Interleukin-1α − 899 (+ 4845) C→T polymorphism increases the risk of chronic periodontitis: Evidence from a meta-analysis of 23 case–control studies

Many epidemiological studies have indicated that interleukin-1α (IL-1α) − 899 (+ 4845) C→T polymorphism increases the risk of chronic periodontitis (CP), whereas some studies have reported opposite results. Accordingly, the aim of this meta-analysis is to investigate the association of the IL-1α − 899 (+ 4845) C→T polymorphism with CP. We searched the PubMed database up to May 1, 2013 and finally obtained 23 case–control studies. After data extraction, we performed meta-analysis using Comprehensive Meta-Analysis v2.2 software. The overall result based on the fixed-effect model showed that IL-1α − 899 (+ 4845) C→T polymorphism was significantly associated with increased risk of CP: [odds ratio (OR) = 1.29, 95% confidence interval (CI) = 1.15–1.44, p < 0.001] for T vs. C; (OR = 1.59, 95%CI = 1.22–2.07, p = 0.0005) for TT vs. CC; (OR = 1.30, 95% CI = 1.12–1.51, p = 0.0004) for CT vs. CC; and (OR = 1.40, 95% CI = 1.21–1.61, p < 0.001) for (CT+TT) vs. CC; (OR = 1.47, 95% CI = 1.16–1.87, p = 0.002) for TT vs. (CT+CC). Stratified analyses revealed that there was a significantly increased risk for Caucasians and Asians. In conclusion, current evidence showed that IL-1α − 899 (+ 4845) C→T polymorphism probably increased the risk of CP.     

Propensity score modeling strategies for the causal analysis of observational data

Propensity score methods are used to estimate a treatment effect with observational data. This paper considers the formation of propensity score subclasses by investigating different methods for determining subclass boundaries and the number of subclasses used. We compare several methods: balancing a summary of the observed information matrix and equal-frequency subclasses. Subclasses that balance the inverse variance of the treatment effect reduce the mean squared error of the estimates and maximize the number of usable subclasses.     

Utilizing Propensity Scores to Estimate Causal Treatment Effects with Censored Time-Lagged Data

Observational studies frequently are conducted to compare long-term effects of treatments. Without randomization, patients receiving one treatment are not guaranteed to be prognostically comparable to those receiving another treatment. Furthermore, the response of interest may be right-censored because of incomplete follow-up. Statistical methods that do not account for censoring and confounding may lead to biased estimates. This article presents a method for estimating treatment effects in nonrandomized studies with right-censored responses. We review the assumptions required to estimate average causal effects and derive an estimator for comparing two treatments by applying inverse weights to the complete cases. The weights are determined according to the estimated probability of receiving treatment conditional on covariates and the estimated treatment-specific censoring distribution. By utilizing martingale representations, the estimator is shown to be asymptotically normal and an estimator for the asymptotic variance is derived. Simulation results are presented to evaluate the properties of the estimator. These methods are applied to an observational data set of acute coronary syndrome patients from Duke University Medical Center to estimate the effect of a treatment strategy on the mean 5-year medical cost.     

Ethnology in the metropole: Robert Knox, Robert Gordon Latham and local sites of observational training

Anthropologists have traditionally separated the history of their discipline into two main diverging methodological paradigms: nineteenth-century armchair theorizing, and twentieth-century field-based research. But this tradition obscures both the complexity of the observational practices of early nineteenth-century researchers and the high degree of continuity between these practices and the techniques that came later. While historians have long since abandoned the notion that nineteenth-century ethnologists and anthropologists were merely ‘armchair’ theorists, this paper shows that there is still much to learn once one asks more insistently what the observational practices of early researchers were actually like. By way of bringing out this complexity and continuity, this essay re-examines the work of two well-known British ethnologists, Robert Knox, and Robert Gordon Latham; looking in particular at their methods of observing, analysing and representing different racial groups. In the work of each figure, early training in natural history, anatomy and physiology can be seen to have influenced their observational practices when it came to identifying and classifying human varieties. Moreover, in both cases, Knox and Latham developed locally-based observational training sites.     

Prognostic value of epidermal growth factor receptor in patients with gastric cancer: A meta-analysis

Background

The epidermal growth factor receptor (EGFR) plays important roles in the development of gastric cancer. This study aims to analyze the prognostic value of EGFR in patients with gastric cancer.

Methods

A meta-analysis is performed by searching Cochrane Library, PubMed, EMBASE and Science Direct databases from Jan 1970 to May 2013. Data are extracted from studies evaluating the survival of gastric cancer patients with either positive or negative EGFR expression. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) are calculated.

Results

Totally 1600 cases of gastric cancer patients from five studies are subjected to final analysis. The HR of post-operational survival of patients with positive EGFR expression is 1.16 (95% CI: 0.94–1.43) as compared with those with negative expression, indicating that positive EGFR expression does not significantly predict the poor survival of gastric cancer.

Conclusions

EGFR expression is not an independent predictor for the survival of gastric cancer patients.     

Confidence intervals for uncommon but dramatic responses to treatment

A small literature discusses locally most powerful rank tests when only a fraction of treated subjects respond to treatment. The ranks used in these tests are very different from conventional ranks, being relatively flat for low responses and then rising steeply, and the associated tests are much more powerful than conventional rank tests when, indeed, only a small fraction of treated subjects exhibit dramatic responses. Because the tests are derived from considerations of local power, they do not yield a plausible family of models for effect, and therefore they do not yield confidence intervals for the magnitude of effect formed by inverting the tests. There is a similarity between these tests and another family of tests, originally motivated by different considerations involving peak performance in small subsets. Exploiting this similarity, a method for obtaining confidence statements is proposed. In the case of observational studies, sensitivity to unobserved bias from nonrandom assignment of treatments is also examined. Two examples are used as illustrations: (i) a study of smoking during pregnancy and its effects on birth weight, in which smokers are matched to six controls, and (ii) a matched pair study of damage to DNA among workers at aluminum production plants.