Multiple imputation strategies for missing event times in a multi-state model analysis.

In clinical studies, multi-state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times may not be known. Motivated by a real dataset of patients who received stem cell transplants, we considered the setting in which some event times were exactly observed and some were missing. In our setting, there was little information about the time intervals in which the missing event times occurred and missingness depended on the event type, given the analysis model covariates. These additional challenges limited the usefulness of some missing data methods (maximum likelihood, complete case analysis, and inverse probability weighting). We show that multiple imputation (MI) of event times can perform well in this setting. MI is a flexible method that can be used with any complete data analysis model. Through an extensive simulation study, we show that MI by predictive mean matching (PMM), in which sampling is from a set of observed times without reliance on a specific parametric distribution, has little bias when event times are missing at random, conditional on the observed data. Applying PMM separately for each sub-group of patients with a different pathway through the MSM tends to further reduce bias and improve precision. We recommend MI using PMM methods when performing MSM analysis with Markov models and partially observed event times.

Time-sensitive testing pressures and COVID-19 outcomes: are socioeconomic inequalities over the first year of the pandemic explained by selection bias?

BACKGROUND: There are many ways in which selection bias might impact COVID-19 research. Here we focus on selection for receiving a polymerase-chain-reaction (PCR) SARS-CoV-2 test and how known changes to selection pressures over time may bias research into COVID-19 infection. METHODS: Using UK Biobank (N = 420,231; 55% female; mean age = 66.8 [SD = 8·11]) we estimate the association between socio-economic position (SEP) and (i) being tested for SARS-CoV-2 infection versus not being tested (ii) testing positive for SARS-CoV-2 infection versus testing negative and (iii) testing negative for SARS-CoV-2 infection versus not being tested. We construct four distinct time-periods between March 2020 and March 2021, representing distinct periods of testing pressures and lockdown restrictions and specify both time-stratified and combined models for each outcome. We explore potential selection bias by examining associations with positive and negative control exposures. RESULTS: The association between more disadvantaged SEP and receiving a SARS-CoV-2 test attenuated over time. Compared to individuals with a degree, individuals whose highest educational qualification was a GCSE or equivalent had an OR of 1·27 (95% CI: 1·18 to 1·37) in March-May 2020 and 1·13 (95% CI: 1.·10 to 1·16) in January-March 2021. The magnitude of the association between educational attainment and testing positive for SARS-CoV-2 infection increased over the same period. For the equivalent comparison, the OR for testing positive increased from 1·25 (95% CI: 1·04 to 1·47), to 1·69 (95% CI: 1·55 to 1·83). We found little evidence of an association between control exposures, and any considered outcome. CONCLUSIONS: The association between SEP and SARS-CoV-2 testing changed over time, highlighting the potential of time-specific selection pressures to bias analyses of COVID-19. Positive and negative control analyses suggest that changes in the association between SEP and SARS-CoV-2 infection over time likely reflect true increases in socioeconomic inequalities.

Sample-constrained partial identification with application to selection bias.

Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort studies. We show that existing sensitivity analyses, which are often conservative and difficult to implement, can be formulated in our framework and made significantly more informative via auxiliary information on the population. We conduct a simulation study to evaluate the finite sample performance of our inference procedure, and conclude with a substantive motivating example on the causal effect of education on income in the highly selected UK Biobank cohort. We demonstrate that our method can produce informative bounds using plausible population-level auxiliary constraints. We implement this method in the [Formula: see text] package [Formula: see text].

Quantitative bias analysis in practice: review of software for regression with unmeasured confounding.

BACKGROUND: Failure to appropriately account for unmeasured confounding may lead to erroneous conclusions. Quantitative bias analysis (QBA) can be used to quantify the potential impact of unmeasured confounding or how much unmeasured confounding would be needed to change a study's conclusions. Currently, QBA methods are not routinely implemented, partly due to a lack of knowledge about accessible software. Also, comparisons of QBA methods have focused on analyses with a binary outcome. METHODS: We conducted a systematic review of the latest developments in QBA software published between 2011 and 2021. Our inclusion criteria were software that did not require adaption (i.e., code changes) before application, was still available in 2022, and accompanied by documentation. Key properties of each software tool were identified. We provide a detailed description of programs applicable for a linear regression analysis, illustrate their application using two data examples and provide code to assist researchers in future use of these programs. RESULTS: Our review identified 21 programs with [Formula: see text] created post 2016. All are implementations of a deterministic QBA with [Formula: see text] available in the free software R. There are programs applicable when the analysis of interest is a regression of binary, continuous or survival outcomes, and for matched and mediation analyses. We identified five programs implementing differing QBAs for a continuous outcome: treatSens, causalsens, sensemakr, EValue, and konfound. When applied to one of our illustrative examples, causalsens incorrectly indicated sensitivity to unmeasured confounding whereas the other four programs indicated robustness. sensemakr performs the most detailed QBA and includes a benchmarking feature for multiple unmeasured confounders. CONCLUSIONS: Software is now available to implement a QBA for a range of different analyses. However, the diversity of methods, even for the same analysis of interest, presents challenges to their widespread uptake. Provision of detailed QBA guidelines would be highly beneficial.

Exploring the impact of selection bias in observational studies of COVID-19: a simulation study.

BACKGROUND: Non-random selection of analytic subsamples could introduce selection bias in observational studies. We explored the potential presence and impact of selection in studies of SARS-CoV-2 infection and COVID-19 prognosis. METHODS: We tested the association of a broad range of characteristics with selection into COVID-19 analytic subsamples in the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank (UKB). We then conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to this selection (relative to our defined UK-based adult target populations) when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19. RESULTS: In both cohorts, a broad range of characteristics was related to selection, sometimes in opposite directions (e.g. more-educated people were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB). Higher BMI was associated with higher odds of SARS-CoV-2 infection and death-with-COVID-19. We found non-negligible bias in many simulated scenarios. CONCLUSIONS: Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depend on the outcome definition, the true effect of the risk factor and the assumed selection mechanism; these are likely to differ between studies with different target populations. Bias due to sample selection is a key concern in COVID-19 research based on national registry data, especially as countries end free mass testing. The framework we have used can be applied by other researchers assessing the extent to which their results may be biased for their research question of interest.

Surgeon effects on cataract refractive outcomes are minimal compared with patient comorbidity and gender: an analysis of 490 987 cases.

AIM: To investigate effect of patient age, gender, comorbidities and surgeon on refractive outcomes following cataract surgery. METHODS: Study population: patients on UK national ophthalmic cataract database on cataract operations undertaken between 1 April 2010 and 31 August 2018. Variables examined included gender, age, diabetic retinopathy, glaucoma, high myopia, inherited retinal disease, optic nerve disease, uveitis, pseudoexfoliation, vitreous opacities, retinal pathology, cataract type, previous surgery and posterior capsular rupture. A multivariate normal cross-classified model was fitted to the refractive outcome using Markov Chain Monte Carlo (MCMC) methods with diffuse priors to approximate maximum likelihood estimation. A MCMC chain was generated with a burn-in of 5000 iterations and a monitoring chain of 50 000 iterations. RESULTS: 490 987 cataract operations were performed on 351 864 patients by 2567 surgeons. Myopic and astigmatic errors were associated with posterior capsule rupture (-0.38/+0.04×72), glaucoma (-0.10/+0.05×95), previous vitrectomy (-0.049/+0.03×66) and high myopia (-0.07/+0.03×57). Hyperopic and astigmatic errors were associated with diabetic retinopathy (+0.08/+0.03×104), pseudoexfoliation (+0.07/+0.01×158), male gender (+0.12/+0.05×91) and age (-0.01/+0.06×97 per increasing decade). Inherited retinal disease, optic nerve disease, previous trabeculectomy, uveitis, brunescent/white cataract had no significant impact on the error of the refractive outcome. The effect of patient gender and comorbidity was additive. Surgeons only accounted for 4% of the unexplained variance in refractive outcome. CONCLUSION: Patient comorbidities and gender account for small but statistically significant differences in refractive outcome, which are additive. Surgeon effects are very small.

Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies.

BACKGROUND: Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory. Our aim was to provide an accessible guide along with applied examples to using four sophisticated modelling procedures for describing nonlinear growth trajectories. METHODS: This expository paper provides an illustrative guide to summarising nonlinear growth trajectories for repeatedly measured continuous outcomes using (i) linear spline and (ii) natural cubic spline linear mixed-effects (LME) models, (iii) Super Imposition by Translation and Rotation (SITAR) nonlinear mixed effects models, and (iv) latent trajectory models. The underlying model for each approach, their similarities and differences, and their advantages and disadvantages are described. Their application and correct interpretation of their results is illustrated by analysing repeated bone mass measures to characterise bone growth patterns and their sex differences in three cohort studies from the UK, USA, and Canada comprising 8500 individuals and 37,000 measurements from ages 5-40 years. Recommendations for choosing a modelling approach are provided along with a discussion and signposting on further modelling extensions for analysing trajectory exposures and outcomes, and multiple cohorts. RESULTS: Linear and natural cubic spline LME models and SITAR provided similar summary of the mean bone growth trajectory and growth velocity, and the sex differences in growth patterns. Growth velocity (in grams/year) peaked during adolescence, and peaked earlier in females than males e.g., mean age at peak bone mineral content accrual from multicohort SITAR models was 12.2 years in females and 13.9 years in males. Latent trajectory models (with trajectory shapes estimated using a natural cubic spline) identified up to four subgroups of individuals with distinct trajectories throughout adolescence. CONCLUSIONS: LME models with linear and natural cubic splines, SITAR, and latent trajectory models are useful for describing nonlinear growth trajectories, and these methods can be adapted for other complex traits. Choice of method depends on the research aims, complexity of the trajectory, and available data. Scripts and synthetic datasets are provided for readers to replicate trajectory modelling and visualisation using the R statistical computing software.

Combining Longitudinal Data From Different Cohorts to Examine the Life-Course Trajectory.

Longitudinal data are necessary to reveal changes within an individual as he or she ages. However, rarely will a single cohort study capture data throughout a person's entire life span. Here we describe in detail the steps needed to develop life-course trajectories from cohort studies that cover different and overlapping periods of life. Such independent studies are probably from heterogenous populations, which raises several challenges, including: 1) data harmonization (deriving new harmonized variables from differently measured variables by identifying common elements across all studies); 2) systematically missing data (variables not measured are missing for all participants in a cohort); and 3) model selection with differing age ranges and measurement schedules. We illustrate how to overcome these challenges using an example which examines the associations of parental education, sex, and race/ethnicity with children's weight trajectories. Data were obtained from 5 prospective cohort studies (carried out in Belarus and 4 regions of the United Kingdom) spanning data collected from birth to early adulthood during differing calendar periods (1936-1964, 1972-1979, 1990-2012, 1996-2016, and 2007-2015). Key strengths of our approach include modeling of trajectories over wide age ranges, sharing of information across studies, and direct comparison of the same parts of the life course in different geographical regions and time periods. We also introduce a novel approach of imputing individual-level covariates of a multilevel model with a nonlinear growth trajectory and interactions.

Mendelian randomisation with coarsened exposures.

A key assumption in Mendelian randomisation is that the relationship between the genetic instruments and the outcome is fully mediated by the exposure, known as the exclusion restriction assumption. However, in epidemiological studies, the exposure is often a coarsened approximation to some latent continuous trait. For example, latent liability to schizophrenia can be thought of as underlying the binary diagnosis measure. Genetically driven variation in the outcome can exist within categories of the exposure measurement, thus violating this assumption. We propose a framework to clarify this violation, deriving a simple expression for the resulting bias and showing that it may inflate or deflate effect estimates but will not reverse their sign. We then characterise a set of assumptions and a straight-forward method for estimating the effect of SD increases in the latent exposure. Our method relies on a sensitivity parameter which can be interpreted as the genetic variance of the latent exposure. We show that this method can be applied in both the one-sample and two-sample settings. We conclude by demonstrating our method in an applied example and reanalysing two papers which are likely to suffer from this type of bias, allowing meaningful interpretation of their effect sizes.

Multiple imputation strategies for a bounded outcome variable in a competing risks analysis.

In patient follow-up studies, events of interest may take place between periodic clinical assessments and so the exact time of onset is not observed. Such events are known as "bounded" or "interval-censored." Methods for handling such events can be categorized as either (i) applying multiple imputation (MI) strategies or (ii) taking a full likelihood-based (LB) approach. We focused on MI strategies, rather than LB methods, because of their flexibility. We evaluated MI strategies for bounded event times in a competing risks analysis, examining the extent to which interval boundaries, features of the data distribution and substantive analysis model are accounted for in the imputation model. Candidate imputation models were predictive mean matching (PMM); log-normal regression with postimputation back-transformation; normal regression with and without restrictions on the imputed values and Delord and Genin's method based on sampling from the cumulative incidence function. We used a simulation study to compare MI methods and one LB method when data were missing at random and missing not at random, also varying the proportion of missing data, and then applied the methods to a hematopoietic stem cell transplantation dataset. We found that cumulative incidence and median event time estimation were sensitive to model misspecification. In a competing risks analysis, we found that it is more important to account for features of the data distribution than to restrict imputed values based on interval boundaries or to ensure compatibility with the substantive analysis by sampling from the cumulative incidence function. We recommend MI by type 1 PMM.

Early life exposure to air pollution, green spaces and built environment, and body mass index growth trajectories during the first 5 years of life: A large longitudinal study.

Urban environments are characterized by multiple exposures that may influence body mass index (BMI) growth in early life. Previous studies are few, with inconsistent results and no evaluation of simultaneous exposures. Thus, this study aimed to assess the associations between exposure to air pollution, green spaces and built environment characteristics, and BMI growth trajectories from 0 to 5 years. This longitudinal study used data from an electronic primary care health record database in Catalonia (Spain), including 79,992 children born between January 01, 2011 and December 31, 2012 in urban areas and followed until 5 years of age. Height and weight were measured frequently during childhood and BMI (kg/m2) was calculated. Urban exposures were estimated at census tract level and included: air pollution (nitrogen dioxide (NO2), particulate matter <10 µm (PM10) and <2.5 µm (PM2.5)), green spaces (Normalized Difference Vegetation Index (NDVI) and % green space) and built environment (population density, street connectivity, land use mix, walkability index). Individual BMI trajectories were estimated using linear spline multilevel models with several knot points. In single exposure models, NO2, PM10, PM2.5, and population density were associated with small increases in BMI growth (e.g. ß per IQR PM10 increase = 0.023 kg/m2, 95%CI: 0.013, 0.033), and NDVI, % of green spaces and land use mix with small reductions in BMI growth (e.g. ß per IQR % green spaces increase = -0.015 kg/m2, 95%CI: -0.026, -0.005). These associations were strongest during the first two months of life. In multiple exposure models, most associations were attenuated, with only those for PM10 and land use mix remaining statistically significant. This large longitudinal study suggests that early life exposure to air pollution, green space and built environment characteristics may be associated with small changes in BMI growth trajectories during the first years of life, and that it is important to account for multiple exposures in urban settings.

Bootstrap inference for multiple imputation under uncongeniality and misspecification.

Multiple imputation has become one of the most popular approaches for handling missing data in statistical analyses. Part of this success is due to Rubin's simple combination rules. These give frequentist valid inferences when the imputation and analysis procedures are so-called congenial and the embedding model is correctly specified, but otherwise may not. Roughly speaking, congeniality corresponds to whether the imputation and analysis models make different assumptions about the data. In practice, imputation models and analysis procedures are often not congenial, such that tests may not have the correct size, and confidence interval coverage deviates from the advertised level. We examine a number of recent proposals which combine bootstrapping with multiple imputation and determine which are valid under uncongeniality and model misspecification. Imputation followed by bootstrapping generally does not result in valid variance estimates under uncongeniality or misspecification, whereas certain bootstrap followed by imputation methods do. We recommend a particular computationally efficient variant of bootstrapping followed by imputation.

Selection Bias When Estimating Average Treatment Effects Using One-sample Instrumental Variable Analysis.

Participants in epidemiologic and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an instrumental variable (IV) analysis are not widely understood by practitioners of IV analyses. We use directed acyclic graphs (DAGs) to depict assumptions about the selection mechanism (factors affecting selection) and show how DAGs can be used to determine when a two-stage least squares IV analysis is biased by different selection mechanisms. Through simulations, we show that selection can result in a biased IV estimate with substantial confidence interval (CI) undercoverage, and the level of bias can differ between instrument strengths, a linear and nonlinear exposure-instrument association, and a causal and noncausal exposure effect. We present an application from the UK Biobank study, which is known to be a selected sample of the general population. Of interest was the causal effect of staying in school at least 1 extra year on the decision to smoke. Based on 22,138 participants, the two-stage least squares exposure estimates were very different between the IV analysis ignoring selection and the IV analysis which adjusted for selection (e.g., risk differences, 1.8% [95% CI, -1.5%, 5.0%] and -4.5% [95% CI, -6.6%, -2.4%], respectively). We conclude that selection bias can have a major effect on an IV analysis, and further research is needed on how to conduct sensitivity analyses when selection depends on unmeasured data.

The proportion of missing data should not be used to guide decisions on multiple imputation.

OBJECTIVES: Researchers are concerned whether multiple imputation (MI) or complete case analysis should be used when a large proportion of data are missing. We aimed to provide guidance for drawing conclusions from data with a large proportion of missingness. STUDY DESIGN AND SETTING: Via simulations, we investigated how the proportion of missing data, the fraction of missing information (FMI), and availability of auxiliary variables affected MI performance. Outcome data were missing completely at random or missing at random (MAR). RESULTS: Provided sufficient auxiliary information was available; MI was beneficial in terms of bias and never detrimental in terms of efficiency. Models with similar FMI values, but differing proportions of missing data, also had similar precision for effect estimates. In the absence of bias, the FMI was a better guide to the efficiency gains using MI than the proportion of missing data. CONCLUSION: We provide evidence that for MAR data, valid MI reduces bias even when the proportion of missingness is large. We advise researchers to use FMI to guide choice of auxiliary variables for efficiency gain in imputation analyses, and that sensitivity analyses including different imputation models may be needed if the number of complete cases is small.

Accounting for missing data in statistical analyses: multiple imputation is not always the answer.

BACKGROUND: Missing data are unavoidable in epidemiological research, potentially leading to bias and loss of precision. Multiple imputation (MI) is widely advocated as an improvement over complete case analysis (CCA). However, contrary to widespread belief, CCA is preferable to MI in some situations. METHODS: We provide guidance on choice of analysis when data are incomplete. Using causal diagrams to depict missingness mechanisms, we describe when CCA will not be biased by missing data and compare MI and CCA, with respect to bias and efficiency, in a range of missing data situations. We illustrate selection of an appropriate method in practice. RESULTS: For most regression models, CCA gives unbiased results when the chance of being a complete case does not depend on the outcome after taking the covariates into consideration, which includes situations where data are missing not at random. Consequently, there are situations in which CCA analyses are unbiased while MI analyses, assuming missing at random (MAR), are biased. By contrast MI, unlike CCA, is valid for all MAR situations and has the potential to use information contained in the incomplete cases and auxiliary variables to reduce bias and/or improve precision. For this reason, MI was preferred over CCA in our real data example. CONCLUSIONS: Choice of method for dealing with missing data is crucial for validity of conclusions, and should be based on careful consideration of the reasons for the missing data, missing data patterns and the availability of auxiliary information.

Childhood neurodevelopmental difficulties and risk of adolescent depression: the role of irritability.

BACKGROUND: Children with neurodevelopmental disorders are at increased risk of developing depression. Irritability predicts depression in the general population and is common in children with neurodevelopmental disorders. Thus, it is possible that irritability in children with neurodevelopmental disorders contributes to the link with later depression. This study aimed to (a) examine the association between childhood neurodevelopmental difficulties and adolescent depression and (b) test whether irritability explains this association. METHODS: Children with any neurodevelopmental difficulty at the age of 7-9 (n = 1,697) and a selected, comparison group without any neurodevelopmental difficulty (n = 3,177) were identified from a prospective, UK population-based cohort, the Avon Longitudinal Study of Parents and Children. Neurodevelopmental difficulties were defined as a score in the bottom 5% of the sample on at least one measure of cognitive ability, communication, autism spectrum symptoms, attention-deficit/hyperactivity symptoms, reading or motor coordination. The Development and Well-Being Assessment measured parent-reported child irritability at the age of 7, parent-reported adolescent depression at the age of 10 and 13, and self-reported depression at the age of 15. Depression measures were combined, deriving an outcome of major depressive disorder (MDD) in adolescence. Logistic regression examined the association between childhood neurodevelopmental difficulties and adolescent MDD, controlling for gender. Path analysis estimated the proportion of this association explained by irritability. Analyses were repeated for individual neurodevelopmental problems. RESULTS: Childhood neurodevelopmental difficulties were associated with adolescent MDD (OR = 2.11, 95% CI = 1.24, 3.60, p = .006). Childhood irritability statistically accounted for 42% of this association. On examining each neurodevelopmental difficulty separately, autistic, communication and ADHD problems were each associated with depression, with irritability explaining 29%-51% of these links. CONCLUSIONS: Childhood irritability appears to be a key contributor to the link between childhood neurodevelopmental difficulties and adolescent MDD. High rates of irritability in children with autistic and ADHD difficulties may explain elevated rates of depression in the neurodevelopmental group.

Prevalence and health outcomes of domestic violence amongst clinical populations in Arab countries: a systematic review and meta-analysis.

BACKGROUND: An estimated 30% of women worldwide experience intimate partner violence (IPV) during their lifetime. Exposure to IPV is associated with poor health outcomes and the prevalence of violence may be higher amongst women seeking healthcare. Existing evidence from the Arab region is limited. We conducted a systematic review and meta-analysis of prevalence and health outcomes of domestic violence (IPV or violence from a family member) in clinical populations in Arab countries. METHODS: Using terms related to domestic violence, Arab countries, and date limit > year 2000, we searched seven databases: Medline, EMBASE, PsycINFO, CINAHL, Web of Science: core collection, IBSS, Westlaw, IMEMR. We included observational studies reporting estimates of prevalence or health outcomes of domestic violence amongst women aged > 15 years, recruited while accessing healthcare in Arab countries. Studies that collected data on/after 1st January 2000 and were published in English, Arabic or French were included. Title/abstract screening, full text screening, quality assessment and data extraction were carried out. Extracted data were summarised and meta-analysis was performed where appropriate. RESULTS: 6341 papers were screened and 41 papers (29 studies) met inclusion criteria. Total 19,101 participants from 10 countries were represented in the data. Meta-analysis produced pooled prevalence estimates of lifetime exposure to any type of IPV of 73·3% (95% CI 64·1-81·6), physical IPV 35·6% (95% CI 24·4-47·5), sexual IPV 22% (95% CI 13·3-32) and emotional/psychological IPV 49·8% (95% CI 37·3-62·3). Domestic violence (IPV or family violence) exposure was associated with increased odds of adverse health outcomes: depression OR 3·3 (95% CI 1·7-6·4), sleep problems OR 3·2 (95% CI 1·5-6·8), abortion OR 3·5 (95% CI 1·2-10·2), pain OR 2·6 (95% CI 1·6-4·1) and hypertension OR 1·6 (95% CI 1·2-2·0). CONCLUSIONS: Domestic violence is common amongst women seeking healthcare in Arab countries. Exposure to domestic violence is associated with several poor health outcomes. Further research into domestic violence in the Arab world is required. TRIAL REGISTRATION: Systematic review protocol was registered on PROSPERO: CRD42017071415 .

Interventions to support shared decision making for hypertension: A systematic review of controlled studies.

BACKGROUND: Hypertension (high blood pressure) is a common long-term health condition. Patient involvement in treating and monitoring hypertension is essential. Control of hypertension improves population cardiovascular outcomes. However, for an individual, potential benefits and harms of treatment are finely balanced. Shared decision making has the potential to align decisions with the preferences and values of patients. OBJECTIVE: Determine the effectiveness of interventions to support shared decision making in hypertension. SEARCH STRATEGY: Searches in MEDLINE, EMBASE, CINAHL, Web of Science and PsycINFO up to 30 September 2017. ELIGIBILITY CRITERIA: Controlled studies evaluating the effects of shared decision-making interventions for adults with hypertension compared with any comparator in any setting and reporting any outcome measures. RESULTS: Six studies (five randomized controlled trials) in European primary care were included. Main intervention components were as follows: training for health-care professionals, decision aids, patient coaching and a patient leaflet. Four studies, none at low risk of bias, reported a measure of shared decision making; the intervention increased shared decision making in one study. Four studies reported blood pressure between 6 months and 3 years after the intervention; there was no difference in blood pressure between intervention and control groups in any study. Lack of comparability between studies prevented meta-analysis. CONCLUSIONS: Despite widespread calls for shared decision making to be embedded in health care, there is little evidence to inform shared decision making for hypertension, one of the most common conditions managed in primary care.

Long terms trends in CD4+ cell counts, CD8+ cell counts, and the CD4+ :  CD8+ ratio.

OBJECTIVE: Model trajectories of CD4+ and CD8+ cell counts after starting combination antiretroviral therapy (ART) and use the model to predict trends in these counts and the CD4+ : CD8+ ratio. DESIGN: Cohort study of antiretroviral-naïve HIV-positive adults who started ART after 1997 (ART Cohort Collaboration) with more than 6 months of follow-up data. METHODS: We jointly estimated CD4+ and CD8+ cell count trends and their correlation using a bivariate random effects model, with linear splines describing their population trends, and predicted the CD4+ : CD8+ ratio trend from this model. We assessed whether CD4+ and CD8+ cell count trends and the CD4+ : CD8+ ratio trend varied according to CD4+ cell count at start of ART (baseline), and, whether these trends differed in patients with and without virological failure more than 6 months after starting ART. RESULTS: A total of 39 979 patients were included (median follow-up was 53 months). Among patients with baseline CD4+ cell count at least 50 cells/µl, predicted mean CD8+ cell counts continued to decrease between 3 and 15 years post-ART, partly driving increases in the predicted mean CD4+ : CD8+ ratio. During 15 years of follow-up, normalization of the predicted mean CD4+ : CD8+ ratio (to >1) was only observed among patients with baseline CD4+ cell count at least 200 cells/µl. A higher baseline CD4+ cell count predicted a shorter time to normalization. CONCLUSION: Declines in CD8+ cell count and increases in CD4+ : CD8+ ratio occurred up to 15 years after starting ART. The likelihood of normalization of the CD4+ : CD8+ ratio is strongly related to baseline CD4+ cell count.