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Re: st: scoring system using marginally significant predictors
From
Donald Spady <[email protected]>
To
[email protected]
Subject
Re: st: scoring system using marginally significant predictors
Date
Thu, 14 Mar 2013 11:39:27 -0600
Did you split your population and test the prediction based on one half, against the other (unpredicted) half. Just depending on statistics to provide a scoring system, rather than also maybe using biologically plausible factors (e.g. sex, age staging etc) even if not statistically significant seems to me not a good approach.
Don
On 2013-03-14, at 11:26 AM, Marcos Vinicius <[email protected]> wrote:
> Hello ,
> I would like to share a strategy and have your opinion.
> Backward elimination was used to reach the final multivariate logistic regression model using p<0.10 as entry and stay level From the final model We then created a weighted scoring system by rounding all regression coefficients up to the nearest integer (ie, the smallest integer greater than the estimate). This method was based on the beta coefficients
> Once the final model was defined, we created integer weights for each variable. We calculated these weights by multiplying the model coefficients by 10.As like the article below we considered the statistically significant predictors and also the marginally significant predictors (0.05<p<0.10 ) to determine the scoring system.
>
> Is it ok or the scoring system should be strictly derivated using only statistically significant predictors?
> Thanks,
> Ann Thorac Surg. 2008 Jun;85(6):1938-45. doi: 10.1016/j.athoracsur.2008.03.014.
> Preoperative prediction of the occurrence and severity of complications after esophagectomy for cancer with use of a nomogram
> Thanks,
> Vinicius
>
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Donald Spady
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