ijsmr

International Journal of Statistics in Medical Research

Can a Mendelian Randomization Study Predict the Results of a Clinical Trial? Yes and No
Pages 56-61
Antonio Abbate, Charles A. Dinarello, Mariangela Peruzzi, Sebastiano Sciarretta, Giacomo Frati and Giuseppe Biondi-Zoccai
DOI:
http://dx.doi.org/10.6000/1929-6029.2016.05.01.6
Published: 08 January 2016


Abstract: Randomized controlled trials are considered at the top of the evidence hierarchy. However, in several cases randomized trials cannot be conducted or have not yet been completed. In such settings observational studies may provide important inference, yet traditional statistical adjustment methods fall short of controlling for all potential confounders, as unknown confounders cannot be taken care of by even the most sophisticated statistical tools. The mendelian randomization study is a type of research design which simultaneously exploits random transmission of genes and genetic linkage to obtain inferential estimates from the association between specific genetic variants known to modulate given risk factors and the corresponding outcomes of interests. Despite several developments in this field, there remain several areas of further research, and discrepancies between mendelian randomization studies and the corresponding randomized trials have already been recognized. Nonetheless, it is likely that this novel type of study will be used more commonly in the future, and a working knowledge of its pros, cons, and range of validity is crucial for conscientious interpretation and application. We thus aimed to concisely yet poignantly introduce the scholarly reader to this novel type of research design, notwithstanding that complementarity prevails in most cases over overlap between mendelian randomization studies and randomized trials.

Keywords: Adjustment, Confounding, Inference, Mendelian randomization study, Observational study, Prediction, Randomized controlled trial.
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International Journal of Statistics in Medical Research

Higher Performance of QuantiFERON TB Compared to Tuberculin Skin Test in Latent Tuberculosis Infection Prospective Diagnosis
Pages 62-70
Tarak Dhaouadi, Imen Sfar, Leila Mouelhi, Raoudha Tekaya, Ines Mahmoud, Jalila Bargaoui, Raoudha Daghbouj, Leila Abdelmoula, Taoufik Najjar, Taieb Ben Abdallah and Yousr Gorgi
DOI:
http://dx.doi.org/10.6000/1929-6029.2016.05.01.7
Published: 08 January 2016


Abstract: The Tuberculin skin test (TST) has been used for years in the latent tuberculosis infection (LTBI) diagnosis, but it has, well-documented, low sensitivity and specificity. Interferon-γ release assays (IGRA) has been reported to be more sensitive and specific than TST. Therefore, this study aimed to evaluate the performance of a commercial IGRA, QuantiFERON®-TB Gold In-Tube (QFT-GIT), comparatively to TST in LTBI diagnosis.

Patients and Methods: This study included 238 patients who were candidate for an anti-TNF therapy. The screening for LTBI was performed by both TST and QFT-GIT test for all patients. In order to evaluate the strength of associations, the odds ratios (OR) together with 95% confidence intervals (CI) were calculated. The correlation between QFT-GIT and TST was evaluated using κ statistics.

Results: Sixty-three (26.4%) sera were positive for QFT-GIT with a mean level of IFN-γ of about 1.18 IU/ml, while 81 (34%) patients were positive for TST. Agreement between QFT-GIT and TST was poor (37 QFT-GIT+/TST- and 55 QFT-GIT-/TST+), κ=0.09 (SD=0.065). The positivity of QFT-GIT was not influenced by BCG vaccination or by immunosuppression. Nevertheless, it was significantly associated to both history of an earlier tuberculosis disease (HETD) and its radiological sequel (RS), p=6E-7 and p=1E-8, respectively. Inversely, the TST results were not correlated to either HETD or RS, but the TST positivity was less frequent in immunosuppressed patients (45.5% vs. 73.9%), p=1E-5, OR (95% CI) = 0.29 [0.17-0.52]. Moreover, the extent of both the immunosuppression period and the time elapsed from the last BCG injection was significantly correlated to a lesser TST positivity, p=3E-12 and p=5E-7, respectively. Among the QFT-GIT-/TST+ patients (n=55) whom received an anti-TNF agent without any prophylactic treatment of LTBI, no tuberculosis was detected with a median follow-up of 78 weeks [56-109].

Conclusion: Our study suggests that the QFT-GIT has a higher performance comparatively to TST in the LTBI screening that is unaffected by either BCG vaccination or immunosuppression. Therefore, IGRAs has to replace TST especially in patients who are under consideration for an anti-TNF therapy.

Keywords: Tumor necrosis factor-α inhibitors, latent tuberculosis infection, Tuberculin skin test, QuantiFERON-TB Gold In-Tube.
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International Journal of Statistics in Medical Research

Editorial: Special Issue: Methods for Estimating Treatment Effects forPersons with Multiple Chronic Conditions
Pages 1
Heather Allore

Published: 08 January 2016


Editorial
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Modeling of the Deaths Due to Ebola Virus Disease Outbreak in Western Africa
Pages 306-321
Robert J. Milletich, Norou Diawara and Anna Jeng
DOI:
http://dx.doi.org/10.6000/1929-6029.2015.04.04.1
Published: 03 November 2015


Abstract: Problem: The recent 2014 Ebola virus outbreak in Western Africa is the worst in history. It is imperative that appropriate statistical and mathematical models are used to identify risk factors and to monitor the development and spread of the disease.

Method: Deaths data due to Ebola virus disease (EVD) in Guinea, Liberia, and Sierra Leone from October 10, 2014 to March 24, 2015 were collected via Situation Reports published by the World Health Organization [1]. Conditional autoregressive (CAR) models were applied to account for the spatial dependency in the countries along with the temporal dimension of the disease. Bayesian change-point models were used to identify key changes in growth and drop time points in the spatial distribution of deaths due to EVD within each country. Country-specific Poisson and negative binomial mixed models of covariate effects were applied to understand the between-country variability in deaths due to EVD.

Results: Both CAR models and generalized linear mixed models identified statistically significant covariate effects; however, the CAR models depended on the interval of data analyzed, whereas the mixed models depended on the underlying distribution assumed. Bayesian change-point models identified one significant change-point in the distribution of deaths due to EVD within each country.

Practical Application: CAR models, Bayesian change-point models, and generalized linear mixed models demonstrate useful techniques in modeling the incidence of deaths due to EVD.

Keywords: Ebola Virus Disease, Conditional Autoregressive Model, Bayesian Analysis, Change-Point Model.
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