Encouraging Blood Donation in Patients With a Blood Type in Short Supply - Part 2
Phase: NA
Conditions: Behavior, Health
Sponsor: Geisinger Clinic

Encouraging Blood Donation in Patients With a Blood Type in Short Supply – Part 2 
 
Protocol and Statistical Analysis Plan 
 
NCT05825300 
 
July 3, 2023 
 
 
 
 
 
 
 
 

Protocol 
 
Scientific background 
 
There has been a years-long national shortage of several blood types in the U.S., including in 
the Geisinger community. Previously, the study team collaborated with Miller Keystone, where 
Geisinger refers patients who wish to donate blood and from whom Geisinger receives blood for 
clinical purposes, on an outreach study to encourage blood donation in patients with needed 
blood types. The study demonstrated that, compared with a no-message control group, patient 
portal messages sent to patients with needed blood types increase patients' likelihood of 
attending donation appointments. However, results were ambiguous with respect to which of 
two message versions was most effective. One version, which stated that the patient had a 
needed blood type (blood-type message), caused a numerically, but not significantly, higher 
number of patients who attended appointments compared to the other version, which did not 
state that the patient had a blood type in need but rather informed the recipient of a general 
blood shortage (no-blood-type message). 
 
Because Miller Keystone particularly values reaching new donors, the team ran a preregistered 
exploratory analysis to test whether the messages were differently effective for new donors 
compared to those who had previously donated at a Miller Keystone site. There was a 
significant interaction between previous donor status (previous donor, previous non-donor) and 
message type, such that previous non-donors were relatively more responsive to the blood-type 
message, while previous donors were more responsive to the no-blood-type message. 
However, the groups were uneven with respect to the number of patients that had previously 
donated. Moreover, when the analysis was limited to patients who opened their messages, this 
interaction effect disappeared: blood-type messages were still most effective in previous non-
donors, and there was no difference in message effectiveness among previous donors. These 
follow-up analyses, and the unevenness of previous donors across groups, call into question the 
robustness of the interaction effect. 
 
Objectives 
 
The present study again tested whether the blood-type message is more effective than the no-
blood-type message for patients with needed blood types overall, and separately for previous 
donors and non-donors. Messages in the present study were sent via email rather than via 
patient portal. Randomization occurred at the email-address level to one of the two message 
types to ensure everyone using the same email address received the same message (although 
each patient was sent an individualized message with their name; there was no no-contact 
control condition this time). Email addresses were excluded if they were shared by patients with 
different blood types. Randomization was stratified by whether all patients using the same email 
address are previous donors or not (email addresses shared by previous donors and non-
donors were excluded). 
 
 

Design  
 
This study is a randomized controlled trial with 2 study arms. 
 
Methods 
 
Email addresses of patients enrolled in the study were randomized to the following arms:  
 
1. No-blood-type message – a message that did not mention the patient had a needed 
blood type 
2. Blood-type message – a message that mentioned the patient had a needed blood type 
 
Randomization was stratified by previous donor status of all patients at an email address. 
 
Power analysis 
 
Our sample includes 40,486 patients with a total of 40,130 email addresses. 39,317 email 
addresses are associated with patients who are previous non-donors, and 813 email addresses 
are associated with patients who are previous donors. 
 
Within the previous non-donor group, we will have 80% power to detect an increase in the 
primary outcome from 0.07% (the primary-outcome rate in previous non-donors who were sent 
a no-blood-type message in our previous study) to 0.16%, with a two-tailed alpha of .05. 
 
Within the previous donor group, we will have 80% power to detect an increase in the primary 
outcome from 4.76% (the primary-outcome rate in previous donors who were sent a no-blood-
type message in our previous study) to 9.87%, with a two-tailed alpha of .05. 
 
Note that a portion of the sample will be excluded from analysis (e.g., those whose emails were 
not sent or bounced; see the Analysis notes section for details). 
 
Project status 
 
All emails have been sent. No outcome data have been extracted. 
 
 
Statistical Analysis Plan 
 
Primary outcome 
 
Number of Participants Who Attended a Donation Appointment 
 
Attended a donation appointment within 6 weeks of their message send date, regardless 
of whether they donated. This outcome includes patients who were unable to donate for 

any reason (e.g., low hemoglobin) or patients who showed up to the appointment but 
decided to leave before donating. 
 
[Time Frame: Within 6 weeks of the patient’s message send date] 
 
Question 1: Within patients who are not previous donors, do more patients attend donation 
appointments if they are sent the blood-type message compared with those sent the no-blood-
type message? 
 
Analysis 1: We will test the hypothesis that previous non-donors who are sent a blood-type 
message are more likely to attend a donation appointment than those sent a no-blood-type 
message. We will run an OLS regression with a binary predictor variable indicating whether 
patients were sent the blood-type message or the no-blood-type message. 
 
Question 2: Within patients who are previous donors, do more patients attend donation 
appointments if they are sent the blood-type message compared with those sent the no-blood-
type message? 
 
Analysis 2: We will run Analysis 1 including only patients who are previous donors. 
 
 
Analysis notes 
 
Whether or not a patient is a previous donor was determined by a data pull completed prior to 
randomization. Because the data on previous donors from this pull was used to stratify 
randomization, we will use this data pull to determine whether patients should be included in 
Analysis 1 (previous non-donors) or Analysis 2 (previous donors), even if we later learn the 
patient had previously donated (e.g., if they donated after the pre-randomization data pull but 
before the intervention began). 
 
Analyses will be conducted on the email-address level. For each email address, if any 
associated patient attended a donation appointment, the email address will be “counted” as 
achieving the primary outcome. To ensure that our findings do not depend on patients who 
share an email address, we will also run the analyses above removing all patients who share an 
email address with another patient in the study.  
 
All analyses will exclude patients who scheduled their appointment prior to their message send 
date.  
 
Patients will be excluded from analysis if, according to our records, their emails were not sent 
(e.g., due to being unsubscribed from emails) or for whom our emails bounced. 
 
Timeframes listed refer to the amount of time elapsed from the send date for a given patient. 
For instance, the primary outcome timeframe is 6 weeks; thus, for the purposes of this study, a 

patient will be counted as having donated if they donated blood within 6 weeks of their message 
send date. 
 
As a robustness check, we will rerun the analyses above on the subset of patients who open the 
messages, using a time frame of 6 weeks from the date each individual patient opened their 
message (rather than 6 weeks from the patient’s message send date). 
 
We will run an additional robustness check including only patients who scheduled an 
appointment within the 2 weeks following their message send date. 
 
Recent work suggests that OLS regressions are appropriate in randomized experiments with 
binary outcome variables such as ours (Gomila, 2021). 
 
 
Other prespecified outcomes 
 
We will use the approaches described in Analyses 1 and 2 above to evaluate the impact of the 
intervention on the other prespecified outcome measures listed in the pre-registration: 
 
1. Number of Participants Who Successfully Donated Blood 
 
Attended a donation appointment within 6 weeks of their message send date and 
successfully donated, excluding patients who were turned away from or left their 
appointment without donating. 
 
[Time Frame: Within 6 weeks of the patient’s message send date] 
 
2. Number of Participants Who Scheduled a Blood Donation Appointment 
 
Scheduled an appointment within 2 weeks of their message send date. 
 
[Time Frame: Within 2 weeks of the patient’s message send date] 
 
3. Number of Participants Who Scheduled a Blood Donation Appointment 
 
Scheduled an appointment within 6 weeks of their message send date. 
 
[Time Frame: Within 6 weeks of the patient’s message send date] 
 
 
 
Additional exploratory analyses 
 
1. Primary outcome in the full sample  
 

We will run an OLS regression to test whether the primary outcome varies as a function 
of experimental group in the full sample, without dividing the sample by previous donor 
status. 
 
2. New donors vs. previous donors 
 
We will run an OLS regression to test whether the primary outcome varies as a function 
of experimental group, a bivariate indicator for whether or not the patient previously 
donated blood, and the interaction between these variables. 
 
3. Time to donation 
 
We will run regression models to test whether either intervention message influenced the 
timing (time elapsed since the message was sent) of donations. 
 
4. Type of donation 
 
We will test whether there are differences in donation type (particularly whole blood and 
red blood cell donations) as a function of experimental group. 
 
5. Demographics 
 
We will run regression models to test for main effects of several demographic factors on 
donation behavior, along with interactions between these factors and message group. 
These demographic factors may include binned age (18–24, 25–34, 35–44,45–54, 55–
64, and 65+), sex, race, ethnicity, line of insurance (as a proxy for socioeconomic 
status), and Charlson Comorbidity. 
 
 
References 
 
Gomila, R. (2021). Logistic or linear? Estimating causal effects of experimental treatments on 
binary outcomes using regression analysis. Journal of Experimental Psychology: General, 
150(4), 700–709. https://doi.org/10.1037/xge0000920