Double-blind crossover pilot trial. Outcome is glucose AUC. Will also measure insulin intake.Treatment is PERT vs. Placebo. 5-6 subjects are planned, given the expense. The goal appears to understand the distribution, and 5-6 is probably reasonable. Cannot determine effect size with this number.
Family view on QoL after TBI
Goal is QoL metric following TBI. Interview survivors and loved one of survivors.
308 people included, about 10% of possible ~3,000. Research Match used to recruit. Prior analysis had order effects, could do a similar analysis with this project. Comparing those who completed and those who did not complete will be important, given the recruitment methods. Sample is not representative, will need to address that as well..
VICTR voucher request. Team has had a prior project with VICTR (Frank and Li). This will fit in the framework of a voucher.
Community informed description of informed consent. There is concern that trial participants may not fully understand the concept of randomization.
Participants would be randomized to one of three groups.
Responses will be presented in random order, we will want to determine how order affects choice.
Question is about sample size may be needed. Funding is there to
A correlation will require ~400 subjects, and a difference in proportions will take about the same. Having 386 in the smallest group of interest (for example, low literacy group) is a good way to think about sample size—the other groups can be more.
This should fit in a VICTR framework, should a voucher be desired.
10 years of Glenn surgeries, want to stratify low/high risk, outcome will be composite (e.g. Death, ECMO, Transplant, Readmit). This is for a QC/QA project, goal to prevent complication via ICU intervention. Vanderbilt patients only, n=220. A true risk prediction will require a sample in the thousands, so this won't possible with the current number of patients. This is staged palliation: Norwood, Glenn, Fontan. Time 0 is post Glenn procedure. Rather than risk model, could put structure on degrees of “badness”, a separate project. Or also could develop complication score, using non-fatal outcomes.
How do colonized strains differ from disease causing strains? How do the strains change over time, disease causing and colonization causing? Could do graphically, but question is how to do statistically.
Logistic regression, model time flexibly. Smoothed relative prevalence over time with confidence bands. For sample size: Sample size is set, could just acknowledge this and accept the limitations.
Assistance with analysis of survey data.
Meeting Notes: The data were collected using questionnaires given out during office visits. The questionnaire is about quality of life and preferences for advanced heart failure patients. The sample size is about 1300. This is a sub-study of a larger multi-center study.
Recommendations: 1. Could describe how many patients declined to fill out the questionnaire. 2. For each question, could report proportions and means. 3. For two questions, could use rank correlation to describe their relationship. There is no need to group numbers/categories. 4. Ideally for heart failure study, would have longitudinal data where patients fill in for each day/week what’s their worse outcome. 5. For further help could reach out for cardiovascular medicine biostatistics team (Meng Xu) or apply a VICTR voucher.
Some other graphics to consider: could color code a line plot for one parameter by the change in value of another parameter (though this could be difficult with the small sample size
Normalization would be difficult because the margin of error would be very high – do not recommend doing this
Can look at one parameter (a summary over the 8 subjects – likely the slope) and look at the correlation with a clinical parameter. Would want to report just the confidence interval for this, not the point estimate because that is difficult to estimate
Questions for the clinic: How to report the CIs for mortality rate? Should pairwise differences be displayed between the phases? Should a power calculation be done and how?
Meeting Notes: Wanted to assess if doing EMG adds diagnostic sensitivity or specificity for suspected neuropathy patients. Patients were excluded if they had known diagnoses for neuropathy. Have 89 patients’ data and each patient’s data contain both NCS and EMG. Would like to complete analysis by October.
Recommendations: 1. Using NCS only, patients would be diagnosed as “every nerve is normal”, “one nerve is abnormal”, or “two or more nerves are abnormal”. The first approach is to see for patients in each “bucket”, does EMG provide additional information? Identify the combination of findings that would be clinically meaningful. 2. Prespecify the information. For each “bucket”, what proportion of patients that find EMG useful is clinically meaningful? 3. Second approach is to present the 89 patients in two different ways: one with only NCS and one with both NCS and EMG (now we have 178 cases). Mix the cases up and give them to specialists to make diagnosis. Also ask the specialists their certainty for each diagnosis (could use slider scale or Likert scale). Could see what proportion of cases are discordant. 4. For the second approach, most of the analyses would be descriptive statistics with proportions and confidence intervals. Could look at patient characteristics and see if any characteristic is associated with discordance. Could run a logistic model with discordance as the outcome with patient characteristics as predictors. 5. Recommended to submit a VICTR voucher request. We will help to write the analysis section for the VICTR resource request. May want us to review data collection forms before collecting data.
Meeting Notes: The overall goal is to develop a dosing calculator. The discovery cohort included 28 subjects, and gender, BMI, and dose frequency were found to be associated with dosing levels. Some of the subjects have multiple samples (longitudinal data). First question is how to do a power analysis to validate this? Another aim was to look at b-cell depletion in groups with 2 different treatments. The binary outcome is based both on the literature and clinical decision, verses using a continuous outcome.
Recommendations: For the first aim, use a precision calculation instead of a power analysis. The precision calculation will validate the absolute difference within a pre-specified level. For the original regression, it is important to consider over-fitting (no more than N/15 parameters in the model). Do an internal validation using a resampling procedure. With a small sample size, a large cross validation will be necessary.
Meeting Notes: It was hypothesized that since COVID, numbers for cervical cancer screening would decrease, but they actually have increased. There was a sharp decline in March – May 2020, but it quickly jumped back up, and higher than the past years. The goal is to determine what could be leading to these increases and if there are specific demographic groups that did or did not increase with the rest.
Recommendations: Need to consider reasons why the numbers could be increasing (population increase, new clinics, new providers, changes in the health system, etc.). Defining some kind of denominator will help with making group comparisons. Some ideas for a denominator: get enrollment numbers or number of well women visits. If a denominator can be defined, you can look at a subset of your data that has had multiple screenings over the years and look at gaps between screenings (as a function of age) to see if there is a change. If a denominator cannot be determined, an epidemiologist or someone from the health policy department could be good contacts for discussing a different approach. It may also be possible to look at different events relative to each other, especially if a denominator cannot be defined. If this is a project you would like to make generalizable (and not just for an internal decision), it would be useful to apply for a VICTR voucher.
Meeting Notes: The PPV is the probability of final positive diagnosis given that the test was positive. The odds ratios listed in the table are for the association between age group and final positive diagnosis.
Recommendations: List the PPV for each age group, with their respective CI’s. Also list the difference in the two PPVs, with the respective CI. Keep the current odds ratio table. To cite the RMS package, use the link: hbiostat.org/rms
Meeting Notes: Most of the data are from hematology/oncology department and that was used as the reference group.
Recommendations: 1. For the reference group, either include it in the table or in the footnote. 2. The interpretation of a proportional odds model is “the odds of feeling more comfortable is…”. 3. For tables, present odds ratio with CI is enough. P-values and model fit statistics are not necessary. 4. Recommend PS software for calculating power ( https://biostat.app.vumc.org/wiki/Main/PowerSampleSize ).
Meeting Notes: A retrospective study looking at COVID negative and COVID positive pregnancies at VUMC last year. Variables collected include patient demographics, gestational age of delivery, maternal age at delivery, and other comorbidities.
Recommendations: 1. The next step after getting the data would be schedule another clinic to determine the feasibility of the project. 2. Propensity score matching could be used. 3. Recommend applying for a VICTR voucher if the project is feasible.
Meeting Notes: The goal is to compare pediatric patients with a positive genetic test for obesity to those with a negative test. The data is about 50-50 for positive and negative tests, with about 90 subjects total. Height and weight are both collected over time for all subjects, but there is variability in the number of data points and the overall time for each subject. The hypothesis is that a patient with a positive test for this gene will have more rapid weight gain and an earlier onset of obesity.
Meeting Notes: There are two aims of this study: 1. Find early (within the first 24 hours) predictors for success (survive the entire hospitalization). 2) Define a modified SAVE score with additional parameters. Since ELSO is an international registry, the data are not always complete. Some of the baseline characteristics of the ELSO registry include height, gender, race, hours on heart and lung machine, primary diagnosis. There are more than 16,000 subjects in the ELSO registry.
Recommendations: 1. Check what proportion of each variable is missing. If more than 30% of a variable is missing, might consider drop that variable from the model. For the rest of variables use multiple imputation for the prediction model. 2. Could do a logistic regression model that includes clinically important variables with binary outcome success/failure. Could divide data into training/test sets or do bootstrapping. 3. It would be ideal if we have time to death and time to discharge. In that case we could do survival model instead of logistic model. 4. Apply for VICTR voucher or work through collaboration.
Meeting Notes: POC glucometers are less reliable during hypoglycemia, which frequently occurs in neonates. One of the aims of the study is to quantify the difference in plasma compare device and lab. The sample size was calculated to be 60.
Recommendations: 1. The PS Power and Sample Size Calculation should be able to calculate sample size. Specify power and test used to get sample size in the application. 2. Need 10-20 patients per variable. The multivariate linear regression could include 3-6 variables.