China
Africa
Explore chapters and articles related to this topic
Infantile Colic
Published in Charles Theisler, Adjuvant Medical Care, 2023
Gentle Manual Therapy: A systematic review was conducted of six randomized controlled trials (RCTs) to evaluate the effectiveness of manipulative therapies for infantile colic. The majority of the included trials indicated that infants receiving manipulative therapies had fewer hours of crying per day than infants who did not; this difference was statistically significant.7 Crying time was reduced by an average of 72 minutes per day. This effect was sustained for studies with a low risk of selection bias and attrition bias. Combining all six RCTs suggested that manipulative therapies had a significant positive effect in treating infantile colic.
The Study Population:
Published in Lynne M. Bianchi, Research during Medical Residency, 2022
Lynne M. Bianchi, Luke J. Rosielle
Whenever a study population does not reflect the same diversity as the target population, generalizability is limited. To include participants who represent the target population, you must eliminate or reduce practices that discourage or omit eligible participants. Biases, also called systematic errors, can occur at every stage of the process. For example, selection bias occurs when the inclusion or exclusion criteria are too strict. Recruitment bias is a concern if the study is only advertised at a single clinic or a few similar clinics. Enrollment bias is a problem when an investigator describes the study in such a way that encourages or discourages some individuals to participate. Allocation bias occurs when an investigator assigns participants with certain characteristics to one group and those with other characteristics to another.
Study Limitations to Consider
Published in Lisa Chasan-Taber, Writing Grant Proposals in Epidemiology, Preventive Medicine, and Biostatistics, 2022
Selection bias is bias in the selection of your study population. It can be viewed as a biased way in which participants come into your study. Selection bias is a differential bias and, unlike nondifferential misclassification, can lead to either an overestimate or an underestimate of the true association between your exposure and outcome of interest. Therefore, it is typically considered a more serious study limitation by reviewers. Once selection bias has occurred, no analysis techniques can alleviate it.
An overview of methodological flaws of real-world studies investigating drug safety in the post-marketing setting
Published in Expert Opinion on Drug Safety, 2023
Salvatore Crisafulli, Zakir Khan, Yusuf Karatas, Marco Tuccori, Gianluca Trifirò
Selection bias arises when subjects who are not representative of the population intended to be analyzed are selectively, thus leading to a distortion in exposure-outcome correlation [59]. The most common example of selection bias is the so-called prevalence bias, which occurs when prevalent drug users (i.e. patients already being already treated with a therapy for some time before the start of the follow-up period) are included in an observational study [60]. This may lead to distorted conclusions because prevalent users are generally those who ‘survive’ the early stages of pharmacological treatment and thus tolerate the therapy better, while persons discontinuing treatments due to early adverse reactions (the so-called ‘depletion of susceptibles’) are unintentionally excluded from a safety assessment [61]. For example, if an event is more likely to occur at the beginning of an exposure, the hazard rate calculated in an exposed cohort will tend to decrease over time. Furthermore, covariates for drug use at study entry are often influenced by the previous intake of the drug.
Daily variation in performance measures related to anaerobic power and capacity: A systematic review
Published in Chronobiology International, 2022
Aishwarya Ravindrakumar, Tulasiram Bommasamudram, David Tod, Ben J. Edwards, Hamdi Chtourou, Samuel A. Pullinger
Based on a modified 26-item Downs and Black (1998) checklist, the results of the methodological quality assessment of the included studies ranged from 19 to 26. Reporting (10 items; items 1–10) showed 6 items to be fully met by all studies (Items 1–4, 6 and 9), with 10 studies meeting full criteria for reporting. External validity (3 items; items 11–13) displayed all three items to be met by only 28 studies. Internal validity study bias (7 items; items 14–20) reported 5 items out of 7 items (items 16–20) to be fully met, with one study fully meeting all criteria for internal validity study bias (Souissi et al. 2019b). Confounding selection bias (6 items; items 21–26) reported 2 studies to meet all criteria (Chtourou et al. 2012a, 2012 c), with item 21 met by all studies. Detailed methodological quality assessment scores can be found in Table 5.
Role of community pharmacists in medication management during COVID-19 lockdown
Published in Pathogens and Global Health, 2021
Amal Akour, Eman Elayeh, Razan Tubeileh, Alaa Hammad, Rawan Ya’Acoub, Ala’a B. Al-Tammemi
Inclusion criteria for participation included: (i) Age ≥ 18 years, (ii) Residing in Jordan during the pandemic and its confinement measures, and (iii) Diagnosed with a chronic disease that requires regular medications and follow-up. To make sure that participants fulfilled the inclusion criteria, two questions were added at the beginning of the survey (‘Are you an adult who is at least 18 years-old?’ and ‘Do you suffer from one or more chronic diseases’). Answering ‘No’ to any of these questions prevented the participants from completing the survey. The snowball convenience sampling technique was employed by encouraging participants to share the survey with their own social networks. We decided to collect data via a web-based questionnaire to reduce direct contact with the participants because of the pandemic situation and in order to reach potential participants in a time-efficient way by eliminating geographical boundaries. In addition, during the gradual easing up of governmental restrictions and reopening of chronic diseases clinics, potential participants were contacted again on the same social media platforms during our data collection period to encourage those who had chronic diseases, and those who did not previously participate in our survey, to take part in the study. Selection bias was minimized through clearly identifying the study population (as previously discussed) and by selecting patients using rigorous criteria to avoid confounding results. The patients originated from the same general population [19].