Abstract To minimise health disparities, equitable access to medical treatment is paramount. In a pioneering intervention, National Health Service England’s Hepatitis C virus (HCV) programme has implemented country-wide peer support to boost treatment access. Peer support workers ( peers ) are individuals with relevant lived experience, who promote testing and treatment in marginalised populations underserved by traditional health services. We evaluated the English peers intervention, exploiting its staggered rollout and rich surveillance data between June 2016 and May 2021. Peers increased HCV cases identified by 13·9% (95% credible interval (95% CrI) [5·3, 21·7]), sustained viral responses by 8·0% (95% CrI [–4·4, 18·6]), and drug services referrals by 8·8% (95% CrI [–12·5, 22·6]). The intervention’s effectiveness was magnified during the first COVID-19 lockdown and individuals supported by peers typically belonged to populations with poor treatment access. Our findings indicate that peers can boost equity in treatment access on a national scale.

In target trial emulation (TTE), marginal structural models (MSMs) can be used to characterise per-protocol treatment effects over time. The MSM parameters are often estimated by inverse probability weighting (IPW), with weights estimated by maximum likelihood. However, IPW-based estimators can be unstable in small samples and are sensitive to misspecification of the weight models. An alternative method for estimating the MSM parameters is longitudinal targeted maximum likelihood estimation (LTMLE). LTMLE is double robust and potentially more efficient than IPW. Nevertheless, LTMLE also relies on inverse probability weights and may therefore share the instability of IPW-based estimators. We propose joint calibrated LTMLE, which integrates LTMLE with joint calibrated weights tailored for per-protocol effect estimation in TTE. This calibration of weights improves finite-sample performance by enforcing covariate balance in both the treatment and censoring processes simultaneously. Simulations show that the proposed method has improved efficiency and robustness to weight model misspecification, compared to standard LTMLE. We illustrate the method using a case study to evaluate the effect of highly active antiretroviral therapy on CD4 cell count among HIV-positive women.

BACKGROUND AND AIMS:Opioid use disorder (OUD) treatment guidelines worldwide recommend opioid agonist treatment (OAT) as a long-term, potentially indefinite treatment for managing OUD. However, many individuals express a strong interest in eventually tapering fully off treatment. Current clinical practice guidelines offer relatively limited guidance or evidence on the appropriate timing to initiate a taper. We aimed to determine the safety and comparative effectiveness of different times from completion of OAT induction at which tapering could be considered to maximize the likelihood of taper completion and minimize the risk of mortality. DESIGN:Population-based retrospective observational study and target trial emulation based on nine linked administrative health databases. SETTING:British Columbia, Canada, from 1 January 2010 to 17 March 2020. PARTICIPANTS:Individuals (identified via linkage of nine provincial health administrative databases) completing OAT induction with methadone or buprenorphine/naloxone who were ≥18 years of age with no known pregnancy, no history of cancer or palliative care and not currently incarcerated. We executed both incident-user (no OAT experiences) and prevalent-new-user (no OAT within the past month) analyses. INTERVENTION AND COMPARATOR:The time between completed OAT induction and taper initiation: <3 months, 3-6 months, 6-12 months, compared with 12-48 months. MEASUREMENTS:The primary outcomes were completed taper (reaching a final daily dose of ≤5 mg/day for methadone, or ≤2 mg/0.5 mg/day for buprenorphine/naloxone) and all-cause mortality. A clone-censor-weight approach was used to adjust for informative censoring and balance baseline characteristics between the groups. Logistic regression and pooled logistic regression models were used to estimate odds ratios (ORs) for completed taper and hazard ratios (HRs) for all-cause mortality, respectively, each with 95% compatibility ('confidence') intervals. FINDINGS:We included 17 726 incident users (buprenorphine/naloxone: 36.9%) and 49 515 treatment episodes (buprenorphine/naloxone: 31.2%) from 31 231 prevalent new users who completed induction in the analyses. Among prevalent new users, beginning tapering within 3 months, between 3 and 6 months and between 6 and 12 months of completing induction was associated with an increased likelihood of completed taper [methadone: <3 months: adjusted odds ratio (aOR) = 3.09, 95% compatibility interval (95% CI) = 2.58-3.68; buprenorphine/naloxone: <3 months: aOR = 6.90, 95% CI = 5.19-9.16] but a higher risk of mortality [methadone: <3 months: adjusted hazard ratio (aHR) = 1.18, 95% CI = 1.12-1.25; buprenorphine/naloxone: <3 months: aHR = 1.12, 95% CI = 1.05-1.19], compared with initiating a taper between 12 and 48 months. Similar results were found among incident users. CONCLUSIONS:Although initiating early tapering off opioid agonist treatment may be associated with a greater likelihood of taper completion, this practice also increases the risk of mortality.

BACKGROUND AND AIM:Urine drug testing is often utilized alongside opioid agonist treatment to assess client progress by validating self-reported substance use, monitoring for diversion and supporting clinical decisions for take-home dosing. However, there is a paucity of evidence to support the practice of urine drug testing. We aimed to determine the association of alternative urine drug testing frequencies with opioid agonist treatment discontinuation, compared with no monitoring, among individuals receiving methadone or buprenorphine/naloxone treatment. DESIGN:Population-based retrospective cohort study and target trial emulation based on nine-linked administrative databases. SETTING:British Columbia, Canada, between 1 January 2010 and 17 March 2020. PARTICIPANTS:Individuals with no history of cancer or palliative care, aged 18 or older and no indication of pregnancy who initiated methadone or buprenorphine/naloxone. A total of 18 988 methadone and 11 910 buprenorphine/naloxone recipients were included in the incident user design (individuals with no past opioid agonist treatment experience). MEASUREMENTS:We used a clone-censor-weight approach to estimate hazard ratios with 95% compatibility ("confidence") intervals for treatment discontinuation (lasting at least 5 and 6 days for methadone and buprenorphine, respectively) and all-cause mortality on treatment within 12 months for static urine drug testing strategies. FINDINGS:Under static monitoring strategies, weekly urine drug testing was associated with a slightly reduced risk of discontinuation in the first year of continuous retention in treatment [methadone: adjusted hazard ratio (aHR) = 0.96, 95% compatibility interval (CI) = (0.95-0.98); buprenorphine/naloxone: aHR = 0.95 (0.94-0.97)] compared with no monitoring. The estimated associations of weekly urine drug testing with all-cause mortality were similar in size but extremely imprecise [methadone: aHR = 0.95 (0.78-1.15), buprenorphine/naloxone: aHR = 0.99 (0.62-1.58)]. Less frequent testing demonstrated no observed difference on treatment discontinuation or all-cause mortality compared with no monitoring. CONCLUSION:Compared with no urine drug testing, weekly urine drug testing may be associated with improved opioid agonist treatment retention; however, the high costs attributable to frequent testing may not be cost-effective and requires further evaluation. There was no improvement associated with less frequent testing compared with no monitoring.

Seaman and Keogh (Biometrical Journal 2024) proposed a method for simulating data compatible with a marginal structural model (MSM) for the hazard of a survival time outcome. In this short report, I propose two extensions of this method. First, Seaman and Keogh favoured the use of a Gaussian copula, because this enables the function of the confounder history through which the hazard of failure depends on confounders to be interpreted as a risk score. Here, I describe how this interpretation can be preserved even when a non-Gaussian copula is used. Second, I extend Seaman and Keogh's method to allow simulation of data compatible with a MSM for a cause-specific or subdistribution hazard of failure in the presence of a competing event.

Collider bias occurs when conditioning on a common effect (collider) of two variables X,Y. In this article, we quantify the collider bias in the estimated association between exposure X and outcome Y induced by selecting on one value of a binary collider S of the exposure and the outcome. In the case of logistic regression, it is known that the magnitude of the collider bias in the exposure-outcome regression coefficient is proportional to the strength of interaction δ3 between X and Y in a log-additive model for the collider: P(S=1|X,Y)=exp{δ0+δ1X+δ2Y+δ3XY}. We show that this result also holds under a linear or Poisson regression model for the exposure-outcome association. We then illustrate numerically that even if a log-additive model with interactions is not the true model for the collider, the interaction term in such a model is still informative about the magnitude of collider bias. Finally, we discuss the implications of these findings for methods that attempt to adjust for collider bias, such as inverse probability weighting which is often implemented without including interactions between variables in the weighting model.

The employment of peer supporter workers starting in 2018 was one of the interventions deployed by National Health Service England as part of its Hepatitis C virus (HCV) elimination plan. Peers are individuals with relevant lived experience who educate their communities about the virus and promote testing and treatment. In this paper, we assess the causal effect of the peers intervention on HCV patient case-finding, using data on 22 administrative regions from January 2016 to May 2021. To do this, we develop a Bayesian causal factor analysis model for count outcomes and ordinal interventions. Our method provides uncertainty quantification for all causal estimands of interest, gains efficiency by jointly modelling the intervention assignment process, pre- and post-intervention outcomes, and provides estimates of both conditional average and individual treatment effects (ITEs). For ITEs, we propose a copula-based approach that allows practitioners to perform sensitivity analysis to assumptions made regarding the joint distribution of potential outcomes, that are necessary to estimate these quantities. Our analysis suggests that the introduction of peers led to an increase in HCV patient case-finding. Further, we found that the effect of the intervention increased with intervention intensity, and was stronger during the national COVID-19 lockdown.

INTRODUCTION:Due to inferior safety profile and higher risk of diversion than buprenorphine/naloxone, guidelines typically recommend stringent eligibility criteria such as daily witnessed ingestion of methadone for at least 12 weeks before considering take-home doses. Recent research has focused on whether or not to initiate take-home methadone doses, often using pandemic-era data when temporary prescribing changes provided a natural experiment on the impact of access to take-home doses. However, none of these studies adequately examined the optimal timing and criteria for safely starting take-home doses to enhance treatment outcomes. To determine the optimal timing for initiating methadone take-home doses, we will compare the effects of different initiation times on time to treatment discontinuation, all-cause mortality and acute-care visits among individuals who completed methadone induction in British Columbia, Canada, from 2010 to 2022. METHODS AND ANALYSIS:We propose emulating a target trial using linked population-level health administrative data for all individuals aged 18 or older living in British Columbia, Canada, completing methadone induction between 1 January 2010 and 31 December 2022. The exposure strategies will include no take-home dosing and take-home dose initiation in ≤4, 5-12, 13-24 and 25-52 weeks since completed induction. The outcomes will include the time to treatment discontinuation, all-cause mortality and acute-care visits. We propose a per-protocol analysis with a clone-censor-weighting approach to address the immortal time bias implicit in the comparison of alternative take-home dose initiation times. Subgroup and sensitivity analyses, including cohort restrictions, study timeline variations, eligibility criteria modifications and outcome reclassifications, are proposed to assess the robustness of our results. ETHICS AND DISSEMINATION:The protocol, cohort creation and analysis plan have been classified and approved as a quality improvement initiative by Providence Health Care Research Ethics Board and the Simon Fraser University Office of Research Ethics. Results will be disseminated to local advocacy groups and decision-makers, national and international clinical guideline developers, presented at international conferences and published in peer-reviewed journals.

Doubly robust estimators combine an inverse probability weighting estimator and a mass imputation estimator. Several doubly robust estimators for estimating the population mean (or prevalence) of an outcome have been proposed for integrating outcome and covariate data from a nonprobability survey with covariate data from an auxiliary probability survey. However, the question of how to combine a doubly robust estimate with a corresponding estimate based on outcome data from the auxiliary probability survey alone has only received limited attention. In this paper, we (i) review previously proposed doubly robust estimators, (ii) provide formulae for the variance of doubly robust estimators and the covariance between doubly robust and probability survey estimates, (iii) propose a framework for how to combine efficiently a doubly robust estimate from a nonprobability sample with an estimate based on the auxiliary probability sample alone, and (iv) provide formulae for the variance of such combined estimates.

Sequential trial emulation (STE) is an approach to estimating causal treatment effects by emulating a sequence of target trials from observational data. In STE, inverse probability weighting is commonly utilised to address time-varying confounding and/or dependent censoring. Then structural models for potential outcomes are applied to the weighted data to estimate treatment effects. For inference, the simple sandwich variance estimator is popular but conservative, while nonparametric bootstrap is computationally expensive, and a more efficient alternative, linearised estimating function (LEF) bootstrap, has not been adapted to STE. We evaluated the performance of various methods for constructing confidence intervals (CIs) of marginal risk differences in STE with survival outcomes by comparing the coverage of CIs based on nonparametric/LEF bootstrap, jackknife, and the sandwich variance estimator through simulations. LEF bootstrap CIs demonstrated better coverage than nonparametric bootstrap CIs and sandwich-variance-estimator-based CIs with small/moderate sample sizes, low event rates and low treatment prevalence, which were the motivating scenarios for STE. They were less affected by treatment group imbalance and faster to compute than nonparametric bootstrap CIs. With large sample sizes and medium/high event rates, the sandwich-variance-estimator-based CIs had the best coverage and were the fastest to compute. These findings offer guidance in constructing CIs in causal survival analysis using STE.

Marginal structural models (MSMs) are often used to estimate causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be present. They can be fitted using, e.g., inverse probability of treatment weighting (IPTW). It is important to evaluate the performance of statistical methods in different scenarios, and simulation studies are a key tool for such evaluations. In such simulation studies, it is common to generate data in such a way that the model of interest is correctly specified, but this is not always straightforward when the model of interest is for potential outcomes, as is an MSM. Methods have been proposed for simulating from MSMs for a survival outcome, but these methods impose restrictions on the data-generating mechanism. Here we propose a method that overcomes these restrictions. The MSM can be a marginal structural logistic model for a discrete survival time or a Cox or additive hazards MSM for a continuous survival time. The hazard of the potential survival time can be conditional on baseline covariates, and the treatment variable can be discrete or continuous. We illustrate the use of the proposed simulation algorithm by carrying out a brief simulation study. This study compares the coverage of confidence intervals calculated in two different ways for causal effect estimates obtained by fitting an MSM via IPTW.

Randomised controlled trials (RCTs) are regarded as the gold standard for estimating causal treatment effects on health outcomes. However, RCTs are not always feasible, because of time, budget or ethical constraints. Observational data such as those from electronic health records (EHRs) offer an alternative way to estimate the causal effects of treatments. Recently, the `target trial emulation' framework was proposed by Hernan and Robins (2016) to provide a formal structure for estimating causal treatment effects from observational data. To promote more widespread implementation of target trial emulation in practice, we develop the R package TrialEmulation to emulate a sequence of target trials using observational time-to-event data, where individuals who start to receive treatment and those who have not been on the treatment at the baseline of the emulated trials are compared in terms of their risks of an outcome event. Specifically, TrialEmulation provides (1) data preparation for emulating a sequence of target trials, (2) calculation of the inverse probability of treatment and censoring weights to handle treatment switching and dependent censoring, (3) fitting of marginal structural models for the time-to-event outcome given baseline covariates, (4) estimation and inference of marginal intention to treat and per-protocol effects of the treatment in terms of marginal risk differences between treated and untreated for a user-specified target trial population. In particular, TrialEmulation can accommodate large data sets (e.g., from EHRs) within memory constraints of R by processing data in chunks and applying case-control sampling. We demonstrate the functionality of TrialEmulation using a simulated data set that mimics typical observational time-to-event data in practice.

Abstract Background Risk prediction models are routinely used to assist in clinical decision making. A small sample size for model development can compromise model performance when the model is applied to new patients. For binary outcomes, the calibration slope (CS) and the mean absolute prediction error (MAPE) are two key measures on which sample size calculations for the development of risk models have been based. CS quantifies the degree of model overfitting while MAPE assesses the accuracy of individual predictions. Methods Recently, two formulae were proposed to calculate the sample size required, given anticipated features of the development data such as the outcome prevalence and c-statistic, to ensure that the expectation of the CS and MAPE (over repeated samples) in models fitted using MLE will meet prespecified target values. In this article, we use a simulation study to evaluate the performance of these formulae. Results We found that both formulae work reasonably well when the anticipated model strength is not too high (c-statistic < 0.8), regardless of the outcome prevalence. However, for higher model strengths the CS formula underestimates the sample size substantially. For example, for c-statistic = 0.85 and 0.9, the sample size needed to be increased by at least 50% and 100%, respectively, to meet the target expected CS. On the other hand, the MAPE formula tends to overestimate the sample size for high model strengths. These conclusions were more pronounced for higher prevalence than for lower prevalence. Similar results were drawn when the outcome was time to event with censoring. Given these findings, we propose a simulation-based approach, implemented in the new R package ‘samplesizedev’, to correctly estimate the sample size even for high model strengths. The software can also calculate the variability in CS and MAPE, thus allowing for assessment of model stability. Conclusions The calibration and MAPE formulae suggest sample sizes that are generally appropriate for use when the model strength is not too high. However, they tend to be biased for higher model strengths, which are not uncommon in clinical risk prediction studies. On those occasions, our proposed adjustments to the sample size calculations will be relevant.

Introduction Opioid agonist treatment (OAT) tapering involves a gradual reduction in daily medication dose to ultimately reach a state of opioid abstinence. Due to the high risk of relapse and overdose after tapering, this practice is not recommended by clinical guidelines, however, clients may still request to taper off medication. The ideal time to initiate an OAT taper is not known. However, ethically, taper plans should acknowledge clients’ preferences and autonomy but apply principles of shared informed decision-making regarding safety and efficacy. Linked population-level data capturing real-world tapering practices provide a valuable opportunity to improve existing evidence on when to contemplate starting an OAT taper. Our objective is to determine the comparative effectiveness of alternative times from OAT initiation at which a taper can be initiated, with a primary outcome of taper completion, as observed in clinical practice in British Columbia (BC), Canada.Methods and analysis We propose a population-level retrospective observational study with a linkage of eight provincial health administrative databases in BC, Canada (01 January 2010 to 17 March 2020). Our primary outcomes include taper completion and all-cause mortality during treatment. We propose a ‘per-protocol’ target trial to compare different durations to taper initiation on the likelihood of taper completion. A range of sensitivity analyses will be used to assess the heterogeneity and robustness of the results including assessment of effectiveness and safety.Ethics and dissemination The protocol, cohort creation and analysis plan have been classified and approved as a quality improvement initiative by Providence Health Care Research Ethics Board and the Simon Fraser University Office of Research Ethics. Results will be disseminated to local advocacy groups and decision-makers, national and international clinical guideline developers, presented at international conferences and published in peer-reviewed journals electronically and in print.

Importance:Previous studies on the comparative effectiveness between buprenorphine and methadone provided limited evidence on differences in treatment effects across key subgroups and were drawn from populations who use primarily heroin or prescription opioids, although fentanyl use is increasing across North America. Objective:To assess the risk of treatment discontinuation and mortality among individuals receiving buprenorphine/naloxone vs methadone for the treatment of opioid use disorder. Design, Setting, and Participants:Population-based retrospective cohort study using linked health administrative databases in British Columbia, Canada. The study included treatment recipients between January 1, 2010, and March 17, 2020, who were 18 years or older and not incarcerated, pregnant, or receiving palliative cancer care at initiation. Exposures:Receipt of buprenorphine/naloxone or methadone among incident (first-time) users and prevalent new users (including first and subsequent treatment attempts). Main Outcomes and Measures:Hazard ratios (HRs) with 95% compatibility (confidence) intervals were estimated for treatment discontinuation (lasting ≥5 days for methadone and ≥6 days for buprenorphine/naloxone) and all-cause mortality within 24 months using discrete-time survival models for comparisons of medications as assigned at initiation regardless of treatment adherence ("initiator") and received according to dosing guidelines (approximating per-protocol analysis). Results:A total of 30 891 incident users (39% receiving buprenorphine/naloxone; 66% male; median age, 33 [25th-75th, 26-43] years) were included in the initiator analysis and 25 614 in the per-protocol analysis. Incident users of buprenorphine/naloxone had a higher risk of treatment discontinuation compared with methadone in initiator analyses (88.8% vs 81.5% discontinued at 24 months; adjusted HR, 1.58 [95% CI, 1.53-1.63]), with limited change in estimates when evaluated at optimal dose in per-protocol analysis (42.1% vs 30.7%; adjusted HR, 1.67 [95% CI, 1.58-1.76]). Per-protocol analyses of mortality while receiving treatment exhibited ambiguous results among incident users (0.08% vs 0.13% mortality at 24 months; adjusted HR, 0.57 [95% CI, 0.24-1.35]) and among prevalent users (0.08% vs 0.09%; adjusted HR, 0.97 [95% CI, 0.54-1.73]). Results were consistent after the introduction of fentanyl and across patient subgroups and sensitivity analyses. Conclusions and Relevance:Receipt of methadone was associated with a lower risk of treatment discontinuation compared with buprenorphine/naloxone. The risk of mortality while receiving treatment was similar for buprenorphine/naloxone and methadone, although the CI estimate for the hazard ratio was wide.

OBJECTIVES:Bivalent original/BA.4-5 and monovalent XBB.1.5 mRNA boosters were offered to UK healthcare workers (HCWs) in the autumn of 2023. We aimed to estimate booster vaccine effectiveness (VE) and post-infection immunity among the SIREN HCW cohort over the subsequent 6-month period of XBB.1.5 and JN.1 variant circulation. METHODS:Between October 2023 to March 2024, 2867 SIREN study participants tested fortnightly for SARS-CoV-2 and completed symptoms questionnaires. We used multi-state models, adjusted for vaccination, prior infection, and demographic covariates, to estimate protection against mild/asymptomatic and moderate SARS-CoV-2 infection. RESULTS:Half of the participants (1422) received a booster during October 2023 (280 bivalent, 1142 monovalent), and 536 (19%) had a PCR-confirmed infection over the study period. Bivalent booster VE was 15.1% (-55.4 to 53.6%) at 0-2 months and 4.2% (-46.4 to 37.3%) at 2-4 months post-vaccination. Monovalent booster VE was 44.2% (95% CI 21.7 to 60.3%) at 0-2 months, and 24.1% (-0.7 to 42.9%) at 2-4 months. VE was greater against moderate infection than against mild/asymptomatic infection, but neither booster showed evidence of protection after 4 months. Controlling for vaccination, compared to an infection >2 years prior, infection within the past 6 months was associated with 58.6% (30.3 to 75.4%) increased protection against moderate infection and 38.5% (5.8 to 59.8%) increased protection against mild/asymptomatic infection. CONCLUSIONS:Monovalent XBB.1.5 boosters provided short-term protection against SARS-CoV-2 infection, particularly against moderate symptoms. Vaccine formulations that target the circulating variant may be suitable for inclusion in seasonal vaccination campaigns among HCWs.

Objective In September 2020, 15 861 SARS-CoV-2 case records failed to upload from the Second Generation Surveillance System (SGSS) to the Contact Tracing Advisory Service (CTAS) tool, delaying the contact tracing of these cases. This study used CTAS data to determine the impact of this delay on population health outcomes: transmission events, hospitalisations and mortality. Previously, a modelling study suggested a substantial impact.Design Observational study.Setting England.Population Individuals testing positive for SARS-CoV-2 and their reported contacts.Main outcome measures Secondary attack rates (SARs), hospitalisations and deaths among primary and secondary contacts were calculated, compared with all other concurrent, unaffected cases. Affected SGSS records were matched to CTAS records. Successive contacts and cases were identified and matched to hospital episode and mortality outcomes.Results Initiation of contact tracing was delayed by 3 days on average in the primary cases in the delay group (6 days) compared with the control group (3 days). This was associated with lower completion of contact tracing: 80% (95% CI: 79% to 81%) in delay group and 83% (95% CI: 83% to 84%) in control group. There was some evidence to suggest increased transmission to non-household contacts among those affected by the delay. The SAR for non-household contacts was higher among secondary contacts in the delay group than the control group (delay group: 7.9%, 95% CI: 6.5% to 9.2%; control group: 5.9%, 95% CI: 5.3% to 6.6%). There did not appear to be a significant difference between the delay and control groups in the odds of hospitalisation (crude OR: 1.1 (95% CI: 0.9 to 1.2)) or death (crude OR: 0.7 (95% CI: 0.1 to 4.0)) among secondary contacts.Conclusions Our analysis suggests that the delay in contact tracing had a limited impact on population health outcomes; however, contact tracing was not completed for all individuals, so some transmission events might not be captured.

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.

Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and nontractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modeling multiple outcomes affected by the intervention, and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England's Test and Trace programme for COVID-19.