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A Natural Experiment for Inferring Causal Association between Smoking and Tooth Loss: A Study of a Workplace Contemporary Cohort
Pages 331-336
Takashi Hanioka, Satoru Haresaku, Nao Suzuki, Kaoru Shimada, Takeshi Watanabe, Miki Ojima, Keiko Fujiie and Masako Watanabe
Published: 03 November 2015

Abstract: Background: Natural experiments in former smokers are an important criterion for inferring causality between smoking and tooth loss. We examined how former smoking influenced risk estimate of tooth loss incidence.

Methods: Records of dental check-ups of the work cohort were examined. The sample consisted of data from 1,724 workers recorded at the ages of 40 years and 50 years, and this was analyzed for tooth loss incidence during a 10-year period. Former smokers were categorized into two groups based on whether they quit smoking before or during the observational period. Variables used for adjustment were age, sex, oral and overall health behavior, dental visit, and number of existing teeth immediately prior to observation.

Results: The prevalence of tooth loss incidence and number of teeth lost during the observational period were both higher in current smokers than in never smokers (33.7% vs. 23.9% and 0.83 vs. 0.42, respectively). Incident odds ratio of tooth loss in long-term quitters relative to never smokers was not significant and less than one (incident odds ratio 0.85, 95% confidence interval 0.56–1.29). Incident odds ratios of short-term quitters and current smokers were both significant, though short-term quitters exhibited higher values (1.72, 1.15–2.55) than current smokers (1.48, 1.10–2.00).

Conclusions: The causal interpretation is strengthened by attenuation of the risk in long-term quitters. However, additional factors may influence the risk estimates of former smokers, suggesting potential limitations of a natural experiment for inferring causal association between smoking and tooth loss.


Keywords: Natural experiment, Smoking, Tooth loss, Cohort study, Causal inference.
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International Journal of Statistics in Medical Research

Age Scale for Assessing Activities of Daily Living
Pages 48-56
Rafael Figueroa, Satoshi Seino, Noriko Yabushita, Yoshiro Okubo, Yosuke Osuka, Miyuki Nemoto, Songee Jung and Kiyoji Tanaka
Published: 27 January 2015

Abstract: The purpose of this study was to develop an age scale for assessing activities of daily living (ADL) among community-dwelling adults aged 75 years or older. Participants were 1006 older Japanese: 312 men (79.6 ± 4.3 years) and 694 women, (79.9 ± 5.5 years). Participants completed a battery of 8 performance tests related to ADL and the Barthel index (BI) questionnaire. Spearman rank-order correlation analysis was applied to obtain the correlation of the 8 ADL performance tests with the total BI score. Three variables were high rank-order correlated with BI, secondly those items were subjected to the principal component analysis. The weighted combination of the principal component scores was summed. Resulting in an ADL score (ADLS), women = 0.075 X1 – 0.082 X2 – 0.063 X3 + 0.124, men = 0.051 X1 – 0.105 X2 – 0.099 X3 + 0.249, where X1 = hand-grip strength, X2 = timed up and go, X3 = five-chair sit to stand. Individual ADLS was transformed to an ADL age scale (ADLA). The estimation was – 5.493 ADLS + 79.90 for women, and – 4.272 ADLS + 79.57 for men. Due to the distortion at the regression edges, the equation was corrected as suggested by Dubina et al. ADLA women after correction was = 0.447 (chronological age: CA) – 5.49ADLS + 44.17, men = 0.519CA – 4.27ADLS + 38.26. ADLA can be used to identify or monitor the characteristics of the ADL levels of physical abilities in older Japanese aged 75 years or older.

Keywords: Age assessment, principal component analysis, physical function, 75 years and older, older Japanese.
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International Journal of Statistics in Medical Research

Comparative Analysis of the Effects of Three Antithrombotic Regimens on Clinical Outcomes of Patients with Atrial Fibrillation and Recent Percutaneous Coronary Intervention with Stent. A Retrospective Cohort Study
Pages 260-269
Renato De Vecchis, Cesare Baldi and Francesco Piemonte
Published: 19 August 2015

Abstract: Introduction: Chronic atrial fibrillation (AF), coexisting with a history of recent coronary angioplasty with stent (PCI-S) represents an encoded indication for oral anticoagulation with warfarin (OAC) plus dual antiplatelet therapy (DAPT).

Methods:Using a retrospective cohort study we determined the respective impacts on cardio- vascular outcomes of three different pharmacologic regimens, i.e., triple therapy (TT) with warfarin + clopidogrel and aspirin, dual therapy (DT) with warfarin +clopidogrel or aspirin, dual antiplatelet therapy (DAPT) with clopidogrel + aspirin. Outcomes of interest were all-cause mortality, ischemic cardiac events, ischemic cerebral events, bleeding events. The inclusion criterion was the coexistence of an indication for OAC (e.g., chronic AF) with an indication for dual antiplatelet therapy due to recent PCI-S.

Results: Among the 98 patients enrolled, 48 (49%), 31 (31.6%), and 19(19.4%) patients were prescribed TT, DT, and DAPT, respectively. Throughout a mean follow-up of 378± 15.7days, there were no significant differences between the three regimens for all abovementioned outcomes. In particular, the total frequency of major bleeding was similar in the three groups: 5 cases (10.4%) in TT, one case (3.22%) in DT and no case in DAPT groups (p [chi-square test] = 0.1987).

Conclusions: TT, DT and DAPT displayed similar efficacy and safety. Although the superiority of OAC vs. DAPT for stroke prevention in AF patients has been demonstrated by previous randomized trials, a smaller frequency of high thromboembolic risks' features in DAPT group of the present study may have prevented the observation of a higher incidence of ischemic stroke in this group.

Keywords: Atrial fibrillation, percutaneous coronary intervention, oral anticoagulant therapy, antithrombotic therapy, major adverse cardiovascular events, bleeding.
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International Journal of Statistics in Medical Research

Application of Generalized Additive Models to the Evaluation of Continuous Markers for Classification Purposes
Pages 296-305
Mónica López-Ratón, Mar Rodríguez-Girondo, María Xosé Rodríguez-Álvarez, Carmen Cadarso-Suárez and Francisco Gude
Published: 19 August 2015

Abstract: Background: Receiver operating characteristic (ROC) curve and derived measures as the Area Under the Curve (AUC) are often used for evaluating the discriminatory capability of a continuous biomarker in distinguishing between alternative states of health. However, if the marker shows an irregular distribution, with a dominance of diseased subjects in noncontiguous regions, classification using a single cutpoint is not appropriate, and it would lead to erroneous conclusions. This study sought to describe a procedure for improving the discriminatory capacity of a continuous biomarker, by using generalized additive models (GAMs) for binary data.

Methods: A new classification rule is obtained by using logistic GAM regression models to transform the original biomarker, with the predicted probabilities being the new transformed continuous biomarker. We propose using this transformed biomarker to establish optimal cut-offs or intervals on which to base the classification. This methodology is applied to different controlled scenarios, and to real data from a prospective study of patients undergoing surgery at a University Teaching Hospital, for examining plasma glucose as postoperative infection biomarker.

Results: Both, theoretical scenarios and real data results show that when the risk marker-disease relationship is not monotone, using the new transformed biomarker entails an improvement in discriminatory capacity. Moreover, in these situations, an optimal interval seems more reasonable than a single cutpoint to define lower and higher disease-risk categories.

Conclusions: Using statistical tools which allow for greater flexibility (e.g., GAMs) can optimize the classificatory capacity of a potential marker using ROC analysis. So, it is important to question linearity in marker-outcome relationships, in order to avoid erroneous conclusions.

Keywords: Discriminatory capability, ROC, AUC, optimal cutpoint, biomarker, plasma glucose.
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Examining Biliary Acid Constituents among Gall Bladder Patients: A Bayes Study Using the Generalized Linear Model
Pages 224-239
Puja Makkar, S.K. Upadhyay, V.K. Shukla and R.S. Singh
Published: 21 May 2015

Abstract: The generalized linear model is an important class of models that has wide variety of applications mainly because of its inherent flexibility and generality. The present paper provides an important application of GLM in order to examine different constituents of bile acid in the development of gallstones as well as carcinoma among the gallbladder patients. These constituents may be broadly categorized as primary and secondary bile acids. The paper, in fact, considers two particular cases of GLM based on normal and gamma modelling assumptions and provides the complete Bayes analysis using independent but vague priors for the concerned model parameters. It then analyzes a real data set taken from SS Hospital, Banaras Hindu University, with primary (secondary) bile acids as response variables and secondary (primary) bile acids as the predictors. The authenticity of the assumed models for the given data set is also examined based on predictive simulation ideas.

Keywords: Generalized linear model, vague priors, posterior distribution, biliary acids, gallbladder diseases, predictive simulation, Bayes information criterion.

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