Reviewer's report
Title:Family history of cancer on survival of patients with gastrointestinal cancer
in northern Iran, using frailty models
Version: 1Date:19 January 2011
Reviewer: Hans C. van Houwelingen
Reviewer's report:
The data could be of interest and worth publishing; however the data-analysis is
far from satisfying.
Issues to be addressed:
Major
1. Kaplan-Meier estimates of the survival function should be shown, to visualize
the data and the effect of important predictors, like gender and family history.
2. The p-values of Table I, compare males and females, I presume. That is not
very interesting. I rather had seen the p-values of the log-rank test and hazard
ratios derived from simple Cox models for each covariate separately.
3. It does not make sense to compare the partial likelihood of a Cox model with
the full likelihood of parametric models. Concluding that the Cox model behaves
poorly on the basis of Table II is not correct
4. Table III is a bit incomprehensible. First of all, I would rather omit all predictors
who do not seem to have an effect at all. Secondly, I expect more than one
parameter for categorical covariates with more than two categories like Type and
Ethnicity. Thirdly, I have never seen the standardized variability as used here. It
is just 1/Z^2, where Z=Wald statistic, I presume. You might as well give the
p-values.
5. Adding a gamma-frailty term improves the likelihood. That is fine. However,
that should be interpreted with care. It shows that the simple model can be
improved. Similar improvements could be obtained by adding time-varying
effects in the Cox model, as already proposed by Cox in his original paper. The
existence of “heterogeneity” can only be concluded if you firmly believe that true
model is a simple linear model.
Minor
1. Since all your models have the same number of covariates, you do not need
AIC. You can just compare the log-likelihoods.
2. I do not quite see the usefulness of the deviance residuals plots. They tell the
same story as the likelihoods. If you want to give them, they would need some
explanation for the clinical readers.
Level of interest: An article of limited interest
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
Reviewer's report
Title:Family history of cancer on survival of patients with gastrointestinal cancer
in northern Iran, using frailty models
Version: 1Date:19 January 2011
Reviewer: Hans C. van Houwelingen
Reviewer's report:
The data could be of interest and worth publishing; however the data-analysis is
far from satisfying.
Issues to be addressed:
Major
1. Kaplan-Meier estimates of the survival function should be shown, to visualize
the data and the effect of important predictors, like gender and family history.
2. The p-values of Table I, compare males and females, I presume. That is not
very interesting. I rather had seen the p-values of the log-rank test and hazard
ratios derived from simple Cox models for each covariate separately.
3. It does not make sense to compare the partial likelihood of a Cox model with
the full likelihood of parametric models. Concluding that the Cox model behaves
poorly on the basis of Table II is not correct
4. Table III is a bit incomprehensible. First of all, I would rather omit all predictors
who do not seem to have an effect at all. Secondly, I expect more than one
parameter for categorical covariates with more than two categories like Type and
Ethnicity. Thirdly, I have never seen the standardized variability as used here. It
is just 1/Z^2, where Z=Wald statistic, I presume. You might as well give the
p-values.
5. Adding a gamma-frailty term improves the likelihood. That is fine. However,
that should be interpreted with care. It shows that the simple model can be
improved. Similar improvements could be obtained by adding time-varying
effects in the Cox model, as already proposed by Cox in his original paper. The
existence of “heterogeneity” can only be concluded if you firmly believe that true
model is a simple linear model.
Minor
1. Since all your models have the same number of covariates, you do not need
AIC. You can just compare the log-likelihoods.
2. I do not quite see the usefulness of the deviance residuals plots. They tell the
same story as the likelihoods. If you want to give them, they would need some
explanation for the clinical readers.
Level of interest: An article of limited interest
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
Please review this paper.
user
Reviewer's report
Title:Family history of cancer on survival of patients with gastrointestinal cancer
in northern Iran, using frailty models
Version: 1Date:19 January 2011
Reviewer: Hans C. van Houwelingen
Reviewer's report:
The data could be of interest and worth publishing; however the data-analysis is
far from satisfying.
Issues to be addressed:
Major
1. Kaplan-Meier estimates of the survival function should be shown, to visualize
the data and the effect of important predictors, like gender and family history.
2. The p-values of Table I, compare males and females, I presume. That is not
very interesting. I rather had seen the p-values of the log-rank test and hazard
ratios derived from simple Cox models for each covariate separately.
3. It does not make sense to compare the partial likelihood of a Cox model with
the full likelihood of parametric models. Concluding that the Cox model behaves
poorly on the basis of Table II is not correct
4. Table III is a bit incomprehensible. First of all, I would rather omit all predictors
who do not seem to have an effect at all. Secondly, I expect more than one
parameter for categorical covariates with more than two categories like Type and
Ethnicity. Thirdly, I have never seen the standardized variability as used here. It
is just 1/Z^2, where Z=Wald statistic, I presume. You might as well give the
p-values.
5. Adding a gamma-frailty term improves the likelihood. That is fine. However,
that should be interpreted with care. It shows that the simple model can be
improved. Similar improvements could be obtained by adding time-varying
effects in the Cox model, as already proposed by Cox in his original paper. The
existence of “heterogeneity” can only be concluded if you firmly believe that true
model is a simple linear model.
Minor
1. Since all your models have the same number of covariates, you do not need
AIC. You can just compare the log-likelihoods.
2. I do not quite see the usefulness of the deviance residuals plots. They tell the
same story as the likelihoods. If you want to give them, they would need some
explanation for the clinical readers.
Level of interest: An article of limited interest
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
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