Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects.

Sometimes treatment effects are absent in a subgroup of the population. For example, penicillin has no effect on severe symptoms in individuals infected by resistant Staphylococcus aureus , and codeine has no effect on pain in individuals with certain polymorphisms in the CYP2D6 enzyme. Subgroups where a treatment is ineffective are often called negative control populations or placebo groups. They are leveraged to detect bias in different disciplines. Here we present formal criteria that justify the use of negative control populations to rule out unmeasured confounding and mechanistic (direct) causal effects. We further argue that negative control populations, satisfying our formal conditions, are available in many settings, spanning from clinical studies of infectious diseases to epidemiologic studies of public health interventions. Negative control populations can also be used to rule out placebo effects in unblinded randomized experiments. As a case study, we evaluate the effect of mobile stroke unit dispatches on functional outcomes at discharge in individuals with suspected stroke, using data from a large trial. Our analysis supports the hypothesis that mobile stroke units improve functional outcomes in these individuals.

Estimation of separable direct and indirect effects in continuous time.

Many research questions involve time-to-event outcomes that can be prevented from occurring due to competing events. In these settings, we must be careful about the causal interpretation of classical statistical estimands. In particular, estimands on the hazard scale, such as ratios of cause-specific or subdistribution hazards, are fundamentally hard to interpret causally. Estimands on the risk scale, such as contrasts of cumulative incidence functions, do have a clear causal interpretation, but they only capture the total effect of the treatment on the event of interest; that is, effects both through and outside of the competing event. To disentangle causal treatment effects on the event of interest and competing events, the separable direct and indirect effects were recently introduced. Here we provide new results on the estimation of direct and indirect separable effects in continuous time. In particular, we derive the nonparametric influence function in continuous time and use it to construct an estimator that has certain robustness properties. We also propose a simple estimator based on semiparametric models for the two cause-specific hazard functions. We describe the asymptotic properties of these estimators and present results from simulation studies, suggesting that the estimators behave satisfactorily in finite samples. Finally, we reanalyze the prostate cancer trial from Stensrud et al. (2020).

Causal inference in continuous time: an example on prostate cancer therapy.

In marginal structural models (MSMs), time is traditionally treated as a discrete parameter. In survival analysis on the other hand, we study processes that develop in continuous time. Therefore, Røysland (2011. A martingale approach to continuous-time marginal structural models. Bernoulli 17, 895-915) developed the continuous-time MSMs, along with continuous-time weights. The continuous-time weights are conceptually similar to the inverse probability weights that are used in discrete time MSMs. Here, we demonstrate that continuous-time MSMs may be used in practice. First, we briefly describe the causal model assumptions using counting process notation, and we suggest how causal effect estimates can be derived by calculating continuous-time weights. Then, we describe how additive hazard models can be used to find such effect estimates. Finally, we apply this strategy to compare medium to long-term differences between the two prostate cancer treatments radical prostatectomy and radiation therapy, using data from the Norwegian Cancer Registry. In contrast to the results of a naive analysis, we find that the marginal cumulative incidence of treatment failure is similar between the strategies, accounting for the competing risk of other death.

Sufficient Cause Interaction for Time-to-event Outcomes.

Methods to assess sufficient cause interactions are well developed for binary outcomes. We extend these methods to handle time-to-event outcomes, which occur frequently in medicine and epidemiology. Based on theory for marginal structural models in continuous time, we show how to assess sufficient cause interaction nonparametrically, allowing for censoring and competing risks. We apply the method to study interaction between intensive blood pressure therapy and statin treatment on all-cause mortality.

The surprising implications of familial association in disease risk.

BACKGROUND: A wide range of diseases show some degree of clustering in families; family history is therefore an important aspect for clinicians when making risk predictions. Familial aggregation is often quantified in terms of a familial relative risk (FRR), and although at first glance this measure may seem simple and intuitive as an average risk prediction, its implications are not straightforward. METHODS: We use two statistical models for the distribution of disease risk in a population: a dichotomous risk model that gives an intuitive understanding of the implication of a given FRR, and a continuous risk model that facilitates a more detailed computation of the inequalities in disease risk. Published estimates of FRRs are used to produce Lorenz curves and Gini indices that quantifies the inequalities in risk for a range of diseases. RESULTS: We demonstrate that even a moderate familial association in disease risk implies a very large difference in risk between individuals in the population. We give examples of diseases for which this is likely to be true, and we further demonstrate the relationship between the point estimates of FRRs and the distribution of risk in the population. CONCLUSIONS: The variation in risk for several severe diseases may be larger than the variation in income in many countries. The implications of familial risk estimates should be recognized by epidemiologists and clinicians.

Exploring Selection Bias by Causal Frailty Models: The Magnitude Matters.

Counter-intuitive associations appear frequently in epidemiology, and these results are often debated. In particular, several scenarios are characterized by a general risk factor that appears protective in particular subpopulations, for example, individuals suffering from a specific disease. However, the associations are not necessarily representing causal effects. Selection bias due to conditioning on a collider may often be involved, and causal graphs are widely used to highlight such biases. These graphs, however, are qualitative, and they do not provide information on the real life relevance of a spurious association. Quantitative estimates of such associations can be obtained from simple statistical models. In this study, we present several paradoxical associations that occur in epidemiology, and we explore these associations in a causal, frailty framework. By using frailty models, we are able to put numbers on spurious effects that often are neglected in epidemiology. We discuss several counter-intuitive findings that have been reported in real life analyses, and we present calculations that may expand the understanding of these associations. In particular, we derive novel expressions to explain the magnitude of bias in index-event studies.

Radiologic evaluation of lumps in the male breast.

BACKGROUND: Gynecomastia has a typical appearance on mammography, and occurs frequently in men. However, imaging is often performed on men with breast lumps to exclude breast cancer, which only comprises 1% of male breast masses. PURPOSE: To assess whether ultrasound and fine needle aspiration cytology (FNAC) are necessary investigations when mammograms show classical gynecomastia. MATERIAL AND METHODS: We have retrospectively collected data on male patients referred for mammography during the period 2011-2013 (a total of 539 patients). All radiological images were re-read, and descriptions of ultrasound images were reviewed. Clinical information supplied with the original referrals was assessed, along with pathology and cytology reports. RESULTS: Among the 539 male patients who underwent mammography, 483 were also examined with ultrasound, and 335 were further evaluated with FNAC. Mammograms showed gynecomastia in 350 patients, and among these subjects ultrasound was performed in 340 (97%), FNAC in 261 (75%), and core biopsies in four (1%) patients. The diagnosis gynecomastia was unchanged in all patients who underwent FNAC or biopsy. Malignant tumors were found in eight patients, six of which were invasive ductal carcinomas. CONCLUSION: In patients with a classical appearance of gynecomastia on mammography, supplemental ultrasound, FNAC, or biopsy is superfluous and contributes to unnecessary costs.

[Mendelian randomisation - a genetic approach to an epidemiological method]. / Mendelsk randomisering - genetisk tilnærming til epidemiologisk metode.

BACKGROUND Genetic information is becoming more easily available, and rapid progress is being made in developing methods of illuminating issues of interest. Mendelian randomisation makes it possible to study causes of disease using observational data. The name refers to the random distribution of gene variants in meiosis. The methodology makes use of genes that influence a risk factor for a disease, without influencing the disease itself. In this review article I explain the principles behind Mendelian randomisation and present the areas of application for this methodology.MATERIAL AND METHOD Methodology articles describing Mendelian randomisation were reviewed. The articles were found through a search in PubMed with the combination «mendelian randomization¼ OR «mendelian randomisation¼, and a search in McMaster Plus with the combination «mendelian randomization¼. A total of 15 methodology articles were read in full text. Methodology articles were supplemented by clinical studies found in the PubMed search.RESULTS In contrast to traditional observational studies, Mendelian randomisation studies are not affected by two important sources of error: conventional confounding variables and reverse causation. Mendelian randomisation is therefore a promising tool for studying causality. Mendelian randomisation studies have already provided valuable knowledge on the risk factors for a wide range of diseases. It is nevertheless important to be aware of the limitations of the methodology. As a result of the rapid developments in genetics research, Mendelian randomisation will probably be widely used in future years.INTERPRETATION If Mendelian randomisation studies are conducted correctly, they may help to reveal both modifiable and non-modifiable causes of disease.

Re-evaluating the mythical divide between traditional and novel cardiovascular risk factors in rheumatoid arthritis.

Cardiovascular (CV) risk factors for rheumatoid arthritis (RA) are conventionally classified as 'traditional' and 'novel'. We argue that this classification is obsolete and potentially counterproductive. Further, we discuss problems with the common practice of adjusting for traditional CV risk factors in statistical analyses. These analyses do not target well-defined effects of RA on CV risk. Ultimately, we propose a future direction for cardiorheumatology research that prioritises optimising current treatments and identifying novel therapeutic targets over further categorisation of well-known risk factors.