Deductive semiparametric estimation in Double-Sampling Designs with application to PEPFAR

Non-ignorable dropout is common in studies with long follow-up time, and it can bias study results unless handled carefully. A double-sampling design allocates additional resources to pursue a subsample of the dropouts and find out their outcomes, which can address potential biases due to non-ignorable dropout. It is desirable to construct semiparametric estimators for the double-sampling design because of their robustness properties. However, obtaining such semiparametric estimators remains a challenge due to the requirement of the analytic form of the efficient influence function (EIF), the derivation of which can be ad hoc and difficult for the double-sampling design. Recent work has shown how the derivation of EIF can be made deductive and computerizable using the functional derivative representation of the EIF in nonparametric models. This approach, however, requires deriving the mixture of a continuous distribution and a point mass, which can itself be challenging for complicated problems such as the double-sampling design. We propose semiparametric estimators for the survival probability in double-sampling designs by generalizing the deductive and computerizable estimation approach. In particular, we propose to build the semiparametric estimators based on a discretized support structure, which approximates the possibly continuous observed data distribution and circumvents the derivation of the mixture distribution. Our approach is deductive in the sense that it is expected to produce semiparametric locally efficient estimators within finite steps without knowledge of the EIF. We apply the proposed estimators to estimating the mortality rate in a double-sampling design component of the President's Emergency Plan for AIDS Relief (PEPFAR) program. We evaluate the impact of double-sampling selection criteria on the mortality rate estimates

Choosing profile double-sampling designs for survival estimation with application to PEPFAR evaluation

Most studies that follow subjects over time are challenged by having some subjects who dropout. Double sampling is a design that selects and devotes resources to intensively pursue and find a subset of these dropouts, then uses data obtained from these to adjust naïve estimates, which are potentially biased by the dropout. Existing methods to estimate survival from double sampling assume a random sample. In limited-resource settings, however, generating accurate estimates using a minimum of resources is important. We propose using double-sampling designs that oversample certain profiles of dropouts as more efficient alternatives to random designs. First, we develop a framework to estimate the survival function under these profile double-sampling designs. We then derive the precision of these designs as a function of the rule for selecting different profiles, in order to identify more efficient designs. We illustrate using data from the United States President's Emergency Plan for AIDS Relief-funded HIV care and treatment program in western Kenya. Our results show why and how more efficient designs should oversample patients with shorter dropout times. Further, our work suggests generalizable practice for more efficient double-sampling designs, which can help maximize efficiency in resource-limited settings

Sampling-Based Approaches to Improve Estimation of Mortality among Patient Dropouts: Experience from a Large PEPFAR-Funded Program in Western Kenya

Monitoring and evaluation (M&E) of HIV care and treatment programs is impacted by losses to follow-up (LTFU) in the patient population. The severity of this effect is undeniable but its extent unknown. Tracing all lost patients addresses this but census methods are not feasible in programs involving rapid scale-up of HIV treatment in the developing world. Sampling-based approaches and statistical adjustment are the only scaleable methods permitting accurate estimation of M&E indices.In a large antiretroviral therapy (ART) program in western Kenya, we assessed the impact of LTFU on estimating patient mortality among 8,977 adult clients of whom, 3,624 were LTFU. Overall, dropouts were more likely male (36.8% versus 33.7%; p = 0.003), and younger than non-dropouts (35.3 versus 35.7 years old; p = 0.020), with lower median CD4 count at enrollment (160 versus 189 cells/ml; p<0.001) and WHO stage 3-4 disease (47.5% versus 41.1%; p<0.001). Urban clinic clients were 75.0% of non-dropouts but 70.3% of dropouts (p<0.001). Of the 3,624 dropouts, 1,143 were sought and 621 had their vital status ascertained. Statistical techniques were used to adjust mortality estimates based on information obtained from located LTFU patients. Observed mortality estimates one year after enrollment were 1.7% (95% CI 1.3%-2.0%), revised to 2.8% (2.3%-3.1%) when deaths discovered through outreach were added and adjusted to 9.2% (7.8%-10.6%) and 9.9% (8.4%-11.5%) through statistical modeling depending on the method used. The estimates 12 months after ART initiation were 1.7% (1.3%-2.2%), 3.4% (2.9%-4.0%), 10.5% (8.7%-12.3%) and 10.7% (8.9%-12.6%) respectively. CONCLUSIONS/SIGNIFICANCE ABSTRACT: Assessment of the impact of LTFU is critical in program M&E as estimated mortality based on passive monitoring may underestimate true mortality by up to 80%. This bias can be ameliorated by tracing a sample of dropouts and statistically adjust the mortality estimates to properly evaluate and guide large HIV care and treatment programs

Tuberculosis in Pediatric Antiretroviral Therapy Programs in Low- and Middle-Income Countries: Diagnosis and Screening Practices

Background The global burden of childhood tuberculosis (TB) is estimated to be 0.5 million new cases per year. Human immunodeficiency virus (HIV)-infected children are at high risk for TB. Diagnosis of TB in HIV-infected children remains a major challenge. Methods We describe TB diagnosis and screening practices of pediatric antiretroviral treatment (ART) programs in Africa, Asia, the Caribbean, and Central and South America. We used web-based questionnaires to collect data on ART programs and patients seen from March to July 2012. Forty-three ART programs treating children in 23 countries participated in the study. Results Sputum microscopy and chest Radiograph were available at all programs, mycobacterial culture in 40 (93%) sites, gastric aspiration in 27 (63%), induced sputum in 23 (54%), and Xpert MTB/RIF in 16 (37%) sites. Screening practices to exclude active TB before starting ART included contact history in 41 sites (84%), symptom screening in 38 (88%), and chest Radiograph in 34 sites (79%). The use of diagnostic tools was examined among 146 children diagnosed with TB during the study period. Chest Radiograph was used in 125 (86%) children, sputum microscopy in 76 (52%), induced sputum microscopy in 38 (26%), gastric aspirate microscopy in 35 (24%), culture in 25 (17%), and Xpert MTB/RIF in 11 (8%) children. Conclusions Induced sputum and Xpert MTB/RIF were infrequently available to diagnose childhood TB, and screening was largely based on symptom identification. There is an urgent need to improve the capacity of ART programs in low- and middle-income countries to exclude and diagnose TB in HIV-infected childre

Results of the Cox proportional hazards model of patient dropout.

*<p>Wald test.</p

Kaplan-Meier estimates of patient survival based on methods described in Table 3.

<p>Time is in months since start of CART.</p

Kaplan-Meier estimates of patient survival based on methods described in Table 3.

<p>Time is in months since enrollment.</p

Mortality estimates (%, 95% CI) at one year from enrollment and one year from ART initiation. Overall estimates based on entire cohort.

*<p>Combined estimates were produced as a weighted average of the individual (dropout versus non-dropout) estimates, taking into account the relative size of the two groups.</p

Patient characteristics and comparison between dropouts and non-dropouts.

<p>Frequencies were compared via Pearson's chi-square test. Continuous factors were compared via the Kruskal-Wallis test. Median times from enrollment until CART start were estimated via the method of Kaplan and Meier and were compared by the log-rank test. IQR = Inter-quartile range.</p>*<p>Out of 6,900 total patients (5,178 non-dropouts and 1,722 dropouts) with WHO stage recorded within three months of enrollment.</p

Regional areas and widths of the midsagittal corpus callosum among HIV-infected patients on stable antiretroviral therapies

Recent reports suggest that a growing number of human immunodeficiency virus (HIV)-infected persons show signs of persistent cognitive impairment even in the context of combination antiretroviral therapies (cART). The basis for this finding remains poorly understood as there are only a limited number of studies examining the relationship between CNS injury, measures of disease severity, and cognitive function in the setting of stable disease. This study examined the effects of HIV infection on cerebral white matter using quantitative morphometry of the midsagittal corpus callosum (CC) in 216 chronically infected participants from the multisite HIV Neuroimaging Consortium study currently receiving cART and 139 controls. All participants underwent MRI assessment, and HIV-infected subjects also underwent measures of cognitive function and disease severity. The midsagittal slice of the CC was quantified using two semi-automated procedures. Group comparisons were accomplished using ANOVA, and the relationship between CC morphometry and clinical covariates (current CD4, nadir CD4, plasma and CSF HIV RNA, duration of HIV infection, age, and ADC stage) was assessed using linear regression models. HIV-infected patients showed significant reductions in both the area and linear widths for several regions of the CC. Significant relationships were found with ADC stage and nadir CD4 cell count, but no other clinical variables. Despite effective treatment, significant and possibly irreversible structural loss of the white matter persists in the setting of chronic HIV disease. A history of advanced immune suppression is a strong predictor of this complication and suggests that antiretroviral intervention at earlier stages of infection may be warranted