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Library (Hmisc ) binconf ( 60, 60,method = 'wilson' ). Could look at precision of response for sub-groups (different baseline) also, if that's interesting. 9Sep13 Charles Phillips, Resident, General Pediatrics Mentor: James Gay I have a data set that I would like to have some help with the analysis. I am not sure which test(s) would be the best to identify significance for my data. My data set contains general pediatrics patients admitted to the resident services from July 2009 to June 2012.

I want to measure select patient outcomes before and after the resident duty hour restrictions implemented on July 1, 2011. Specifically I want to compare July 2009-June 2011 vs July 2011-June 2012. One of my questions is, given the fluctuation in patient volume at children's hospital should I break down the data into quarters or months for analysis? The specific markers I want to look at are the following:. 30 Day Readmissions (column E).

Length of stay (column L). RCW Inlier (column M). Charges in 2012 USD (column T) Finally I would like to see if there is a significant difference in the number of patient errors as measured by ICD9 codes. I have sent a list of the codes I would like to compare.

As a control I would like all of the same information for the nonteaching hospitalist team which did not change in structure for the measured time periods. The only major change during that time frame was the structure of the resident hours. The attendings were consistent. The reason I was wanting to look at the nonteaching hospitalist team as well is that in theory nothing should have changed for that team and we hoped to use that as a proxy for any unmeasured variance over that time period. I have already tried my best to create a homogenous group of patients (even within gen peds) based on their clinical severity by limiting the group to patients with a relative cost weight (a surrogate for complexity) of less than 1.

Need to consider cost vs. Charges, separating diagnoses present on admission from those that developed as complications during hospitalization. Suggest fitting monthly trends on all outcome variables before the change and a separate trend after the change. General statistical test would test whether the two curves are really one curve but allow for slopes/nonlinearity.

Need to code 'errors'; do you analyzing them individually or in groups or by summing the number? Celeste Ojeda Hemmingway MD, Assistant Professor, OB-Gyn, Assoc. Residency Prog Dir I am planning to come to Monday's biostat clinic to get help on how to mine my dataset in SPSS. It's an educational project looking at an assessment tool and I want to think of opportunities I have to validate the tool I'm using. I will bring my SPSS dataset with me, but I suspect we will just get started on it.

This is an assessment tool looking at evaluating resident surgical/procedural skills. I would like to validate the tool and look at potential ways to take out information. There is a rater and a proceduralist and they get a numerical score as well as a compositite assessment of competency. I would like to consider ways to validate it (inter-rater reliability - though this is tricky because only one rater present, categorical by year etc) and perhaps correlate the numerical score to the composite competency score. I fear this might need a little more explanation and I will be happy to bring the tool, examples, and the data file.

Part of Masters in Health project (MHPE). 5-point Lickert scales for skills assessment; overall competency scale; watch for lack of variability in ordinal choices. Looking at April-June. New residents starting in July. Can the survey instrument be shortened? How to validate it. Evaluators are uniquely coded; validation would be strengthened by using pairs where the evaluators are different.

Basic method of evaluation in the past: compare competency on one procedure with competency on previous and next procedure. Literature has, for different procedures, summed the items and compared to global skills checklist. Start with scatterplots and other graphs. Is a hierarchical analysis needed because of nesting?

Multi-level model needed?. Basic statistical measure: Spearman rho rank correlation between one item (or sums of items) and global assessment. Could assess relationship between years of experience and scores (individual procedure plus global). Can have a statistical model for resident scores where a smooth function of calendar time is included.

Use actual date of evaluation. Other covariates: age, number of previous procedures done, etc. Try to lot all raw data. Can scenarios help with validation?. Look at variablity across evaluations for different evaluators; evaluators who vary the most may be the most discriminating Susan Salazar, Assistant Prof, OB/Gyn, working with Eduardo Dias and Meghan Hendrickson. Mentor: Kim Fortner I would be interested in meeting with a statistician about design study and statistical interpretation. My study involves the use of a hand held ultrasound machine and I want to demonstrate that it improves workflow (i.e.

Decreases length of stay) for women in our triage unit. I also want to show that it shortens the time of our 'code' in labor and delivery (obstetrical emergency). I am collecting the data for these events for the months of Feb-May so I will have a baseline for comparison. So far, I have 36 women who received ultrasounds in triage from Feb-May. I'm thinking just a paired t test or possibly ANOVA, but I'd love your input. I will be applying for a VICTR grant as soon as I have the design study and statistical analysis framework ready.

Standard non-portable machine has a significant warmup time. Length of time in triage is of key interest. 2 types of randomized designs (individual vs. Randomize so that a given day is all-in or all-out).

Will remove part two (the staff satisfaction survey) due to lack of reasonable tool. Will keep the resident OBET exploratory arm as a type of qualitative data collection that may lead to development of another study.

Estimated 20 hours of biostatistical support from VICTR 19Aug13 Sharmin Basher, Clinical Fellow, Division of Cardiovascular Medicine See I am planning to investigate the effectiveness of supplementary written information given to women during cardiovascular disease prevention counseling compared to verbal counseling alone. I'm randomizing patients who are new to prevention counseling into an intervention arm (verbal counseling with written supplements) and control arm (only verbal counseling, no written supplements). Both groups will receive survey prior to the visit to assess their knowledge.

The intervention group will receive a pamphlet emphasizing what is discussed verbally during the visit and the control group will only receive verbal counseling. Both groups will take the test again in 1 week to assess their knowledge. I am not sure what sample size I would need to determine a difference in knowledge. The survey I am using is a valid and reliable tool and is comprised of 25 questions. I've attached the paper that describes the development of the tool.

On page 66, they mention in Table 2 the means and standard deviations. In order to determine my sample size, what SD should I use? I've calculated that I should have 20 patients in each cohort for a total of 40 subjects. Muldowney has asked that I randomize the groups by stratifying by patient education level and by the person providing the verbal counseling (there will be 2 people providing verbal counseling: Dr. Emily Kurtz and myself). Stephanie Sohl, Department of Medicine I would like guidance on conducting and interpreting a logistic regression (outcome is two categories that are nearly evenly split; N=191).

Materials are in /clinics/general/sohl. Discussed treating more of the continuous and ordinal variables as continuous to increase power (age, education, duration of relationship, number of visits, etc.). Number of candidate variables (candidate d.f.) that can be 'safely' analyzed (i.e., the fitted model would likely replicate in another similar sample): one rule of thumb is to have no more than m/10 candidate d.f. Require (Hmisc ) binconf ( 14, 159 + 14 ) PointEst Lower Upper 0.08092486 0.04881523 0.1312413. Can use a t-test or better: Wilcoxon-Mann-Whitney 2-sample rank-sum test to compare age for those upgraded vs. Not upgraded. For location: chi-square test for a 2x2 table.

Power is limited by 14; confidence limits for differences will keep limited sample size in perspective Ashley Karpinos; Med-Peds/VA Quality Scholars; MPH student. Cross-sectional study to determine prevalence of hypertension in collegiate male athletes esp. Setwd ( '/home/bigconf/clinic' ) library (foreign ) countdata lower limit of normal (time = infinity if died) 28 Feb 2011 Elizabeth Moore, Nursing. Planning a Cochrane neonatal review for intervention: skin-to-skin contact of NICU child with mother. Outcomes are breast feeding, mother-infant attachment, and adverse events. Some suggestions from protocol review:. Using fixed or random effects in analysis due to diversity in control and skin-to-skin conditions.

May try to account for dose-response effect (dose being the frequency and amount of time there was skin-to-skin contact). May plan to subset analysis for different control conditions.

Typically if there are 3 studies, random effects models are used for meta-analysis. Avoid sensitivity analyses unless there is a clear decision rule for differences in models, better to use robust methods. Just used odds ratios, not both odds and risk ratios. In addition to individually randomized trials, they suggest cluster randomized and crossover trials may be included. Will consider cluster RT if estimates of intervention are adjusted for baseline differences in patient population. Crossover trials may have carryover effects, so only data from the first randomized time period would be included. Request VICTR funding to work with Chris Fonnesbeck.

Kathy Hartman and Melissa work with many Cochrane reviews in Epidemiology. Dan Kaizer, Cardiology. Want to plot the impact of polymorphism on absolute risk. Consider x-axis with probability of afib as a function of all variables. Then include the polymorphism in the model and plot the probability of afib on the y-axis. Performed logistic regression with interaction between statin and polymorphism in SPSS. Difficult to contrast groups in SPSS.

To get the OR and 95% CI of interaction effect, take antilog(Effect), antilog(Effect+-1.96.SE). How do you combine four regression estimates if you have four confidence intervals? Consider a weighted average of the odds-ratios. Veronica Oates, TSU Family and Consumer Sciences.

Survey of 52 parents on parent/child interaction. 10 questions on parent-child interaction, interested in developing a scale to compare with other scales. Testing for validity and reliability of measuring a construct with survey questions is a study in itself.

Consider searching for validated instrument when possible. 21 Feb 2011 Alicia Fadiel, Epidemiology.

Time to event analysis for polymorphisms associated with disease progression-free/overall survival in Shanghai breast cancer study. There are three studies: SPCS1, SPCS2, SPCSS.

Time of diagnosis is start time. Surveillance for progression/death should be similar across studies. 8 polymorphisms of interest looking at research maturity over time (false positive biomarker findings) for different studies. Initial research is either less precise or biased towards 'winning' biomarkers. For Kaplan-Meier plots, try confidence band for difference in survival curves from SPCS1 to SPCS2. Try an interval chart (e.g., dot plot or Forrest plot) to show hazard ratios and confidence intervals by Stages 1/2.

Consider including stage in the Cox PH models and test for interaction between genotype.stage. IF significant interaction, then 'estimated effects of a genotype are in disagreement with each other by study'. Merida Grant, Psychology. Interested in learning more about mixed effects models for analyzing repeated measures in stimulus experiments. A nice summary graphic for longitudinal data is the 'spaghetti plot' with time on the x-axis and response on the y-axis - each subject has one line. Sometimes a LOWESS (locally weighted smoother) curve is fit to summarize the trend. Karen Rufus, OTTED.

Karen is preparing a dissertation proposal and would like feedback on methods. She plans to survey 15 directors at 15 weight loss centers to examine predictors of success/adherence. Suggest collecting objective information in addition to the opinions of directors. 15 centers may not be sufficient to detect differences, though patient level data may be recovered. Consider polling more centers, but not at the cost of a poor response rate. Next step would be to prepare a data analysis plan.

31 Jan 2011 Evan Brittain, Cardiology. Interested in the agreement of two software users in MRI measurement. Try estimating the pairwise difference and calculating the confidence interval to determine difference among users. This method does not give consideration for repeated measures. To compare users, a linear mixed effects model will account for variability within raters and repeated measures per patient.

Quinn Wells, Cardiology. Interested in modeling the effect of two continuous variables (and their interaction) on the occurrence of a heart related event (binary). Rather than cutting the continuous variable to tertiles, try using logistic regression with an interaction term. Logistic Regression Model lrm(formula = form, data = dat) Frequencies of Responses 0 1 18 11 Obs Max Deriv Model L.R. P C Dxy 29 0.6 4.81 3 0.1862 0.629 0.258 Gamma Tau-a R2 Brier 0.259 0.126 0.208 0.204 Coef S.E.

Wald Z P Intercept 1.3057699 1.669843 0.78 0.4342 PDGFABBBngmL -0.0859901 0.069629 -1.23 0.2168 VEGFpgmL -0.0112985 0.009698 -1.16 0.2440 PDGFABBBngmL. VEGFpgmL 0.0005501 0.000435 1.26 0.2060 Effects Response: Collat Factor Low High Diff.

Lower 0.95 Upper 0.95 PDGFABBBngmL 18.480 38.284 19.804 -0.33 0.70 -1.69 1.04 Odds Ratio 18.480 38.284 19.804 0.72 NA 0.18 2.83 VEGFpgmL 80.644 154.370 73.726 0.18 0.35 -0.50 0.86 Odds Ratio 80.644 154.370 73.726 1.20 NA 0.61 2.35 Linear Regression Model ols(formula = EF PDGFABBBngmL. VEGFpgmL, data = dat) n Model L.R. R2 Sigma 29 1.203 3 0.04063 13.54 Residuals: Min 1Q Median 3Q Max -20.293 -9.316 -1.266 7.484 32.356 Coefficients: Value Std. Require (Hmisc ) tang <- csv. Get ( 'tang.csv', lowernames = TRUE ) tang <- upData (tang, rename = c (patient.= 'patient', patient.age = 'age', previous.abnormal.pap.yes.no.= 'previous.abnormal.pap', pregnant.yes.no.= 'pregnant' )) names (tang ) <- gsub ( 'hpv.' Require (Hmisc ) binconf ( 50, 100 ) n <- 100; binconf (n / 2, n ) n <- 200; binconf (n / 2, n ) n <- 400; binconf (n / 2, n ) n <- 800; binconf (n / 2, n ) n <- 200; 1.96.

sqrt (.25 /n +.25 /n ) n <- 400; 1.96. sqrt (.25 /n +.25 /n ). Beware of the difficulty of estimating relative errors when error rates are low. Regression models can account for multiple characteristics simultaneously. Outcome could be binary (error/no error) ordinal (to capture severity of error). If want to model 5 covariates would need at least 200 + 20.5/Prob(error) = 700 cases if overall Prob(error) = 0.2. This is a target sample size to achieve good predictive accuracy for many covariate combinations.

533 are need of Prob(error) = 0.3. Number of covariates is the number of continuous + no.

Binary + sum of k where k = number of levels of categorical variables less one, for those having 3 or more categories. 5 category + 3 category + 10 category = 15 parameters to estimate + intercept instead of 5. Precision of odds ratio when there are N subjects in each of two groups (fold-change or multiplicative margin of error). N <- 640; exp ( 1.96. sqrt ( 4 /n + 4 /n )). N=640 in each group will allow estimation of an odds ratio to within a factor of 1.25.

May need to audit cases in which neither reviewer found an error 17Aug09 Natasha and Carrie Geisberg, Cardiology. Studying release of vegf. Ubuntu mpeg stream clip download for mac.

Should she consider the location? Means <- c ( 3.25, 6.9, 8.6 ) sds <- c ( 0.4, 0.9, 0.2 ). sqrt ( c ( 8, 8, 10 )) sds 1 1.1313708 2.5455844 0.6324555 plot (means, sds ) sds.

sqrt ( 2 ) 1 1.6000000 3.6000000 0.8944272 # pooled estimate of SD: n. N <- 5 sqrt ((n - 1 )/ qchisq (.025,n - 1 )) sqrt ((n - 1 )/ qchisq (.975,n - 1 )). Would have to take an SD estimate from the pilot study with a grain of salt (i.e., multiply it by 2.87). Would need n=25 to get multiplicative moe 1Kg. Took picture of skin immediately after removal and up to 36h later.

15 patients; 1 had only 2 readings before art line came out; 1 had 21; avg.

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