Incidence, risk factors and causes of death in an HIV care programme with a large proportion of injecting drug users.

Objectives  To identify factors influencing mortality in an HIV programme providing care to large numbers of injecting drug users (IDUs) and patients co-infected with hepatitis C (HCV). Methods  A longitudinal analysis of monitoring data from HIV-infected adults who started antiretroviral therapy (ART) between 2003 and 2009 was performed. Mortality and programme attrition rates within 2 years of ART initiation were estimated. Associations with individual-level factors were assessed with multivariable Cox and piece-wise Cox regression. Results  A total of 1671 person-years of follow-up from 1014 individuals was analysed. Thirty-four percent of patients were women and 33% were current or ex-IDUs. 36.2% of patients (90.8% of IDUs) were co-infected with HCV. Two-year all-cause mortality rate was 5.4 per 100 person-years (95% CI, 4.4-6.7). Most HIV-related deaths occurred within 6 months of ART start (36, 67.9%), but only 5 (25.0%) non-HIV-related deaths were recorded during this period. Mortality was higher in older patients (HR = 2.50; 95% CI, 1.42-4.40 for ≥40 compared to 15-29 years), and in those with initial BMI < 18.5 kg/m(2) (HR = 3.38; 95% CI, 1.82-5.32), poor adherence to treatment (HR = 5.13; 95% CI, 2.47-10.65 during the second year of therapy), or low initial CD4 cell count (HR = 4.55; 95% CI, 1.54-13.41 for <100 compared to ≥100 cells/μl). Risk of death was not associated with IDU status (P = 0.38). Conclusion  Increased mortality was associated with late presentation of patients. In this programme, death rates were similar regardless of injection drug exposure, supporting the notion that satisfactory treatment outcomes can be achieved when comprehensive care is provided to these patients

Predictors of functional dependency in Parkinson’s disease

Financial disclosures/conflicts of interest: Dr Macleod was funded by a Clinical Academic Fellowship from the Chief Scientist Office of the Scottish Government and received grant funding from Parkinson’s UK, the Wellcome Trust, University of Aberdeen, and NHS Grampian endowments relating to this research. Dr Counsell received grant funding from Parkinson’s UK, National Institute for Health Research, the Scottish Chief Scientist Office, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, NHS Grampian endowments and SPRING relating to this research. We declare we have no conflicts of interest. Financial support: This study was funded by Parkinson’s UK, the Scottish Chief Scientist Office, NHS Grampian endowments, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, and SPRING.  Peer reviewedPublisher PD

Efficacy of Liver Transplantation in Patients with Primary Biliary Cirrhosis

No controlled trials have been performed to assess the efficacy of liver transplantation. Because of the marked improvement in survival after liver transplantation since 1981, random assignment of patients to a control group not undergoing transplantation is considered clinically inappropriate. To assess the efficacy of liver transplantation in patients with primary biliary cirrhosis, we compared survival in 161 patients with this diagnosis who had undergone a liver transplantation with survival in patients with the same diagnosis who had been treated conservatively. The comparison was performed with use of a recently developed statistical technique, the Mayo model. All patients had undergone liver transplantation between March 1980 and June 1987 and were followed for a median of 25 months. Three months after liver transplantation, the Kaplan–Meier survival probabilities in the recipients were substantially higher than the Mayo-model “simulated-control” survival probabilities (P<0.001). At two years, the Kaplan–Meier survival probability was 0.74, whereas the mean Mayo-model survival probability was 0.31. The patients who were at low risk according to the Mayo model had the best probability of survival after liver transplantation; however, patients at all risk levels who had undergone liver transplantation had higher probabilities of survival than those who had not. We conclude that liver transplantation is an efficacious treatment in patients with advanced primary biliary cirrhosis. (N Engl J Med 1989; 320:1709–13.), LIVER transplantation has been accepted clinically as a lifesaving treatment in various end-stage liver diseases, including primary biliary cirrhosis.1,2 However, no controlled trials have been performed to evaluate the efficacy of this procedure. Indeed, because there has been a marked improvement since 1981 in survival after transplantation, random assignment of patients with advanced liver disease to a nontransplantation control group is considered to be clinically inappropriate. At the Mayo Clinic, a Cox regression model for predicting the probability of survival in patients with conservatively treated primary biliary cirrhosis has been developed.3 To provide control data for assessing the efficacy of…. © 1989, Massachusetts Medical Society. All rights reserved

Caesarean section and risk of unexplained stillbirth in subsequent pregnancy

Background Caesarean section is associated with an increased risk of disorders of placentation in subsequent pregnancies, but effects on the rate of antepartum stillbirth are unknown. We aimed to establish whether previous caesarean delivery is associated with an increased risk of antepartum stillbirth. Methods We linked pregnancy discharge data from the Scottish Morbidity Record (1980–98) and the Scottish Stillbirth and Infant Death Enquiry (1985–98). We estimated the relative risk of antepartum stillbirth in second pregnancies using time-to-event analyses. Findings For 120 633 singleton second births, there were 68 antepartum stillbirths in 17 754 women previously delivered by caesarean section (2–39 per 10 000 women per week) and 244 in 102879 women previously delivered vaginally (1·44; p&lt;0·001). Risk of unexplained stillbirth associated with previous caesarean delivery differed significantly with gestational age (p=0·04); the excess risk was apparent from 34 weeks (hazard ratio 2·23 [95% Cl 1·48–3·36]). Risk was not attenuated by adjustment for maternal characteristics or outcome of the first pregnancy (2·74 [1·74–4·30]). The absolute risk of unexplained stillbirth at or after 39 weeks' gestation was 1·1 per 1000 women who had had a previous caesarean section and 0·5 per 1000 in those who had not. The difference was due mostly to an excess of unexplained stillbirths among women previously delivered by caesarean section. Interpretation Delivery by caesarean section in the first pregnancy could increase the risk of unexplained stillbirth in the second. In women with one previous caesarean delivery, the risk of unexplained antepartum stillbirth at or after 39 weeks' gestation is about double the risk of stillbirth or neonatal death from intrapartum uterine rupture

A special case of reduced rank models for identification and modelling of time varying effects in survival analysis

Flexible survival models are in need when modelling data from long term follow-up studies. In many cases, the assumption of proportionality imposed by a Cox model will not be valid. Instead, a model that can identify time varying effects of fixed covariates can be used. Although there are several approaches that deal with this problem, it is not always straightforward how to choose which covariates should be modelled having time varying effects and which not. At the same time, it is up to the researcher to define appropriate time functions that describe the dynamic pattern of the effects. In this work, we suggest a model that can deal with both fixed and time varying effects and uses simple hypotheses tests to distinguish which covariates do have dynamic effects. The model is an extension of the parsimonious reduced rank model of rank 1. As such, the number of parameters is kept low, and thus, a flexible set of time functions, such as b-splines, can be used. The basic theory is illustrated along with an efficient fitting algorithm. The proposed method is applied to a dataset of breast cancer patients and compared with a multivariate fractional polynomials approach for modelling time-varying effects. Copyright © 2016 John Wiley & Sons, Ltd

Treatment outcomes of new tuberculosis patients hospitalized in Kampala, Uganda: a prospective cohort study.

BACKGROUND: In most resource limited settings, new tuberculosis (TB) patients are usually treated as outpatients. We sought to investigate the reasons for hospitalisation and the predictors of poor treatment outcomes and mortality in a cohort of hospitalized new TB patients in Kampala, Uganda. METHODS AND FINDINGS: Ninety-six new TB patients hospitalised between 2003 and 2006 were enrolled and followed for two years. Thirty two were HIV-uninfected and 64 were HIV-infected. Among the HIV-uninfected, the commonest reasons for hospitalization were low Karnofsky score (47%) and need for diagnostic evaluation (25%). HIV-infected patients were commonly hospitalized due to low Karnofsky score (72%), concurrent illness (16%) and diagnostic evaluation (14%). Eleven HIV uninfected patients died (mortality rate 19.7 per 100 person-years) while 41 deaths occurred among the HIV-infected patients (mortality rate 46.9 per 100 person years). In all patients an unsuccessful treatment outcome (treatment failure, death during the treatment period or an unknown outcome) was associated with duration of TB symptoms, with the odds of an unsuccessful outcome decreasing with increasing duration. Among HIV-infected patients, an unsuccessful treatment outcome was also associated with male sex (P = 0.004) and age (P = 0.034). Low Karnofsky score (aHR = 8.93, 95% CI 1.88 - 42.40, P = 0.001) was the only factor significantly associated with mortality among the HIV-uninfected. Mortality among the HIV-infected was associated with the composite variable of CD4 and ART use, with patients with baseline CD4 below 200 cells/µL who were not on ART at a greater risk of death than those who were on ART, and low Karnofsky score (aHR = 2.02, 95% CI 1.02 - 4.01, P = 0.045). CONCLUSION: Poor health status is a common cause of hospitalisation for new TB patients. Mortality in this study was very high and associated with advanced HIV Disease and no use of ART

Previous caesarean delivery and the risk of unexplained stillbirth: retrospective cohort study and meta-analysis.

OBJECTIVE: To determine whether caesarean delivery in the first pregnancy is a risk factor for unexplained antepartum stillbirth in a second pregnancy. DESIGN: A population-based retrospective cohort study and meta-analysis. SETTING: All maternity units in Scotland. PARTICIPANTS: A cohort of 128 585 second births, 1999-2008. METHODS: Time-to-event analysis and random-effects meta-analysis. MAIN OUTCOME MEASURE: Risk of unexplained antepartum stillbirth in a second pregnancy. RESULTS: There were 88 stillbirths among 23 688 women with a previous caesarean delivery (2.34 per 10 000 women per week) and 288 stillbirths in 104 897 women who had previously delivered vaginally (1.67 per 10 000 women per week, P = 0.002). When analysed by cause, women with a previous caesarean delivery had an increased risk of unexplained stillbirth (hazard ratio, HR 1.47; 95% confidence interval, 95% CI 1.12-1.94; P = 0.006) and, as previously observed, the excess risk was apparent from 34 weeks of gestation onwards. The risk did not differ in relation to the indication of the caesarean delivery, and was independent of maternal characteristics and previous obstetric complications. We identified three other comparable studies (two in North America and one in Europe), and meta-analysis of these studies showed a statistically significant association between previous caesarean delivery and the risk of antepartum stillbirth in the second pregnancy (pooled HR 1.40; 95% CI 1.10-1.77; P = 0.006). CONCLUSIONS: Women who have had a previous caesarean delivery are at increased risk of unexplained stillbirth in the second pregnancy. TWEETABLE ABSTRACT: Caesarean first delivery is associated with an increased risk of unexplained stillbirth in the next pregnancy.The work was supported by the NIHR Cambridge Comprehensive Biomedical Research Centre. The funding bodies had no role in any aspect of the conduct, analysis or presentation of this study.This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1111/1471-0528.1346

An approach to trial design and analysis in the era of non-proportional hazards of the treatment effect

Background: Most randomized controlled trials with a time-to-event outcome are designed and analysed under the proportional hazards assumption, with a target hazard ratio for the treatment effect in mind. However, the hazards may be non-proportional. We address how to design a trial under such conditions, and how to analyse the results. Methods: We propose to extend the usual approach, a logrank test, to also include the Grambsch-Therneau test of proportional hazards. We test the resulting composite null hypothesis using a joint test for the hazard ratio and for time-dependent behaviour of the hazard ratio. We compute the power and sample size for the logrank test under proportional hazards, and from that we compute the power of the joint test. For the estimation of relevant quantities from the trial data, various models could be used; we advocate adopting a pre-specified flexible parametric survival model that supports time-dependent behaviour of the hazard ratio. Results: We present the mathematics for calculating the power and sample size for the joint test. We illustrate the methodology in real data from two randomized trials, one in ovarian cancer and the other in treating cellulitis. We show selected estimates and their uncertainty derived from the advocated flexible parametric model. We demonstrate in a small simulation study that when a treatment effect either increases or decreases over time, the joint test can outperform the logrank test in the presence of both patterns of non-proportional hazards. Conclusions: Those designing and analysing trials in the era of non-proportional hazards need to acknowledge that a more complex type of treatment effect is becoming more common. Our method for the design of the trial retains the tools familiar in the standard methodology based on the logrank test, and extends it to incorporate a joint test of the null hypothesis with power against non-proportional hazards. For the analysis of trial data, we propose the use of a pre-specified flexible parametric model that can represent a time-dependent hazard ratio if one is present

Optimal Patch-Leaving Behaviour: A Case Study Using The Parasitoid Cotesia rebecula

1. Parasitoids are predicted to spend longer in patches with more hosts, but previous work on Cotesia rubecula (Marshall) has not upheld this prediction. Tests of theoretical predictions may be affected by the definition of patch leaving behaviour, which is often ambiguous. 2. In this study whole plants were considered as patches and assumed that wasps move within patches by means of walking or flying. Within-patch and between-patch flights were distinguished based on flight distance. The quality of this classification was tested statistically by examination of log-survivor curves of flight times. 3. Wasps remained longer in patches with higher host densities, which is consistent with predictions of the marginal value theorem (Charnov 1976). Under the assumption that each flight indicates a patch departure, there is no relationship between host density and leaving tendency. 4. Oviposition influences the patch leaving behaviour of wasps in a count down fashion (Driessen et al. 1995), as predicted by an optimal foraging model (Tenhumberg, Keller & Possingham 2001). 5. Wasps spend significantly longer in the first patch encountered following release, resulting in an increased rate of superparasitism

Multiple imputation in Cox regression when there are time-varying effects of covariates.

In Cox regression, it is important to test the proportional hazards assumption and sometimes of interest in itself to study time-varying effects (TVEs) of covariates. TVEs can be investigated with log hazard ratios modelled as a function of time. Missing data on covariates are common and multiple imputation is a popular approach to handling this to avoid the potential bias and efficiency loss resulting from a "complete-case" analysis. Two multiple imputation methods have been proposed for when the substantive model is a Cox proportional hazards regression: an approximate method (Imputing missing covariate values for the Cox model in Statistics in Medicine (2009) by White and Royston) and a substantive-model-compatible method (Multiple imputation of covariates by fully conditional specification: accommodating the substantive model in Statistical Methods in Medical Research (2015) by Bartlett et al). At present, neither accommodates TVEs of covariates. We extend them to do so for a general form for the TVEs and give specific details for TVEs modelled using restricted cubic splines. Simulation studies assess the performance of the methods under several underlying shapes for TVEs. Our proposed methods give approximately unbiased TVE estimates for binary covariates with missing data, but for continuous covariates, the substantive-model-compatible method performs better. The methods also give approximately correct type I errors in the test for proportional hazards when there is no TVE and gain power to detect TVEs relative to complete-case analysis. Ignoring TVEs at the imputation stage results in biased TVE estimates, incorrect type I errors, and substantial loss of power in detecting TVEs. We also propose a multivariable TVE model selection algorithm. The methods are illustrated using data from the Rotterdam Breast Cancer Study. R code is provided