Interpretation of Research Discussion

Interpretation of Research Discussion

Interpretation of Research Discussion

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Article Analysis 1

Article Citation and Permalink (APA format) Basch, E., Deal, A. M., Kris, M. G., Scher, H. I., Hudis, C. A., Sabbatini, P., Schrag, D. (2016). Symptom Monitoring With Patient-Reported Outcomes During Routine Cancer Treatment: A Randomized Controlled Trial. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 34(6), 557–565.

Link: 10.1200/JCO.2015.63.0830

Hailu, Fikadu Balcha et al. “Diabetes Self-Management Education (DSME) – Effect on Knowledge, Self-Care Behavior, and Self-Efficacy Among Type 2 Diabetes Patients in Ethiopia: A Controlled Clinical Trial.” Diabetes, metabolic syndrome and obesity: targets and therapy vol. 12 2489-2499.

Link: 10.2147/DMSO.S223123

 

Periasamy, U., Sidik, S. M., Rampal, L., Fadhilah, S. I., Akhtari-Zavare, M., & Mahmud, R. (2017). Effect of chemotherapy counseling by pharmacists on quality of life and psychological outcomes of oncology patients in Malaysia: a randomized control trial. Health and quality of life outcomes, 15(1), 104.

Link:https://doi.org/10.1186/s12955-017-0680-2

Point Description Description Description
Broad Topic Area/Title Symptom monitoring during routine cancer care using patient-reported outcomes. Effects of diabetes self-management education (DSME) intervention on patients’ self-reported levels of diabetes knowledge, self-care behaviors, and self-efficacy. Effectiveness of a chemotherapy counseling module by pharmacists among oncology patients.
Identify Independent and Dependent Variables and Type of Data for the Variables Symptoms reporting (discrete data )

Health-related quality of life (HRQL) (Nominal Data)

Emergency room (ER) visits, hospitalizations, and survival (Continuous data).

Diabetes self-management education (DSME) intervention, Usual care (Discrete data).

Diabetes knowledge, self-care behaviors, and self-efficacy (Nominal data)

Repetitive Counselling (discrete data)

Quality of life (Nominal data)

Psychological issues (continuous data)

Population of Interest for the Study Patients initiating chemotherapy at Memorial Sloan Kettering Cancer Center (MSK) in New York for metastatic breast, genitourinary, gynecologic, or lung cancers Adult patients with T2DM attending Jimma University Medical Centre (JUMC) in Ethiopia. Malaysia patients above 18 years old in different stages of cancers which undergoing their 1th and 2nd cycles of chemotherapy and able to read.
Sample 766 patients

Computer –experienced – 539

Computer inexperienced – 227

(150 assigned to STAR and 75 to usual care)

At endpoint 142

78 intervention group participants

64 comparison group participants

161 patients

Intervention 81

Control = 80

Sampling Method  Participants were first grouped as computer experienced and computer inexperienced depending on computer and email use and then randomized in respective groups. Excel’s random number generator assigned 120 patients to intervention group and 120 patients to comparison group Random sampling was used to group eligible patients.
Descriptive Statistics (Mean, Median, Mode; Standard Deviation)

 

Mean time on study 7.4 months

Median time was 3.7 months (range, 0.25 to 49)

Mean of 16 clinic visits per patient (range, 1 to 114).

Mean age 47 (10) years

Mean  number of living with diabetes: intervention group  10 (6) years and comparison group 12 (7) years

Intervention group mean diabetes knowledge score, 11.33 out of 20, comparison group, 10.61 out of 20.

Intervention group participants performed foot care for a mean of 5.80 days per week, compared to 5.26 days for the comparison group

 

Average age of the respondents was 65 years (mean = 65.49 ± 1.4; 95% CI = 64.08–66.90)

Mean differences of quality of life 81.95

Inferential Statistics

 

Mean HRQL scores declined by less in the intervention arm compared with usual care (1.4- v 7.1-point drop; P < .001

Significant differences in quality-adjusted survival were observed during this 1-year period for all patients (mean of 8.0 v 8.7 months; P = .004) and were statistically significant in both subgroups

Mean DKS score significantly increased by 0.76 in the intervention group and decreased by 0.16 in the comparison group from baseline to endpoint (p = 0.044)

mean number of days per week that the intervention group participants followed specific dietary recommendations significantly increased by 2.65 days from baseline to endpoint (p = 0.019)

The mean differences of anxiety −0.31, (−0.59–0.03; p = 0.028) and depression −0.56, (−0.85–0.27; p = 0.000) for the intervention group was significantly lower compared to the control group from baseline until 3rd follow up.

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The hypothesis is the question the researcher wants to answer, the clinical inquiry in healthcare, the research design, how the data is gathered and analyzed is determined by the question or hypothesis. In healthcare we aim to find correlations and answers within the data to provide for better patient population outcomes. Correlation does not prove causation. Clinical significance determines whether the research has a practical application to an individual or a group. It also is used to determine health care decisions made by leadership. Clinical significance is the application in improving the quality of life of an individual and provides the bridge from health research to patient care (Ambrose, 2018).

The confidence interval helps to reject the null hypothesis.  The confidence interval is an interval estimate for the mean. It is a range of

Interpretation of Research Discussion
Interpretation of Research Discussion

values that are set close to the mean either in a positive or negative direction. For the null to be rejected, 95% of the values need to be set close to the mean. The range of values determines the effect. While there is not 100% certainty that either of these possibilities could be true, the CI reflects the risk of the researcher being wrong. It is important that the statistical analysis of the data and its associated probability are true. The basis of rejection or failure to reject the null hypothesis is based on the CI of 95%. A CI of 95% says that 95% of research projects like the one completed will include the true mean, but 5% will not, meaning that there are five chances in 100 of being wrong. Reducing the confidence interval increases the risk for error (Ambrose, 2018).

A CI informs the investigator and the reader about the power of the study and whether or not the data are compatible with a clinically significant treatment effect. Confidence intervals also provide a more appropriate means of analysis for studies that seek to describe or explain, rather than to make decisions about treatment efficacy.

The logic of hypothesis testing uses a decision-making mode of thinking which is more suitable to randomized controlled trials (RCTs) of health care interventions. Hypothesis testing to determine statistical significance was initially intended to be used only in randomized experiments such as RCTs which are typically not feasible in clinical research involving identification of risk factors, etiology, clinical diagnosis, or prognosis. The use of CIs allows for hypothesis testing and it allows a more flexible approach to analysis that accounts for the objectives of each investigation (Savage, 2003).

The use of hypothesis testing and confidence intervals can be seen in quality improvement projects throughout an organization. In healthcare, we aim to find correlations and answers to our questions (hypothesis) within the data to provide better patient outcomes. Through these projects, we ask the question, find, plan and implement processes or the evidence, and evaluate the outcomes by building a concept or framework for the investigation. In my place of work the rate of readmission can be projected through the use of hypothesis testing by inputting those precautionary factors that can help in the reduction of patients coming back to hospital after they have been discharge. While at the same time, the confidence interval is use to determine average rehospitalization within any particular month, and this help to improve the quality of service provided by the organization

Reference:

Ambrose, J. (2018). What are statistics and why are they important to health science. In Applied statistics for health care (1 ed.). Grand Canyon University: Grand Canyon University.

Savage, S. (2003). Advantages of confidence intervals in clinical research. Retrieved from: https://www.redorbit.com/news/science/18686/advantages_of_confidence_intervals_in_clinical_research/