patient sample may be generalized to our larger, complete population of HIV-positive patients. However, results can significantly vary when analyzing smaller or pseudo-random samples, particularly if some patient data are not completely missing at random, such as viral load results.
OBJECTIVES: To compare two human immunodeficiency virus (HIV) cohorts to determine whether a pseudo-random sample can represent the entire study population. STUDY DESIGN AND SETTING:HIV-positivepatients receiving care at eight sites in seven Asian countries. The TREAT Asia HIV Observational database (TAHOD) pseudo-randomly selected a patient sample, while TREAT Asia HIV Observational database-Low Intensity Transfer (TAHOD-LITE) included all patients. We compared patient demographics, CD4 count, and HIV viral load testing for each cohort. Risk factors associated with CD4 count response, HIV viral load suppression (<400 copies/mL), and survival were determined for each cohort. RESULTS: There were 2,318 TAHOD patients and 14,714 TAHOD-LITE patients. Patient demographics, CD4 count, and HIV viral load testing rates were broadly similar between the cohorts. CD4 count response and all-cause mortality were consistent among the cohorts with similar risk factors. HIV viral load response appeared to be superior in TAHOD and many risk factors differed, possibly due to viral load being tested on a subset of patients. CONCLUSION: Our study gives the first empirical evidence that analysis of risk factors for completely ascertained end points from our pseudo-randomly selected patient sample may be generalized to our larger, complete population of HIV-positivepatients. However, results can significantly vary when analyzing smaller or pseudo-random samples, particularly if some patient data are not completely missing at random, such as viral load results.
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