Selection of confounding variables should not be based on observed associations with exposure

In observational studies, selection of confounding variables for adjustment is often based on observed baseline incomparability. The aim of this study was to evaluate this selection strategy. We used clinical data on the effects of inhaled long-acting beta-agonist (LABA) use on the risk of mortality among patients with obstructive pulmonary disease to illustrate the impact of selection of confounding variables for adjustment based on baseline comparisons. Among 2,394 asthma and COPD patients included in the analyses, the LABA ever-users were considerably older than never-users, but cardiovascular co-morbidity was equally prevalent (19.9% vs. 19.9%). Adjustment for cardiovascular co-morbidity status did not affect the crude risk ratio (RR) for mortality: crude RR 1.19 (95% CI 0.93–1.51) versus RR 1.19 (95% CI 0.94–1.50) after adjustment for cardiovascular co-morbidity. However, after adjustment for age (RR 0.95, 95% CI 0.76–1.19), additional adjustment for cardiovascular co-morbidity status did affect the association between LABA use and mortality (RR 1.01, 95% CI 0.80–1.26). Confounding variables should not be discarded based on balanced distributions among exposure groups, because residual confounding due to the omission of confounding variables from the adjustment model can be relevant

Variable selection: current practice in epidemiological studies

Selection of covariates is among the most controversial and difficult tasks in epidemiologic analysis. Correct variable selection addresses the problem of confounding in etiologic research and allows unbiased estimation of probabilities in prognostic studies. The aim of this commentary is to assess how often different variable selection techniques were applied in contemporary epidemiologic analysis. It was of particular interest to see whether modern methods such as shrinkage or penalized regression were used in recent publications. Stepwise selection methods remained the predominant method for variable selection in publications in epidemiological journals in 2008. Shrinkage methods were not used in any of the reviewed articles. Editors, reviewers and authors have insufficiently promoted the new, less controversial approaches of variable selection in the biomedical literature, whereas statisticians may not have adequately addressed the method’s feasibility

Measures and models for causal inference in cross-sectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression

<p>Abstract</p> <p>Background</p> <p>Several papers have discussed which effect measures are appropriate to capture the contrast between exposure groups in cross-sectional studies, and which related multivariate models are suitable. Although some have favored the Prevalence Ratio over the Prevalence Odds Ratio -- thus suggesting the use of log-binomial or robust Poisson instead of the logistic regression models -- this debate is still far from settled and requires close scrutiny.</p> <p>Discussion</p> <p>In order to evaluate how accurately true causal parameters such as Incidence Density Ratio (IDR) or the Cumulative Incidence Ratio (CIR) are effectively estimated, this paper presents a series of scenarios in which a researcher happens to find a preset ratio of prevalences in a given cross-sectional study. Results show that, provided essential and non-waivable conditions for causal inference are met, the CIR is most often inestimable whether through the Prevalence Ratio or the Prevalence Odds Ratio, and that the latter is the measure that consistently yields an appropriate measure of the Incidence Density Ratio.</p> <p>Summary</p> <p>Multivariate regression models should be avoided when assumptions for causal inference from cross-sectional data do not hold. Nevertheless, if these assumptions are met, it is the logistic regression model that is best suited for this task as it provides a suitable estimate of the Incidence Density Ratio.</p

Elevated antiphospholipid antibody titers and adverse pregnancy outcomes: analysis of a population-based hospital dataset

<p>Abstract</p> <p>Background</p> <p>The primary objective of this study was to determine if elevated antiphospholipid antibody titers were correlated with the presence of preeclampsia/eclampsia, systemic lupus erythematosus (SLE), placental insufficiency, and a prolonged length of stay (PLOS), in women who delivered throughout Florida, USA.</p> <p>Methods</p> <p>Cross-sectional analyses were conducted using a statewide hospital database. Prevalence odds ratios (OR) were calculated to quantify the association between elevated antiphospholipid antibody titers and four outcomes in 141,286 women who delivered in Florida in 2001. The possibility that the relationship between elevated antiphospholipid antibody titers and the outcomes of preeclampsia/eclampsia, placental insufficiency, and PLOS, may have been modified by the presence of SLE was evaluated in a multiple logistic regression model by creating a composite interaction term.</p> <p>Results</p> <p>Women with elevated antiphospholipid antibody titers (n = 88) were older, more likely to be of white race and not on Medicaid than women who did not have elevated antiphospholipid antibody titers. Women who had elevated antiphospholipid antibody titers had an increased adjusted odds ratio for preeclampsia and eclampsia, (OR = 2.93 p = 0.0015), SLE (OR = 61.24 p < 0.0001), placental insufficiency (OR = 4.58 p = 0.0003), and PLOS (OR = 3.93 p < 0.0001). Patients who had both an elevated antiphospholipid antibody titer and SLE were significantly more likely than the comparison group (women without an elevated titer who did not have SLE) to have the outcomes of preeclampsia, placental insufficiency and PLOS.</p> <p>Conclusion</p> <p>This exploratory epidemiologic investigation found moderate to very strong associations between elevated antiphospholipid antibody titers and four important outcomes in a large sample of women.</p

Assessing the impact of prescribed medicines on health outcomes

This paper reviews methods that can be used to assess the impact of medicine use on population health outcomes. In the absence of a gold standard, we argue that a convergence of evidence from different types of studies using multiple methods of independent imperfection provides the best bases for attributing improvements in health outcomes to the use of medicines. The major requirements are: good evidence that a safe and effective medicine is being appropriately prescribed; covariation between medicine use and improved health outcomes; and being able to discount alternative explanations of the covariation (via covariate adjustment, propensity analyses and sensitivity analyses), so that medicine use is the most plausible explanation of the improved health outcomes. The strongest possible evidence would be provided by the coherence of the following types of evidence: (1) individual linked data showing that patients are prescribed the medicine, there are reasonable levels of patient compliance, and there is a relationship between medicine use and health improvements that is not explained by other factors; (2) ecological evidence of improvements in these health outcomes in the population in which the medicine is used. Confidence in these inferences would be increased by: the replication of these results in comparable countries and consistent trends in population vital statistics in countries that have introduced the medicine; and epidemiological modelling indicating that changes observed in population health outcomes are plausible given the epidemiology of the condition being treated

Tamoxifen's protection against breast cancer recurrence is not reduced by concurrent use of the SSRI citalopram

Tamoxifen remains an important adjuvant therapy to reduce the rate of breast cancer recurrence among patients with oestrogen-receptor-positive tumours. Cytochrome P-450 2D6 metabolises tamoxifen to metabolites that more readily bind the oestrogen receptor. This enzyme also metabolises selective serotonin reuptake inhibitors (SSRI), so these widely used drugs – when taken concurrently – may reduce tamoxifen's prevention of breast cancer recurrence. We studied citalopram use in 184 cases of breast cancer recurrence and 184 matched controls without recurrence after equivalent follow-up. Cases and controls were nested in a population of female residents of Northern Denmark with stages I–III oestrogen-receptor-positive breast cancer 1985–2001 and who took tamoxifen for 1, 2, or most often for 5 years. We ascertained prescription histories by linking participants' central personal registry numbers to prescription databases from the National Health Service. Seventeen cases (9%) and 21 controls (11%) received at least one prescription for the SSRI citalopram while taking tamoxifen (adjusted conditional odds ratio=0.85, 95% confidence interval=0.42, 1.7). We also observed no reduction of tamoxifen effectiveness among regular citalopram users (⩾30% overlap with tamoxifen use). These results suggest that concurrent use of citalopram does not reduce tamoxifen's prevention of breast cancer recurrence

Simpson's paradox visualized: The example of the Rosiglitazone meta-analysis

<p>Abstract</p> <p>Background</p> <p>Simpson's paradox is sometimes referred to in the areas of epidemiology and clinical research. It can also be found in meta-analysis of randomized clinical trials. However, though readers are able to recalculate examples from hypothetical as well as real data, they may have problems to easily figure where it emerges from.</p> <p>Method</p> <p>First, two kinds of plots are proposed to illustrate the phenomenon graphically, a scatter plot and a line graph. Subsequently, these can be overlaid, resulting in a overlay plot. The plots are applied to the recent large meta-analysis of adverse effects of rosiglitazone on myocardial infarction and to an example from the literature. A large set of meta-analyses is screened for further examples.</p> <p>Results</p> <p>As noted earlier by others, occurrence of Simpson's paradox in the meta-analytic setting, if present, is associated with imbalance of treatment arm size. This is well illustrated by the proposed plots. The rosiglitazone meta-analysis shows an effect reversion if all trials are pooled. In a sample of 157 meta-analyses, nine showed an effect reversion after pooling, though non-significant in all cases.</p> <p>Conclusion</p> <p>The plots give insight on how the imbalance of trial arm size works as a confounder, thus producing Simpson's paradox. Readers can see why meta-analytic methods must be used and what is wrong with simple pooling.</p