Methods to assess seasonal effects in epidemiological studies of infectious diseases—exemplified by application to the occurrence of meningococcal disease

AbstractSeasonal variation in occurrence is a common feature of many diseases, especially those of infectious origin. Studies of seasonal variation contribute to healthcare planning and to the understanding of the aetiology of infections. In this article, we provide an overview of statistical methods for the assessment and quantification of seasonality of infectious diseases, as exemplified by their application to meningococcal disease in Denmark in 1995-2011. Additionally, we discuss the conditions under which seasonality should be considered as a covariate in studies of infectious diseases. The methods considered range from the simplest comparison of disease occurrence between the extremes of summer and winter, through modelling of the intensity of seasonal patterns by use of a sine curve, to more advanced generalized linear models. All three classes of method have advantages and disadvantages. The choice among analytical approaches should ideally reflect the research question of interest. Simple methods are compelling, but may overlook important seasonal peaks that would have been identified if more advanced methods had been applied. For most studies, we suggest the use of methods that allow estimation of the magnitude and timing of seasonal peaks and valleys, ideally with a measure of the intensity of seasonality, such as the peak-to-low ratio. Seasonality may be a confounder in studies of infectious disease occurrence when it fulfils the three primary criteria for being a confounder, i.e. when both the disease occurrence and the exposure vary seasonally without seasonality being a step in the causal pathway. In these situations, confounding by seasonality should be controlled as for any confounder

Insights into different results from different causal contrasts in the presence of effect-measure modification

Purpose: Both propensity score (PS) matching and inverse probability of treatment weighting (IPTW) allow causal contrasts, albeit different ones. In the presence of effect-measure modification, different analytic approaches produce different summary estimates. Methods: We present a spreadsheet example that assumes a dichotomous exposure, covariate, and outcome. The covariate can be a confounder or not and a modifier of the relative risk (RR) or not. Based on expected cell counts, we calculate RR estimates using five summary estimators: Mantel-Haenszel (MH), maximum likelihood (ML), the standardized mortality ratio (SMR), PS matching, and a common implementation of IPTW. Results: Without effect-measure modification, all approaches produce identical results. In the presence of effect-measure modification and regardless of the presence of confounding, results from the SMR and PS are identical, but IPTW can produce strikingly different results (e.g., RR = 0.83 vs. RR = 1.50). In such settings, MH and ML do not estimate a population parameter and results for those measures fall between PS and IPTW. Conclusions: Discrepancies between PS and IPTW reflect different weighting of stratum-specific effect estimates. SMR and PS matching assign weights according to the distribution of the effect-measure modifier in the exposed subpopulation, whereas IPTW assigns weights according to the distribution of the entire study population. In pharmacoepidemiology, contraindications to treatment that also modify the effect might be prevalent in the population, but would be rare among the exposed. In such settings, estimating the effect of exposure in the exposed rather than the whole population is preferable

Pharmacoepidemiology and "in silico" drug evaluation: is there common ground?

Shortcomings of randomized trials have long been recognized by medical researchers (1,2). These shortcomings include a reduced range of risk factors for the outcome among patients enrolled in pre-approval trials. The reduced range enhances the validity of the trials, but the patient populations are highly selected compared with the broad range of risk factors represented among patients who are eventually treated with the drug. This restriction may reduce the generalizability of the findings from those studied to those with different baseline risks

Statistical inference in abstracts of major medical and epidemiology journals 1975–2014: a systematic review

Since its introduction in the twentieth century, null hypothesis significance testing (NHST), a hybrid of significance testing (ST) advocated by Fisher and null hypothesis testing (NHT) developed by Neyman and Pearson, has become widely adopted but has also been a source of debate. The principal alternative to such testing is estimation with point estimates and confidence intervals (CI). Our aim was to estimate time trends in NHST, ST, NHT and CI reporting in abstracts of major medical and epidemiological journals. We reviewed 89,533 abstracts in five major medical journals and seven major epidemiological journals, 1975–2014, and estimated time trends in the proportions of abstracts containing statistical inference. In those abstracts, we estimated time trends in the proportions relying on NHST and its major variants, ST and NHT, and in the proportions reporting CIs without explicit use of NHST (CI-only approach). The CI-only approach rose monotonically during the study period in the abstracts of all journals. In Epidemiology abstracts, as a result of the journal’s editorial policy, the CI-only approach has always been the most common approach. In the other 11 journals, the NHST approach started out more common, but by 2014, this disparity had narrowed, disappeared or reversed in 9 of them. The exceptions were JAMA, New England Journal of Medicine, and Lancet abstracts, where the predominance of the NHST approach prevailed over time. In 2014, the CI-only approach is as popular as the NHST approach in the abstracts of 4 of the epidemiology journals: the American Journal of Epidemiology (48%), the Annals of Epidemiology (55%), Epidemiology (79%) and the International Journal of Epidemiology (52%). The reporting of CIs without explicitly interpreting them as statistical tests is becoming more common in abstracts, particularly in epidemiology journals. Although NHST is becoming less popular in abstracts of most epidemiology journals studied and some widely read medical journals, it is still very common in the abstracts of other widely read medical journals, especially in the hybrid form of ST and NHT in which p values are reported numerically along with declarations of the presence or absence of statistical significance

Changing predictors of statin initiation in US women over two decades

Purpose: To describe changing roles of predictors of statin initiation before and after incident coronary heart disease, and before and after publication of National Cholesterol Education Program Adult Treatment Panel-III (ATP-III) guidelines in a cohort of US women. Methods: We identified 34 382 women enrolled into the Women's Health Study from 1993 to 1995 and followed up until 2012. Proportions of previous nonusers initiating statins were described over time. We used multivariable linear regression models to estimate adjusted initiation proportion differences (IPDs) for initiation overall, separately before and after incident coronary heart disease, and separately for ATP-II and ATP-III time periods. Results: Key predictors of initiation overall were self-reported total cholesterol, and previous incident coronary heart disease, cerebrovascular disease, and diabetes. Adjusted IPDs (percentage) for total cholesterol > 240 vs <200 mg/dL were 7.5 (95% confidence interval [CI], 7.0-8.0) and 9.3 (95% CI, 8.7-9.9) during ATP-II and ATP-III time periods, respectively. Adjusted IPDs in women with diabetes were 7.0 (95% CI, 6.3-7.8) and 11.9 (95% CI, 6.7-17.0) for primary and secondary prevention, respectively, and 3.1 (95% CI, 2.1-4.0) and 9.2 (95% CI 8.2-10.2) for before and after ATP-III, respectively. Conclusions: Secular trends reflected evolution toward risk factor-based treatment indications for statin initiation with increased initiation among diabetics and women with normal and borderline cholesterol. The role of serum cholesterol changed over time, though the character was scale (multiplicative vs additive) dependent. In pharmacoepidemiologic studies of statins, strength of confounding by important variables sometimes unmeasured in claims data, such as cholesterol level, may be calendar time dependent

Performance of propensity score calibration - A simulation study

Confounding can be a major source of bias in nonexperimental research. The authors recently introduced propensity score calibration (PSC), which combines propensity scores and regression calibration to address confounding by variables unobserved in the main study by using variables observed in a validation study. Here, the authors assess the performance of PSC using simulations in settings with and without violation of the key assumption of PSC: that the error-prone propensity score estimated in the main study is a surrogate for the gold-standard propensity score (i.e., it contains no additional information on the outcome). The assumption can be assessed if data on the outcome are available in the validation study. If data are simulated allowing for surrogacy to be violated, results depend largely on the extent of violation. If surrogacy holds, PSC leads to bias reduction between 32% and 106% (>100% representing overcorrection). If surrogacy is violated, PSC can lead to an increase in bias. Surrogacy is violated when the direction of confounding of the exposure-disease association caused by the unobserved variable(s) differs from that of the confounding due to observed variables. When surrogacy holds, PSC is a useful approach to adjust for unmeasured confounding using validation data

A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods

Objective: Propensity score (PS) analyses attempt to control for confounding in nonexperimental studies by adjusting for the likelihood that a given patient is exposed. Such analyses have been proposed to address confounding by indication, but there is little empirical evidence that they achieve better control than conventional multivariate outcome modeling. Study Design and Methods: Using PubMed and Science Citation Index, we assessed the use of propensity scores over time and critically evaluated studies published through 2003. Results: Use of propensity scores increased from a total of 8 reports before 1998 to 71 in 2003. Most of the 177 published studies abstracted assessed medications (N = 60) or surgical interventions (N = 51), mainly in cardiology and cardiac surgery (N = 90). Whether PS methods or conventional outcome models were used to control for confounding had little effect on results in those studies in which such comparison was possible. Only 9 of 69 studies (13%) had an effect estimate that differed by more than 20% from that obtained with a conventional outcome model in all PS analyses presented. Conclusions: Publication of results based on propensity score methods has increased dramatically, but there is little evidence that these methods yield substantially different estimates compared with conventional multivariable methods

Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: Nonsteroidal antiinflammatory drugs and short-term mortality in the elderly

Little is known about optimal application and behavior of exposure propensity scores (EPS) in small studies. In a cohort of 103,133 elderly Medicaid beneficiaries in New Jersey, the effect of nonsteroidal antiinflammatory drug use on 1-year all-cause mortality was assessed (1995-1997) based on the assumption that there is no protective effect and that the preponderance of any observed effect would be confounded. To study the comparative behavior of EPS, disease risk scores, and "conventional" disease models, the authors randomly resampled 1,000 subcohorts of 10,000, 1,000, and 500 persons. The number of variables was limited in disease models, but not EPS and disease risk scores. Estimated EPS were used to adjust for confounding by matching, inverse probability of treatment weighting, stratification, and modeling. The crude rate ratio of death was 0.68 for users of nonsteroidal antiinflammatory drugs. "Conventional" adjustment resulted in a rate ratio of 0.80 (95% confidence interval: 0.77, 0.84). The rate ratio closest to 1 (0.85) was achieved by inverse probability of treatment weighting (95% confidence interval: 0.82, 0.88). With decreasing study size, estimates remained further from the null value, which was most pronounced for inverse probability of treatment weighting (n = 500: rate ratio = 0.72, 95% confidence interval: 0.26, 1.68). In this setting, analytic strategies using EPS or disease risk scores were not generally superior to "conventional" models. Various ways to use EPS and disease risk scores behaved differently with smaller study size

Subgroup analyses to determine cardiovascular risk associated with nonsteroidal antiinflammatory drugs and coxibs in specific patient groups

Objective. To explore the extent to which clinical characteristics influence the association between cyclooxygenase 2 inhibitors (coxibs) and/or nonselective nonsteroidal antiinflammatory drugs (NSAIDs) and increased cardiovascular disease (CVD) risk in specific patient subgroups. There is substantial concern regarding the potential cardiovascular adverse effects of selective coxibs and nonselective NSAIDs, but many patients with arthritis experience important clinical benefits from these agents. Methods. The study population consisted of Medicare beneficiaries also eligible for a drug benefits program for older adults during the years 1999-2004. We calculated the relative risk (RR) for CVD events (myocardial infarction [MI], stroke, congestive heart failure, and cardiovascular death) among users of coxibs or nonselective NSAIDs in the prior 6 months compared with nonusers. We assessed biologic interaction between these medication exposures and important patient characteristics. Results. In the primary cohort, we identified 76,082 new users of coxibs, 53,014 new users of nonselective NSAIDs, and 46,558 nonusers. Compared with nonusers, the adjusted RR of CVD events for new users of each agent increased for rofecoxib (RR 1.22, 95% confidence interval [95% CI] 1.14, 1.30) and decreased for naproxen (RR 0.79, 95% CI 0.67, 0.93). Several patient characteristics were found to increase the risk of CVD events among users of some agents in both the primary and secondary cohorts, including age ≥80 years, hypertension, prior MI, prior CVD, rheumatoid arthritis, chronic renal disease, and chronic obstructive pulmonary disease. Rofecoxib and ibuprofen appeared to confer an increased risk in multiple patient subgroups. Conclusion. Many nonselective NSAIDs and coxibs are not associated with an increased risk of CVD events. However, several patient characteristics identify important subgroups that may be at an increased risk when using specific agents