Comparison of retention in observational cohorts and nested simulated HIV vaccine efficacy trials in the key populations in Uganda.

BACKGROUND: Outcomes in observational studies may not best estimate those expected in the HIV vaccine efficacy trials. We compared retention in Simulated HIV Vaccine Efficacy Trials (SiVETs) and observational cohorts drawn from two key populations in Uganda. METHODS: Two SiVETs were nested within two observational cohorts, one in Fisherfolk (FF) and another one in Female Sex Workers (FSW). Adult participants in each observational cohort were screened for enrolment into SiVETs. Those screened-out or not screened continued participation in the observational (non-SiVET) cohorts. SiVET participants were administered a licensed hepatitis B vaccine in a schedule that mimicked an actual HIV vaccine efficacy trial. Both cohorts were followed for 12 months and retention was assessed through dropout, defined as lost to follow up, being uncontactable, refusal to continue or missing the last study clinic visit. Dropout rates were compared using Poisson models giving rate ratios and 95% confidence intervals (95%CI). RESULTS: Out of 1525 participants (565 FF and 960 FSW), 572 (38%) were enrolled into SiVETs (282-FF and 290-FSW), and 953 (62%) remained in the non-SiVET cohorts. Overall, 326 (101 SiVET, 225 non-SiVET) dropped out in 1260 Person Years of Observation (PYO), a dropout rate of 25.9 /100 PYO (95%CI: 23.2-28.8); fewer dropped out in the SiVET cohorts (18.4, 95% CI: 15.1-22.4) than in the non-SiVET cohorts (31.6, 95% CI: 27.8-36.1), rate ratio (RR) =0.58, 95% CI: 0.46-0.73. In all cohorts, the dropout was more marked in FSW than in FF population. Duration lived in community was associated with dropout in both SiVETs and religion in both non-SiVET cohorts. CONCLUSION: The rate of dropout was lower in SiVET compared to non-SiVET cohort. Though the difference in dropout between SiVET and non-SiVET was generally similar, the actual dropout rates were higher in the FSW population. Conduct of SiVETs in these key populations could mean that designing HIV Vaccine Efficacy Trials will benefit from lower dropout rate shown in SiVET than non-SiVET observational cohort.

Centre effects and case-mix in early rheumatoid arthritis observational cohorts: a narrative review.

OBJECTIVES: Observational cohort studies in early RA are a key source of evidence, despite inconsistencies in methodological approaches. This narrative review assesses the spectrum of methodologies used in addressing centre-level effect and case-mix adjustment in early RA observational cohort studies. METHODS: An electronic search was undertaken to identify observational prospective cohorts of >100 patients recruited from two or more centres, within 2 years of an RA or early inflammatory arthritis diagnosis. References and author publication lists of all studies from eligible cohorts were assessed for additional cohorts. RESULTS: Thirty-four unique cohorts were identified from 204 studies. Seven percent of studies considered centre in their analyses, most commonly as a fixed effect in regression modelling. Reporting of case-mix variables in analyses varied widely. The number of variables considered in case-mix adjustment was higher following publication of the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement in 2007. CONCLUSION: Centre effect is unreported or inadequately accounted for in the majority of RA observational cohorts, potentially leading to spurious inferences and obstructing comparisons between studies. Inadequate case-mix adjustment precludes meaningful comparisons between centres. Appropriate methodology to account for centre and case-mix adjustment should be considered at the outset of analyses.

Not-in-trial simulation I: Bridging cardiovascular risk from clinical trials to real-life conditions.

AIMS: The assessment of heart rate-corrected QT (QTc) interval prolongation relies on the evidence of drug effects in healthy subjects. This study demonstrates the relevance of pharmacokinetic-pharmacodynamic (PKPD) relationships to characterize drug-induced QTc interval prolongation and explore the discrepancies between clinical trials and real-life conditions. METHODS: d,l-Sotalol data from healthy subjects and from the Rotterdam Study cohort were used to assess treatment response in a phase I setting and in a real-life conditions, respectively. Using modelling and simulation, drug effects at therapeutic doses were predicted in both populations. RESULTS: Inclusion criteria were shown to restrict the representativeness of the trial population in comparison to real-life conditions. A significant part of the typical patient population was excluded from trials due to weight and baseline QTc interval criteria. Relative risk was significantly different between sotalol users with and without heart failure, hypertension, diabetes and myocardial infarction (P < 0.01). Although drug effects do cause an increase in the relative risk of QTc interval prolongation, the presence of diabetes represented an increase from 4.0 [95% confidence interval (CI) 2.7-5.8] to 6.5 (95% CI 1.6-27.1), whilst for myocardial infarction it increased from 3.4 (95% CI 2.3-5.13) to 15.5 (95% CI 4.9-49.3). CONCLUSIONS: Our findings show that drug effects on QTc interval do not explain the observed QTc values in the population. The prevalence of high QTc values in the real-life population can be assigned to co-morbidities and concomitant medications. These findings substantiate the need to account for these factors when evaluating the cardiovascular risk of medicinal products.

Lessons learned from over a decade of data audits in international observational HIV cohorts in Latin America and East Africa.

Introduction: Routine patient care data are increasingly used for biomedical research, but such "secondary use" data have known limitations, including their quality. When leveraging routine care data for observational research, developing audit protocols that can maximize informational return and minimize costs is paramount. Methods: For more than a decade, the Latin America and East Africa regions of the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium have been auditing the observational data drawn from participating human immunodeficiency virus clinics. Since our earliest audits, where external auditors used paper forms to record audit findings from paper medical records, we have streamlined our protocols to obtain more efficient and informative audits that keep up with advancing technology while reducing travel obligations and associated costs. Results: We present five key lessons learned from conducting data audits of secondary-use data from resource-limited settings for more than 10 years and share eight recommendations for other consortia looking to implement data quality initiatives. Conclusion: After completing multiple audit cycles in both the Latin America and East Africa regions of the IeDEA consortium, we have established a rich reference for data quality in our cohorts, as well as large, audited analytical datasets that can be used to answer important clinical questions with confidence. By sharing our audit processes and how they have been adapted over time, we hope that others can develop protocols informed by our lessons learned from more than a decade of experience in these large, diverse cohorts.

Cancer and autoimmunity: Harnessing longitudinal cohorts to probe the link.

In many autoimmune rheumatic diseases, there is an increased risk of cancer compared to the general population. While reasons for this increased risk have not been elucidated, it has been hypothesized that the link between cancer and autoimmunity may be bidirectional. For instance, chronic inflammation and damage from the rheumatic disease or its therapies may trigger malignant transformation; conversely, antitumor immune responses targeting cancers may become cross-reactive resulting in autoimmunity. In rare rheumatic diseases, longitudinal observational studies can play a critical role in studying these complex relationships, thereby enabling investigators to quantify the extent of cancer risk, identify unique clinical phenotypes associated with cancer, investigate the biological link between these conditions, and define optimal strategies for screening and treatment of the underlying cancer. In this review, we discuss recent data on cancer in the rheumatic diseases and suggest a research agenda to address several gaps in our current knowledge base.

A longitudinal model for disease progression was developed and applied to multiple sclerosis.

OBJECTIVES: To develop a model of disease progression using multiple sclerosis (MS) as an exemplar. STUDY DESIGN AND SETTINGS: Two observational cohorts, the University of Wales MS (UoWMS), UK (1976), and British Columbia MS (BCMS) database, Canada (1980), with longitudinal disability data [the Expanded Disability Status Scale (EDSS)] were used; individuals potentially eligible for MS disease-modifying drugs treatments, but who were unexposed, were selected. Multilevel modeling was used to estimate the EDSS trajectory over time in one data set and validated in the other; challenges addressed included the choice and function of time axis, complex observation-level variation, adjustments for MS relapses, and autocorrelation. RESULTS: The best-fitting model for the UoWMS cohort (404 individuals, and 2,290 EDSS observations) included a nonlinear function of time since onset. Measurement error decreased over time and ad hoc methods reduced autocorrelation and the effect of relapse. Replication within the BCMS cohort (978 individuals and 7,335 EDSS observations) led to a model with similar time (years) coefficients, time [0.22 (95% confidence interval {CI}: 0.19, 0.26), 0.16 (95% CI: 0.10, 0.22)] and log time [-0.13 (95% CI: -0.39, 0.14), -0.15 (95% CI: -0.70, 0.40)] for BCMS and UoWMS, respectively. CONCLUSION: It is possible to develop robust models of disability progression for chronic disease. However, explicit validation is important given the complex methodological challenges faced.