Prior event rate ratio adjustment for hidden confounding in observational studies of treatment effectiveness: a pairwise Cox likelihood approach.

Observational studies provide a rich source of information for assessing effectiveness of treatment interventions in many situations where it is not ethical or practical to perform randomized controlled trials. However, such studies are prone to bias from hidden (unmeasured) confounding. A promising approach to identifying and reducing the impact of unmeasured confounding is prior event rate ratio (PERR) adjustment, a quasi-experimental analytic method proposed in the context of electronic medical record database studies. In this paper, we present a statistical framework for using a pairwise approach to PERR adjustment that removes bias inherent in the original PERR method. A flexible pairwise Cox likelihood function is derived and used to demonstrate the consistency of the simple and convenient alternative PERR (PERR-ALT) estimator. We show how to estimate standard errors and confidence intervals for treatment effect estimates based on the observed information and provide R code to illustrate how to implement the method. Assumptions required for the pairwise approach (as well as PERR) are clarified, and the consequences of model misspecification are explored. Our results confirm the need for researchers to consider carefully the suitability of the method in the context of each problem. Extensions of the pairwise likelihood to more complex designs involving time-varying covariates or more than two periods are considered. We illustrate the application of the method using data from a longitudinal cohort study of enzyme replacement therapy for lysosomal storage disorders. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Bias and sensitivity analysis when estimating treatment effects from the cox model with omitted covariates.

Omission of relevant covariates can lead to bias when estimating treatment or exposure effects from survival data in both randomized controlled trials and observational studies. This paper presents a general approach to assessing bias when covariates are omitted from the Cox model. The proposed method is applicable to both randomized and non-randomized studies. We distinguish between the effects of three possible sources of bias: omission of a balanced covariate, data censoring and unmeasured confounding. Asymptotic formulae for determining the bias are derived from the large sample properties of the maximum likelihood estimator. A simulation study is used to demonstrate the validity of the bias formulae and to characterize the influence of the different sources of bias. It is shown that the bias converges to fixed limits as the effect of the omitted covariate increases, irrespective of the degree of confounding. The bias formulae are used as the basis for developing a new method of sensitivity analysis to assess the impact of omitted covariates on estimates of treatment or exposure effects. In simulation studies, the proposed method gave unbiased treatment estimates and confidence intervals with good coverage when the true sensitivity parameters were known. We describe application of the method to a randomized controlled trial and a non-randomized study.

Ultra-low Ultraviolet Radiation in Office Lighting Can Moderate Seasonal Vitamin D Cycle: A Pilot Study.

BACKGROUND/AIM: Ultraviolet-B (UV-B) radiation initiates vitamin D synthesis in the skin, making sun exposure a major source of vitamin D. We aimed to determine whether office lighting containing ultra-low levels of UV-B radiation could modify the winter decline in vitamin D status in the UK, while being safe and well tolerated. PATIENTS AND METHODS: Twenty commercial office desk lamps were modified with the addition of UV-B LEDs. Ten hospital office administrative staff received UV-modified lamps with UV-on, and 10 staff received identical placebo lamps with UV switched off, in a double-blind, cross-over pilot study during the winter of 2021/22. Circulating 25-hydroxyvitamin D [25(OH)D] was measured every 4 weeks for 20 weeks: at baseline and during an 8-week trial period, 4-week washout, and a cross-over 8-week trial period. RESULTS: The linear regression combining the complete datasets for phase 1 and 2 of the trial showed that an 8-week UV light intervention significantly increased 25OHD by 7.13 nmol/l with a p-Value=0.02, compared to the placebo group. Similar results were confirmed by cross-over analyses using the datasets of those completing both phases of the trial both with and without using the inverse probability weighing method to handle dropouts. CONCLUSION: The UV-B-modified lighting was well-tolerated and safe with weekly doses of UV-B of 0.5 - 0.9 Standard Erythema Dose [SED=100 Jm-2 erythema weighted UV radiation] measured at chest level. This ultra-low dosing was effective in reducing the winter decline in vitamin D status.

Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review.

OBJECTIVES: Motivated by recent calls to use electronic health records for research, we reviewed the application and development of methods for addressing the bias from unmeasured confounding in longitudinal data. STUDY DESIGN AND SETTING: Methodological review of existing literature. We searched MEDLINE and EMBASE for articles addressing the threat to causal inference from unmeasured confounding in nonrandomized longitudinal health data through quasi-experimental analysis. RESULTS: Among the 121 studies included for review, 84 used instrumental variable analysis (IVA), of which 36 used lagged or historical instruments. Difference-in-differences (DiD) and fixed effects (FE) models were found in 29 studies. Five of these combined IVA with DiD or FE to try to mitigate for time-dependent confounding. Other less frequently used methods included prior event rate ratio adjustment, regression discontinuity nested within pre-post studies, propensity score calibration, perturbation analysis, and negative control outcomes. CONCLUSION: Well-established econometric methods such as DiD and IVA are commonly used to address unmeasured confounding in nonrandomized longitudinal studies, but researchers often fail to take full advantage of available longitudinal information. A range of promising new methods have been developed, but further studies are needed to understand their relative performance in different contexts before they can be recommended for widespread use.