1. S1316 analysis details Garnet Anderson Katie Arnold
SWOG Statistical Center
Fred Hutchinson Cancer Research Center
October 24, 2014

2. Design of S1316 Consent to Randomization Surgery Surgery
Yes
Best Medical Care
Consent to Randomization
Surgery
My goal is to provide you with additional details about the analysis so that you will understand some of the important features of the design.
We have proposed a hybrid design, combining a small randomized trial with a parallel observational study. The general concept is that all patients with malignant bowel obstruction that leads to a hospital admission but is not considered acute, can be treated either with surgery or medical therapy but that it is not known which provides the best outcome, when measured as days spent outside of the hospital over the subsequent 3 months. It is this equipose—our lack of data to support one approach over the other, that motivates this study and in fact, justifies randomization.
We didn’t propose a full-scale randomized trial for feasibility reasons. The data are very sparse to support the key design features of such a trial so it would be difficult to defend any full-scale design. In addition, we have limited experience, and in fact no experience in SWOG itself, in recruiting to such a trial, in this clearly vulnerable population.
We didn’t propose an observational study because it was unclear that the results for this question would be interpretable, because of the multiple factors and potential sources of bias that can enter into this decision, some of which could be difficult to measure or control for.
So as SWOG’s entrée into this area, we have proposed a novel design where we have married what should be considered a pilot trial that involves randomizing 50 patients, with a larger observational study of 130 patients. We believe this is a practical approach to developing an evidence base in this important area. It is possible that the results may not be definitive, but if we are able to complete the study as designed, it will give the best information to date on this question.
If patients consent to randomized, they will be allocated in equal proportions to surgery or best medical care, with stratification for initial disease site.
For patients who are eligible and willing to participate but not be randomized, they and their health care providers will document their choice of initial treatment approach, again surgery or best medical care, at the time of consent, and this will be considered their treatment assignment for the purposes of analysis. No
Best Medical Care

3. Key variables in S1316 analysis
Outcome: Number of “good” days (D)
Counted from day of randomization or enrollment through day 91
Design factor: Consent to be randomized (C)
Treatment: Surgery vs Best Medical Care (S)
Adjustment factors (X)
Cancer site: colorectal vs ovarian vs other
Patient and disease characteristics
Provider characteristics
The key elements of the analysis are shown here.
The Outcome is the number of “good” days over the 91 days from enrollment, where good is defined as not hospitalized.
The analysis needs to account for several other variables.
First, we will need to account for the potential of patients who consent to randomization being different from those who do not. I’ll call this indicator variable C for consent.
There is the traditional treatment variable, in this case S, indicating Surgery or not.
Finally, there are several other adjustment variables that will be considered.
The initial cancer site, which is a stratification variable in the randomization scheme and should also be accounted for in the analysis of the observational component. We will also want to consider controlling for other patient and disease characteristics such as age, sex, disease stage, etc. Finally, we would like to control for selected health care provider characteristics, such as specialty of the care provider.

4. Analysis uses a regression model using data from both cohorts
Good Days (D) = function of : a0 + a1*X Baseline, adjustment factors + a2*C Difference between randomized and non-randomized cohorts + b1* S Effect of surgery + b2* S*C Difference in effect of surgery between randomized and non-randomized cohorts
The primary analysis will be based on a regression model that incorporates all of these factors.
Specifically, we will model good days as a function of an baseline mean which is alpha 0 as well as the adjustment factors.
We then include the design factor C to control for any other unexplained difference in the randomized and non-randomized cohorts.
We include the treatment variable S to indicate the effect of surgery, relative to best medical care, as represented by beta 1.
We include an interaction between surgery and consent, to allow for the potential of the surgery effect to differ between the randomized and non-randomized cohort.

5. Analysis approach
Intent-to-treat (ITT) and pseudo ITT based on treatment choice at consent
After controlling for all known confounders, determine if surgery effect is similar across cohorts (Is b2 = 0?)
If so, estimated effect of surgery is b1 based on all data
If not,
b1 is the estimated surgery effect based on RCT alone
b2 is an estimate of the bias in the non-randomized study
The modelling strategy has 2 somewhat unusual features. The randomized trial will use the standard intent-to-treat approach, in which all patients outcome results will be attributed to the arm to which they are randomized, regardless of whether this is how they were treated.
For the non-randomized or observational component, we try to mirror that approach as closely as possible. In this pseudo-ITT approach, we will analyze the outcomes for patients in the non-randomized component using the treatment assignment selected and documented at time of consent, regardless of whether that was the treatment approach actually implemented.
The second unusual aspect, at least when compared to a typicaly randomized trial analysis, is the need to measure and control for confounders. Our approach will be to model the adjustment factors as best we can with the aim of making the effect of surgery the same in both cohorts. That is, can we make the beta 2 be zero in this analysis by adjusting for these confounders? If so, then beta 1, the coefficient associated with the surgery variable becomes the sole estimate of treatment effect and it is based on the entire dataset. If beta 2 is not zero, then it is an estimate of the bias in the non-randomized component and beta 1, the effect of surgery, is based on the randomized trial alone.

6. Design/analysis of S1316 b1 b1+b2 Surgery Surgery Best Medical Care
Yes b1
Best Medical Care
Consent to Randomization
Surgery
To try to make this a little more visual, beta 1 measures the difference between surgery and best medical care in the randomized component. Beta 1 plus beta 2 measures the similar difference in the non-randomized component. If beta 2 is statistically equivalent to zero, the treatment effect in both components is beta 1 and it is acceptable to combine the data. We will get a reasonably precise estimate of the treatment effect using the combined data.
If beta 2 is not zero, we will we learn that there is inherent bias in an observational study of this nature and we will have a preliminary estimate of the treatment effect, based on the randomize trial alone, and that will guide what the next research steps should be.
b1+b2 No
Best Medical Care

7. Some critical aspects for implementing hybrid design
Need patients randomized at each site
Monitoring treatment choices in the non- randomized component
Collection of confounder variables for the multivariate analysis
Need all patients managed and monitored identically after initial treatment choice
For this hybrid approach to be robust, we need each site to randomize patients as well as enroll into the observational component.
We also need to monitor the treatment choices in the non-randomized component because if it is terribly off-balance (e.g., 90% choose surgery) we will not have enough data to in the two groups to support this analysis.
We are collecting a broader range of potential confounders in this study, because of the non-randomized component. We need these on each patient to assure that we can appropriately adjust for these factors
The analysis also assumes that all patients are managed and monitored identically after initial treatment choice is made—that is, regardless of whether they consent to randomization or select their treatment with their care provider, all remaining aspects of the protocol is identical. This is another step in eliminating any bias between the randomized and non-randomized component.