Quantitative Variables4-5 Explain how quantitative variables were handled in the analyses

Quantitative Variables4-5
Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why.

Explanation on the ways and rationale the quantitative statistics are categorized, the cut-points and groups mean and median values are suggested. The number of cases, control, person at risk, person-time at risk and etc. should be stated for each group whenever the results are tabulated.
In this study, only the way to categorize type of mutation has been described. However, the methods and reasons to determine other groupings were not mentioned.

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Median value was reported for the diagnosis of first breast cancer, but not for the other categories.

For tables, the data for cases and control were reported.

Statistical Methods
(a) Describe all statistical methods, including those used to control for cofounding.

The statistical methods were not comprehensively described, but were listed under statistical analysis.

If the categories being differentiated are not identical, the potential cofounders should be adjusted by stratification or multivariate analysis.7 Particular methods for the choice of variants and data analysis should be clarified; novel approaches referenced; and statistical software adapted being described. Sufficiently detailed outline of all these are recommended4-5, that the authors should concentrate on explaining the methods instead of stating the statistical tests.5
(b) Describe any methods used to examine subgroups and interactions4-5.

Subgroup analyses and interactions, whether its planned or emerged during data analysis should be reported, and describing procedures to assess the effect and association across categories is essential as it allows the displayed relationship to be identified in the form of hypothesis testing or generating. Interaction is when a determinant altered the effect of another, and should be completely reported. This criterion was not identified in this study.
(c) Explain how missing data were addressed4-5.
Missing data is normal in observational studies. It is suggested that the number and reason of lost data is reported, and the resulting participants excluded determined. For investigations reporting lost data, nature and presumption of the analysis should be described.
In this study, only number of missing values have been reported under the tables and no further explanation was made.

(d) Case-control study – If applicable, explain how matching of cases and controls was addressed.
Complete explanation of technique employed to evaluate the information is required for viewers to determine whether the matched design was properly included in the analysis.4
Comprehensive description was not done, but the methods were stated:

P value was used to compare cases and controls:

(e) Describe any sensitive analysis.

Sensitive analysis is helpful to examine whether the major results are uniform with those achieved using other examination procedure or presumption.8 In this study, sensitive analysis was not described.

(a) Report numbers of individuals at each stage of study – eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed
The numbers of individuals at each stage of study are stated under the methods and results part of this case-control study. The numbers of individuals that potentially eligible are 1243, examined for eligibility are from 1243 cases, confirmed eligible and included in study are 209 cases and 384 controls, and completing follow-up are only for controls.

(b) Give reasons for non-participation at each stage
Reasons for non-participation either for case or control are not stated under results part but stated clearly under the methods part of this case-control study.

(c) Consider use of a flow diagram
Flow diagram is not being used in this case-control study. All of the results are illustrated in table form. According to STROBE checklist, flow diagram is needed to be including in a case-control study. Flow diagram helps the readers to follow clearly on the process of examining the eligibility of cases and controls. Readers can clearly identify the amount of samples that are not eligible due to failing to meet the requirements.


Descriptive data
(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders
1343025930910The characteristics of study participants are stated in Table 1 while the information on exposures is in Table 2. For potential confounders, they are stated under the methods part of this case-control study.

(b) Indicate number of participants with missing data for each variable of interest
The numbers of participants with missing data are stated clearly below Table 1 and Table 2.


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