Accurate evaluation of programs that treat and monitor HIV/AIDS patients around the world is central for fighting the epidemic. A major obstacle for program evaluation is patient dropout. An important such program is the President's Emergency Plan for AIDS Relief (PEPFAR). The US has been sponsoring PEPFAR ($63 billion for 2004-2013), and methods to accurately estimate patient survival are central to guide US management of the program. However, PEPFAR experiences high dropout rates (e.g., 39% in two years). Standard survival methods use only the observed non-dropout data, with no objective information for the dropouts. Such methods can be severely biased when dropout patients differ from nondropouts after accounting for observed information. To provide valid evaluation, in earlier work we have used a richer design known as """"""""double-sampling"""""""". This design re-allocates increased resources to target, intensively pursue and find a subset of the dropouts. These double-sampled dropouts are intended to represent the non-double-sampled dropouts, and to provide objective information for the entire cohort. Although standard methods have been known for double-sampling in surveys, we have shown earlier that standard survival methods fail when double-sampling is used in continuous enrollment programs such as PEPFAR. Also, we have shown earlier that standard evaluation without double-sampling can dramatically underestimate mortality in PEPFAR by a factor of 5. The proposed methods will build on our earlier work with the framework of """"""""principal stratification"""""""". The success of that framework increases the potential impact of this proposal. The proposed new methods are developed for three specific aims, motivated by PEPFAR in East Africa.
(Aim 1). Develop methods to estimate the performance of follow-up programs by using data from a given double- sampling design. In this aim we will develop methods to estimate survival from double-sampling designs that select patients based on their history characteristics before dropout. This is also important for the next two aims.
(Aim 2). Develop methods to create double-sampling designs that produce most accurate estimation of a pro- gram's performance given fixed resources. Evidence shows that information specific to a patient is important for what double-sampling designs provide best information about a program. Here, we will create patient-dependent double-sampling designs that maximize the accuracy given resources to estimate survival in such programs.
(Aim 3). Develop double-sampling designs to best target clinical goals.
Aim 1 can use the dropout patients'clinical history to predict those with highest mortality risk. These predictions can constrain the design to ensure to double-sample all such patients to better serve them medically.
In Aim 3, we will create designs that maximize the accuracy of estimation and best benefit patients.
Accurate evaluation of programs monitoring HIV/AIDS patients, such as US-sponsored PEPFAR, is critical for fighting the epidemic, but is threatened by high rates of patient dropout. Evidence shows that dramatically inaccurate evaluations of existing methods can be corrected if the design concentrates resources to ascertain post-dropout information for a subset of the dropouts (double-sampling), but the use of double-sampling is limited because accurate and integrated methods for such designs of follow-up programs are lacking. This project proposes to develop integrated double-sampling designs and methods to accurately evaluate such monitoring programs and best benefit patients given limited resources.
|Li, Xinran; Ding, Peng; Rubin, Donald B (2018) Asymptotic theory of rerandomization in treatment-control experiments. Proc Natl Acad Sci U S A 115:9157-9162|
|Gutman, R; Rubin, D B (2017) Estimation of causal effects of binary treatments in unconfounded studies with one continuous covariate. Stat Methods Med Res 26:1199-1215|
|Bakoyannis, Giorgos; Yu, Menggang; Yiannoutsos, Constantin T (2017) Semiparametric regression on cumulative incidence function with interval-censored competing risks data. Stat Med 36:3683-3707|
|Bind, Marie-Abele C; Rubin, Donald B (2017) Bridging observational studies and randomized experiments by embedding the former in the latter. Stat Methods Med Res :962280217740609|
|Narayan, Anand; Hong, Kelvin; Streiff, Michael et al. (2016) The Impact of Cancer on the Clinical Outcome of Patients After Inferior Vena Cava Filter Placement: A Retrospective Cohort Study. Am J Clin Oncol 39:294-301|
|Go, Vivian F; Latkin, Carl; Le Minh, Nguyen et al. (2016) Variations in the Role of Social Support on Disclosure Among Newly Diagnosed HIV-Infected People Who Inject Drugs in Vietnam. AIDS Behav 20:155-64|
|Lee, Joseph J; Rubin, Donald B (2015) Valid randomization-based p-values for partially post hoc subgroup analyses. Stat Med 34:3214-22|
|Bakoyannis, Giorgos; Yiannoutsos, Constantin T (2015) Impact of and Correction for Outcome Misclassification in Cumulative Incidence Estimation. PLoS One 10:e0137454|
|Frangakis, Constantine E; Qian, Tianchen; Wu, Zhenke et al. (2015) Deductive derivation and turing-computerization of semiparametric efficient estimation. Biometrics 71:867-74|
|Yu, Menggang; Yiannoutsos, Constantin T (2015) Marginal and Conditional Distribution Estimation from Double-Sampled Semi-Competing Risks Data. Scand Stat Theory Appl 42:87-103|
Showing the most recent 10 out of 31 publications