Can Internet-Based Sexual Health Services Increase Diagnoses of Sexually Transmitted Infections (STI)? Protocol for a Randomized Evaluation of an Internet-Based STI Testing and Results Service.

Background: Ensuring rapid access to high quality sexual health services is a key public health objective, both in the United Kingdom and internationally. Internet-based testing services for sexually transmitted infections (STIs) are considered to be a promising way to achieve this goal. This study will evaluate a nascent online STI testing and results service in South East London, delivered alongside standard face-to-face STI testing services. Objective: The aim of this study is to establish whether an online testing and results services can (1) increase diagnoses of STIs and (2) increase uptake of STI testing, when delivered alongside standard face-to-face STI testing services. Methods: This is a single-blind randomized controlled trial. We will recruit 3000 participants who meet the following eligibility criteria: 16-30 years of age, resident in the London boroughs of Lambeth and Southwark, having at least one sexual partner in the last 12 months, having access to the Internet and willing to take an STI test. People unable to provide informed consent and unable to read and understand English (the websites will be in English) will be excluded. Baseline data will be collected at enrolment. This includes participant contact details, demographic data (date of birth, gender, ethnicity, and sexual orientation), and sexual health behaviors (last STI test, service used at last STI test and number of sexual partners in the last 12 months). Once enrolled, participants will be randomly allocated either (1) to an online STI testing and results service (Sexual Health 24) offering postal self-administered STI kits for chlamydia, gonorrhoea, syphilis, and HIV; results via text message (short message service, SMS), except positive results for HIV, which will be delivered by phone; and direct referrals to local clinics for treatment or (2) to a conventional sexual health information website with signposting to local clinic-based sexual health services. Participants will be free to use any other interventions or services during the trial period. At 6 weeks from randomization we will collect self-reported follow-up data on service use, STI tests and results, treatment prescribed, and acceptability of STI testing services. We will also collect objective data from participating STI testing services on uptake of STI testing, STI diagnoses and treatment. We hypothesise that uptake of STI testing and STI diagnoses will be higher in the intervention arm. Our hypothesis is based on the assumption that the intervention is less time-consuming, more convenient, more private, and incur less stigma and embarrassment than face-to-face STI testing pathways. The primary outcome measure is diagnosis of any STI at 6 weeks from randomization and our co-primary outcome is completion of any STI test at 6 weeks from randomization. We define completion of a test, as samples returned, processed, and results delivered to the intervention and/or clinic settings. We will use risk ratios to calculate the effect of the intervention on our primary outcomes with 95% confidence intervals. All analyses will be based on the intention-to-treat (ITT) principle. Results: This study is funded by Guy’s and St Thomas’ Charity and it has received ethical approval from NRES Committee London-Camberwell St Giles (Ref 14/LO/1477). Research and Development approval has been obtained from Kings College Hospital NHS Foundation Trust and Guy’s and St Thomas’ NHS Foundation Trust. Results are expected in June 2016. Conclusions: This study will provide evidence on the effectiveness of an online STI testing and results service in South East London. Our findings may also be generalizable to similar populations in the United Kingdom

Multiple imputation of multiple multi-item scales when a full imputation model is infeasible

Background Missing data in a large scale survey presents major challenges. We focus on performing multiple imputation by chained equations when data contain multiple incomplete multi-item scales. Recent authors have proposed imputing such data at the level of the individual item, but this can lead to infeasibly large imputation models. Methods We use data gathered from a large multinational survey, where analysis uses separate logistic regression models in each of nine country-specific data sets. In these data, applying multiple imputation by chained equations to the individual scale items is computationally infeasible. We propose an adaptation of multiple imputation by chained equations which imputes the individual scale items but reduces the number of variables in the imputation models by replacing most scale items with scale summary scores. We evaluate the feasibility of the proposed approach and compare it with a complete case analysis. We perform a simulation study to compare the proposed method with alternative approaches: we do this in a simplified setting to allow comparison with the full imputation model. Results For the case study, the proposed approach reduces the size of the prediction models from 134 predictors to a maximum of 72 and makes multiple imputation by chained equations computationally feasible. Distributions of imputed data are seen to be consistent with observed data. Results from the regression analysis with multiple imputation are similar to, but more precise than, results for complete case analysis; for the same regression models a 39 % reduction in the standard error is observed. The simulation shows that our proposed method can perform comparably against the alternatives. Conclusions By substantially reducing imputation model sizes, our adaptation makes multiple imputation feasible for large scale survey data with multiple multi-item scales. For the data considered, analysis of the multiply imputed data shows greater power and efficiency than complete case analysis. The adaptation of multiple imputation makes better use of available data and can yield substantively different results from simpler techniques

The effect of biological heterogeneity on R-CHOP treatment outcome in diffuse large B-cell lymphoma across five international regions

Addressing the global burden of cancer, understanding its diverse biology, and promoting appropriate prevention and treatment strategies around the world has become a priority for the United Nations and International Atomic Energy Agency (IAEA), the WHO, and International Agency for Research on Cancer (IARC). The IAEA sponsored an international prospective cohort study to better understand biology, treatment response, and outcomes of diffuse large B-cell lymphoma (DLBCL) in low and middle-income countries across five UN-defined geographical regions. We report an analysis of biological variation in DLBCL across seven ethnic and environmentally diverse populations. In this cohort of 136 patients treated to a common protocol, we demonstrate significant biological differences between countries, characterized by a validated prognostic gene expression score (p < .0001), but International Prognostic Index (IPI)-adjusted survivals in all participating countries were similar. We conclude that DLBCL treatment outcomes in these populations can be benchmarked to international standards, despite biological heterogeneity

INTEREST: INteractive Tool for Exploring REsults from Simulation sTudies

Simulation studies allow us to explore the properties of statistical methods. They provide a powerful tool with a multiplicity of aims; among others: evaluating and comparing new or existing statistical methods, assessing violations of modelling assumptions, helping with the understanding of statistical concepts, and supporting the design of clinical trials. The increased availability of powerful computational tools and usable software has contributed to the rise of simulation studies in the current literature. However, simulation studies involve increasingly complex designs, making it difficult to provide all relevant results clearly. Dissemination of results plays a focal role in simulation studies: it can drive applied analysts to use methods that have been shown to perform well in their settings, guide researchers to develop new methods in a promising direction, and provide insights into less established methods. It is crucial that we can digest relevant results of simulation studies. Therefore, we developed INTEREST: an INteractive Tool for Exploring REsults from Simulation sTudies. The tool has been developed using the Shiny framework in R and is available as a web app or as a standalone package. It requires uploading a tidy format dataset with the results of a simulation study in R, Stata, SAS, SPSS, or comma-separated format. A variety of performance measures are estimated automatically along with Monte Carlo standard errors; results and performance summaries are displayed both in tabular and graphical fashion, with a wide variety of available plots. Consequently, the reader can focus on simulation parameters and estimands of most interest. In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations and readers to explore them freely

Practical Use of Multiple Imputation

Multiple imputation is a flexible technique for handling missing data that is widely used in medical research. Its properties are understood well for some simple settings but less so for the complex settings in which it is typically applied. The three research topics considered in thesis consider incomplete continuous covariates when the analysis model involves nonlinear functions of one or more of these. Chapters 2–4 evaluate two imputation techniques known as predictive mean matching and local residual draws, which may protect against bias when the imputation model is misspecified. Following a review of the literature, I focus on how to match, the appropriate size of donor pool, and whether transformation can improve imputation. Neither method performs as well as hoped when the imputation model is misspecified but both can offer some protection against imputation model misspecification. Chapter 5 investigates strategies for imputing the ratio of two variables. Various ‘active’ and ‘passive’ strategies are critiqued, applied to two datasets and compared in a simulation study. (‘Active’ indicates the ratio is imputed directly within a model; ‘passive’ means it is calculated externally to the imputation model.) Without prior transformation, passive imputation after imputing the numerator and denominator should be avoided, but other methods require less caution. Chapter 6 proposes techniques for combining multiple imputation with (multivariable) fractional polynomial methods. A new technique for imputing dimension-one fractional polynomials is developed and nested in a chained-equations procedure. Two candidate methods for estimating exponents in the fractional polynomial model, using Wald statistics and log-likelihoods, are assessed via simulation. Finally, the type I error and power are compared for model selection procedures based on Wald and likelihood-ratio type tests. Both methods can out-perform complete cases analysis, with the Wald method marginally better than likelihood-ratio tests