Nonlinear multiple imputation for continuous covariate within semiparametric Cox model: application to HIV data in Senegal.

Multiple imputation is commonly used to impute missing covariate in Cox semiparametric regression setting. It is to fill each missing data with more plausible values, via a Gibbs sampling procedure, specifying an imputation model for each missing variable. This imputation method is implemented in several softwares that offer imputation models steered by the shape of the variable to be imputed, but all these imputation models make an assumption of linearity on covariates effect. However, this assumption is not often verified in practice as the covariates can have a nonlinear effect. Such a linear assumption can lead to a misleading conclusion because imputation model should be constructed to reflect the true distributional relationship between the missing values and the observed values. To estimate nonlinear effects of continuous time invariant covariates in imputation model, we propose a method based on B-splines function. To assess the performance of this method, we conducted a simulation study, where we compared the multiple imputation method using Bayesian splines imputation model with multiple imputation using Bayesian linear imputation model in survival analysis setting. We evaluated the proposed method on the motivated data set collected in HIV-infected patients enrolled in an observational cohort study in Senegal, which contains several incomplete variables. We found that our method performs well to estimate hazard ratio compared with the linear imputation methods, when data are missing completely at random, or missing at random.

Operational research and HIV policy and guidelines: lessons from a study of patients lost to follow-up from a public antiretroviral treatment program in Cameroon.

Can operations and implementation research guide today's unprecedented efforts to scale-up HIV/AIDS prevention, treatment, care, and support in resource-limited settings? Our study of patients with HIV/AIDS who were first seen at the Central Hospital (Yaoundé, Cameroon) to begin antiretroviral therapy demonstrates the value of using operations research to explore programs, policies, and guidelines used in health care. We studied one group of patients, those lost to follow-up. Our findings confirmed the value of early treatment, systems to follow individuals, free treatment, and resources that enable operations research. We encourage health-care workers and program managers to perform operational research in their own context, and we emphasize the importance of allocating adequate human, financial, and logistic resources for this activity. Finally, we stress that the health-care workers, program managers, and researchers must work together to better inform policy and guidelines.

Monitoring of HIV viral loads, CD4 cell counts, and clinical assessments versus clinical monitoring alone for antiretroviral therapy in rural district hospitals in Cameroon (Stratall ANRS 12110/ESTHER): a randomised non-inferiority trial.

BACKGROUND: Scaling up of antiretroviral therapy in low-resource countries is done on the basis of decentralised, integrated HIV care in rural facilities; however, laboratory monitoring is generally unavailable. We aimed to assess the effectiveness and safety of clinical monitoring alone (CLIN) in terms of non-inferiority to laboratory and clinical monitoring (LAB). METHODS: We did a randomised, open-label, non-inferiority trial in nine rural district hospitals in Cameroon. Eligible participants were adults (≥18 years) infected with HIV-1 group M (WHO disease stage 3-4) who had not previously received antiretroviral therapy, and were followed-up for 2 years by health-care workers in routine activities. We randomly assigned participants (1:1) to CLIN or LAB (counts of HIV viral load and CD4 cell every 6 months) groups with a computer-generated list. The primary outcome was non-inferiority of CLIN to LAB in terms of increase in CD4 cell count with a non-inferiority margin of 25%. We did all analyses in participants who attended at least one follow-up visit. This trial is registered with ClinicalTrials.gov, number NCT00301561. FINDINGS: 238 (93%) of 256 participants assigned to CLIN and 221 (93%) of 237 assigned to LAB were eligible for analysis. CLIN was not non-inferior to LAB; the mean increase in CD4 cell count was 175 cells per µL (SD 190, 95% CI 151-200) with CLIN and 206 (190, 181-231) with LAB (difference -31 [-63 to 2] and non-inferiority margin -52 [-58 to -45]). Furthermore, in the predefined secondary outcome of treatment changes, 13 participants (6%) in the LAB group switched to second-line regimens whereas no participants in the CLIN group did so (p<0·0001). By contrast, other predefined secondary outcomes were much the same in both groups-viral suppression (<40 copies per mL; 465 [49%] of 952 measurements in CLIN vs 456 [52%] of 884 in LAB), HIV resistance (23 [10%] of 238 participants vs 22 [10%] of 219 participants), mortality (44 [18%] of 238 vs 32 [14%] of 221), disease progression (85 [36%] of 238 vs 64 [29%] of 221), adherence (672 [63%] of 1067 measurements vs 621 [61%] of 1011), loss to follow-up (21 [9%] of 238 vs 17 [8%] of 221), and toxic effects (46 [19%] of 238 vs 56 [25%] of 221). INTERPRETATION: Our findings support WHO's recommendation for laboratory monitoring of antiretroviral therapy. However, the small differences that we noted between the strategies suggest that clinical monitoring alone could be used, at least temporarily, to expand antiretroviral therapy in low-resource settings. FUNDING: French National Agency for Research on AIDS (ANRS) and Ensemble pour une Solidarité Thérapeutique Hospitalière En Réseau (ESTHER).