In the last few years, stakeholders in the scientific community have raised alarms about a perceived lack of reproducibility of scientific results. In reaction, guidelines for journals have been ...promulgated and grant applicants have been asked to address the rigor and reproducibility of their proposed projects. Neither solution addresses a primary culprit, which is the culture of null hypothesis significance testing that dominates statistical analysis and inference. In an innovative research enterprise, selection of results for further evaluation based on null hypothesis significance testing is doomed to yield a low proportion of reproducible results and a high proportion of effects that are initially overestimated. In addition, the culture of null hypothesis significance testing discourages quantitative adjustments to account for systematic errors and quantitative incorporation of prior information. These strategies would otherwise improve reproducibility and have not been previously proposed in the widely cited literature on this topic. Without discarding the culture of null hypothesis significance testing and implementing these alternative methods for statistical analysis and inference, all other strategies for improving reproducibility will yield marginal gains at best.
The Charlson comorbidity index is often used to control for confounding in research based on medical databases. There are few studies of the accuracy of the codes obtained from these databases. We ...examined the positive predictive value (PPV) of the ICD-10 diagnostic coding in the Danish National Registry of Patients (NRP) for the 19 Charlson conditions.
Among all hospitalizations in Northern Denmark between 1 January 1998 and 31 December 2007 with a first-listed diagnosis of a Charlson condition in the NRP, we selected 50 hospital contacts for each condition. We reviewed discharge summaries and medical records to verify the NRP diagnoses, and computed the PPV as the proportion of confirmed diagnoses.
A total of 950 records were reviewed. The overall PPV for the 19 Charlson conditions was 98.0% (95% CI; 96.9, 98.8). The PPVs ranged from 82.0% (95% CI; 68.6%, 91.4%) for diabetes with diabetic complications to 100% (one-sided 97.5% CI; 92.9%, 100%) for congestive heart failure, peripheral vascular disease, chronic pulmonary disease, mild and severe liver disease, hemiplegia, renal disease, leukaemia, lymphoma, metastatic tumour, and AIDS.
The PPV of NRP coding of the Charlson conditions was consistently high.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Summary Much preclinical and epidemiological evidence supports the anticancer effects of statins. Epidemiological evidence does not suggest an association between statin use and reduced incidence of ...breast cancer, but does support a protective effect of statins—especially simvastatin—on breast cancer recurrence. Here, we argue that the existing evidence base is sufficient to justify a clinical trial of breast cancer adjuvant therapy with statins and we advocate for such a trial to be initiated without delay. If a protective effect of statins on breast cancer recurrence is supported by trial evidence, then the indications for a safe, well tolerated, and inexpensive treatment can be expanded to improve outcomes for breast cancer survivors. We discuss several trial design opportunities—including candidate predictive biomarkers of statin safety and efficacy—and offer solutions to the key challenges involved in the enrolment, follow-up, and analysis of such a trial.
Background & Aims At least 40% of patients with cirrhosis have comorbidities that increase mortality. We developed a cirrhosis-specific comorbidity scoring system (CirCom) to help determine how these ...comorbidities affect mortality and compared it with the generic Charlson Comorbidity Index. Methods We used data from nationwide health care registries to identify Danish citizens diagnosed with cirrhosis in 1999−2008 (n = 12,976). They were followed through 2010 and characterized by 34 comorbidities. We used Cox regression to assign severity weights to comorbidities with an adjusted mortality hazard ratio (HR) ≥1.20. Each patient's CirCom score was based on, at most, 2 of these comorbidities. Performance was measured with Harrell's C statistic and the Net Reclassification Index (NRI) and results were compared with those obtained using the Charlson Index (based on 17 comorbidities). Findings were validated in 2 separate cohorts of patients with alcohol-related cirrhosis or chronic hepatitis C. Results The CirCom score included chronic obstructive pulmonary disease, acute myocardial infarction, peripheral arterial disease, epilepsy, substance abuse, heart failure, nonmetastatic cancer, metastatic cancer, and chronic kidney disease; 24.2% of patients had 1 or more of these, and mortality correlated with the CirCom score. Patients' CirCom score correlated with their Charlson Comorbidity Index (Kendall's τ = 0.57; P < .0001). Compared with the Charlson Index, the CirCom score increased Harrell's C statistic by 0.6% (95% confidence interval: 0.3%−0.8%). The NRI for the CirCom score was 5.2% (95% confidence interval: 3.7%−6.9%), and the NRI for the Charlson Index was 3.6% (95% confidence interval: 2.3%−5.0%). Similar results were obtained from the validation cohorts. Conclusions We developed a scoring system to predict mortality among patients with cirrhosis based on 9 comorbidities. This system had higher C statistic and NRI values than the Charlson Comorbidity Index, and is easier to use. It could therefore be a preferred method to predict death or survival of patients and for use in epidemiologic studies.
Abstract
Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or ...procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study’s inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.
IMPORTANCE: Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data with machine-learning approaches in psychiatry may ...help advance the understanding of suicide risk. OBJECTIVE: To examine sex-specific risk profiles for death from suicide using machine-learning methods and data from the population of Denmark. DESIGN, SETTING, AND PARTICIPANTS: A case-cohort study nested within 8 national Danish health and social registries was conducted from January 1, 1995, through December 31, 2015. The source population was all persons born or residing in Denmark as of January 1, 1995. Data were analyzed from November 5, 2018, through May 13, 2019. EXPOSURES: Exposures included 1339 variables spanning domains of suicide risk factors. MAIN OUTCOMES AND MEASURES: Death from suicide from the Danish cause of death registry. RESULTS: A total of 14 103 individuals died by suicide between 1995 and 2015 (10 152 men 72.0%; mean SD age, 43.5 18.8 years and 3951 women 28.0%; age, 47.6 18.8 years). The comparison subcohort was a 5% random sample (n = 265 183) of living individuals in Denmark on January 1, 1995 (130 591 men 49.2%; age, 37.4 21.8 years and 134 592 women 50.8%; age, 39.9 23.4 years). With use of classification trees and random forests, sex-specific differences were noted in risk for suicide, with physical health more important to men’s suicide risk than women’s suicide risk. Psychiatric disorders and possibly associated medications were important to suicide risk, with specific results that may increase clarity in the literature. Generally, diagnoses and medications measured 48 months before suicide were more important indicators of suicide risk than when measured 6 months earlier. Individuals in the top 5% of predicted suicide risk appeared to account for 32.0% of all suicide cases in men and 53.4% of all cases in women. CONCLUSIONS AND RELEVANCE: Despite decades of research on suicide risk factors, understanding of suicide remains poor. In this study, the first to date to develop risk profiles for suicide based on data from a full population, apparent consistency with what is known about suicide risk was noted, as well as potentially important, understudied risk factors with evidence of unique suicide risk profiles among specific subpopulations.
Good practices for quantitative bias analysis LASH, Timothy L; FOX, Matthew P; MACLEHOSE, Richard F ...
International journal of epidemiology,
12/2014, Letnik:
43, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Quantitative bias analysis serves several objectives in epidemiological research. First, it provides a quantitative estimate of the direction, magnitude and uncertainty arising from systematic ...errors. Second, the acts of identifying sources of systematic error, writing down models to quantify them, assigning values to the bias parameters and interpreting the results combat the human tendency towards overconfidence in research results, syntheses and critiques and the inferences that rest upon them. Finally, by suggesting aspects that dominate uncertainty in a particular research result or topic area, bias analysis can guide efficient allocation of sparse research resources. The fundamental methods of bias analyses have been known for decades, and there have been calls for more widespread use for nearly as long. There was a time when some believed that bias analyses were rarely undertaken because the methods were not widely known and because automated computing tools were not readily available to implement the methods. These shortcomings have been largely resolved. We must, therefore, contemplate other barriers to implementation. One possibility is that practitioners avoid the analyses because they lack confidence in the practice of bias analysis. The purpose of this paper is therefore to describe what we view as good practices for applying quantitative bias analysis to epidemiological data, directed towards those familiar with the methods. We focus on answering questions often posed to those of us who advocate incorporation of bias analysis methods into teaching and research. These include the following. When is bias analysis practical and productive? How does one select the biases that ought to be addressed? How does one select a method to model biases? How does one assign values to the parameters of a bias model? How does one present and interpret a bias analysis?. We hope that our guide to good practices for conducting and presenting bias analyses will encourage more widespread use of bias analysis to estimate the potential magnitude and direction of biases, as well as the uncertainty in estimates potentially influenced by the biases.
Objectives To examine 25 year trends in first time hospitalisation for acute myocardial infarction in Denmark, subsequent short and long term mortality, and the prognostic impact of sex and ...comorbidity.Design Nationwide population based cohort study using medical registries.Setting All hospitals in Denmark.Subjects 234 331 patients with a first time hospitalisation for myocardial infarction from 1984 through 2008.Main outcome measures Standardised incidence rate of myocardial infarction and 30 day and 31–365 day mortality by sex. Comorbidity categories were defined as normal, moderate, severe, and very severe according to the Charlson comorbidity index, and were compared by means of mortality rate ratios based on Cox regression.Results The standardised incidence rate per 100 000 people decreased in the 25 year period by 37% for women (from 209 to 131) and by 48% for men (from 410 to 213). The 30 day, 31–365 day, and one year mortality declined from 31.4%, 15.6%, and 42.1% in 1984–8 to 14.8%, 11.1%, and 24.2% in 2004–8, respectively. After adjustment for age at time of myocardial infarction, men and women had the same one year risk of dying. The mortality reduction was independent of comorbidity category. Comparing patients with very severe versus normal comorbidity during 2004–8, the mortality rate ratio, adjusted for age and sex, was 1.96 (95% CI 1.83 to 2.11) within 30 days and 3.89 (3.58 to 4.24) within 31–365 days.Conclusions The rate of first time hospitalisation for myocardial infarction and subsequent short term mortality both declined by nearly half between 1984 and 2008. The reduction in mortality occurred for all patients, independent of sex and comorbidity. However, comorbidity burden was a strong prognostic factor for short and long term mortality, while sex was not.
Abstract
Measurement error is pervasive in epidemiologic research. Epidemiologists often assume that mismeasurement of study variables is nondifferential with respect to other analytical variables ...and then rely on the heuristic that “nondifferential misclassification will bias estimates towards the null.” However, there are many exceptions to the heuristic for which bias towards the null cannot be assumed. In this paper, we compile and characterize 7 exceptions to this rule and encourage analysts to take a more critical and nuanced approach to evaluating and discussing bias from nondifferential mismeasurement.
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