Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
David Hoaglin <dchoaglin@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Is it valid to use the individual ratios (i.e. Xi/Yi) in the dependent or independent part of a regression model? |

Date |
Mon, 28 May 2012 14:04:04 -0400 |

Dear Jinn-Yuh, 1. ACR is clearly a ratio variable. If you are using ACR as the dependent variable in a regression, however, you are not dealing with its distribution as a free-standing variable (see my earlier comment). 2. The article on propagation of error and CV assumes a ratio of independent random variables, each of which has a normal distribution. (For each of the links that you sent, I looked only at the abstract.) It is not clear how, if at all, to apply its results to ACR, which is the ratio of quantities that are (as I understand it) not independent and not normally distributed. 3. 1) The abstract of the article from the PREVEND study says that the association is stronger for spot urine ACR than for spot urinary albumin concentration or the reciprocal of spot urine creatinine. I read that statement as interpreting the three values of the hazard ratio (1.41 vs. 1.26 and 1.16). It would be stronger if the difference were significant. Each of those hazard ratios is per SD in the log scale, which is a reasonable unit, but perhaps not the only unit. 3. 2) As a statistician, I claim no expertise on the choice of predictor variables for inclusion in your models. Though widespread, language such as "independent of" is not helpful in reporting results. Without reading the article, I suspect that the statement means that, after adjusting for the contribution of 24-hour urinary albumin excretion to ACR, body weight, 24-hour urinary creatinine excretion, age, and gender made significant contributions. If all of those variables were predictors in a single multiple-regression model, the results give the contribution of each after adjusting for the contributions of all of the others. If each was used in a separate regression, with 24-hour urinary albumin excretion as the only other predictor, then the adjustment is only for 24-hour urinary albumin excretion. Determining which variables should be included in a regression on the data of your patients is part of your analysis. 4. The abstract for "Ratio index variables or ANCOVA" contains too little information for me to understand what the authors did. As I discussed in an earlier message, in advance of the model-building phase of an analysis, the choice of statistical model is usually unclear. Clarifying the choice for a particular set of data requires both statistical judgment and substantive (clinical) judgment. Even if a model were well established in the literature, some skeptical model-building would often be appropriate. Models sometimes become established without adequate analysis. David Hoaglin On Mon, May 28, 2012 at 10:47 AM, <guhjy@kmu.edu.tw> wrote: > 1. Why is the ACR of a group of patients not a ratio distribution? > 2. The coefficient of variation is always higher for the ratio (X/Y) > than for either X or Y (http://www.ncbi.nlm.nih.gov/pubmed/17434158). > 3. In the PREVEND (Prevention of Renal and Vascular End-stage Disease) > study (http://www.ncbi.nlm.nih.gov/pubmed/22383750): > 1) The hazard ratio (95% CI) for predicting CV events were 1.41 (1.25, > 1.58) for spot urine ACR, 1.26 (1.1, 1.43) for spot urine urinary > albumin concentration, and 1.16 (1.01, 1.32) for the reciprocal of > spot urine creatinine, respectively. The 95% CI of the three HR > overlapped (i.e. the three HR were similar) although the author > claimed that ACR predicts better than either urinary albumin or > urinary creatinine. > 2) Body weight, 24 hour-urinary creatinine excretion, age and gender > predict ACR independent of 24 hour-urinary albumin excretion. In other > words, 24 hour-urinary albumin excretion is not the only determinant > of ACR. > 4. In "Ratio index variables or ANCOVA? Fisher's cats revisited > (http://www.ncbi.nlm.nih.gov/pubmed/19337988), Tu YK cautioned about > the use of ratios wherever the underlying biological relationships > among epidemiological variables are unclear (urinary concentration is > not the only determinant of urinary creatinine in this case), and > hence the choice of statistical model is also unclear. > > Jinn-Yuh * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Is it valid to use the individual ratios (i.e. Xi/Yi) in the dependent or independent part of a regression model?***From:*guhjy@kmu.edu.tw

**Re: st: Is it valid to use the individual ratios (i.e. Xi/Yi) in the dependent or independent part of a regression model?***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Is it valid to use the individual ratios (i.e. Xi/Yi) in the dependent or independent part of a regression model?***From:*Steve Samuels <sjsamuels@gmail.com>

*From:*guhjy@kmu.edu.tw

*From:*Tirthankar Chakravarty <tirthankar.chakravarty@gmail.com>

*From:*guhjy@kmu.edu.tw

*From:*Nick Cox <njcoxstata@gmail.com>

*From:*guhjy@kmu.edu.tw

*From:*David Hoaglin <dchoaglin@gmail.com>

*From:*guhjy@kmu.edu.tw

*From:*David Hoaglin <dchoaglin@gmail.com>

*From:*guhjy@kmu.edu.tw

*From:*David Hoaglin <dchoaglin@gmail.com>

*From:*guhjy@kmu.edu.tw

- Prev by Date:
**Re: st: Difference-in-differences analysis with binary data (repeated cross-sectional data)** - Next by Date:
**st: Stata and CPu usage** - Previous by thread:
- Next by thread:
**RE: st: Is it valid to use the individual ratios (i.e. Xi/Yi) in the dependent or independent part of a regression model?** - Index(es):