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Denna Wheeler edited MethodologyWe_searched_the_PubMed_database__.html
about 8 years ago
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(e.g., animal studies, genome-wide association studies, adult survivors of
pediatric ALL, and genetic studies). Meta-analyses were excluded to avoid
redundancy since the primary constituent studies were included. The remaining
182 articles were retained for coding. (See Prisma
Diagram).
An
Diagram).
We formulated an
abstraction manualwas formulated by D.H. to standardize the coding process. Each person was partnered with another so
that all of the process. The coding process was
conducted in pairs to ensure that at least two researchers reviewed once over. The papers were analyzed for nine each study. Nine specific pieces of information: study characteristics were
documented: outcome, measurement device, method of aggregation, primacy of
outcome, whether the outcome was a harm or side effect of an intervention,
study design, study type, metric, & sample size. With regards to metric,
when an outcome element was implicitly specified, we considered it specified.
For example, because quantifying survival is, by definition, measuring time to
event, specific metric for survival analysis outcomes was always coded as time
to event. For survival, remission, and relapse, measurement device was coded as
“N/A” because, other than a calendar, there is no measurement device. For
outcomes reported using scales (e.g., NCI-CTC), metric was coded as "value
at a time point" unless otherwise specified within the body of the
article. When coding sample size for studies including non-pediatric-ALL
research, all study participants were counted, including adult patients and
pediatric patients with a cancer other than ALL. For the final step in coding, Finally, outcomes were grouped
into eight domains for analysis: 1) Survival; 2) Mortality; 3) Remission; 4)
Relapse; 5) Response to Treatment; 6) Adverse Event; 7) Cognitive Event; 8)
Other.
Analysis:
STATA Other.
Analysis:
STATA software was used to analyze frequency of appearance of unique outcomes and the specification of the nine outcome elements outlined in the abstraction manual. Unique outcomes were then placed in the eight broad domains listed above and run through STATA to reveal larger trends in reporting.
In order to structure a visual representation and calculate centrality of clinical outcomes in pediatric leukemia, a matrix was constructed. The foundation of this social network was formed using a basis of frequency of connections across outcomes, termed co-occurrences. Each outcome and the number of times it co-occurred with other specific outcomes were recorded in a spreadsheet. Reviewers C.C.W. and W.D.B. produced the network structure with a symmetrically duplicated matrix, ultimately serving to verify the co-occurrences.
We imported the network matrix onto UCINET and used Netdraw software. Each outcome was uploaded onto the program in the order of total co-occurrences. Thus, each outcome was sized in increasingly larger nodes, the plots of FIGURE **; the larger the size of the node, then the larger number of total co-occurrences this outcome maintains across outcomes in pediatric acute lymphoblastic leukemia. Next, the spring embedding function was applied to group outcomes around the largest nodes. This was accomplished by grouping less connected outcomes around nodes in a pattern of descending number of co-occurrences until the network became too dense for coherency. Next, a superstructure was formed, according to FIGURE **, which represents the social network architecture of outcomes.
