Prenatal exposure to benzodiazepine and z-hypnotics and fifth-grade scholastic skills - emulating target trials using data from the Norwegian Mother, Father and Child Cohort Study.

Evidence is limited regarding the effect of prenatal benzodiazepine and z-hypnotic exposure and long-term neurodevelopment in childhood. The objective of this study was to investigate the effects of initiating benzodiazepine or z-hypnotic treatment in early, mid and late pregnancy on fifth-grade numeracy and literacy scholastic skills in children, by emulating three target trials. The trials are identical except for the timing of enrollment and the number of eligible individuals. Eligibility to the trials required a history of anxiety and/or depression prior to pregnancy. We used data from the Norwegian Mother, Father and Child Cohort Study, linked to the Medical Birth Registry of Norway, to emulate the trials. We adjusted for baseline covariates that were available at time 0 for each trial by inverse probability of treatment weighting using the propensity score. The findings of this study did not show any effect of mothers' initiation of treatment with benzodiazepines or z-hypnotics in early, mid or late pregnancy on the children's 5th grade test scores in numeracy and literacy. The study results provide reassurance for patients in need of benzodiazepines and z-hypnotics during pregnancy; however, these findings need to be interpreted with caution due to low study power in some of the analyses.

Separable and controlled direct effects for competing events: Estimation of component specific effects on sickness absence.

In many settings, it is reasonable to think of treatment as consisting of a number of components, either because this is the case in practice or because it is conceptually possible to decompose treatment into separate components due to the way in which it exerts effects on the outcome of interest. For competing events, the treatment decomposition idea has recently been suggested to separate effects of treatments on the outcome of interest from effects mediated through competing events using so-called separable effects. Like the idea of separating effects of exposure, it has been pointed out that ideas from mediation analysis generally may help to clarify the interpretation of existing estimands used in competing events settings. One example is the use of the controlled direct effect, to conceptualize the effects of interventions preventing the competing event from occurring. In this article, we identify the controlled direct effect as a component specific effect and discuss the merits of this estimand when the prevented event is non terminal and other methods of effects separation are problematic. Our motivating example is the study of a policy initiative, introduced in 2001, aimed at reducing long term sickness absence (SA) in Norway. The initiative consists of different components, one being to encourage use of graded SA, which is considered a key tool in the Nordic countries to reduce long term SA. The analysis makes use of longitudinal registry data for 113 808 individuals, followed from the time of first SA.

Association of physical activity with overall mortality among long-term testicular cancer survivors: A longitudinal study.

Physical activity (PA) has been associated with reduced mortality among cancer survivors, but no study has focused on testicular cancer survivors (TCSs). We aimed to investigate the association of PA measured twice during survivorship with overall mortality in TCSs. TCSs treated during 1980 to 1994 participated in a nationwide longitudinal survey between 1998 to 2002 (S1: n = 1392) and 2007 to 2009 (S2: n = 1011). PA was self-reported by asking for the average hours per week of leisure-time PA in the past year. Responses were converted into metabolic equivalent task hours/week (MET-h/wk) and participants were categorized into: Inactives (0 MET-h/wk), Low-Actives (2-6 MET-h/wk), Actives (10-18 MET-h/wk) and High-Actives (20-48 MET-h/wk). Mortality from S1 and S2, respectively, was analyzed using the Kaplan-Meier estimator and Cox proportional hazards models until the End of Study (December 31, 2020). Mean age at S1 was 45 years (SD 10.2). Nineteen percent (n = 268) of TCSs died between S1 and EoS, with 138 dying after S2. Compared to Inactives at S1, the mortality risk among Actives was 51% lower (HR 0.49, 95% CI: 0.29-0.84) with no further mortality reduction among High-Actives. At S2, the mortality risk was at least 60% lower among the Actives, High-Actives and even the Low-Actives compared to the Inactives. Persistent Actives (≥10 MET-h/wk at S1 and S2) had a 51% lower mortality risk compared to Persistent Inactives (<10 MET-h/wk at S1 and S2; HR 0.49, 95% CI: 0.30-0.82). During long-term survivorship after TC treatment, regular and maintained PA were associated with an overall mortality risk reduction of at least 50%.

Third dose mRNA vaccination against SARS-CoV-2 reduces medical complaints seen in primary care: a matched cohort study.

BACKGROUND: SARS-CoV-2 mRNA vaccination has been associated with both side effects and a reduction in COVID-related complaints due to the decrease in COVID-19 incidence. We aimed to investigate if individuals who received three doses of SARS-CoV-2 mRNA vaccines had a lower incidence of (a) medical complaints and (b) COVID-19-related medical complaints, both as seen in primary care, when compared to individuals who received two doses. METHODS: We conducted a daily longitudinal exact one-to-one matching study based on a set of covariates. We obtained a matched sample of 315,650 individuals aged 18-70 years who received the 3rd dose at 20-30 weeks after the 2nd dose and an equally large control group who did not. Outcome variables were diagnostic codes as reported by general practitioners or emergency wards, both alone and in combination with diagnostic codes of confirmed COVID-19. For each outcome, we estimated cumulative incidence functions with hospitalization and death as competing events. RESULTS: We found that the number of medical complaints was lower in individuals aged 18-44 years who received three doses compared to those who received two doses. The differences in estimates per 100,000 vaccinated were as follows: fatigue 458 less (95% confidence interval: 355-539), musculoskeletal pain 171 less (48-292), cough 118 less (65-173), heart palpitations 57 less (22-98), shortness of breath 118 less (81-149), and brain fog 31 less (8-55). We also found a lower number of COVID-19-related medical complaints: per 100,000 individuals aged 18-44 years vaccinated with three doses, there were 102 (76-125) fewer individuals with fatigue, 32 (18-45) fewer with musculoskeletal pain, 30 (14-45) fewer with cough, and 36 (22-48) fewer with shortness of breath. There were no or fewer differences in heart palpitations (8 (1-16)) or brain fog (0 (- 1-8)). We observed similar results, though more uncertain, for individuals aged 45-70 years, both for medical complaints and for medical complaints that were COVID-19 related. CONCLUSIONS: Our findings suggest that a 3rd dose of SARS-CoV-2 mRNA vaccine administered 20-30 weeks after the 2nd dose may reduce the incidence of medical complaints. It may also reduce the COVID-19-related burden on primary healthcare services.

Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models.

Longitudinal observational data on patients can be used to investigate causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for estimating such effects by controlling for the time-dependent confounding that typically occurs. The most commonly used is marginal structural models (MSM) estimated using inverse probability of treatment weights (IPTW) (MSM-IPTW). An alternative, the sequential trials approach, is increasingly popular, and involves creating a sequence of "trials" from new time origins and comparing treatment initiators and non-initiators. Individuals are censored when they deviate from their treatment assignment at the start of each "trial" (initiator or noninitiator), which is accounted for using inverse probability of censoring weights. The analysis uses data combined across trials. We show that the sequential trials approach can estimate the parameters of a particular MSM. The causal estimand that we focus on is the marginal risk difference between the sustained treatment strategies of "always treat" vs "never treat." We compare how the sequential trials approach and MSM-IPTW estimate this estimand, and discuss their assumptions and how data are used differently. The performance of the two approaches is compared in a simulation study. The sequential trials approach, which tends to involve less extreme weights than MSM-IPTW, results in greater efficiency for estimating the marginal risk difference at most follow-up times, but this can, in certain scenarios, be reversed at later time points and relies on modelling assumptions. We apply the methods to longitudinal observational data from the UK Cystic Fibrosis Registry to estimate the effect of dornase alfa on survival.

Strategies to manage auxiliary pain medications in chronic pain trials: a topical review.

Chronic pain trials commonly allow auxiliary pain medications such as rescue and concomitant analgesics in addition to the randomized treatment. Changes in auxiliary pain medications after randomization represent intercurrent events that may affect either the interpretation or the existence of the measurements associated with the clinical question of interest, complicating the assessment of treatment efficacy. In chronic pain trials, pain intensity typically varies and patients may take the auxiliary medications 1 day but not the next or increase and decrease the dosages temporarily while continuing their randomized study medication. This distinctive feature of auxiliary pain medications as an intercurrent event has received little attention in the literature. Further clarifications on how to manage these issues are therefore pressing. Here we provide perspectives on issues related to auxiliary pain medication-related intercurrent events in randomized controlled chronic pain trials considering the strategies suggested in the E9(R1) addendum to the ICH guideline on statistical principles for clinical trials.

Letter to the editor regarding "Covid-19 transmission in fitness centers in Norway - a randomized trial".

In a recently published paper in BMC Public Health we read about a randomized trial on Covid-19 transmission performed in five fitness centers in Oslo, Norway, during the spring of 2020. In our opinion, this study has major shortcomings in design and methodology, which have not been addressed by the authors.

Impact of the Norwegian Agreement for a More Inclusive Working Life on diagnosis-specific sickness absence in young adults: a difference-in-difference analysis.

BACKGROUND: The Norwegian Agreement for a More Inclusive Working Life (the IA Agreement) aims to reduce sickness absence (SA) and increase work participation. Potential impacts of the IA Agreement have not been thoroughly evaluated. The study aimed to estimate the impact of the IA Agreement on musculoskeletal and psychological SA prevalence and duration among young adult men and women, and to identify whether the impact was modified by economic activity or SA grade. METHODS: Data from national registries were combined for 372,199 individuals born in Norway 1967-1976. ICPC-2 codes identified musculoskeletal (L) and psychological (P) diagnoses. A difference-in-difference method compared prevalence and mean duration of first SA > 16 days between 2000 and 2005 separately for men and women working in IA companies relative to non-IA companies. Analyses were adjusted for mean company size and stratified by economic activity and SA grade (full/graded). Average marginal change was calculated with 95% confidence intervals (CI). RESULTS: The impacts of the IA Agreement on SA prevalence were mixed as the direction and size of marginal changes varied according to diagnosis, gender, and economic activity. However, there was a general tendency towards reduced mean SA duration for both diagnosis groups, and in particular men with musculoskeletal SA (- 16.6 days, 95% CI -25.3, - 7.9). Individuals with full SA in IA companies had greater reductions in mean SA duration. Only the wholesale and retail economic activity indicated a beneficial contribution of the IA Agreement for both SA prevalence and duration, in both diagnoses and genders. CONCLUSIONS: Potential impacts of the IA Agreement on SA in young men and women varied according to diagnosis and economic activity. However, results indicated that the IA Agreement could reduce SA duration. Further research should identify reasons for gender and economic activity differences.

Immune checkpoint blockade in the treatment of advanced non-small cell lung cancer - predictors of response and impact of previous radiotherapy.

BACKGROUND: The implementation of immune checkpoint inhibitors (ICI) into the standard care of advanced non-small cell lung cancer (NSCLC) has improved prognosis for this group of patients. However, long-term survival is rare. The aim of the study was to identify predictors of response and, especially, to investigate the impact radiotherapy might have on duration of response. MATERIAL AND METHODS: The association between pretreatment patient/tumor characteristics and progression-free survival (PFS), overall survival (OS), and lung cancer-specific survival was investigated in 78 patients receiving an ICI as ≥2nd line treatment for advanced NSCLC, using Cox regression analysis. Due to competing risk, cause-specific deaths were also examined with cumulative incidence plots. RESULTS: Median OS was 12.6 months (95% CI 7.8-18.2) and median PFS 4.1 months (95% CI 3.0-6.2), after median follow-up time of 49.7 months (range 20.9-51.5). Increasing CRP and neutrophil/lymphocyte ratio (NLR), were associated with poor PFS (CRP: HR 1.49, 95% CI 1.12-1.98; NLR: HR 1.59, 95% CI 1.22-1.85) and OS (CRP: HR 1.94, 95% CI 1.47-2.56; NLR: HR 1.54, 95% CI 1.27-1.87). Radiotherapy prior to immunotherapy was not significantly associated with patient outcome. However, when the dataset was split at 6 months of follow-up, to be able to identify early and late predictors of prognosis, we found that patients receiving radiotherapy <6 months prior to immunotherapy had better PFS (HR: 0.27, 95% CI 0.09-0.84) and lung cancer-specific survival (HR: 0.41, 95% CI 0.18-0.95) after the first 6 months of follow-up, while increasing CRP (PFS: HR1.61, 95% CI 1.21-2.14; OS: HR2.04, 95% CI 1.51-2.74) and NLR (PFS: HR 1.57, 95% CI 1.29-1.91; OS: HR 1.63, 95% CI 1.35-1.97) were predictors of poor short-term prognosis. CONCLUSIONS: Radiotherapy may be of importance to achieve a long-lasting response to immunotherapy, while indicators of systemic inflammation can help in identifying patients with poor short-term prognosis.

Simulating longitudinal data from marginal structural models using the additive hazard model.

Observational longitudinal data on treatments and covariates are increasingly used to investigate treatment effects, but are often subject to time-dependent confounding. Marginal structural models (MSMs), estimated using inverse probability of treatment weighting or the g-formula, are popular for handling this problem. With increasing development of advanced causal inference methods, it is important to be able to assess their performance in different scenarios to guide their application. Simulation studies are a key tool for this, but their use to evaluate causal inference methods has been limited. This paper focuses on the use of simulations for evaluations involving MSMs in studies with a time-to-event outcome. In a simulation, it is important to be able to generate the data in such a way that the correct forms of any models to be fitted to those data are known. However, this is not straightforward in the longitudinal setting because it is natural for data to be generated in a sequential conditional manner, whereas MSMs involve fitting marginal rather than conditional hazard models. We provide general results that enable the form of the correctly specified MSM to be derived based on a conditional data generating procedure, and show how the results can be applied when the conditional hazard model is an Aalen additive hazard or Cox model. Using conditional additive hazard models is advantageous because they imply additive MSMs that can be fitted using standard software. We describe and illustrate a simulation algorithm. Our results will help researchers to effectively evaluate causal inference methods via simulation.

A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models.

Multi-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation of many parameters of interest. This is a well known problem for the standard Aalen-Johansen estimator of transition probabilities and several alternative estimators, not relying on the Markov assumption, have been suggested. A particularly simple approach known as landmarking have resulted in the Landmark-Aalen-Johansen estimator. Since landmarking is a stratification method a disadvantage of landmarking is data reduction, leading to a loss of power. This is problematic for "less traveled" transitions, and undesirable when such transitions indeed exhibit Markov behaviour. Introducing the concept of partially non-Markov multi-state models, we suggest a hybrid landmark Aalen-Johansen estimator for transition probabilities. We also show how non-Markov transitions can be identified using a testing procedure. The proposed estimator is a compromise between regular Aalen-Johansen and landmark estimation, using transition specific landmarking, and can drastically improve statistical power. We show that the proposed estimator is consistent, but that the traditional variance estimator can underestimate the variance of both the hybrid and landmark estimator. Bootstrapping is therefore recommended. The methods are compared in a simulation study and in a real data application using registry data to model individual transitions for a birth cohort of 184 951 Norwegian men between states of sick leave, disability, education, work and unemployment.

Seasonal and pandemic influenza during pregnancy and risk of fetal death: A Norwegian registry-based cohort study.

Previous studies of fetal death with maternal influenza have been inconsistent. We explored the effect of maternal influenza-like illness (ILI) in pregnancy on the risk of fetal death, distinguishing between diagnoses during regular influenza seasons and the 2009/2010 pandemic and between trimesters of ILI. We used birth records from the Medical Birth Registry of Norway to identify fetal deaths after the first trimester in singleton pregnancies (2006-2013). The Norwegian Directorate of Health provided dates of clinical influenza diagnoses by primary-health-care providers, whereas dates of laboratory-confirmed influenza A (H1N1) diagnoses were provided by the Norwegian Surveillance System for Communicable Diseases. We obtained dates and types of influenza vaccinations from the Norwegian Immunisation Registry. Cox proportional-hazards regression models were fitted to estimate hazard ratios (HRs) of fetal death, with associated 95% confidence intervals (CIs), comparing women with and without an ILI diagnosis in pregnancy. There were 2510 fetal deaths among 417,406 eligible pregnancies. ILI during regular seasons was not associated with increased risk of fetal death: adjusted HR = 0.90 (95% CI 0.64-1.27). In contrast, ILI during the pandemic was associated with substantially increased risk of fetal death, with an adjusted HR of 1.75 (95% CI 1.21-2.54). The risk was highest following first-trimester ILI (adjusted HR = 2.28 [95% CI 1.45-3.59]). ILI during the pandemic-but not during regular seasons-was associated with increased risk of fetal death in the second and third trimester. The estimated effect was strongest with ILI in first trimester.

Estimates of global seasonal influenza-associated respiratory mortality: a modelling study.

BACKGROUND: Estimates of influenza-associated mortality are important for national and international decision making on public health priorities. Previous estimates of 250 000-500 000 annual influenza deaths are outdated. We updated the estimated number of global annual influenza-associated respiratory deaths using country-specific influenza-associated excess respiratory mortality estimates from 1999-2015. METHODS: We estimated country-specific influenza-associated respiratory excess mortality rates (EMR) for 33 countries using time series log-linear regression models with vital death records and influenza surveillance data. To extrapolate estimates to countries without data, we divided countries into three analytic divisions for three age groups (<65 years, 65-74 years, and ≥75 years) using WHO Global Health Estimate (GHE) respiratory infection mortality rates. We calculated mortality rate ratios (MRR) to account for differences in risk of influenza death across countries by comparing GHE respiratory infection mortality rates from countries without EMR estimates with those with estimates. To calculate death estimates for individual countries within each age-specific analytic division, we multiplied randomly selected mean annual EMRs by the country's MRR and population. Global 95% credible interval (CrI) estimates were obtained from the posterior distribution of the sum of country-specific estimates to represent the range of possible influenza-associated deaths in a season or year. We calculated influenza-associated deaths for children younger than 5 years for 92 countries with high rates of mortality due to respiratory infection using the same methods. FINDINGS: EMR-contributing countries represented 57% of the global population. The estimated mean annual influenza-associated respiratory EMR ranged from 0·1 to 6·4 per 100 000 individuals for people younger than 65 years, 2·9 to 44·0 per 100 000 individuals for people aged between 65 and 74 years, and 17·9 to 223·5 per 100 000 for people older than 75 years. We estimated that 291 243-645 832 seasonal influenza-associated respiratory deaths (4·0-8·8 per 100 000 individuals) occur annually. The highest mortality rates were estimated in sub-Saharan Africa (2·8-16·5 per 100 000 individuals), southeast Asia (3·5-9·2 per 100 000 individuals), and among people aged 75 years or older (51·3-99·4 per 100 000 individuals). For 92 countries, we estimated that among children younger than 5 years, 9243-105 690 influenza-associated respiratory deaths occur annually. INTERPRETATION: These global influenza-associated respiratory mortality estimates are higher than previously reported, suggesting that previous estimates might have underestimated disease burden. The contribution of non-respiratory causes of death to global influenza-associated mortality should be investigated. FUNDING: None.

Comparison of the associations between non-traditional and traditional indices of adiposity and cardiovascular mortality: an observational study of one million person-years of follow-up.

BACKGROUND/OBJECTIVE: The most widely used adiposity index, body mass index (BMI), is not optimal to evaluate cardiovascular (CV) risk associated with overweight and obesity. We aimed to explore the association between traditional and non-traditional adiposity indices and CV mortality, and compare their discriminative ability for CV death. METHODS: We studied participants (age 19-79 years, BMI ≥18.5 kg/m2) from the population-based Norwegian Nord-Trøndelag Health Study 2 (HUNT 2). Traditional indices explored were BMI, waist circumference (WC) and waist- to-hip ratio, whereas non-traditional were estimated total body fat (eTBF), which is a sex-specific fat%-index, index of central obesity (WC/height) and a body shape index (ABSI) [WC/(BMI2/3 × âˆšheight)]. Associations between the traditional and non-traditional indices and CV death, obtained from the Norwegian Cause of Death Registry, were explored by Cox proportional hazard regression, and the indices' discriminative ability by Harrell's C statistics. RESULTS: Baseline assessments were done from 1995 to 1997 and the population (n = 61,016, 52% women) was observed for 17.7 ± 4.2 years (until 2016), yielding 1,080,473.6 person-years of follow-up. Thirteen thousand one hundred and ninety five (21.6%) subjects died, of whom 4908 (37.2%) died from CV causes. Across genders, eTBF had the strongest association to CV death (unadjusted hazard ratios [HRs] 4th vs. 1st quartile in women and men 13.38 [95% confidence interval (CI): 11.05-16.22] and 9.32 [8.03-10.81], respectively), together with index of central obesity in women and ABSI in men. The other indices showed weaker associations, in particular BMI in men: 1.73 [1.56-1.93]. Age adjustment attenuated the associations, but the pattern remained. In concordance with this, C-statistics was C = 0.725 [0.713-0.737] in women and 0.711 [0.701-0.721] in men for eTBF, and C = 0.622 [0.610-0.634] in women and 0.551 [0.541-0.562] in men for BMI. CONCLUSION: eTBF, a sex-specific total body fat index, was more strongly associated with CV death than other adiposity indices and may be a suitable clinical tool for assessment of obesity-associated CV risk.

Global test for high-dimensional mediation: Testing groups of potential mediators.

We address the problem of testing whether a possibly high-dimensional vector may act as a mediator between some exposure variable and the outcome of interest. We propose a global test for mediation, which combines a global test with the intersection-union principle. We discuss theoretical properties of our approach and conduct simulation studies that demonstrate that it performs equally well or better than its competitor. We also propose a multiple testing procedure, ScreenMin, that provides asymptotic control of either familywise error rate or false discovery rate when multiple groups of potential mediators are tested simultaneously. We apply our approach to data from a large Norwegian cohort study, where we look at the hypothesis that smoking increases the risk of lung cancer by modifying the level of DNA methylation.

Long-Term Survival after Invasive or Conservative Strategy in Elderly Patients with non-ST-Elevation Myocardial Infarction: A Prospective Cohort Study.

BACKGROUND: The optimal management of elderly patients with non-ST-segment elevation myocardial infarction (NSTEMI) is still discussed. We aimed to study short- and long-term survival in NSTEMI patients ≥75 years managed with an invasive or a conservative strategy. METHODS: NSTEMI patients admitted to Oslo University Hospital Ulleval during 2005-2011 were included consecutively in a prospective registry. Vital status until December 31, 2013, was obtained from the Norwegian Cause of Death Registry. Patients ≥75 years were identified, and 30-day and 7-year survival were analyzed. Logistic- and Cox regression was used to estimate OR and hazard ratio (HR) for death in the invasive versus conservative group, adjusting for registered confounders. RESULTS: There were 2,064 NSTEMI patients ≥75 years (48.2% women); 1,200 (58.1%) were treated with an invasive strategy, and were younger, more likely to be male and previously revascularized compared to 864 (41.9%) patients treated conservatively (p < 0.0001 for all). Survival at 30-day was 94.9% in the invasive and 76.6% in the conservative group. For 30-day survivors, 7-year survival was 47.4% (95% CI 42.9-51.8) and 11.6% (95% CI 8.3-15.6), respectively. After multivariate adjustment, an invasive strategy was associated with lower long-term risk (adjusted HR [aHR] 0.49 [95% CI 0.41-0.59]). Actual revascularization was associated with lower risk of long-term mortality compared to angiography only (aHRPCI 0.73 [95% CI 0.59-0.90], aHRCABG 0.43 [95% CI 0.28-0.65]). CONCLUSION: In this real-life cohort of NSTEMI patients ≥75 years, 30-day survival was 95%, and 7-year survival was 47% with an invasive strategy. Revascularized patients had a superior long-term prognosis. With a conservative strategy, short- and long-term survival was lower, probably due to selection bias and unmeasured confounding.

Landmark estimation of transition probabilities in non-Markov multi-state models with covariates.

In non-Markov multi-state models, the traditional Aalen-Johansen (AJ) estimator for state transition probabilities is generally not valid. An alternative, suggested by Putter and Spitioni, is to analyse a subsample of the full data, consisting of the individuals present in a specific state at a given landmark time-point. The AJ estimator of occupation probabilities is then applied to the landmark subsample. Exploiting the result by Datta and Satten, that the AJ estimator is consistent for state occupation probabilities even in non-Markov models given that censoring is independent of state occupancy and times of transition between states, the landmark Aalen-Johansen (LMAJ) estimator provides consistent estimates of transition probabilities. So far, this approach has only been studied for non-parametric estimation without covariates. In this paper, we show how semi-parametric regression models and inverse probability weights can be used in combination with the LMAJ estimator to perform covariate adjusted analyses. The methods are illustrated by a simulation study and an application to population-wide registry data on work, education and health-related absence in Norway. Results using the traditional AJ estimator and the LMAJ estimator are compared, and show large differences in estimated transition probabilities for highly non-Markov multi-state models.

Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study.

Highly active antiretroviral therapy (HAART) has proved efficient in increasing CD4 counts in many randomized clinical trials. Because randomized trials have some limitations (e.g., short duration, highly selected subjects), it is interesting to assess the effect of treatments using observational studies. This is challenging because treatment is started preferentially in subjects with severe conditions. This general problem had been treated using Marginal Structural Models (MSM) relying on the counterfactual formulation. Another approach to causality is based on dynamical models. We present three discrete-time dynamic models based on linear increments models (LIM): the first one based on one difference equation for CD4 counts, the second with an equilibrium point, and the third based on a system of two difference equations, which allows jointly modeling CD4 counts and viral load. We also consider continuous-time models based on ordinary differential equations with non-linear mixed effects (ODE-NLME). These mechanistic models allow incorporating biological knowledge when available, which leads to increased statistical evidence for detecting treatment effect. Because inference in ODE-NLME is numerically challenging and requires specific methods and softwares, LIM are a valuable intermediary option in terms of consistency, precision, and complexity. We compare the different approaches in simulation and in illustration on the ANRS CO3 Aquitaine Cohort and the Swiss HIV Cohort Study.

Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia.

BACKGROUND: This paper studies the effect of mosquito abundance and malaria incidence in the last 3 weeks, and their interaction, on the hazard of time to malaria in a previously studied cohort of children in Ethiopia. METHODS: We model the mosquito abundance and time to malaria data jointly in a Bayesian framework. RESULTS: We found that the interaction of mosquito abundance and incidence plays a prominent role on malaria risk. We quantify and compare relative risks of various factors, and determine the predominant role of the interaction between incidence and mosquito abundance in describing malaria risk. Seasonal rain patterns, distance to a water source of the households, temperature and relative humidity are all significant in explaining mosquito abundance, and through this affect malaria risk. CONCLUSION: Analyzing jointly the time to malaria data and the mosquito abundance allows a precise comparison of factors affecting the spread of malaria. The effect of the interaction between mosquito abundances and local presence of malaria parasites has an important effect on the hazard of time to malaria, beyond abundance alone. Each additional one km away from the dam gives an average reduction of malaria relative risk of 5.7%. The importance of the interaction between abundance and incidence leads to the hypothesis that preventive intervention could advantageously target the infectious population, in addition to mosquito control, which is the typical intervention today.