[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
Ricardo Ovaldia <ovaldia@yahoo.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Comparing change in rates - frustrating problem, please help |

Date |
Thu, 5 Feb 2004 16:07:33 -0800 (PST) |

Thank you Joseph and Kieran. Obviously this was not the easy question I though it was. I have spent several days contemplating the answers and playing around with my data. Although I find Kieran's conditional logistic approach appealing, I understand and agree with Joseph's concerns and objections. Faced with the need to analyze these data and the eventual submission for publication I fear that reviewers may disagree with which ever method I select. The issue becomes more complicated when one considers the effect of additional covariates such as sex on the intervention. Regardless of all this, I appreciate tremendously Joseph and Kieran comments and time thinking about this problem. Ricardo. --- Joseph Coveney <jcoveney@bigplanet.com> wrote: > > Kieran McCaul posted results from a randomized > parallel-group design study to > illustrate the use of conditional logistic > regression. The study randomized > households to an intervention designed to promote > banning of smoking in the > home. Policy in the home was measured before and > after intervention. Kieran > invited Ricardo and I to respond with what we think > of advocating conditional > logistic regression to assess the efficacy of the > intervention for before-and- > after studies based upon the results posted for that > study. > > I don't claim to speak for Ricardo, but his original > question related to > imbalances in the baseline rates of the outcome > between the two parallel > intervention groups. It appears that Kieran's study > was successful in its > randomization (or used stratified randomization and > didn't lose too many > households to dropout), because the proportions of > households banning smoking > at baseline were nearly identical between the > intervention groups. With > essentially identical rates of baseline, there would > be little or no cause for > concern about confounding due to it and little > statistical difference in > including baseline as a covariate. And, in fact, > both conditional logistic > regression approach and the so-called ANCOVA-like > multiple logistic regression > approach give essentially similar results in this > balanced study. (I think the > same would have obtained for Ricardo's study had the > baseline rates of seatbelt > use been similar between the two intervention > groups.) > > But, let's look at the issue of which approach is > more suitable when the > concern is, as it was for Ricardo, to analyze an > intervention effect _in the > face of an imbalance in the baseline rates of an > outcome_. > > If Kieran will indulge me one more time to use a > fictional dataset to > illustrate a point, let's say that Kieran's > randomization method did not > stratify on baseline household smoking policy, and > suffered an unfortunate > imbalance due to chance, for instance a 50 : 50 > ratio of households banning > smoking at baseline in the nonintervention group, > but a 75 : 25 ratio in the > intervention group. Let's say that 2 of the 50 > households that previously > banned smoking in the nonintervention group now > permit it, a worsening of 4% > (if your health policy is to ban smoking), and that > only 1 of the 50 households > that didn't ban smoking now do so in the > nonintervention group, a meager > improvement of 2%. Let's say that 4 of the 75 > households that banned smoking > at baseline switched and permitted smoking in the > home after the intervention, > and 2 of the 25 households that didn't ban smoking > switched as a result of the > intervention. The results of the intervention are a > slightly greater 5.3% > worsening (compare to 4%) in the former nonbanning > household population, but a > much greater 8% (compare to 2%) improvement among > the formerly permissive > households. > > Now, the effects of intervention are no great > shakes, but I think that it would > be safe to say that it's not *nothing*, especially > if you somehow take into > account the possible confounding effect of the > chance unfortunate imbalance in > baseline policy between treatment groups. > > But, by the conditional logistic regression > approach, it *is* nothing--the odds > ratio for both nonintervention and intervention > groups is 0.5 (McNemar's test > uses only the off-diagonal values and ignores the > diagonal values) so the ratio > of the two odds ratios is 1.0, and this is what the > conditional logistic > regression dutifully reports: the period term is > 0.5 and the interaction > term's odds ratio is 1.0 with a Z-statistic of 0.00 > and a p-value of 1.00. > Granted, the confidence interval encompasses a lot, > but the point estimate and > hypothesis test for the interaction term (which is > ostensibly the effect of > intervention) just don't give the same take-home > message as inspection of the > data. So, my conclusion differs from Kieran's on > this; I don't think that > conditional logistic regression is valid to test for > differences between > treatment effects (differences between treatment > differences, which are between- > subject effects) in parallel-group designs with a > repeated binary outcome > measure, especially in the presence of baseline > differences in the outcome > measure, which are ignored in the conditional > logistic model. > > In contrast, the ANCOVA-like, baseline-as-covariate > multiple regression > approach does provide a separate, and I think > competent, handling of baseline > differences and their potential for confounding. In > the fictitious example, > this approach shows the pronounced effect of > baseline smoking policy as > expected, and it shows that the odds ratio for > intervention isn't 1.0 given > baseline differences between intervention groups. > The saturated model (with > the interaction term) also helps to put the > potential for confounding into > perspective. (The do-file for all of this is below > for anyone interested.) > > It seems that at least some of the discrepancy > between the two approaches > reflects Simpson's paradox. This is the same > underlying phenomenon that > results in bias in logistic regression coefficients > (and in nonlinear > regression, in general) when important covariates > are left out of the model. > This is what Frank E. Harrell Jr.'s lecture dealt > with in the URL given in my > last posting. And it relates to the > "noncollapsibility of odds ratios" that > epidemiologists sometimes refer to. > > In fairness to us all (Kieran, Ricardo and me), it > seems that the matter of > which approach is better isn't completely settled > even for *linear* models, > where this incollapsibility-of-odds-ratios > phenomenon and the incidental > parameters problem don't apply: there is a thread > ("Repeated measures and > including time zero response as baseline covariate") > on sci.stat.consult that > was started on May 7 of last year by Frank Harrell. > Professor Harrell wrote a > well received book on regression modeling and is now > chairman of a department > of biostatistics, yet even he asks, "Has anyone come > across some practical > guidance for when to include the first measured > response (at time zero) as a > baseline covariate as opposed to the first repeated > measurement in a > longitudinal data analysis?" > > Joseph Coveney > > ------------------------------------------------------------------------------- > > clear > tempfile tmp > set obs 100 > generate byte ban0 = _n > _N / 4 > generate byte ban1 = ban0 > replace ban1 = !ban1 in 50/53 > replace ban1 = !ban1 in 1/2 > * > * Intervention group > * > display 4 / 75 // switching by banners > display 2 / 25 // switching by permitters > mcc ban1 ban0 > generate byte intervention = 1 > save `tmp' > clear > set obs 100 > generate byte ban0 = _n > _N / 2 > generate byte ban1 = ban0 > replace ban1 = !ban1 in 50/52 > * > * Nonintervention group > * > === message truncated === ===== Ricardo Ovaldia, MS Statistician Oklahoma City, OK __________________________________ Do you Yahoo!? Yahoo! Finance: Get your refund fast by filing online. http://taxes.yahoo.com/filing.html * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: Comparing change in rates - frustrating problem: questionable results***From:*Ricardo Ovaldia <ovaldia@yahoo.com>

**References**:**Re: st: Comparing change in rates - frustrating problem, please help***From:*Joseph Coveney <jcoveney@bigplanet.com>

- Prev by Date:
**st: Re: SUEST and Xtreg.** - Next by Date:
**st: overlay in ltable** - Previous by thread:
**Re: st: Comparing change in rates - frustrating problem, please help** - Next by thread:
**st: Comparing change in rates - frustrating problem: questionable results** - Index(es):

© Copyright 1996–2015 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |