Review for 10.1186/1471-230X-11-133 (2011)

Reviewer's report
Title:The MLH1 2101C>A (Q701K) variation is related to risk of gastric cancer in
Chinese males
Version: 2Date:12 August 2011
Reviewer: Eugenia Flores
Reviewer's report:
The authors perform an assessment of the association between mutations in the
DNA repair gene MLH1, with the risk of gastric cancer in Chinese population.
The study was conducted in 236 patients with gastric cancer and 240 controls
matched for age and sex of the provinces of Jiangsu and Anhui. The question is
clear the authors try to determine the significance of the association between
mutations in the MLH1 gene with gastric cancer.
Major Compulsory Revisions
RESULTS
1. Table 1, is important to have information on family history of cancer in the
controls, and if possible, data from other exposure factors for both groups, which
allows for evaluation of potential differences between cases and controls, useful
information for confusion assessment by statistical models.
2. Authors should indicate the distribution of cases and controls by province, and
if possible by ethnicity, for statistical evaluation of confounding by population
stratification, and to avoid bias.
3. In the literature (Kleinbaum and Klein, 2002) suggests three forms of statistical
analysis to the case-control studies matched: 1) Stratified analysis, 2) McNemar
analysis, 3) Logistic modeling.
One way to perform the stratified analysis is by using a chi-square
Mantel-Haenszel (M-H) when studying pairs (case-control) with similar
characteristics, where each pair forms a stratum. In this study indicate having
studied 236 cases and 240 controls matched for age and sex data shown in
Table 1 (p = 0.997 p = 0.915, respectively), however, if 1 case, matched with 1
control, have 236 or 240 pairs, I guess that the information in table 1, where we
have 178 cases for men and 58 women and 180 control men and 60 women,
failed the 1:1 ratio, making it difficult statistical analysis from the perspective of
paired data. The odds ratio (OR) of M-H is an estimate of summary adjusted for
matching variables. The McNemar test is exactly equal to the M-H chi square;
both are based on couples and not in individuals for the case-control studies
matched. Multiple logistic modeling is a suitable method when you need to
control for variables not included in the matching, as could be: family history,
ethnicity or place of origin, alcohol consumption, smoking, etc, variables that are
potential confounding. Conditional logistic regression model is appropriate for
paired data (when you have partners like). When this is not possible, an
alternative is to use non-conditional logistic regression, modeling appropriate to
be used to present the ORs, CI and p value in Table 2, statistical models should
be adjusted for matching variables (age and sex), and other variables not
considered in the matching, such as family history, smoking, alcohol
consumption and place of origin, or other factors. In the discussion referred
information of drinking and smoking in men, is striking that these data have not
been incorporated into the results.
4. If possible, using the above logistic model, to evaluate the interaction between
the sex variable and the genotypes of MLH1 2101C>A, which is the association
that seeks to demonstrate in Table 3.
5. Using the statistical program STATA v.11.1, I performed the calculations of
ORs controlled by the gender with the information in Table 3, and the M-H test
for homogeneity of the results, indicate that not there is sufficient evidence that
the ORs are different, so we cannot conclude that gender is a real effect modifier.
Similar results are obtained by unconditional logistic regression. On the other
hand, the amplitude of the confidence interval (1.04 - 68.06) of the M-H OR in
men (Table 3) indicates uncertainty of the estimator and therefore can be thought
of an estimator biased or confused. However, one cannot exclude that the
association is real. The intervals may be more accurate with a larger sample size
6. I believe that Table 3 should be eliminated or replaced by the results of logistic
regression. One of the limitations of stratified analysis is that there is loss of
statistical power, increasing the type 1 error.
7. It is recommended that the results of Table 4, are made by haplotype analysis,
and also perform the calculation of linkage disequilibrium. Analysis of current and
relevant in the field of genetic association studies. There are several programs in
which you can perform this analysis: Haploview, Haplo Stats, SNPStats, etc.
8. Authors should evaluate the Hardy-Weinberg equilibrium in controls for SNPs
DISCUSSION
9. Authors should restructure the discussion.
Minor Essential Revisions:
1. In methods: instead of normal use to refer to patients without cancer, using the
word control in the first paragraph of clinical samples .... A total of 240 normal
age-matched and sex individuals ... (similar situation in the first paragraph of
results).
2. In the third paragraph of methods (Mutation screening), I consider it important
to indicate the Primer sequences, and a summary of the conditions of PCR, so
that results can be replicated anywhere.
3. The discussion in the penultimate paragraph indicate:……However, it was only
marginally associated with the riesk of gastric cancer (p=0.069), this value cannot
indicate that there is a marginal association, the limit value set by the authors
was 0.05. It is clear that age is a confounding variable. The risk of developing
any type of cancer increases with age.
4. Strengthen the discussion on the limitations of the study, related to the low
frequencies of SNPs and mutations found, as well, the sample size.
5. At the end of the conclusions instead of…..it has been demonstrated….. that
the MLH1 2101 C>A mutation may contribute to an increased risk of gastric
cancer in males. Changed to: it was found that the MLH1 2101 C>A mutation
.........
Referencia
Kleinbaum DG and Klein M (2002). Logistic Regression: A self learning text.
Springer-Verlag, 2nd Ed.
Level of interest: An article whose findings are important to those with closely
related research interests
Quality of written English: Acceptable
Statistical review: Yes, and I have assessed the statistics in my report.
Declaration of competing interests:
I declare that I have no competing interests