A causal framework for classical statistical estimands in failure-time settings with competing events

Data-driven subclassification of ANCA-associated vasculitis: model-based clustering of a federated international cohort

BACKGROUND

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a heterogenous autoimmune disease. While traditionally stratified into two conditions, granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), the subclassification of ANCA-associated vasculitis is subject to continued debate. Here we aim to identify phenotypically distinct subgroups and develop a data-driven subclassification of ANCA-associated vasculitis, using a large real-world dataset.

METHODS

In the collaborative data reuse project FAIRVASC (Findable, Accessible, Interoperable, Reusable, Vasculitis), registry records of patients with ANCA-associated vasculitis were retrieved from six European vasculitis registries: the Czech Registry of ANCA-associated vasculitis (Czech Republic), the French Vasculitis Study Group Registry (FVSG; France), the Joint Vasculitis Registry in German-speaking Countries (GeVas; Germany), the Polish Vasculitis Registry (POLVAS; Poland), the Irish Rare Kidney Disease Registry (RKD; Ireland), and the Skåne Vasculitis Cohort (Sweden). We performed model-based clustering of 17 mixed-type clinical variables using a parsimonious mixture of two latent Gaussian variable models. Clinical validation of the optimal cluster solution was made through summary statistics of the clusters' demography, phenotypic and serological characteristics, and outcome. The predictive value of models featuring the cluster affiliations were compared with classifications based on clinical diagnosis and ANCA specificity. People with lived experience were involved throughout the FAIRVASVC project.

FINDINGS

A total of 3868 patients diagnosed with ANCA-associated vasculitis between Nov 1, 1966, and March 1, 2023, were included in the study across the six registries (Czech Registry n=371, FVSG n=1780, GeVas n=135, POLVAS n=792, RKD n=439, and Skåne Vasculitis Cohort n=351). There were 2434 (62·9%) patients with GPA and 1434 (37·1%) with MPA. Mean age at diagnosis was 57·2 years (SD 16·4); 2006 (51·9%) of 3867 patients were men and 1861 (48·1%) were women. We identified five clusters, with distinct phenotype, biochemical presentation, and disease outcome. Three clusters were characterised by kidney involvement: one severe kidney cluster (555 [14·3%] of 3868 patients) with high C-reactive protein (CRP) and serum creatinine concentrations, and variable ANCA specificity (SK cluster); one myeloperoxidase (MPO)-ANCA-positive kidney involvement cluster (782 [20·2%]) with limited extrarenal disease (MPO-K cluster); and one proteinase 3 (PR3)-ANCA-positive kidney involvement cluster (683 [17·7%]) with widespread extrarenal disease (PR3-K cluster). Two clusters were characterised by relative absence of kidney involvement: one was a predominantly PR3-ANCA-positive cluster (1202 [31·1%]) with inflammatory multisystem disease (IMS cluster), and one was a cluster (646 [16·7%]) with predominantly ear-nose-throat involvement and low CRP, with mainly younger patients (YR cluster). Compared with models fitted with clinical diagnosis or ANCA status, cluster-assigned models demonstrated improved predictive power with respect to both patient and kidney survival.

INTERPRETATION

Our study reinforces the view that ANCA-associated vasculitis is not merely a binary construct. Data-driven subclassification of ANCA-associated vasculitis exhibits higher predictive value than current approaches for key outcomes.

FUNDING

European Union's Horizon 2020 research and innovation programme under the European Joint Programme on Rare Diseases.

Causal Inference for Continuous Multiple Time Point Interventions

There are limited options to estimate the treatment effects of variables which are continuous and measured at multiple time points, particularly if the true dose-response curve should be estimated as closely as possible. However, these situations may be of relevance: in pharmacology, one may be interested in how outcomes of people living with-and treated for-HIV, such as viral failure, would vary for time-varying interventions such as different drug concentration trajectories. A challenge for doing causal inference with continuous interventions is that the positivity assumption is typically violated. To address positivity violations, we develop projection functions, which reweigh and redefine the estimand of interest based on functions of the conditional support for the respective interventions. With these functions, we obtain the desired dose-response curve in areas of enough support, and otherwise a meaningful estimand that does not require the positivity assumption. We developg $$ g $$ -computation type plug-in estimators for this case. Those are contrasted with g-computation estimators which are applied to continuous interventions without specifically addressing positivity violations, which we propose to be presented with diagnostics. The ideas are illustrated with longitudinal data from HIV positive children treated with an efavirenz-based regimen as part of the CHAPAS-3 trial, which enrolled children< 13 $$ <13 $$ years in Zambia/Uganda. Simulations show in which situations a standard g-computation approach is appropriate, and in which it leads to bias and how the proposed weighted estimation approach then recovers the alternative estimand of interest.

Statin use is associated with less ST-elevation versus non-ST-elevation myocardial infarction in a nationwide study

BACKGROUND AND AIMS

Statin therapy reduces myocardial infarction rate but whether it is associated with a shift of ST-elevation myocardial infarction (STEMI) towards non-ST-elevation myocardial infarction (non-STEMI) remains unknown. Thus, we tested the hypothesis that statin use is associated with less STEMI relative to non-STEMI in first-time myocardial infarction.

METHODS

In a nationwide study, including 66,896 patients with first-time myocardial infarction between 2010 and 2021, we obtained multivariable risk estimates for STEMI versus non-STEMI according to any statin use, cumulated statin use, and daily statin dose. Furthermore, we obtained hazard ratios for 60-day mortality (5545 deaths) following myocardial infarction according to type of infarction.

RESULTS

Odds ratios for STEMI versus non-STEMI were 0.81 (95 % CI:0.77-0.85) and 1.07 (1.01-1.13) in current and previous statin users compared to never statin users. Cumulated statin exposure yielded odds ratios of 0.96 (0.87-1.07) for <2 statin-years, 0.87 (0.79-0.95) for 2-4.9 statin-years, 0.80 (0.74-0.87) for 5-10 statin-years, and 0.75 (0.70-0.80) for >10 statin-years compared to never users. Corresponding odds ratios for statin dose intensity were 0.89 (0.84-0.95) for low-intensity, 0.77 (0.73-0.82) for moderate-intensity, and 0.70 (0.63-0.77) for high-intensity. Results were similar in multiple sensitivity analyses and using a cohort design. The hazard ratio for 60-day mortality after first-time STEMI versus non-STEMI was 2.24 (2.13-2.37).

CONCLUSIONS

In this nationwide study, prior statin use is associated with less STEMI relative to non-STEMI in a dose dependent manner. This indicates that statin therapy, in addition to reducing myocardial infarction event rates, also result in a less severe presentation of myocardial infarctions.

Association of early statin initiation during COVID-19 admission with inpatient mortality at an academic health system in Illinois, March 2020 to September 2022: a target trial emulation using observational data

OBJECTIVE

We assessed the association of early statin initiation with inpatient mortality among hospitalised COVID-19 patients.

DESIGN, SETTING AND PARTICIPANTS

This observational study emulated a hypothetical target trial using electronic health records data from Northwestern Medicine Health System, Illinois, 2020-2022. We included patients who were ≥40 years, admitted ≥48 hours for COVID-19 from March 2020 to August 2022 and had no evidence of statin use before admission.

INTERVENTIONS

Individuals who initiated any statins within 48 hours of admission were compared with individuals who did not initiate statins during this period.

PRIMARY OUTCOME MEASURES

Inpatient mortality at hospital days 7, 14, 21 and 28 were determined using hospital records. Risk differences between exposure groups were calculated using augmented inverse propensity weighting (AIPW) with SuperLearner.

RESULTS

A total of 8893 individuals (24.5% early statin initiators) were included. Early initiators tended to be older, male and have higher comorbidity burdens. Unadjusted day 28 mortality was higher in early initiators (6.0% vs 3.6%). Adjusted analysis showed slightly higher inpatient mortality risk at days 7 (RD: 0.5%, 95% CI: 0.2 to 0.8) and 21 (RD: 0.6%, 95% CI: 0.04 to 1.1), but not days 14 (RD: 0.4%, 95% CI: -0.03 to 0.9) and 28 (RD: 0.4%, 95% CI: -0.2 to 1.1). Sensitivity analyses using alternative modelling approaches showed no difference between groups.

CONCLUSIONS

Early statin initiation was not associated with lower mortality contrasting with findings of previous observational studies. Trial emulation helped in identifying and addressing sources of bias incompletely addressed by previous work. Statin use may be indicated for other conditions but not COVID-19.

The inverse association between cancer history and incident cognitive impairment: Addressing attrition bias

INTRODUCTION

Cancer is inversely associated with cognitive impairment. Whether this is due to statistical handling of attrition (death and censoring) is unknown.

METHODS

We quantified associations between cancer history and incident cognitive impairment among Health, Aging, and Body Composition Study participants without baseline cognitive impairment or stroke (n = 2604) using multiple competing-risks models and their corresponding estimands: cause-specific, subdistribution, and marginal hazards, plus composite-outcome (cognitive impairment or all-cause mortality) hazards. All-cause mortality was also modeled.

RESULTS

After covariate adjustment (demographics, apolipoprotein E ε4, lifestyle, health conditions), cause-specific and marginal hazard ratios (HRs) were similar to each other (≈ 0.84; P values < 0.05). The subdistribution HR was 0.764 (95% confidence interval [CI] = 0.645-0.906), and composite-outcome Cox model HR was 1.149 (95% CI = 1.016-1.299). Cancer history was positively associated with all-cause mortality (HR = 1.813; 95% CI = 1.525-2.156).

DISCUSSION

Cause-specific, subdistribution, and marginal hazards models produced inverse associations between cancer and cognitive impairment. Competing risk models answer slightly different questions, and estimand choice influenced findings here.

HIGHLIGHTS

Cancer history is inversely associated with incident cognitive impairment. Findings were robust to handling of competing risks of death. All models also addressed possible informative censoring bias. Cancer history was associated with 16% lower hazard of cognitive impairment. Cancer history was associated with 81% higher all-cause mortality hazard.

Comparative effectiveness of bovine carotid artery xenograft and polytetrafluoroethylene in hemodialysis access revision

BACKGROUND

When hemodialysis arteriovenous accesses fail, autogenous options are often limited. Non-autogenous conduit choices include bovine carotid artery xenografts (BCAG) and expanded polytetrafluoroethylene (PTFE), yet their comparative effectiveness in hemodialysis access revision remains largely unknown.

METHODS

A cohort study was performed from a prospectively collected institutional database from August 2010 to July 2021. All patients undergoing an arteriovenous access revision with either BCAG or PTFE were followed for up to 3 years from their index access revision. Revision was defined as graft placement to address a specific problem of an existing arteriovenous access while maintaining one or more of the key components of the original access (e.g. inflow, outflow, and cannulation zone). Outcomes were measured starting at the date of the index revision procedure. The primary outcome was loss of secondary patency at 3 years. Secondary outcomes included loss of post-intervention primary patency, rates of recurrent interventions, and 30-day complications. Pooled logistic regression was used to estimate inverse probability weighted marginal structural models for the time-to-event outcomes of interest.

RESULTS

A total of 159 patients were included in the study, and 58% received access revision with BCAG. Common indications for revision included worn out cannulation zones (32%), thrombosis (18%), outflow augmentation (16%), and inflow augmentation (13%). Estimated risk of secondary patency loss at 3 years was lower in the BCAG group (8.6%, 3.9-15.1) compared to the PTFE group (24.8%, 12.4-38.7). Patients receiving BCAG experienced a 60% decreased relative risk of secondary patency loss at 3 years (risk ratio 0.40, 0.14-0.86). Recurrent interventions occurred at similar rates in the BCAG and PTFE groups, with 1.86 (1.31-2.43) and 1.60 (1.07-2.14) interventions at 1 year, respectively (hazard ratio 1.22, 0.74-1.96).

CONCLUSIONS

Under the conditions of this contemporary cohort study, use of BCAG in upper extremity hemodialysis access revision decreased access abandonment when compared to PTFE.

Disentangling discordant vitamin D associations with prostate cancer incidence and fatality in a large, nested case-control study

BACKGROUND

Published analyses of prostate cancer nested case-control and survival data in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study cohort suggested that men with higher baseline vitamin D [25(OH)D] concentrations have both (i) increased prostate cancer risk and (ii) decreased prostate cancer-specific fatality.

METHODS

To investigate possible factors responsible for a spurious association with prostate cancer fatality, we reanalysed baseline serum vitamin D associations with prostate cancer risk and prostate cancer-specific fatality in case-control data nested within the ATBC Study (1000 controls and 1000 incident prostate cancer cases). Conditional logistic regression and Cox proportion hazard models were used, respectively, to estimate odds ratios for risk and hazard ratios for prostate cancer-specific fatality, overall and by disease aggressiveness. We replicated these case-control analyses using baseline serum measurements of alpha-tocopherol (vitamin E), beta-carotene and retinol (vitamin A), and used the entire ATBC Study cohort (n = 29 085) to estimate marginal associations between these baseline vitamins and prostate cancer incidence and fatality following blood collection.

RESULTS

Vitamin D analyses agreed closely with those originally published, with opposite risk and fatality associations. By contrast, the analyses of alpha-tocopherol, beta-carotene and retinol yielded concordant associations for prostate cancer incidence and prostate cancer-specific fatality.

CONCLUSIONS

We found evidence of neither artefacts in the nested prostate cancer case-control data set nor detection or collider biases in the fatality analyses. The present findings therefore support a valid inverse (i.e. beneficial) association between vitamin D and prostate cancer-specific survival that warrants further evaluation, including possibly in controlled trials.

Comparative Effectiveness of Switching to Bictegravir From Dolutegravir-, Efavirenz-, or Raltegravir-Based Antiretroviral Therapy Among Individuals With HIV Who are Virologically Suppressed

Background

We aimed to determine the effectiveness of switching to bictegravir in maintaining an undetectable viral load (<50 copies/mL) among people with HIV (PWH) as compared with continuing dolutegravir-, efavirenz-, or raltegravir-based antiretroviral therapy using nationwide observational data from Mexico.

Methods

We emulated 3 target trials comparing switching to bictegravir vs continuing with dolutegravir, efavirenz, or raltegravir. Eligibility criteria were PWH aged ≥16 years with a viral load <50 copies/mL and at least 3 months of current antiretroviral therapy (dolutegravir, efavirenz, or raltegravir) between July 2019 and September 2021. Weekly target trials were emulated during the study period, and individuals were included in every emulation if they continued to be eligible. The main outcome was the probability of an undetectable viral load at 3 months, which was estimated via an adjusted logistic regression model. Estimated probabilities were compared via differences, and 95% CIs were calculated via bootstrap. Outcomes were also ascertained at 12 months, and sensitivity analyses were performed to test our analytic choices.

Results

We analyzed data from 3 028 619 PWH (63 581 unique individuals). The probability of an undetectable viral load at 3 months was 2.9% (95% CI, 1.9%-3.8%), 1.3% (95% CI, .9%-1.6%), and 1.2% (95% CI, .8%-1.7%) higher when switching to bictegravir vs continuing with dolutegravir, efavirenz, and raltegravir, respectively. Similar results were observed at 12 months and in other sensitivity analyses.

Conclusions

Our findings suggest that switching to bictegravir could be more effective in maintaining viral suppression than continuing with dolutegravir, efavirenz, or raltegravir.

Racial and ethnic differences in the risk of dementia diagnosis under hypothetical blood pressure-lowering interventions: The Multi-Ethnic Study of Atherosclerosis

INTRODUCTION

Substantial racial and ethnic disparities in hypertension and dementia exist in the United States. We evaluated the effect of maintaining systolic blood pressure (SBP) below clinical thresholds on dementia incidence.

METHODS

We included 6806 Multi-Ethnic Study of Atherosclerosis participants (44 to 84 years old). We implemented the parametric g-formula to simulate the hypothetical interventions to reduce SBP below 120 and 140 mmHg over time, accounting for time-varying confounding. We estimated risk ratios (RRs) and risk differences for dementia incidence at 19 years.

RESULTS

The RRs (95% confidence intervals [CIs]) comparing an intervention reducing SBP below 120 mmHg to no intervention were 0.93 (0.87 to 0.99) for total sample, 0.95 (0.88 to 1.02) for White, 0.90 (0.79 to 1.02) for Black, 0.90 (0.78 to 1.05) for Latino, and 1.16 (0.83 to 1.55) for Chinese American participants. Results for lowering SBP below 140 mmHg and with death as competing event were attenuated.

DISCUSSION

The reduction of SBP below 120 mmHg over time has modest effects on reducing dementia incidence. More work is needed to understand the heterogeneity across racial and ethnic groups.

HIGHLIGHTS

There is a potential beneficial effect in lowering SBP to reduce the risk of dementia, which may vary by race and ethnicity. The percentage of participants who would need intervention on blood pressure to meet clinical thresholds is greater for Black and Latino communities. Results are sensitive to the way that death is specified in the research question and analysis.

Educational inequalities in multimorbidity at older ages: a multi-generational population-based study

BACKGROUND

Social inequalities in multimorbidity may occur due to familial and/or individual factors and may differ between men and women. Using population-based multi-generational data, this study aimed to (1) assess the roles of parental and individual education in the risk of multimorbidity and (2) examine the potential effect modification by sex.

METHODS

Data were analysed from 62 060 adults aged 50+ who participated in the Survey of Health, Ageing and Retirement in Europe, comprising 14 European countries. Intergenerational educational trajectories (exposure) were High-High (reference), Low-High, High-Low and Low-Low, corresponding to parental-individual educational attainments. Multimorbidity (outcome) was ascertained between 2013 and 2020 as self-reported occurrence of ≥2 diagnosed chronic conditions. Inequalities were quantified as multimorbidity-free years lost (MFYL) between the ages of 50 and 90 and estimated via differences in the area under the standardized cumulative risk curves. Effect modification by sex was assessed via stratification.

RESULTS

Low individual education was associated with higher multimorbidity risk regardless of parental education. Compared to the High-High trajectory, Low-High was associated with -0.2 MFYL (95% confidence intervals: -0.5 to 0.1), High-Low with 3.0 (2.4-3.5), and Low-Low with 2.6 (2.3-2.9) MFYL. This pattern was observed for both sexes, with a greater magnitude for women. This effect modification was not observed when only diseases diagnosed independently of healthcare-seeking behaviours were examined.

CONCLUSIONS

Individual education was the main contributor to intergenerational inequalities in multimorbidity risk among older European adults. These findings support the importance of achieving a high education to mitigate multimorbidity risk.

Semiparametric g-computation for survival outcomes with time-fixed exposures: An illustration

PURPOSE

Generalized (g-) computation is a useful tool for causal inference in epidemiology. However, in settings when the outcome is a survival time subject to right censoring, the standard pooled logistic regression approach to g-computation requires arbitrary discretization of time, parametric modeling of the baseline hazard function, and the need to expand one's dataset. We illustrate a semiparametric Breslow estimator for g-computation with time-fixed treatments and survival outcomes that is not subject to these limitations.

METHODS

We compare performance of the Breslow g-computation estimator to the pooled logistic g-computation estimator in simulations and illustrate both approaches to estimate the effect of a 3-drug vs 2-drug antiretroviral therapy regimen among people with HIV.

RESULTS

In simulations, both approaches performed well at the end of follow-up. The pooled logistic approach was biased at times between the endpoints of the discrete time intervals used, while the Breslow approach was not. In the example, both approaches estimated a 1-year risk difference of about 6 % in favor of the 3-drug regimen, but the shape of the survival curves differed.

CONCLUSIONS

The Breslow g-computation estimator of counterfactual risk functions does not rely on strong parametric assumptions about the time-to-event distribution or onerous dataset expansions.

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.

Colchicine for cardiovascular and limb risk reduction in Medicare beneficiaries with peripheral artery disease: emulation of target trials

Aims

Recent evidence from randomized trials demonstrates that colchicine can reduce the risk of major adverse cardiovascular events (MACE) in patients with coronary artery disease. Colchicine's effect on lower-extremity peripheral artery disease (PAD) is not known.

Methods and results

To make inferences about the real-world effectiveness of colchicine in PAD, we emulated two target trials leveraging the variable prescribing practice of adding colchicine vs. a non-steroidal anti-inflammatory drug (NSAID) to urate-lowering therapy in patients with gout and PAD. Emulated Trial 1 compared colchicine initiators with NSAID initiators. Emulated Trial 2 compared long-term (indefinite) and short-term (3 months) treatment strategies after initiating colchicine. Eligible individuals were those continuously enrolled in Medicare receiving care at a multicentre academic health system between July 2007 and December 2019. The primary outcome for both trials was a 2 year composite of major adverse limb events (MALE), MACE, and all-cause mortality. Secondary outcomes included MALE and death, MACE and death, and individual components of the primary outcome. Inverse probability weighting was used to adjust for confounding. Percentile-based 95% confidence intervals (CIs) were estimated using non-parametric bootstrapping. A total of 1820 eligible patients were included; the mean age was 77 years [standard deviation (SD) 7], 32% were female, and 9% were non-White. The mean (SD) duration of colchicine and NSAID therapy was 247 (345) and 137 (237) days, respectively. In the emulation of Trial 1, the risk of the primary composite outcome of MALE, MACE, and death at 2 years was 29.9% (95% CI 27.2%, 32.3%) in the colchicine group and 31.5% (28.3%, 34.6%) in the NSAID group, with a risk difference of -1.7% (95% CI -6.5%, 3.1%) and a risk ratio of 0.95 (95% CI 0.83, 1.07). Similar findings were noted in the emulation of Trial 2, with a risk of the primary composite outcome at 2 years of 30.7% (95% CI 23.7%, 38.1%) in the long-term colchicine group and 33.4% (95% CI 29.4%, 37.7%) in the short-term group, with a risk difference of -2.7% (95% CI -10.3%, 5.4%) and risk ratio of 0.92 (95% CI 0.70, 1.16).

Conclusion

In a real-world sample of patients with PAD and gout, estimates of the effect of colchicine were consistent across two analyses and provided no conclusive evidence that colchicine decreased the risk of adverse cardiovascular or limb events and death. The cardiovascular and limb benefits of colchicine in older, comorbid populations with PAD and advanced systematic atherosclerosis remain uncertain.

On variance estimation of the inverse probability-of-treatment weighting estimator: A tutorial for different types of propensity score weights

Propensity score methods, such as inverse probability-of-treatment weighting (IPTW), have been increasingly used for covariate balancing in both observational studies and randomized trials, allowing the control of both systematic and chance imbalances. Approaches using IPTW are based on two steps: (i) estimation of the individual propensity scores (PS), and (ii) estimation of the treatment effect by applying PS weights. Thus, a variance estimator that accounts for both steps is crucial for correct inference. Using a variance estimator which ignores the first step leads to overestimated variance when the estimand is the average treatment effect (ATE), and to under or overestimated estimates when targeting the average treatment effect on the treated (ATT). In this article, we emphasize the importance of using an IPTW variance estimator that correctly considers the uncertainty in PS estimation. We present a comprehensive tutorial to obtain unbiased variance estimates, by proposing and applying a unifying formula for different types of PS weights (ATE, ATT, matching and overlap weights). This can be derived either via the linearization approach or M-estimation. Extensive R code is provided along with the corresponding large-sample theory. We perform simulation studies to illustrate the behavior of the estimators under different treatment and outcome prevalences and demonstrate appropriate behavior of the analytical variance estimator. We also use a reproducible analysis of observational lung cancer data as an illustrative example, estimating the effect of receiving a PET-CT scan on the receipt of surgery.

Association of platinum-based chemotherapy with live birth and infertility in female survivors of adolescent and young adult cancer

OBJECTIVE

To estimate the effect of platinum-based chemotherapy on live birth (LB) and infertility after cancer, in order to address a lack of treatment-specific fertility risks for female survivors of adolescent and young adult cancer, which limits counseling on fertility preservation decisions.

DESIGN

Retrospective cohort study.

SETTING

US administrative database.

PATIENTS

We identified incident breast, colorectal, and ovarian cancer cases in females aged 15-39 years who received platinum-based chemotherapy or no chemotherapy and matched them to females without cancer.

INTERVENTION

Platinum-based chemotherapy.

MAIN OUTCOME MEASURES

We estimated the effect of chemotherapy on the incidence of LB and infertility after cancer, overall, and after accounting for competing events (recurrence, death, and sterilizing surgeries).

RESULTS

There were 1,287 survivors in the chemotherapy group, 3,192 in the no chemotherapy group, and 34,147 women in the no cancer group, with a mean age of 33 years. Accounting for competing events, the overall 5-year LB incidence was lower in the chemotherapy group (3.9%) vs. the no chemotherapy group (6.4%). Adjusted relative risks vs. no chemotherapy and no cancer groups were 0.61 (95% confidence interval [CI] 0.42-0.82) and 0.70 (95% CI 0.51-0.93), respectively. The overall 5-year infertility incidence was similar in the chemotherapy group (21.8%) compared with the no chemotherapy group (20.7%). The adjusted relative risks vs. no chemotherapy and no cancer groups were 1.05 (95% CI 0.97-1.15) and 1.42 (95% CI 1.31-1.53), respectively.

CONCLUSIONS

Cancer survivors treated with platinum-based chemotherapy experienced modestly increased adverse fertility outcomes. The estimated effects of platinum-based chemotherapy were affected by competing events, suggesting the importance of this analytic approach for interpretations that ultimately inform clinical fertility preservation decisions.

Towards a Clearer Causal Question Underlying the Association Between Cancer and Dementia

BACKGROUND

Several observational studies have described an inverse association between cancer diagnosis and subsequent dementia risk. Multiple biologic mechanisms and potential biases have been proposed in attempts to explain this association. One proposed explanation is the opposite expression of Pin1 in cancer and dementia, and we use this explanation and potential drug target to illustrate the required assumptions and potential sources of bias for inferring an effect of Pin1 on dementia risk from analyses measuring cancer diagnosis as a proxy for Pin1 expression.

METHODS

We used data from the Rotterdam Study, a population-based cohort. We estimate the association between cancer diagnosis (as a proxy for Pin1) and subsequent dementia diagnosis using two different proxy methods and with confounding and censoring for death addressed with inverse probability weights. We estimate and compare the complements of a weighted Kaplan-Meier survival estimator at 20 years of follow-up.

RESULTS

Out of 3634 participants, 899 (25%) were diagnosed with cancer, of whom 53 (6%) had dementia, and 567 (63%) died. Among those without cancer, 15% (411) were diagnosed with dementia, and 667 (24%) died over follow-up. Depending on the confounding and selection bias control, and the way in which cancer was used as a time-varying proxy exposure, the risk ratio for dementia diagnosis ranged from 0.71 (95% confidence interval [CI] = 0.49, 0.95) to 1.1 (95% CI = 0.79, 1.3).

CONCLUSION

Being explicit about the underlying mechanism of interest is key to maximizing what we can learn from this cancer-dementia association given available or readily collected data, and to defining, detecting, and preventing potential biases.

Bias of the additive hazard model in the presence of causal effect heterogeneity

Hazard ratios are prone to selection bias, compromising their use as causal estimands. On the other hand, if Aalen's additive hazard model applies, the hazard difference has been shown to remain unaffected by the selection of frailty factors over time. Then, in the absence of confounding, observed hazard differences are equal in expectation to the causal hazard differences. However, in the presence of effect (on the hazard) heterogeneity, the observed hazard difference is also affected by selection of survivors. In this work, we formalize how the observed hazard difference (from a randomized controlled trial) evolves by selecting favourable levels of effect modifiers in the exposed group and thus deviates from the causal effect of interest. Such selection may result in a non-linear integrated hazard difference curve even when the individual causal effects are time-invariant. Therefore, a homogeneous time-varying causal additive effect on the hazard cannot be distinguished from a time-invariant but heterogeneous causal effect. We illustrate this causal issue by studying the effect of chemotherapy on the survival time of patients suffering from carcinoma of the oropharynx using data from a clinical trial. The hazard difference can thus not be used as an appropriate measure of the causal effect without making untestable assumptions.

The built-in selection bias of hazard ratios formalized using structural causal models

It is known that the hazard ratio lacks a useful causal interpretation. Even for data from a randomized controlled trial, the hazard ratio suffers from so-called built-in selection bias as, over time, the individuals at risk among the exposed and unexposed are no longer exchangeable. In this paper, we formalize how the expectation of the observed hazard ratio evolves and deviates from the causal effect of interest in the presence of heterogeneity of the hazard rate of unexposed individuals (frailty) and heterogeneity in effect (individual modification). For the case of effect heterogeneity, we define the causal hazard ratio. We show that the expected observed hazard ratio equals the ratio of expectations of the latent variables (frailty and modifier) conditionally on survival in the world with and without exposure, respectively. Examples with gamma, inverse Gaussian and compound Poisson distributed frailty and categorical (harming, beneficial or neutral) distributed effect modifiers are presented for illustration. This set of examples shows that an observed hazard ratio with a particular value can arise for all values of the causal hazard ratio. Therefore, the hazard ratio cannot be used as a measure of the causal effect without making untestable assumptions, stressing the importance of using more appropriate estimands, such as contrasts of the survival probabilities.

Prospective benchmarking of an observational analysis in the SWEDEHEART registry against the REDUCE-AMI randomized trial

Prospective benchmarking of an observational analysis against a randomized trial increases confidence in the benchmarking process as it relies exclusively on aligning the protocol of the trial and the observational analysis, while the trials findings are unavailable. The Randomized Evaluation of Decreased Usage of Betablockers After Myocardial Infarction (REDUCE-AMI, ClinicalTrials.gov ID: NCT03278509) trial started recruitment in September 2017 and results are expected in 2024. REDUCE-AMI aimed to estimate the effect of long-term use of beta blockers on the risk of death and myocardial following a myocardial infarction with preserved left ventricular systolic ejection fraction. We specified the protocol of a target trial as similar as possible to that of REDUCE-AMI, then emulated the target trial using observational data from Swedish healthcare registries. Had everyone followed the treatment strategy as specified in the target trial protocol, the observational analysis estimated a reduction in the 5-year risk of death or myocardial infarction of 0.8 percentage points for beta blockers compared with no beta blockers; effects ranging from an absolute reduction of 4.5 percentage points to an increase of 2.8 percentage points in the risk of death or myocardial infarction were compatible with our data under conventional statistical criteria. Once results of REDUCE-AMI are published, we will compare the results of our observational analysis against those from the trial. If this prospective benchmarking is successful, it supports the credibility of additional analyses using these observational data, which can rapidly deliver answers to questions that could not be answered by the initial trial. If benchmarking proves unsuccessful, we will conduct a "postmortem" analysis to identify the reasons for the discrepancy. Prospective benchmarking shifts the investigator focus away from an endeavour to use observational data to obtain similar results as a completed randomized trial, to a systematic attempt to align the design and analysis of the trial and the observational analysis.

Biased effects of pre-diagnostic physical activity on breast cancer survival: Systematic review and meta-analysis

BACKGROUND

Pre-diagnostic physical activity is reported to improve survival for women with breast cancer. However, studies of pre-diagnostic exposures and cancer survival are susceptible to bias, made clear when applying a target trial framework. We investigated the impact of selection bias, immortal time bias, confounding and bias due to inappropriate adjustment for post-exposure variables in a systematic review and meta-analysis of pre-diagnostic physical activity and survival after breast cancer.

METHODS

Medline, Embase and Emcare were searched from inception to November 2021 for studies examining pre-diagnostic physical activity and overall or breast cancer-specific survival for women with breast cancer. Random-effects meta-analysis was used to estimate pooled hazard ratios (HRs) and 95% confidence intervals (CIs) comparing highest versus lowest pre-diagnostic physical activity. Subgroup meta-analyses were used to compare HRs of studies with and without different biases. ROBINS-E was used to assess risk of bias.

RESULTS

We included 22 studies. Women with highest versus lowest pre-diagnostic physical activity had higher overall and breast cancer-specific survival across most analyses. The overall risk of bias was high. We observed marked differences in estimated HRs between studies that did and did not adjust for post-exposure variables or have immortal time bias. All studies were at risk of selection bias due to participants becoming eligible for study when they have survived to post-exposure events (e.g., breast cancer diagnosis). Insufficient studies were available to investigate confounding.

CONCLUSION

Biases can substantially change effect estimates. Due to misalignment of treatment assignment (before diagnosis), eligibility (survival to post-exposure events) and start of follow-up, bias is difficult to avoid. It is difficult to lend a causal interpretation to effect estimates from studies of pre-diagnostic physical activity and survival after cancer. Biased effect estimates that are difficult to interpret may be less useful for clinical or public health policy applications.

Do the associations of daily steps with mortality and incident cardiovascular disease differ by sedentary time levels? A device-based cohort study

OBJECTIVES

This study aims to examine the associations of daily step count with all-cause mortality and incident cardiovascular disease (CVD) by sedentary time levels and to determine if the minimal and optimal number of daily steps is modified by high sedentary time.

METHODS

Using data from the UK Biobank, this was a prospective dose-response analysis of total daily steps across low (<10.5 hours/day) and high (≥10.5 hours/day) sedentary time (as defined by the inflection point of the adjusted absolute risk of sedentary time with the two outcomes). Mortality and incident CVD was ascertained through 31 October 2021.

RESULTS

Among 72 174 participants (age=61.1±7.8 years), 1633 deaths and 6190 CVD events occurred over 6.9 (±0.8) years of follow-up. Compared with the referent 2200 steps/day (5th percentile), the optimal dose (nadir of the curve) for all-cause mortality ranged between 9000 and 10 500 steps/day for high (HR (95% CI)=0.61 (0.51 to 0.73)) and low (0.69 (0.52 to 0.92)) sedentary time. For incident CVD, there was a subtle gradient of association by sedentary time level with the lowest risk observed at approximately 9700 steps/day for high (0.79 (0.72 to 0.86)) and low (0.71 (0.61 to 0.83)) sedentary time. The minimal dose (steps/day associated with 50% of the optimal dose) of daily steps was between 4000 and 4500 steps/day across sedentary time groups for all-cause mortality and incident CVD.

CONCLUSIONS

Any amount of daily steps above the referent 2200 steps/day was associated with lower mortality and incident CVD risk, for low and high sedentary time. Accruing 9000-10 500 steps/day was associated with the lowest mortality risk independent of sedentary time. For a roughly equivalent number of steps/day, the risk of incident CVD was lower for low sedentary time compared with high sedentary time.

Selection Bias in Health Research: Quantifying, Eliminating, or Exacerbating Health Disparities?

Purpose of review

To summarize recent literature on selection bias in disparities research addressing either descriptive or causal questions, with examples from dementia research.

Recent findings

Defining a clear estimand, including the target population, is essential to assess whether generalizability bias or collider-stratification bias are threats to inferences. Selection bias in disparities research can result from sampling strategies, differential inclusion pipelines, loss to follow-up, and competing events. If competing events occur, several potentially relevant estimands can be estimated under different assumptions, with different interpretations. The apparent magnitude of a disparity can differ substantially based on the chosen estimand. Both randomized and observational studies may misrepresent health disparities or heterogeneity in treatment effects if they are not based on a known sampling scheme.

Conclusion

Researchers have recently made substantial progress in conceptualization and methods related to selection bias. This progress will improve the relevance of both descriptive and causal health disparities research.

An Overview of Regression Models for Adverse Events Analysis

Over the last few years, several review articles described the adverse events analysis as sub-optimal in clinical trials. Indeed, the context surrounding adverse events analyses often imply an overwhelming number of events, a lack of power to find associations, but also a lack of specific training regarding those complex data. In randomized controlled trials or in observational studies, comparing the occurrence of adverse events according to a covariable of interest (e.g., treatment) is a recurrent question in the analysis of drug safety data, and adjusting other important factors is often relevant. This article is an overview of the existing regression models that may be considered to compare adverse events and to discuss model choice regarding the characteristics of the adverse events of interest. Many dimensions may be relevant to compare the adverse events between patients, (e.g., timing, recurrence, and severity). Recent efforts have been made to cover all of them. For chronic treatments, the occurrence of intercurrent events during the patient follow-up usually needs the modeling approach to be adapted (at least with regard to their interpretation). Moreover, analysis based on regression models should not be limited to the estimation of relative effects. Indeed, absolute risks stemming from the model should be presented systematically to help the interpretation, to validate the model, and to encourage comparison of studies.

Causal relationships between pain, medical treatments, and knee osteoarthritis: A graphical causal model to guide analyses

OBJECTIVE

Randomized controlled trials (RCTs) are a gold standard for estimating the benefits of clinical interventions, but their decision-making utility can be limited by relatively short follow-up time. Longer-term follow-up of RCT participants is essential to support treatment decisions. However, as time from randomization accrues, loss to follow-up and competing events can introduce biases and require covariate adjustment even for intention-to-treat effects. We describe a process for synthesizing expert knowledge and apply this to long-term follow-up of an RCT of treatments for meniscal tears in patients with knee osteoarthritis (OA).

METHODS

We identified 2 post-randomization events likely to impact accurate assessment of pain outcomes beyond 5 years in trial participants: loss to follow-up and total knee replacement (TKR). We conducted literature searches for covariates related to pain and TKR in individuals with knee OA and combined these with expert input. We synthesized the evidence into graphical models.

RESULTS

We identified 94 potential covariates potentially related to pain and/or TKR among individuals with knee OA. Of these, 46 were identified in the literature review and 48 by expert panelists. We determined that adjustment for 50 covariates may be required to estimate the long-term effects of knee OA treatments on pain.

CONCLUSION

We present a process for combining literature reviews with expert input to synthesize existing knowledge and improve covariate selection. We apply this process to the long-term follow-up of a randomized trial and show that expert input provides additional information not obtainable from literature reviews alone.

Dealing with time-dependent exposures and confounding when defining and estimating attributable fractions-Revisiting estimands and estimators

The population-attributable fraction (PAF) is commonly interpreted as the proportion of events that can be ascribed to a certain exposure in a certain population. Its estimation is sensitive to common forms of time-dependent bias in the face of a time-dependent exposure. Predominant estimation approaches based on multistate modeling fail to fully eliminate such bias and, as a result, do not permit a causal interpretation, even in the absence of confounding. While recently proposed multistate modeling approaches can successfully eliminate residual time-dependent bias, and moreover succeed to adjust for time-dependent confounding by means of inverse probability of censoring weighting, inadequate application, and misinterpretation prevails in the medical literature. In this paper, we therefore revisit recent work on previously proposed PAF estimands and estimators in settings with time-dependent exposures and competing events and extend this work in several ways. First, we critically revisit the interpretation and applied terminology of these estimands. Second, we further formalize the assumptions under which a causally interpretable PAF estimand can be identified and provide analogous weighting-based representations of the identifying functionals of other proposed estimands. This representation aims to enhance the applied statistician's understanding of different sources of bias that may arise when the aim is to obtain a valid estimate of a causally interpretable PAF. To illustrate and compare these representations, we present a real-life application to observational data from the Ghent University Hospital ICUs to estimate the fraction of ICU deaths attributable to hospital-acquired infections.

Effectiveness and safety of direct oral anticoagulation vs. warfarin in frail patients with atrial fibrillation

AIMS

Although frail patients with atrial fibrillation (AF) carry a high risk of stroke and treatment-related bleeding complications, evidence for the safety and effectiveness of anticoagulation remains sparse. This study investigated the effectiveness and safety of direct oral anticoagulant (DOAC) vs. warfarin in frail AF patients.

METHODS AND RESULTS

Nationwide registry-based cohort study including 32 048 anticoagulation naïve frail patients (median age 80 years, 53% female) with incident AF during 2012-20. Frailty was assessed using the hospital frailty risk score. To address baseline confounding, we applied inverse probability of treatment weighting (IPTW) and marginal structural models with weighted pooled regression to compute weighted hazard ratios (wHRs) and risk differences for thromboembolism and major bleeding comparing specific DOAC doses with warfarin. After AF diagnosis, 6747 (21.1%) initiated warfarin, 17 076 (50.3%) initiated standard-dose DOAC, and 9179 (28.6%) initiated reduced-dose DOAC. Comparative effectiveness analyses in the IPTW pseudo-populations revealed similar thromboembolism risk between standard-dose DOAC and warfarin [wHR 0.95, 95% confidence interval (CI) 0.80-1.13] and between reduced-dose DOAC and warfarin (wHR 0.97, 95% CI 0.77-1.23). The 1-year thromboembolic event-free survival difference was -0.2% for DOAC, regardless of dosing, vs. warfarin. Major bleeding risk was significantly lower with standard-dose DOAC (wHR 0.69, 95% CI 0.59-0.87) and reduced-dose DOAC (wHR 0.67, 95% CI 0.55-0.81) vs. warfarin. The 1-year bleeding risk difference with DOAC ranged from -1.3% to -3.0%.

CONCLUSION

Our findings indicate comparable thromboembolism risk and significantly lower bleeding risk with both standard and reduced DOAC regimens compared with warfarin in frail AF patients in routine care.

Human papillomavirus infection and cardiovascular mortality: a cohort study

BACKGROUND AND AIMS

High-risk human papillomavirus (HR-HPV) infection-a well-established risk factor for cervical cancer-has associations with cardiovascular disease (CVD). However, its relationship with CVD mortality remains uncertain. This study examined the associations between HR-HPV infection and CVD mortality.

METHODS

As part of a health examination, 163 250 CVD-free Korean women (mean age: 40.2 years) underwent HR-HPV screening and were tracked for up to 17 years (median: 8.6 years). National death records identified the CVD mortality cases. Hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD mortality were estimated using Cox proportional hazard regression analyses.

RESULTS

During 1 380 953 person-years of follow-up, 134 CVD deaths occurred, with a mortality rate of 9.1 per 105 person-years for HR-HPV(-) women and 14.9 per 105 person-years for HR-HPV(+) women. After adjustment for traditional CVD risk factors and confounders, the HRs (95% CI) for atherosclerotic CVD (ASCVD), ischaemic heart disease (IHD), and stroke mortality in women with HR-HPV infection compared with those without infection were 3.91 (1.85-8.26), 3.74 (1.53-9.14), and 5.86 (0.86-40.11), respectively. The association between HR-HPV infection and ASCVD mortality was stronger in women with obesity than in those without (P for interaction = .006), with corresponding HRs (95% CI) of 4.81 (1.55-14.93) for obese women and 2.86 (1.04-7.88) for non-obese women.

CONCLUSIONS

In this cohort study of young and middle-aged Korean women, at low risks for CVD mortality, those with HR-HPV infection had higher death rates from CVD, specifically ASCVD and IHD, with a more pronounced trend in obese individuals.

Post-traumatic stress disorder as a risk factor for major adverse cardiovascular events: a cohort study of a South African medical insurance scheme

AIMS

Prior research, largely focused on US male veterans, indicates an increased risk of cardiovascular disease among individuals with post-traumatic stress disorder (PTSD). Data from other settings and populations are scarce. The objective of this study is to examine PTSD as a risk factor for incident major adverse cardiovascular events (MACEs) in South Africa.

METHODS

We analysed reimbursement claims (2011-2020) of a cohort of South African medical insurance scheme beneficiaries aged 18 years or older. We calculated adjusted hazard ratios (aHRs) for associations between PTSD and MACEs using Cox proportional hazard models and calculated the effect of PTSD on MACEs using longitudinal targeted maximum likelihood estimation.

RESULTS

We followed 1,009,113 beneficiaries over a median of 3.0 years (IQR 1.1-6.0). During follow-up, 12,662 (1.3%) persons were diagnosed with PTSD and 39,255 (3.9%) had a MACE. After adjustment for sex, HIV status, age, population group, substance use disorders, psychotic disorders, major depressive disorder, sleep disorders and the use of antipsychotic medication, PTSD was associated with a 16% increase in the risk of MACEs (aHR 1.16, 95% confidence interval (CI) 1.05-1.28). The risk ratio for the effect of PTSD on MACEs decreased from 1.59 (95% CI 1.49-1.68) after 1 year of follow-up to 1.14 (95% CI 1.11-1.16) after 8 years of follow-up.

CONCLUSION

Our study provides empirical support for an increased risk of MACEs in males and females with PTSD from a general population sample in South Africa. These findings highlight the importance of monitoring cardiovascular risk among individuals diagnosed with PTSD.

Estimating Post-treatment Recurrence After Multidrug-Resistant Tuberculosis Treatment Among Patients With and Without Human Immunodeficiency Virus: The Impact of Assumptions About Death and Missing Follow-up

BACKGROUND

Quantification of recurrence risk following successful treatment is crucial to evaluating regimens for multidrug- or rifampicin-resistant (MDR/RR) tuberculosis (TB). However, such analyses are complicated when some patients die or become lost during post-treatment follow-up.

METHODS

We analyzed data on 1991 patients who successfully completed a longer MDR/RR-TB regimen containing bedaquiline and/or delamanid between 2015 and 2018 in 16 countries. Using 5 approaches for handling post-treatment deaths, we estimated 6-month post-treatment TB recurrence risk overall and by HIV status. We used inverse-probability weighting to account for patients with missing follow-up and investigated the impact of potential bias from excluding these patients without applying inverse-probability weights.

RESULTS

The estimated TB recurrence risk was 7.4/1000 (95% credible interval: 3.3-12.8) when deaths were handled as non-recurrences and 7.6/1000 (3.3-13.0) when deaths were censored and inverse-probability weights were applied to account for the excluded deaths. The estimated risks of composite recurrence outcomes were 25.5 (15.3-38.1), 11.7 (6.4-18.2), and 8.6 (4.1-14.4) per 1000 for recurrence or (1) any death, (2) death with unknown or TB-related cause, or (3) TB-related death, respectively. Corresponding relative risks for HIV status varied in direction and magnitude. Exclusion of patients with missing follow-up without inverse-probability weighting had a small impact on estimates.

CONCLUSIONS

The estimated 6-month TB recurrence risk was low, and the association with HIV status was inconclusive due to few recurrence events. Estimation of post-treatment recurrence will be enhanced by explicit assumptions about deaths and appropriate adjustment for missing follow-up data.

Hypothetical 22-Year Intervention With the Dietary Approaches to Stop Hypertension and Risk of Heart Failure in a General Population

We used design principles of target trial methodology to emulate the effect of sustained adherence to the Dietary Approaches to Stop Hypertension (DASH) diet on the 22-year risk of heart failure. Women and men aged 45-83 years without previous heart failure, who answered questionnaires in 1997 from the Swedish Mammography Cohort and the Cohort of Swedish Men, were eligible. Follow-up questionnaires were sent in 2008-2009. Incidence of heart failure was ascertained using the Swedish Patient Register, updated until December 31, 2019. The parametric g-formula was used to estimate the 22-year risk of heart failure under sustained adherence to a population-adapted DASH diet compared with no intervention. Intakes before 1997 for before-baseline adjustment was available only for women. In total, 31,238 women and 34,939 men were eligible. The 22-year risk of heart failure was 14.5% with long-term adherence to the DASH diet compared with 15.2% with no intervention (risk difference = -0.7%, 95% confidence interval: 1.6, 0.0%) in women and correspondingly in men 15.3% vs. 16.2% (risk difference = -0.9%, 95% confidence interval: -1.6, -0.2%). Our hypothetical intervention suggests that sustained adherence to the population-adapted DASH diet may reduce risk of heart failure in middle-aged and elderly Swedish women and men.

Causal Effects of Stochastic PrEP Interventions on HIV Incidence Among Men Who Have Sex With Men

Antiretroviral preexposure prophylaxis (PrEP) is highly effective in preventing human immunodeficiency virus (HIV) infection, but uptake has been limited and inequitable. Although interventions to increase PrEP uptake are being evaluated in clinical trials among men who have sex with men (MSM), those trials cannot evaluate effects on HIV incidence. Estimates from observational studies of the causal effects of PrEP-uptake interventions on HIV incidence can inform decisions about intervention scale-up. We used longitudinal electronic health record data from HIV-negative MSM accessing care at Fenway Health, a community health center in Boston, Massachusetts, from January 2012 through February 2018, with 2 years of follow-up. We considered stochastic interventions that increased the chance of initiating PrEP in several high-priority subgroups. We estimated the effects of these interventions on population-level HIV incidence using a novel inverse-probability weighted estimator of the generalized g-formula, adjusting for baseline and time-varying confounders. Our results suggest that even modest increases in PrEP initiation in high-priority subgroups of MSM could meaningfully reduce HIV incidence in the overall population of MSM. Interventions tailored to Black and Latino MSM should be prioritized to maximize equity and impact.

Prognostic value of aspartate aminotransferase/alanine aminotransferase ratio in hepatocellular carcinoma after hepatectomy

BACKGROUND

The prognostic significance of the aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio in hepatocellular carcinoma remains uncertain. The aim of the current study was to evaluate the association between the AST/ALT ratio and prognosis in patients with hepatocellular carcinoma after hepatectomy, and to explore the role of underlying liver diseases as mediators.

METHODS

This retrospective study included patients with hepatocellular carcinoma who underwent hepatectomy between January 2014 and January 2018 at two Chinese hospitals. The maximally selected rank statistic and g-computation approach were used to quantify and visualize the association between the AST/ALT ratio and overall survival or recurrence-free survival. The role of mediators (chronic hepatitis B, hepatic steatosis and liver cirrhosis) was analysed.

RESULTS

Among the 1519 patients (mean(s.d.) age at baseline, 50.5(11.3) years), 1309 (86.2%) were male. During a median follow-up of 46.0 months, 514 (33.8%) patients died and 358 (23.6%) patients experienced recurrence. The optimal cut-off value for the AST/ALT ratio was 1.4, and the AST/ALT ratio greater than or equal to 1.4 was independently associated with a 39.0% increased risk of death and a 30.0% increased risk of recurrence (overall survival: hazard ratio (HR), 1.39; 95% c.i. 1.15 to 1.68; recurrence-free survival: HR, 1.30; 95% c.i. 1.12 to 1.52) after adjusting for confounders. Chronic hepatitis B significantly mediated the association of the ratio of AST/ALT with both overall survival and recurrence-free survival (20.3% for overall survival; 20.1% for recurrence-free survival).

CONCLUSION

The AST/ALT ratio greater than or equal to 1.4 was associated with shorter overall survival and recurrence-free survival in patients with hepatocellular carcinoma after hepatectomy, and chronic hepatitis B may play a role in their association.

Causal inference with recurrent and competing events

Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports injuries in athletes. Competing events, such as death, complicate causal inference in studies of recurrent events because once a competing event occurs, an individual cannot have more recurrent events. Several statistical estimands have been studied in recurrent event settings, with and without competing events. However, the causal interpretations of these estimands, and the conditions that are required to identify these estimands from observed data, have yet to be formalized. Here we use a formal framework for causal inference to formulate several causal estimands in recurrent event settings, with and without competing events. When competing events exist, we clarify when commonly used classical statistical estimands can be interpreted as causal quantities from the causal mediation literature, such as (controlled) direct effects and total effects. Furthermore, we show that recent results on interventionist mediation estimands allow us to define new causal estimands with recurrent and competing events that may be of particular clinical relevance in many subject matter settings. We use causal directed acyclic graphs and single world intervention graphs to illustrate how to reason about identification conditions for the various causal estimands based on subject matter knowledge. Furthermore, using results on counting processes, we show that our causal estimands and their identification conditions, which are articulated in discrete time, converge to classical continuous time counterparts in the limit of fine discretizations of time. We propose estimators and establish their consistency for the various identifying functionals. Finally, we use the proposed estimators to compute the effect of blood pressure lowering treatment on the recurrence of acute kidney injury using data from the Systolic Blood Pressure Intervention Trial.

Causal survival analysis under competing risks using longitudinal modified treatment policies

Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, ordinal, or continuous treatments measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to a competing event that precludes observation of the event of interest. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as [Formula: see text]-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.

Accounting for Competing Events When Evaluating Long-Term Outcomes in Survivors of Critical Illness

The clinical trajectory of survivors of critical illness after hospital discharge can be complex and highly unpredictable. Assessing long-term outcomes after critical illness can be challenging because of possible competing events, such as all-cause death during follow-up (which precludes the occurrence of an event of particular interest). In this perspective, we explore challenges and methodological implications of competing events during the assessment of long-term outcomes in survivors of critical illness. In the absence of competing events, researchers evaluating long-term outcomes commonly use the Kaplan-Meier method and the Cox proportional hazards model to analyze time-to-event (survival) data. However, traditional analytical and modeling techniques can yield biased estimates in the presence of competing events. We present different estimands of interest and the use of different analytical approaches, including changes to the outcome of interest, Fine and Gray regression models, cause-specific Cox proportional hazards models, and generalized methods (such as inverse probability weighting). Finally, we provide code and a simulated dataset to exemplify the application of the different analytical strategies in addition to overall reporting recommendations.

Compliance-Adjusted Estimates of Aspirin Effects Among Older Persons in the ASPREE Randomized Trial

The Aspirin in Reducing Events in the Elderly (ASPREE) Trial recruited 19,114 participants across Australia and the United States during 2010-2014. Participants were randomized to receive either 100 mg of aspirin daily or matching placebo, with disability-free survival as the primary outcome. During a median 4.7 years of follow-up, 37% of participants in the aspirin group permanently ceased taking their study medication and 10% commenced open-label aspirin use. In the placebo group, 35% and 11% ceased using study medication and commenced open-label aspirin use, respectively. In order to estimate compliance-adjusted effects of aspirin, we applied rank-preserving structural failure time models. The results for disability-free survival and most secondary endpoints were similar in intention-to-treat and compliance-adjusted analyses. For major hemorrhage, cancer mortality, and all-cause mortality, compliance-adjusted effects of aspirin indicated greater risks than were seen in intention-to-treat analyses. These findings were robust in a range of sensitivity analyses. In accordance with the original trial analyses, compliance-adjusted results showed an absence of benefit with aspirin for primary prevention in older people, along with an elevated risk of clinically significant bleeding.

Examining the Relationship Between Multilevel Resilience Resources and Cardiovascular Disease Incidence, Overall and by Psychosocial Risks, Among Participants in the Jackson Heart Study, the Multi-Ethnic Study of Atherosclerosis, and the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study

We examined relationships between resilience resources (optimism, social support, and neighborhood social cohesion) and cardiovascular disease (CVD) incidence and assessed potential effect-measure modification by psychosocial risk factors (e.g., stress, depression) among adults without CVD in 3 cohort studies (2000-2018): the Jackson Heart Study, the Multi-Ethnic Study of Atherosclerosis, and the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study. We fitted adjusted Cox models accounting for within-neighborhood clustering while censoring at dropout or non-CVD death. We assessed for effect-measure modification by psychosocial risks. In secondary analyses, we estimated standardized risk ratios using inverse-probability-weighted Aalen-Johansen estimators to account for confounding, dropout, and competing risks (non-CVD deaths) and obtained 95% confidence intervals (CIs) using cluster bootstrapping. For high and medium (versus low) optimism (n = 6,243), adjusted hazard ratios (HRs) for incident CVD were 0.94 (95% CI: 0.78, 1.13) and 0.90 (95% CI: 0.75, 1.07), respectively. Corresponding HRs were 0.88 (95% CI: 0.74, 1.04) and 0.92 (95% CI: 0.79, 1.06) for social support (n = 7,729) and 1.10 (95% CI: 0.94, 1.29) and 0.99 (95% CI: 0.85, 1.16) for social cohesion (n = 7,557), respectively. Some psychosocial risks modified CVD HRs. Secondary analyses yielded similar findings. For optimism and social support, an inverse relationship was frequently most compatible with the data, but a positive relationship was also compatible. For neighborhood social cohesion, positive and null relationships were most compatible. Thus, specific resilience resources may be potential intervention targets, especially among certain subgroups.

Evaluating Model Specification When Using the Parametric G-Formula in the Presence of Censoring

The noniterative conditional expectation (NICE) parametric g-formula can be used to estimate the causal effect of sustained treatment strategies. In addition to identifiability conditions, the validity of the NICE parametric g-formula generally requires the correct specification of models for time-varying outcomes, treatments, and confounders at each follow-up time point. An informal approach for evaluating model specification is to compare the observed distributions of the outcome, treatments, and confounders with their parametric g-formula estimates under the "natural course." In the presence of loss to follow-up, however, the observed and natural-course risks can differ even if the identifiability conditions of the parametric g-formula hold and there is no model misspecification. Here, we describe 2 approaches for evaluating model specification when using the parametric g-formula in the presence of censoring: 1) comparing factual risks estimated by the g-formula with nonparametric Kaplan-Meier estimates and 2) comparing natural-course risks estimated by inverse probability weighting with those estimated by the g-formula. We also describe how to correctly compute natural-course estimates of time-varying covariate means when using a computationally efficient g-formula algorithm. We evaluate the proposed methods via simulation and implement them to estimate the effects of dietary interventions in 2 cohort studies.

Causal inference for oncology: past developments and current challenges

In this paper, we review some important early developments on causal inference in medical statistics and epidemiology that were inspired by questions in oncology. We examine two classical examples from the literature and point to a current area of ongoing methodological development, namely the estimation of optimal adaptive treatment strategies. While causal approaches to analysis have become more routine in oncology research, many exciting challenges and open problems remain, particularly in the context of censored outcomes.

A nonparametric instrumental approach to confounding in competing risks models

This paper discusses nonparametric identification and estimation of the causal effect of a treatment in the presence of confounding, competing risks and random right-censoring. Our identification strategy is based on an instrumental variable. We show that the competing risks model generates a nonparametric quantile instrumental regression problem. Quantile treatment effects on the subdistribution function can be recovered from the regression function. A distinguishing feature of the model is that censoring and competing risks prevent identification at some quantiles. We characterize the set of quantiles for which exact identification is possible and give partial identification results for other quantiles. We outline an estimation procedure and discuss its properties. The finite sample performance of the estimator is evaluated through simulations. We apply the proposed method to the Health Insurance Plan of Greater New York experiment.

Propensity score weighting methods for causal subgroup analysis with time-to-event outcomes

Evaluating causal effects of an intervention in pre-specified subgroups is a standard goal in comparative effectiveness research. Despite recent advancements in causal subgroup analysis, research on time-to-event outcomes has been lacking. This article investigates the propensity score weighting method for causal subgroup survival analysis. We introduce two causal estimands, the subgroup marginal hazard ratio and subgroup restricted average causal effect, and provide corresponding propensity score weighting estimators. We analytically established that the bias of subgroup-restricted average causal effect is determined by subgroup covariate balance. Using extensive simulations, we compare the performance of various combinations of propensity score models (logistic regression, random forests, least absolute shrinkage and selection operator, and generalized boosted models) and weighting schemes (inverse probability weighting, and overlap weighting) for estimating the causal estimands. We find that the logistic model with subgroup-covariate interactions selected by least absolute shrinkage and selection operator consistently outperforms other propensity score models. Also, overlap weighting generally outperforms inverse probability weighting in terms of balance, bias and variance, and the advantage is particularly pronounced in small subgroups and/or in the presence of poor overlap. We applied the methods to the observational Comparing Options for Management: PAtient-centered REsults for Uterine Fibroids study to evaluate the causal effects of myomectomy versus hysterectomy on the time to disease recurrence in a number of pre-specified subgroups of patients with uterine fibroids.

Children's cognitive performance and suicide risk through middle adulthood

BACKGROUND

Longitudinal studies show that lower cognitive performance in adolescence and early adulthood is associated with higher risk of suicide death throughout adulthood. However, it is unclear whether this cognitive vulnerability originates earlier in childhood since studies conducted in children are scarce and have inconsistent results.

METHODS

Vital status of 49,853 individuals born between 1959 and 1966 to participants in the Collaborative Perinatal Project cohort was determined by a probabilistic linkage to the National Death Index, covering all US deaths occurring from 1979 through 2016. Cox proportional hazard models were used to examine associations of general, verbal, and non-verbal intelligence at ages 4 and 7, and academic skills at age 7 with suicide death coded according to ICD-9/10 criteria, while accounting for sociodemographic and pregnancy factors previously associated with suicide in this sample.

RESULTS

By the end of 2016, 288 cohort members had died by suicide. Cognitive performance at 7 years on tests with verbal components was associated with suicide risk (average vs. high verbal intelligence, HR = 1.97, 95% CI 1.05-3.71; low vs. high spelling skills, HR = 2.02, 95% CI 1.16-3.51; low vs. high reading skills, HR = 2.01, 95% CI 1.27-3.17). Associations were still evident, especially for verbal intelligence and reading skills, but hazard ratios were attenuated after adjusting for prenatal and sociodemographic factors at birth (verbal intelligence, HR = 1.97, 95% CI 1.03-3.78; spelling, HR = 1.61, 95% CI 0.90-2.88; reading, HR = 1.67, 95% CI 1.02-2.72).

CONCLUSIONS

Childhood neurocognitive performance is associated with vulnerability to suicide mortality through middle-adulthood, suggesting that there might be a cognitive diathesis for suicide originating in early childhood. Future studies should examine how multiple domains of childhood cognitive performance contribute to vulnerability to suicide risk, including by increasing risk for social and environmental factors that are associated not only with suicide but also with many types of psychiatric disorders.

Single-world intervention graphs for defining, identifying, and communicating estimands in clinical trials

Confusion often arises when attempting to articulate target estimand(s) of a clinical trial in plain language. We aim to rectify this confusion by using a type of causal graph called the Single-World Intervention Graph (SWIG) to provide a visual representation of the estimand that can be effectively communicated to interdisciplinary stakeholders. These graphs not only display estimands, but also illustrate the assumptions under which a causal estimand is identifiable by presenting the graphical relationships between the treatment, intercurrent events, and clinical outcomes. To demonstrate its usefulness in pharmaceutical research, we present examples of SWIGs for various intercurrent event strategies specified in the ICH E9(R1) addendum, as well as an example from a real-world clinical trial for chronic pain. code to generate all the SWIGs shown is this paper is made available. We advocate clinical trialists adopt the use of SWIGs in their estimand discussions during the planning stages of their studies.

Plasma concentrations of per- and polyfluoroalkyl substances in pregnancy and breastfeeding duration in Project Viva

BACKGROUND

Per- and polyfluoroalkyl substances (PFAS) may disrupt mammary gland development and function; thereby inhibiting milk supply and breastfeeding duration. However, conclusions on the potential effects of PFAS and breastfeeding duration are limited by prior epidemiologic studies that inconsistently adjusted for past cumulative breastfeeding duration and by a lack of examination of the joint effects of PFAS mixtures.

METHODS

In Project Viva, a longitudinal cohort that enrolled pregnant participants from 1999 to 2002 in the greater Boston, MA area, we studied 1079 women who ever attempted to lactate. We investigated associations of plasma concentrations of select PFAS in early pregnancy (mean: 10.1 weeks gestation) with breastfeeding termination by 9 months, after which women typically cite self-weaning as the reason for terminating breastfeeding. We used Cox regression for single-PFAS models and quantile g-computation for mixture models, adjusting for sociodemographics, prior breastfeeding duration, and weeks of gestation at the time of blood draw.

RESULTS

We detected 6 PFAS [perfluorooctane sulfonate; perfluorooctanoate (PFOA); perfluorohexane sulfonate; perfluorononanoate; 2-(N-ethyl-perfluorooctane sulfonamido) acetate (EtFOSAA); 2-(N-methyl-perfluorooctane sulfonamide) acetate (MeFOSAA)] in >98 % of samples. Sixty percent of lactating women terminated breastfeeding by 9 months postpartum. Women with higher plasma concentrations of PFOA, EtFOSAA, and MeFOSAA had a greater hazard of terminating breastfeeding in the first 9 months postpartum [HR (95 % CI) per doubling concentration: 1.20 (1.04, 1.38) for PFOA; 1.10 (1.01, 1.20) for EtFOSAA; 1.18 (1.08, 1.30) for MeFOSAA]. In the quantile g-computation model, simultaneously increasing all PFAS in the mixture by one quartile was associated with 1.17 (95 % CI: 1.05, 1.31) greater hazard of terminating breastfeeding in the first 9 months.

CONCLUSION

Our findings suggest that exposure to PFAS may be associated with reduced breastfeeding duration and draw further attention to environmental chemicals that may dysregulate human lactation.

Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data

BACKGROUND

Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks.

METHODS

For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented.

RESULTS

Our study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results.

CONCLUSIONS

Extending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.

Understanding the Accumulation of Health-Related Inequities Over the Life Course Using the Mean Cumulative Count

Understanding how health inequities develop over time is necessary to inform interventions, but methods for doing so are underutilized. We provide an example of the accumulation of stressful life events using the mean cumulative count (MCC), which estimates the expected number of events per person as a function of time, allowing for censoring and competing events. Data came from the National Longitudinal Survey of Youth 1997, a nationally representative data set. To compare the MCC with standard practice, we present the proportions of persons experiencing 1, 2, and ≥3 stressful events and the cumulative probability of experiencing at least 1 event by the end of follow-up. Our sample included 6,522 individuals aged 18-33 years who were followed for a median of 14 years. Using the MCC, by age 20 years the expected number of encounters was 56 events per 100 participants for Black non-Hispanic persons, 47 per 100 for White non-Hispanic persons, and 50 per 100 for Hispanic persons. By age 33 years, inequities grew to 117, 99, and 108 events per 100 persons, respectively. The MCC revealed that inequities in stressful events accumulate over the course of early adulthood, partially driven by repeat events; this information was not evident from conventional approaches. This method can be used to identify intervention points for disrupting the accumulation of repeat events to improve health equity.

Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome

Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle-Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package, which includes all methods discussed in this paper.