SAS and R code for probabilistic quantitative bias analysis for misclassified binary variables and binary unmeasured confounders

Adjustment for duration of employment in occupational epidemiology

PURPOSE

In occupational epidemiology, the healthy worker survivor effect can manifest as a time-dependent confounder because healthier workers can accrue greater amounts of exposure over longer periods of employment. For example, in occupational studies of radiation exposure that focus on cumulative annualized radiation dose, workers can accrue greater amounts of cumulative radiation exposure over longer periods of employment, while workers with longer periods of employment can transition into jobs with a reduced potential for annualized radiation exposure. The extent to which confounding arising from the healthy worker survivor effect impacts radiation risk estimates is unknown.

METHODS

We assessed the impact of the healthy worker survivor effect on estimates of radiation risk among nuclear workers in a Million Person Study cohort. In simulation studies, we contrasted the ability of marginal structural Cox models with inverse probability weighting and Cox proportional hazards models to account for time-dependent confounding arising from the healthy worker survivor effect.

RESULTS

Marginal structural Cox models and Cox proportional hazards models with flexible functional forms for duration of employment provided reliable results.

CONCLUSIONS

It is crucial to flexibly adjust for duration of employment to account for confounding arising from the healthy worker survivor effect in occupational epidemiology.

Estimating the impact of bias in causal epidemiological studies: the case of health outcomes following assisted reproduction

Researchers interested in causal questions must deal with two sources of error: random error (random deviation from the true mean value of a distribution), and bias (systematic deviance from the true mean value due to extraneous factors). For some causal questions, randomization is not feasible, and observational studies are necessary. Bias poses a substantial threat to the validity of observational research and can have important consequences for health policy developed from the findings. The current piece describes bias and its sources, outlines proposed methods to estimate its impacts in an observational study, and demonstrates how these methods may be used to inform debate on the causal relationship between medically assisted reproduction (MAR) and health outcomes, using cancer as an example. In doing so, we aim to enlighten researchers who work with observational data, especially regarding the health effects of MAR and infertility, on the pitfalls of bias, and how to address them. We hope that, in combination with the provided example, we can convince readers that estimating the impact of bias in causal epidemiologic research is not only important but necessary to inform the development of robust health policy and clinical practice recommendations.

Menopausal hormone therapy and risk of sarcoidosis: a population-based nested case-control study in Sweden

Sarcoidosis incidence peaks in women between 50 and 60 years old, which coincides with menopause, suggesting that certain sex hormones, mainly estrogen, may play a role in disease development. We investigated whether menopausal hormone therapy (MHT) was associated with sarcoidosis risk in women and whether the risk varied by treatment type. We performed a nested case-control study (2007-2020) including incident sarcoidosis cases from the Swedish National Patient Register (n = 2593) and matched (1:10) to general population controls (n = 20,003) on birth year, county, and living in Sweden at the time of sarcoidosis diagnosis. Dispensations of MHT were obtained from the Swedish Prescribed Drug Register before sarcoidosis diagnosis/matching. Adjusted odds ratios (aOR) of sarcoidosis were estimated using conditional logistic regression. Ever MHT use was associated with a 25% higher risk of sarcoidosis compared with never use (aOR 1.25, 95% CI 1.13-1.38). When MHT type and route of administration were considered together, systemic estrogen was associated with the highest risk of sarcoidosis (aOR 1.51, 95% CI 1.23-1.85), followed by local estrogen (aOR 1.25, 95% CI 1.11-1.42), while systemic estrogen-progestogen combined was associated with the lowest risk compared to never users (aOR 1.12, 95% CI 0.96-1.31). The aOR of sarcoidosis did not differ greatly by duration of MHT use. Our findings suggest that a history of MHT use is associated with increased risk of sarcoidosis, with women receiving estrogen administered systemically having the highest risk.

Quantitative Assessment of Systematic Bias: A Guide for Researchers

Observational research provides valuable opportunities to advance oral health science but is limited by vulnerabilities to systematic bias, including unmeasured confounding, errors in variable measurement, or bias in the creation of study populations and/or analytic samples. The potential influence of systematic biases on observed results is often only briefly mentioned among the discussion of limitations of a given study, despite existing methods that support detailed assessments of their potential effects. Quantitative bias analysis is a set of methodological techniques that, when applied to observational data, can provide important context to aid in the interpretation and integration of observational research findings into the broader body of oral health research. Specifically, these methods were developed to provide quantitative estimates of the potential magnitude and direction of the influence of systematic biases on observed results. We aim to encourage and facilitate the broad adoption of quantitative bias analyses into observational oral health research. To this end, we provide an overview of quantitative bias analysis techniques, including a step-by-step implementation guide. We also provide a detailed appendix that guides readers through an applied example using real data obtained from a prospective observational cohort study of preconception periodontitis in relation to time to pregnancy. Quantitative bias analysis methods are available to all investigators. When appropriately applied to observational studies, findings from such studies can have a greater impact in the broader research context.