Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data.

The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log-relative hazards, and there has been some work estimating pure risk. Yet there are few examples of these options in the medical literature, and we could not find programs currently online to analyze these various options. We therefore present a unified approach and R software to facilitate such analyses. We used influence functions adapted to the various design and analysis options together with variance calculations that take the two-phase sampling into account. This work clarifies when the widely used "robust" variance estimate of Barlow (Biometrics 50:1064-1072, 1994) is appropriate. The corresponding R software, CaseCohortCoxSurvival, facilitates analysis with and without stratification and/or weight calibration, for subcohort sampling with or without replacement. We also allow for phase-two data to be missing at random for stratified designs. We provide inference not only for log-relative hazards in the Cox model, but also for cumulative baseline hazards and covariate-specific pure risks. We hope these calculations and software will promote wider use of more efficient and principled design and analysis options for case-cohort studies.

The Time-Dependent Association Between Irritable Bowel Syndrome and All-Cause and Cause-Specific Mortality: A Prospective Cohort Study Within the UK Biobank.

INTRODUCTION: Irritable bowel syndrome (IBS) is one of the most common functional gastrointestinal disorders, but few studies have evaluated mortality risks among individuals with IBS. We explored the association between IBS and all-cause and cause-specific mortality in the UK Biobank. METHODS: We included 502,369 participants from the UK Biobank with mortality data through 2022. IBS was defined using baseline self-report and linkage to primary care or hospital admission data. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause and cause-specific mortality using multivariable Cox proportional hazards regression models within partitioned follow-up time categories (0-5, >5-10, and >10 years). RESULTS: A total of 25,697 participants (5.1%) had a history of IBS at baseline. After a median follow-up of 13.7 years, a total of 44,499 deaths occurred. Having an IBS diagnosis was strongly associated with lower risks of all-cause (HR = 0.70, 95% CI = 0.62-0.78) and all-cancer (HR = 0.69, 95% CI = 0.60-0.79) mortality in the first 5 years of follow-up. These associations were attenuated over follow-up, but even after 10 years of follow-up, associations remained inverse (all-cause: HR = 0.89, 95% CI = 0.84-0.96; all-cancer: HR = 0.87, 95% CI = 0.78-0.97) after full adjustment. Individuals with IBS had decreased risk of mortality from breast, prostate, and colorectal cancers in some of the follow-up time categories. DISCUSSION: We found that earlier during follow-up, having diagnosed IBS was associated with lower mortality risk, and the association attenuated over time. Additional studies to understand whether specific factors, such as lifestyle and healthcare access, explain the inverse association between IBS and mortality are needed.

On the use of cross-validation for the calibration of the adaptive lasso.

Cross-validation is the standard method for hyperparameter tuning, or calibration, of machine learning algorithms. The adaptive lasso is a popular class of penalized approaches based on weighted L1 -norm penalties, with weights derived from an initial estimate of the model parameter. Although it violates the paramount principle of cross-validation, according to which no information from the hold-out test set should be used when constructing the model on the training set, a "naive" cross-validation scheme is often implemented for the calibration of the adaptive lasso. The unsuitability of this naive cross-validation scheme in this context has not been well documented in the literature. In this work, we recall why the naive scheme is theoretically unsuitable and how proper cross-validation should be implemented in this particular context. Using both synthetic and real-world examples and considering several versions of the adaptive lasso, we illustrate the flaws of the naive scheme in practice. In particular, we show that it can lead to the selection of adaptive lasso estimates that perform substantially worse than those selected via a proper scheme in terms of both support recovery and prediction error. In other words, our results show that the theoretical unsuitability of the naive scheme translates into suboptimality in practice, and call for abandoning it.

Increasing efficiency and reducing bias when assessing HPV vaccination efficacy by using nontargeted HPV strains.

Studies of vaccine efficacy often record both the incidence of vaccine-targeted virus strains (primary outcome) and the incidence of nontargeted strains (secondary outcome). However, standard estimates of vaccine efficacy on targeted strains ignore the data on nontargeted strains. Assuming nontargeted strains are unaffected by vaccination, we regard the secondary outcome as a negative control outcome and show how using such data can (i) increase the precision of the estimated vaccine efficacy against targeted strains in randomized trials and (ii) reduce confounding bias of that same estimate in observational studies. For objective (i), we augment the primary outcome estimating equation with a function of the secondary outcome that is unbiased for zero. For objective (ii), we jointly estimate the treatment effects on the primary and secondary outcomes. If the bias induced by the unmeasured confounders is similar for both types of outcomes, as is plausible for factors that influence the general risk of infection, then we can use the estimated efficacy against the secondary outcomes to remove the bias from estimated efficacy against the primary outcome. We demonstrate the utility of these approaches in studies of HPV vaccines that only target a few highly carcinogenic strains. In this example, using nontargeted strains increased precision in randomized trials modestly but reduced bias in observational studies substantially.

Causal inference under over-simplified longitudinal causal models.

Many causal models of interest in epidemiology involve longitudinal exposures, confounders and mediators. However, repeated measurements are not always available or used in practice, leading analysts to overlook the time-varying nature of exposures and work under over-simplified causal models. Our objective is to assess whether - and how - causal effects identified under such misspecified causal models relates to true causal effects of interest. We derive sufficient conditions ensuring that the quantities estimated in practice under over-simplified causal models can be expressed as weighted averages of longitudinal causal effects of interest. Unsurprisingly, these sufficient conditions are very restrictive, and our results state that the quantities estimated in practice should be interpreted with caution in general, as they usually do not relate to any longitudinal causal effect of interest. Our simulations further illustrate that the bias between the quantities estimated in practice and the weighted averages of longitudinal causal effects of interest can be substantial. Overall, our results confirm the need for repeated measurements to conduct proper analyses and/or the development of sensitivity analyses when they are not available.