| 1. |
- Benamer, Hani T S, et al.
(författare)
-
Arab nations lagging behind other Middle Eastern countries in biomedical research: a comparative study.
- 2009
-
Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 9:Apr 17
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Analysis of biomedical research and publications in a country or group of countries is used to monitor research progress and trends. This study aims to assess the performance of biomedical research in the Arab world during 2001-2005 and to compare it with other Middle Eastern non-Arab countries. METHODS: PubMed and Science Citation Index Expanded (SCI-expanded) were searched systematically for the original biomedical research publications and their citation frequencies of 16 Arab nations and three non-Arab Middle Eastern countries (Iran, Israel and Turkey), all of which are classified as middle or high income countries. RESULTS: The 16 Arab countries together have 5775 and 14,374 original research articles listed by PubMed and SCI-expanded, respectively, significantly less (p < 0.001) than the other three Middle Eastern countries (25,643 and 49,110). The Arab countries also scored less when the data were normalized to population, gross domestic product (GDP), and GDP/capita. The publications from the Arab countries also have a significantly lower (p < 0.001) citation frequency. CONCLUSION: The Arab world is producing fewer biomedical publications of lower quality than other Middle Eastern countries. Studies are needed to clarify the causes and to propose strategies to improve the biomedical research status in Arab countries.
|
|
| 2. |
|
|
| 3. |
- Flynn, Terry N, et al.
(författare)
-
Estimating preferences for a dermatology consultation using Best-Worst Scaling : comparison of various methods of analysis.
- 2008
-
Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 8, s. 76-
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Additional insights into patient preferences can be gained by supplementing discrete choice experiments with best-worst choice tasks. However, there are no empirical studies illustrating the relative advantages of the various methods of analysis within a random utility framework.METHODS: Multinomial and weighted least squares regression models were estimated for a discrete choice experiment. The discrete choice experiment incorporated a best-worst study and was conducted in a UK NHS dermatology context. Waiting time, expertise of doctor, convenience of attending and perceived thoroughness of care were varied across 16 hypothetical appointments. Sample level preferences were estimated for all models and differences between patient subgroups were investigated using covariate-adjusted multinomial logistic regression.RESULTS: A high level of agreement was observed between results from the paired model (which is theoretically consistent with the 'maxdiff' choice model) and the marginal model (which is only an approximation to it). Adjusting for covariates showed that patients who felt particularly affected by their skin condition during the previous week displayed extreme preference for short/no waiting time and were less concerned about other aspects of the appointment. Higher levels of educational attainment were associated with larger differences in utility between the levels of all attributes, although the attributes per se had the same impact upon choices as those with lower levels of attainment. The study also demonstrated the high levels of agreement between summary analyses using weighted least squares and estimates from multinomial models.CONCLUSION: Robust policy-relevant information on preferences can be obtained from discrete choice experiments incorporating best-worst questions with relatively small sample sizes. The separation of the effects due to attribute impact from the position of levels on the latent utility scale is not possible using traditional discrete choice experiments. This separation is important because health policies to change the levels of attributes in health care may be very different from those aiming to change the attribute impact per se. The good approximation of summary analyses to the multinomial model is a useful finding, because weighted least squares choice totals give better insights into the choice model and promote greater familiarity with the preference data.
|
|
| 4. |
|
|
| 5. |
- Fottrell, Edward, et al.
(författare)
-
Demonstrating the robustness of population surveillance data : implications of error rates on demographic and mortality estimates
- 2008
-
Ingår i: BMC Medical Research Methodology. - : BioMed Central. - 1471-2288. ; 8, s. Article nr 13-
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: As in any measurement process, a certain amount of error may be expected in routine population surveillance operations such as those in demographic surveillance sites (DSSs). Vital events are likely to be missed and errors made no matter what method of data capture is used or what quality control procedures are in place. The extent to which random errors in large, longitudinal datasets affect overall health and demographic profiles has important implications for the role of DSSs as platforms for public health research and clinical trials. Such knowledge is also of particular importance if the outputs of DSSs are to be extrapolated and aggregated with realistic margins of error and validity.METHODS: This study uses the first 10-year dataset from the Butajira Rural Health Project (BRHP) DSS, Ethiopia, covering approximately 336,000 person-years of data. Simple programmes were written to introduce random errors and omissions into new versions of the definitive 10-year Butajira dataset. Key parameters of sex, age, death, literacy and roof material (an indicator of poverty) were selected for the introduction of errors based on their obvious importance in demographic and health surveillance and their established significant associations with mortality. Defining the original 10-year dataset as the 'gold standard' for the purposes of this investigation, population, age and sex compositions and Poisson regression models of mortality rate ratios were compared between each of the intentionally erroneous datasets and the original 'gold standard' 10-year data.RESULTS: The composition of the Butajira population was well represented despite introducing random errors, and differences between population pyramids based on the derived datasets were subtle. Regression analyses of well-established mortality risk factors were largely unaffected even by relatively high levels of random errors in the data.CONCLUSION: The low sensitivity of parameter estimates and regression analyses to significant amounts of randomly introduced errors indicates a high level of robustness of the dataset. This apparent inertia of population parameter estimates to simulated errors is largely due to the size of the dataset. Tolerable margins of random error in DSS data may exceed 20%. While this is not an argument in favour of poor quality data, reducing the time and valuable resources spent on detecting and correcting random errors in routine DSS operations may be justifiable as the returns from such procedures diminish with increasing overall accuracy. The money and effort currently spent on endlessly correcting DSS datasets would perhaps be better spent on increasing the surveillance population size and geographic spread of DSSs and analysing and disseminating research findings.
|
|
| 6. |
- Nemes, Szilard, 1977, et al.
(författare)
-
Bias in odds ratios by logistic regression modelling and sample size.
- 2009
-
Ingår i: BMC medical research methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- Background: In epidemiological studies researchers use logistic regression as an analytical tool to study the association of a binary outcome to a set of possible exposures. Methods: Using a simulation study we illustrate how the analytically derived bias of odds ratios modelling in logistic regression varies as a function of the sample size. Results: Logistic regression overestimates odds ratios in studies with small to moderate samples size. The small sample size induced bias is a systematic one, bias away from null. Regression coefficient estimates shifts away from zero, odds ratios from one. Conclusion: If several small studies are pooled without consideration of the bias introduced by the inherent mathematical properties of the logistic regression model, researchers may be mislead to erroneous interpretation of the results.
|
|
| 7. |
|
|
