No abstract

Background Health-related quality of life (HRQoL) has become a major endpoint to assess the clinical benefit of new therapeutic strategies in oncology clinical trials. Typically, HRQoL outcomes are analyzed using linear mixed models (LMMs). However, longitudinal analysis of HRQoL in the presence of missing data remains complex and unstandardized. Our objective was to compare the modeling alternatives that account for informative dropout. Methods We investigated three alternative methods—the selection model (SM), pattern-mixture model (PMM), and shared-parameters model (SPM)—in relation to the LMM. We first compared them on the basis of methodological arguments highlighting their advantages and drawbacks. Then, we applied them to data from a randomized clinical trial that included 267 patients with advanced esophageal cancer for the analysis of four HRQoL dimensions evaluated using the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire. Results We highlighted differences in terms of outputs, interpretation, and underlying modeling assumptions; this methodological comparison could guide the choice of method according to the context. In the application, none of the four models detected a significant difference between the two treatment arms. The estimated effect of time on HRQoL varied according to the method: for all analyzed dimensions, the PMM estimated an effect that contrasted with those estimated by the SM and SPM; the LMM estimated effects were confirmed by the SM (on two of four HRQoL dimensions) and SPM (on three of four HRQoL dimensions). Conclusions The PMM, SM, or SPM should be used to confirm or invalidate the results of LMM analysis when informative dropout is suspected. Of these three alternative methods, the SPM appears to be the most interesting from both theoretical and practical viewpoints. Trial registration This study is registered with ClinicalTrials.gov , number NCT00861094 .

No abstract

Background Data on HIV seroconversion among men who have sex with men (MSM) using pre-exposure prophylaxis (PrEP) in West Africa are needed. This study aimed to document HIV seroconversion and associated determinants, PrEP adherence, plasma drug concentrations, and HIV drug resistance in MSM using event-driven or daily PrEP in Burkina Faso, Côte d’Ivoire, Mali, and Togo. Methods A prospective cohort study was conducted in 2017-2021 among HIV-seronegative MSM aged 18 or over who were at high risk of HIV infection. Participants could choose between event-driven and daily PrEP, switch regimens, and discontinue or restart PrEP. The determinants of HIV incidence were investigated using a multivariate mixed-effects Poisson regression analysis. Results A total of 647 participants were followed for a total time of 1229.3 person-years. Of 5371 visits, event-driven PrEP was chosen in 3873 (72.1%), and daily PrEP in 1400 (26.1%). HIV incidence was 2.4 per 100 person-years (95% CI 1.5-3.6) for event-driven PrEP, and 0.6 per 100 person-years (95% CI 0.1-2.3) for daily PrEP (adjusted incidence rate ratio 4.40, 95% CI 1.00-19.36, p=0.050). Adequate adherence was lower with event-driven than daily PrEP (44.3% vs 74.9%, p<0.001). Plasma drug concentrations were undetectable in 92 (97.9%) of the 94 measures taken for 23 participants who seroconverted. Only 1 participant had resistance to PrEP drugs. Conclusions HIV seroconversions mainly occurred in participants who chose event-driven PrEP. The study’s data highlighted major difficulties with adherence to this regimen. Improving adherence to event-driven PrEP constitutes a major research and public health priority in this context.

Background Antimicrobial resistance to macrolides and fluoroquinolones in Mycoplasma genitalium among men who have sex with men (MSM) is worryingly high in high-resource countries. Data in Africa are lacking. We aimed to assess the burden of M. genitalium including the presence of resistance associated mutations (RAMs) in M. genitalium among MSM using HIV pre-exposure prophylaxis in Burkina Faso, Côte d’Ivoire, Mali and Togo. Methods MSM were included in a prospective cohort study (2017-2021). Molecular detection of M. genitalium in urine, anorectal, and pharyngeal samples was performed at baseline and after 6 and 12 months. Detection of RAMs to macrolides and fluoroquinolones was performed by sequencing the 23S rRNA, the parC and gyrA gene. A sample was found to be possibly resistant to fluoroquinolones if alterations were found in ParC position 83/87. Results Of 598 participants, 173 (28.9%) were positive at least once for M. genitalium and global point-prevalence was 19.4%. Interestingly, 238/250 (95.2%) infections were asymptomatic and 72/138 M. genitalium infections with follow-up data (52.2%) cleared during the study. Only one macrolide RAM was found (0.6%). Prevalence of fluoroquinolones RAMs was 11.3% overall, ranging from 2.4% in Burkina Faso to 17.5% in Mali. Conclusions Although M. genitalium was highly prevalent in these MSM, macrolide resistance was almost non-existent. Nevertheless, more than 10% of the samples was possibly resistant to fluoroquinolones. Heterogeneity in the prevalence of fluoroquinolone RAMs between countries may be explained by different antimicrobial consumption in humans and animals.

When investigating disease etiology, twin data provide a unique opportunity to control for confounding and disentangling the role of the human genome and exposome. However, using appropriate statistical methods is fundamental for exploiting such potential. We aimed to critically review the statistical approaches used in twin studies relating exposure to early life health conditions. We searched PubMed, Scopus, Web of Science, and Embase (2011–2021). We identified 32 studies and nine classes of methods. Five were conditional approaches (within-pair analyses): additive-common-erratic (ACE) models (11 studies), generalized linear mixed models (GLMMs, five studies), generalized linear models (GLMs) with fixed pair effects (four studies), within-pair difference analyses (three studies), and paired-sample tests (two studies). Four were marginal approaches (unpaired analyses): generalized estimating equations (GEE) models (five studies), GLMs with cluster-robust standard errors (six studies), GLMs (one study), and independent-sample tests (one study). ACE models are suitable for assessing heritability but require adaptations for binary outcomes and repeated measurements. Conditional models can adjust by design for shared confounders, and GLMMs are suitable for repeated measurements. Marginal models may lead to invalid inference. By highlighting the strengths and limitations of commonly applied statistical methods, this review may be helpful for researchers using twin designs.

Health-related quality of life (HRQoL) is an important endpoint in cancer clinical trials. Analysis of HRQoL longitudinal data is plagued by missing data, notably due to dropout. Joint models are increasingly receiving attention for modelling longitudinal outcomes and the time-to-dropout. However, dropout can be informative or non-informative depending on the cause. MethodsWe propose using a joint model that includes a competing risks sub-model for the cause-specific time-to-dropout.We compared a competing risks joint model (CR JM) that distinguishes between two causes of dropout with a standard joint model (SJM) that treats all the dropouts equally. First, we applied the CR JM and SJM to data from 267 patients with advanced oesophageal cancer from the randomized clinical trial PRODIGE 5/ACCORD 17 to analyse HRQoL data in the presence of dropouts unrelated and related to a clinical event. Then, we compared the models using a simulation study. ResultsWe showed that the CR JM performed as well as the SJM in situations where the risk of dropout was the same whatever the cause. In the presence of both informative and non-informative dropouts, only the SJM estimations were biased, impacting the HRQoL estimated parameters. ConclusionThe systematic collection of the reasons for dropout in clinical trials would facilitate the use of CR JMs, which could be a satisfactory approach to analysing HRQoL data in presence of both informative and non-informative dropout.Trial registration: This study is registered with ClinicalTrials.gov, number NCT00861094.

No abstract