ijsmr
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Abstract: In matched cohort studies exposed and unexposed individuals are matched on certain characteristics to form clusters to reduce potential confounding effects. Data in these studies are clustered and thus dependent due to matching. When the outcome is a Poisson count, specialized methods have been proposed for sample size estimation. However, in practice the variance of the counts often exceeds the mean (i.e. counts are overdispersed), so that Poisson methods don’t apply. We propose a simple approach for calculating statistical power and sample size for clustered Poisson data when the proportion of exposed subjects in a cluster is constant across clusters. We extend the approach to clustered count data with overdispersion, which is common in practice. We evaluate these approaches with simulation studies and apply them to a matched cohort study examining the association of parental depression with health care utilization. Simulation results show that the methods for estimating power and sample size performed reasonably well under the scenarios examined and were robust in the presence of mixed exposure proportions up to 30%. Keywords: Clustered Poisson data, Overdispersion, Subject heterogeneity, Statistical power, Sample size.Download Full Article |
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Abstract: Motivated by HIV retention, we present an application of the smooth test of goodness-of-fit under right-censoring to time to first occurrence of a recurrent event. The smooth test applied here is an extension of Neyman’s smooth test to a class of hazard functions for the initial distribution of a recurrent failure-time event. We estimate the baseline hazard function of time-to-first loss to follow-up, using a Block, Borges and Savits (BBS) minimal repair model of the data (n = 2,987,72% censored). Simulations were conducted at various percentages of censoring to assess the performance of the smooth test. Results show that the smooth test performed well under right-censoring. Keywords: BBS model, Hazard function, Loss to follow-up, Neyman’s smooth test, Recurrent events, Retention in HIV care. |
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Abstract: Introduction: Libya is a North African country classified under the Eastern Mediterranean Regional Office. In response to the general paucity of literature regarding cancer in Libya, this study aims to analyze various risk factors for breast cancer among patients in Benghazi, Libya. Material and Methods: Using records from a major primary oncology clinic, data was gathered from breast cancer patients. A total of 301 patients were diagnosed with breast cancer in the study period. For the purpose of risk factor determination, this hospital-based case control study consisted of 212 recently diagnosed cases of breast cancer attending the oncology clinic at Al-Jamhouria hospital in Benghazi. Age matched controls (n=219) were randomly enrolled from other medical departments of Al-Jamhouria hospital and the general population visiting the hospital. Chi square was used to assess significance of the risk factors and the corresponding odds ratio (O.R.) and 95% CI were calculated to assess the magnitude of associations.
Results:A total of 1478 cases presented to the gynecological oncology clinic at Al-Jamhouria hospital during the period of 2007-2008. Of these cases, around 20% (n=301) were breast cancer patients. The average age of presentation was 49 years + S.D 13 years, with most of the cases (61%, n=184) being premenopausal. Over 90% (n=273) of breast cancer patients are diagnosed at stage II or later. More than 16% of cases seek medical attention when the malignancy has already reached stage IV. Diabetes, hypertension and family history of other malignancies were found to significantly increase the risk of developing breast cancer.
Discussions: A range of socioeconomic risk factors were also analyzed (i.e. parity, breastfeeding etc…) and some were found to be protective. Libyan breast cancer cases are slightly older compared to the rest of the Arab world, but are younger than their counterparts in the West. The major issue in the Libyan scenario is delayed presentation which significantly worsens the prognosis. Hence, all the recommendations focus on increased awareness, the implementation of a national cancer control plan and a national screening program and training healthcare professions in palliative care. Keywords: Breast cancer, Libya, Arab World, Epidemiological studies, Early Detection of Cancer.Download Full Article |
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Abstract: In this study, we fit the two-parameter Weibull distribution to an HIV retention data and assess the fit using a smooth test of goodness-of-fit. The smooth test described here is a score test and is derived as an extension of the Neyman’s smooth test. Simulations are conducted to compare the power of the smooth test with the power of each of three empirical goodness-of-fit tests for the Weibull distribution. Results show that the smooth tests of order three and four are more powerful than the three empirical goodness-of-fit tests. For validation, we used retention data from an HIV care setting in Kenya. Keywords: Goodness-of-fit, Loss to follow-up, Neyman’s smooth test, Retention in HIV care, Weibull distribution. |
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Abstract: The issue of borderline p-value seems to divide health scientists into two schools of thought. One school of thought argues that when the p-value is greater than or equal to the statistical significance cut-off level of 0.05, it should not be considered statistically significant and the null hypothesis should be accepted no matter how close the p-value is to the 0.05. The other school of thought believes that by doing so one might be committing a Type 2 error and possibly missing valuable information. In this paper, we discuss an approach to address this issue and suggest the test of random duplication of participants as a way to interpret study outcomes when the statistical significance is borderline. This discussion shows the irrefutability of the concept of borderline statistical significance, however, it is important that one demonstrates whether a borderline statistical significance is truly borderline or not. Since the absence of statistical significance is not necessarily evidence of absence of effect, one needs to double check if a borderline statistical significance is indeed borderline or not. The p-value should not be looked at as a rule of thumb for accepting or rejecting the null hypothesis but rather as a guide for further action or analysis that leads to correct conclusions. Keywords: P-value, Sample Size, Statistical Significance, Borderline Significance, Participant Random Duplication.Download Full Article |