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.

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.