Management of Acute Bronchitis in Switzerland With Pelargonium Sidoides Extract EPs®7630 Versus Usual Care - a Pragmatic, Open-label, Randomised Controlled Trial
Phase: NA
Conditions: Acute Bronchitis
Sponsor: University of Fribourg

 
 
 
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Statistical Analysis Plan (SAP) 
Management of acute bronchitis in Switzerland with Pelar-
gonium sidoides extract EPS®7630 versus usual care – a 
pragmatic, open-label, randomized controlled trial 
PHYTOBRONCH 
Administrative Information 
Trial registration number: 
NCT05916768 
SAP version: 
SAP version number with dates.  
1 of 20.03.2025 
Protocol version: 
V3 of 24.10.2024 
 
 
 

 
 
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Abbreviations 
 
ABSS 
Acute Bronchitis Severity Score 
AE 
Adverse event 
AIC 
Akaike Information Criterion 
BIC 
Bayesian Information Criterion 
BIHAM 
Berner Institut für Hausarztmedizin 
FAIR 
Findable, Accessible, Interoperable and Re-usable 
FAS 
Full Analysis Set 
GBTM 
Group-Based Trajectory Modeling 
ICE 
Intercurrent Event 
IML 
Institut de Médecine de Famille 
ITT 
Intention To Treat 
MAR 
Missing At Random 
MICE 
Multiple Imputation with Chained Equations 
PCP 
Primary Care Physician 
PP 
Per Protocol 
SAE 
Severe Adverse Event 
SAF 
Safety Population 
SAP 
Statistical Analysis Plan 
 
 

 
 
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Contents 
1. 
Introduction ....................................................................................................................... 5 
1.1 
Background and rationale ................................................................................................... 5 
1.2 
Objectives ........................................................................................................................... 5 
1.3 
Timing of SAP writing ......................................................................................................... 5 
2. 
Study Methods .................................................................................................................. 6 
2.1 
Trial design ......................................................................................................................... 6 
2.2 
Randomization .................................................................................................................... 6 
2.3 
Sample size ........................................................................................................................ 6 
2.4 
Framework .......................................................................................................................... 6 
2.5 
Statistical interim analyses and stopping guidance ............................................................ 6 
2.6 
Timing of final analysis ....................................................................................................... 6 
2.7 
Timing of outcome assessments ........................................................................................ 7 
2.8 
Blinding ............................................................................................................................... 7 
3. 
Data Management ............................................................................................................. 7 
3.1 
Data export ......................................................................................................................... 7 
3.2 
Data validation .................................................................................................................... 7 
3.3 
Data preparation ................................................................................................................. 7 
3.4 
Data sharing (if applicable) ................................................................................................. 7 
4. 
Statistical Principles ......................................................................................................... 7 
4.1 
Confidence intervals and P values ...................................................................................... 7 
4.2 
Analysis populations ........................................................................................................... 7 
4.2.1 
Full analysis set (FAS) ........................................................................................................ 7 
4.2.2 
Per-protocol (PP) ................................................................................................................ 8 
4.2.3 
Safety population (SAF) ...................................................................................................... 8 
4.3 
Estimands ........................................................................................................................... 8 
5. 
Trial Population ............................................................................................................... 11 
5.1 
Screening data .................................................................................................................. 11 

 
 
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5.2 
Eligibility ............................................................................................................................ 11 
5.3 
Baseline patient characteristics ........................................................................................ 11 
5.4 
Adherence and protocol deviations ................................................................................... 11 
5.5 
Withdrawal/follow-up ......................................................................................................... 12 
6. 
Analysis ........................................................................................................................... 12 
6.1 
Outcome definitions .......................................................................................................... 12 
6.2 
Analysis methods .............................................................................................................. 13 
6.2.1 
Primary analysis ............................................................................................................... 13 
6.2.2 
Secondary analyses.......................................................................................................... 13 
6.2.3 
Sensitivity analyses .......................................................................................................... 14 
6.2.4 
Subgroup analyses ........................................................................................................... 14 
6.2.5 
Additional analyses ........................................................................................................... 14 
6.2.6 
Assessment of statistical assumptions ............................................................................. 14 
6.3 
Interim analyses ................................................................................................................ 14 
6.4 
Missing data ...................................................................................................................... 14 
6.5 
Safety evaluation .............................................................................................................. 15 
6.6 
Subproject (if applicable) .................................................................................................. 15 
6.7 
Statistical software ............................................................................................................ 15 
6.8 
Quality control ................................................................................................................... 15 
7. 
Changes from the Protocol ............................................................................................ 15 
8. 
References ...................................................................................................................... 15 
 
 
 

 
 
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1. 
Introduction  
1.1 
Background and rationale  
Although most respiratory tract infections, including acute bronchitis, are caused by viruses, 53% 
of diagnosed patients receive antibiotics in Europe, representing one of the main reasons for pre-
scribing antibiotics in adults. This inappropriate use is a major contributor to resistance to antibiot-
ics and is therefore a global threat to public health. Furthermore, symptomatic treatments used in 
this indication have not yet shown convincing benefits. Therefore, there are no effective conven-
tional medicines to treat acute bronchitis. 
Phytotherapy could be a useful and promising resource for the development of candidate drugs for 
the treatment of acute bronchitis. A review of the literature identified one plant (Pelargonium si-
doides), which has a standardized extract, EPs®7630 registered on the market in Switzerland un-
der the name of Kaloba®, that may be effective as a symptomatic treatment in the management of 
acute bronchitis. The PHYTOBRONCH study aims to investigate the efficacy of Kaloba® in the 
management of acute bronchitis to find alternatives to the ineffective or inappropriate treatments that 
are currently used in the management of this disease. 
1.2 
Objectives 
The primary objective is to assess the effectiveness of Kaloba® to treat acute bronchitis compared to 
usual care to reduce: 
• 
Reduction of symptom severity 
• 
Antibiotics use 
Secondary objectives are: 
• 
To assess the incidence of adverse events (AEs) and severe adverse events (SAEs) 
• 
To estimate the proportion of primary care physicians (PCPs) who agree to participate in the 
study 
1.3 
Timing of SAP writing 
This SAP version was written and finalized before enrollment of participants was finished. 
 
 
 

 
 
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2. 
Study Methods 
2.1 
Trial design 
This is a pragmatic, open-label, superiority randomized controlled trial, which takes place in ~40 PCP 
in practices and 5 walk-in clinics. The intervention group receives Kaloba® for 7 days and up to 21 
days. The control group receives usual care, with symptomatic treatments freely chosen by the PCP 
(phytotherapy excluded). Usual care is not standardized between PCPs. A delayed use of antibiotics 
can be used in both groups. Prescription of paracetamol is allowed, as well as any routine medication 
pre-reviewed by the PCP and not counter-indicated. 
2.2 
Randomization 
Participants are randomized in a 1:1 ratio to either the intervention group or the control group, with 
stratification according to PCP. Randomization was performed using blocks of equal size (size=4). An 
independent statistician built the randomization table. 
Study group allocation is done using the web-based system REDCap (hosted by the University of Fri-
bourg), which also contains the electronic case report form (eCRF). The PCP uses the button “ran-
domize” in REDCap to allocate each patient to a group. 
2.3 
Sample size  
The sample size was calculated for each of the co-primary outcomes, using a Bonferroni correction 
for the significance level (.05 / 2 = .025). It was replicated in Stata 18 as follows: 
• 
Reduction of symptom severity: Using a two-sample means test with power = 80%, a two-
sided significance level = 2.5%, an allocation ratio 1:1, one day of difference between groups 
(Δ=1), and a standard deviation of 3 days assumed to be the same in both groups (1), we 
needed n=346 (173 in each group). 
• 
Antibiotics use: Using a two-sample proportions test (Pearson’s chi-squared test) with power 
= 80%, a two-sided significance level = 2.5%, an allocation ratio 1:1, a proportion of 60% in 
the control group and 45% in the intervention group (Δ=15) (2), we needed n=420 (210 in 
each group). 
The largest sample size estimation was selected: n=420. As this approach is rather conservative 
(e.g., it does not consider the fact that the co-primary outcomes may be correlated), we did not ac-
count for potential drop-out in the sample size calculation.  
2.4 
Framework 
This is a superiority trial, comparing participants who receive Kaloba® to those who receive usual 
care. 
2.5 
Statistical interim analyses and stopping guidance 
No interim analysis is planned. 
2.6 
Timing of final analysis 
All outcomes will be analyzed after study completion, once the database is cleaned, validated, and 
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2.7 
Timing of outcome assessments 
Participants answer daily a web-based questionnaire (from day 1 to 21, stopping at symptom resolu-
tion) and a phone call at days 4, 8, and 22. A paper version of the questionnaire is also available for 
participants who are unable to answer online.  
2.8 
Blinding 
This study is not blinded. 
3. 
Data Management 
3.1 
Data export 
The database is hosted in the REDCap database system of University of Fribourg. It will be ex-
ported as a CSV file and imported in Stata 18 for analyses. Data will be stored on the server of the 
University of Bern for analyses. 
3.2 
Data validation 
Central data monitoring is performed by the IMF at the University of Fribourg. Data will be cleaned 
and validated by the data manager of the University of Bern. We will check for outliers, implausible 
values, and missing data, as well as inconsistencies. We will particularly check outcomes.  
3.3 
Data preparation 
Outcomes will be created as explained in section 6.  
3.4 
Data sharing (if applicable) 
Once the statistical analysis completed, data will be prepared for sharing and storage. Documents will 
include database and metadata (e.g., SAP, codebook, and statistical code). They will be sent back to 
the University of Fribourg. They will be shared on FAIR data repository, if requested by the sponsor-
investigator. 
4. 
Statistical Principles 
4.1 
Confidence intervals and P values 
Statistical tests will be two-sided with a 5% significance level, except for the co-primary outcomes, for 
which a 2.5% significance level is used. We will present 95% confidence intervals, used to assess the 
precision of the estimates. We will not calculate p-values for baseline comparisons. 
4.2 
Analysis populations 
4.2.1 
Full analysis set (FAS) 
The full analysis set (FAS) will include all randomized participants, regardless of protocol violations or 
discontinuation. Analyses will be conducted following the intention-to-treat (ITT) principle, with partici-
pants analyzed according to their assigned group. 
For the secondary outcome on PCPs, the FAS will include all PCPs invited to participate in the trial, 
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4.2.2 
Per-protocol (PP) 
Per-protocol (PP) analyses will be conducted in participants who do not have any intercurrent events 
(ICEs). ICEs include:  
• 
ICE1: Non-respect of the treatment strategy, including cross-over (use phytotherapy in the 
control group or not used Kaloba® in the intervention group), non-adherence to treatment 
(adherence is defined as missing the treatment at least 50% over the period of symptoms 
(and maximum for 7 days), use of antibiotics at baseline, use medications other than those 
authorized (exclusion criteria 12, section 5.2), discontinuation of treatment in the intervention 
group (e.g., due to AEs or lack of efficacy), already using Kaloba® or Umckaloabo® in the 
control group (exclusion criteria 11, section 5.2). 
• 
ICE2: Inclusion despite presence of an exclusion criterion (criteria 1 to 10, section 5.2) or lack 
of an inclusion criterion (criteria 1 to 3, section 5.2). 
• 
ICE3: Terminal events, such as death. 
4.2.3 
Safety population (SAF) 
The safety population (SAF) will be participants who received at least one medication (Kaloba® or 
usual care). 
4.3 
Estimands 
We define the following estimands for the outcomes. 
Estimand E1.1: This estimand quantifies the effectiveness of the treatment assessed with the reduc-
tion of symptom severity (co-primary outcome).  
Outcome of interest: Number of days needed to achieve a 50% reduction on the Acute Bronchitis Se-
verity Score (ABSS) after peak of symptoms, assessed at max. 22 days. The peak of symptoms is de-
fined by the highest ABSS (or the initial score if there is no peak). This is a normally distributed count 
outcome (3). 
Population-level summary measure of outcome: adjusted* mean difference. 
Participant-set of interest: FAS. 
Handling of intercurrent events: All participants are included. 
Handling of missing data: Missing data will be multiple imputed (see section 6.4). 
* 1) Adjusted for the type of recruitment (PCP/walk-in clinic) and 2) adjusted for the following factors at 
baseline: type of recruitment, age, sex, level of education, smoking status, beginning of symptoms 
(number of days), baseline ABSS, and prescriptions for corticosteroid containing oral medication or 
inhalers (yes/no), including Prednisone, Symbicort, and Vannair.  
Estimand E1.2: This estimand quantifies the effectiveness of the treatment assessed with the reduc-
tion of symptom severity (co-primary outcome) in the PP population. 
Outcome of interest: Same as estimand 1.1. 
Population-level summary measure of outcome: same as estimand 1.1. 
Participant-set of interest: PP. 
Handling of intercurrent events: Participants with ICE1-3 are excluded from the analysis. The data will 
be weighted according to inverse probability weighting for ICE 1 and 2 (see section 6.2.2). 
Handling of missing data:  As ICE cannot be assessed in participants who drop out or are lost to fol-
low-up, we will not use multiple imputation. We will use inverse probability of attrition weights to ac-
count for attrition and multiple imputation for missing baseline covariates (see section 6.4). 

 
 
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Estimand E2.1: This estimand quantifies the efficacy of the treatment assessed with the proportion of 
antibiotics use (co-primary outcome). 
Outcome of interest: Use of antibiotics after the initial visit (binary). 
Population-level summary measure of outcome: adjusted* risk ratio. 
Participant-set of interest: FAS. 
Handling of intercurrent events: All participants are included. Participants who took antibiotics at base-
line will be considered as failures (having antibiotics). 
Handling of missing data: Missing data will be multiple imputed (see section 6.4). 
* Same adjustment as E1.1. If there are too few participants, we will use analyses adjusting separately 
for each covariate + type of recruitment. 
Estimand E2.2: This estimand quantifies the efficacy of the treatment assessed with the proportion of 
antibiotics use (co-primary outcome) in the PP population. 
Outcome of interest: same as estimand 2.1. 
Population-level summary measure of outcome: same as estimand 2.1. 
Participant-set of interest: PP. 
Handling of intercurrent events: Participants with ICE1-3 are excluded from the analysis. The data will 
be weighted according to inverse probability weighting for ICE 1 and 2 (see section 6.2.2). 
Handling of missing data: As ICE cannot be assessed in participants who drop out or are lost to fol-
low-up, we will not use multiple imputation. We will use inverse probability of attrition weights to ac-
count for attrition and multiple imputation for missing baseline covariates (see section 6.4). 
Estimand E3.1: This estimand quantifies the incidence of AEs and SAEs. 
Outcome of interest: Presence of any adverse or severe adverse event (binary) during the follow-up 
(up to 22 days). 
Population-level summary measure of outcome: adjusted* hazard ratio. 
Participant-set of interest: SAF. 
Handling of intercurrent events: All participants are included. 
Handling of missing data: No missing data, participants are censored when the study ends (at day 22) 
or at the day of last contact. 
* 1) Adjusted for the type of recruitment (PCP/walk-in clinic) and 2) adjusted for the following factors at 
baseline: type of recruitment, age, sex, level of education, smoking status, beginning of symptoms 
(number of days), and baseline ABSS. If there are too few participants, we will use analyses adjusting 
separately for each covariate + type of recruitment. 
Estimand E3.2: This estimand quantifies the incidence of AEs and SAEs. 
Outcome of interest: same as estimand 3.1. 
Population-level summary measure of outcome: same as estimand 3.1. 
Participant-set of interest: SAF without ICE2. 
Handling of intercurrent events: Participants with ICE2 are excluded from the analysis. The data will 
be weighted according to inverse probability weighting for ICE 2 (see section 6.2.2). Participants will 
be censored when they deviate from the treatment strategy (ICE1) and when they have a terminal 
event (ICE3). 
Handling of missing data: No missing data, participants are censored when the study ends (at day 22) 
or at the day of last contact. 

 
 
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Estimand E4: This estimand quantifies the proportion of PCPs who agree to participate in the study. 
Outcome of interest: PCP agrees or declines to participate in the study (binary). 
Population-level summary measure of outcome: Percentage. 
Participant-set of interest: FAS PCPs. 
Handling of intercurrent events: All PCPs are included. 
Handling of missing data: No missing data (missing data are considered as refusal to participate in 
the study). 
 
 
 
 
 

 
 
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5. 
Trial Population 
5.1 
Screening data 
Not available. 
5.2 
Eligibility 
The target study participants are outpatients with acute bronchitis (with or without COVID-19). 
Inclusion criteria are: 
1. At least 18 years old  
2. Consulting for the first time as part of this bronchial episode 
3. Consulting for:  
a. Acute cough (≤ 8 days) as the main symptom 
b. Illness (≤ 8 days) in which cough is not the main symptom, but the PCP believes that 
acute bronchitis is the most likely diagnosis 
Exclusion criteria are: 
1. Infection requiring antibiotics 
2. Pneumonia 
3. Non-infectious causes (COPD, asthma, pulmonary embolism, heart failure, gastro-esopha-
geal reflux, allergy) 
4. Incapacity of judgment (dementia, psychosis or severe depression) 
5. Important risk of bleeding (severe thrombocytopenia and anticoagulant intake) 
6. Inability to complete the diary 
7. Pregnancy or breastfeeding 
8. Immunological deficiencies 
9. Severe hepatic disease 
10. Known hypersensitivity to Pelargonium sidoides extract or excipients of the trial medication 
11. Currently taking Kaloba® or Umckaloabo® for this current episode of acute bronchitis 
12. Taking anticoagulants, immunosuppressants, chemotherapy or immunotherapy 
A CONSORT patient flow diagram will be drawn. 
5.3 
Baseline patient characteristics 
Baseline parameters will be presented as means and standard deviations for continuous variables, or 
percentages and n for categorical variables. 
The baseline table will include standardized mean/proportion differences to have an overview of the 
balance between groups, but no p-values or confidence intervals. 
Relevant baseline covariates include: 
- 
Socio-demographics: sex, age, level of education (primary, secondary, or university) 
- 
Risk factors: smoking (yes/no), comorbidities (hypertension, diabetes, cardiovascular disease, 
cancer, obesity),  
- 
Illness-related variables: number of days since the beginning of symptoms, baseline ABSS, 
medications already used (cough suppressant, expectorant, anti-inflammatory, febrifuge, 
other) 
- 
Medical background: COVID-19 vaccination. 
5.4 
Adherence and protocol deviations 
Protocol violations will be summarized by percentages and n in each group. 

 
 
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5.5 
Withdrawal/follow-up 
Withdrawal from the study and lost-to-follow-up will be summarized by percentage and n in each 
group. 
6. 
Analysis 
6.1 
Outcome definitions 
Reduction of symptom severity. The first co-primary endpoint uses the patient-reported outcome 
Acute Bronchitis Severity Score (ABSS), which quantifies the severity of 5 symptoms related to acute 
bronchitis for the previous 24 hours. It is assessed on a scale from 0 to 4, with the sum score ranging 
between 0 and 20 (see Table 1) (3). Participants will complete the ABSS daily on an online diary, until 
symptom resolution and up to 21 days. If the participant does not answer, the ABSS will be evaluated 
in phone interviews (days 4, 8, and 22). 
 
Table 1. Acute Bronchitis Severity Score (ABSS) 
Symptom 
0 
1 
2 
3 
4 
Overall severity of illness 
Very mild 
Mild 
Moderate 
Serious 
Very serious 
Day cough 
1-2 times/day 
3-5 times/day 
6-10 times/day 
11-20 times/day 
>20 times/day 
Night cough 
1-2 times/night 
3-5 times/night 
6-10 times/night 
11-20 times/night 
>20 times/night 
Limit daily activity 
None 
Mild 
Moderate 
Severe 
Very severe 
Subjective fever 
None 
Mild 
Moderate 
Severe 
Very severe 
 
The first co-primary is the number of days needed to achieve a 50% reduction in the ABSS after peak 
of symptoms (1). The peak of symptoms is defined as the highest ABSS observed during the study.  
The procedure to calculate the first co-primary endpoint is the following: 
- 
Calculate daily ABSS (sum score of the 5 symptoms) 
- 
Calculate the peak (max. ABSS observed) 
- 
Calculate daily percentage change in the ABSS after the peak 
- 
Calculate which day corresponds to a 50% decrease 
- 
Calculate the number of days between the peak and the 50% decrease 
If there is no peak, the first day will be used as the peak. If the 50% decrease is not reached, we will 
set the number of days to the maximum (22 days). Imputation will be used for missing values on daily 
ABSS (see section 6.4). We will run a sensitivity analysis computing the primary outcome and using 
multiple imputation on the primary outcome.  
Antibiotics use. The second co-primary endpoint is the proportion of participants who take antibiotics 
after the initial visit (≥ day 2). It is defined as 0=no antibiotics during the same illness episode and 
1=antibiotics use during the same illness period. Antibiotics use is assessed daily in the diary (day 1 
to 21) until symptom resolution and at the three phone calls at day 4, 8, and 22. 
AEs and SAEs. The first secondary endpoint will include AEs, predefined according to the dictionary 
MedDRA and SAEs, defined according to the HRA. Assessments of severity (5 categories, from 
grade 0 to 5) and causality (5 categories, from “not related” to “definitely related”) will also be evalu-
ated. We will compute a single endpoint with 0=no AE or SAE during the study and 1=at least one AE 
or SAE during the study period. We will also create separate endpoints for AEs and SAEs and types 
of AEs/AEs. The date of the (S)AEs will also be recorded, to capture the time until (S)AE. 

 
 
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PCP agreement to participate in the study. The second secondary endpoint will assess whether the 
PCP agrees to participate in the study. Participation will be defined as signing the agreement and en-
rolling participants. Non-participation will be defined as refusing or signing but not enrolling partici-
pants. 
6.2 
Analysis methods 
6.2.1 
Primary analysis 
The primary analysis will be done according to the FAS (ITT) and PP sets specified in section 4.2, us-
ing the adjusted model (4, 5). The randomization group will be used as the main predictor, adjusting 
for the stratification factors and relevant predefined covariates. To account for the fact that partici-
pants are clustered in PCP practices, we will use bootstrapped standard errors (1000 reps). A random 
seed will be predefined for replicability. The intraclass coefficient is expected to be small. All effects 
will be shown using marginal point estimates (accounting for adjustment variables) and 95% confi-
dence intervals. 
For the first co-primary endpoint, reduction in ABSS, we will use a linear regression, e.g.: 
bootstrap, reps(1000) seed(XXX): regress y x1 x2 
This endpoint should be normally distributed, as observed in previous studies (3). However, if it is not 
normally distributed, we will use a log-transformation after adding the constant 1.  
For the second co-primary endpoint, proportion of antibiotics use, we will use a logistic regression, 
e.g.: 
bootstrap, reps(1000) seed(XXX): glm y x1 x2, fam(bin) link(log) eform 
We will use multiple imputation to handle missing values on covariates and outcomes (see section 
6.4). If there is collinearity between covariates, we will remove one colinear variable based on theoret-
ical reasons. 
6.2.2 
Secondary analyses 
The secondary analysis will be done according to the FAS (ITT) and PP sets specified in section 4.2. 
Models will also be adjusted for the stratification factor and relevant covariates, with bootstrapped 
standard errors, as described for the primary analysis. Effects will be shown using marginal point esti-
mates (accounting for adjustment variables) and 95% confidence intervals. 
For the first secondary endpoint, AEs and SAEs, we will use a Cox regression, e.g.: 
stset timevar, failure(y) id(idvar) 
bootstrap, reps(1000) seed(XXX): stcox x1 x2 
We will use multiple imputation to handle missing values on covariates (see section 6.4). There won’t 
be missing values on (S)AEs, as participants who drop out from the study will be right-censored. If 
there is collinearity between covariates, we will remove one colinear variable based on theoretical 
reasons. If the assumption of proportionality of hazard is not met, we will stratify on the problematic 
variable(s). 
For the second secondary endpoint, proportion of PCP who agree to participate, we will calculate a 
percentage with a 95% confidence interval, using Wilson intervals which are superior for small sam-
ples without being overly conservative like exact methods (6), e.g.: 
cci proportions n x, Wilson 
For analyses on the PP set, we will run the same analyses as described in section 6.2.1. In addition, 
we will use inverse probability weighting for ICE1 and ICE2. We will derive stabilized weights sepa-
rately and combined them by multiplying them. The covariates used to create weights are the follow-
ing. For ICE1, we will use for GP/walk-in clinic, age, sex, education, beginning of symptoms (number 
of days), and baseline ABSS. For ICE2, we will use for GP/walk-in clinic. We will truncate weights at 

 
 
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the 99th percentile. The weights will then be used in the main adjusted model to re-balance randomi-
zation groups. We will check whether balance was achieved using standardized mean/proportion dif-
ferences, which should be lower than .25 (7, 8). As balance may not be achieved using multiple impu-
tation, we will use simple imputation in models using inverse probability weighting. 
6.2.3 
Sensitivity analyses 
If the first co-primary endpoint (reduction in ABSS) is not normally distributed, we will run sensitiv-
ity analyses using negative-binomial regression (if data are positively skewed) or other transfor-
mations (i.e., square root transformation for positively-skewed data or square for negatively-
skewed data). 
6.2.4 
Subgroup analyses 
We will perform stratified analyses on the two primary endpoints for: 
- 
Sex (men and women) 
- 
Type of site (PCPs and walk-in clinics) 
- 
Smoking status 
While COVID-19 was a pre-planned analysis in the protocol, it is no longer possible since there is no 
testing for SARS-CoV-2 infection. 
6.2.5 
Additional analyses 
In addition, we will explore: 
Time to clinical improvement: We will test how long it takes to achieve symptom recovery using a Cox 
regression with the randomization group predicting time to complete resolution of symptoms (adjusted 
model with multiple imputation, as described in section 6.2.2). 
Change in ABSS: We will test the trajectory of ABSS over time. In a first step, we will calculate a 
group-based trajectory model (GBTM) to identify trajectories of symptom severity over time, coding 
the score to 0 when symptom recovery is achieved. GBTM is kind of finite mixture modelling that iden-
tifies groups of individuals who share a similar trajectory based on a multinomial strategy with maxi-
mum likelihood estimation (9). The number of trajectories will be selected using usual fit indices: 
Akaike Information Criterion (AIC), Bayesian information criterion (BIC), loglikelihood, entropy, and 
the percentage of participants in each class (≥ 5%%). We will then test whether the group member-
ship is associated with the randomization group using a multinomial regression with bootstrapped 
standard errors, adjusting for covariates. Odds-ratios will be reported. 
Time to antibiotics use: We will test how long it takes to use antibiotics using a Cox regression, with 
the randomization group predicting time to antibiotics use (adjusted model with multiple imputation, as 
described in section 6.2.2). 
6.2.6 
Assessment of statistical assumptions 
Statistical assumptions will be assessed using appropriate tools and diagnostic plots. Alternative mod-
els have been described above. 
6.3 
Interim analyses 
No interim analyses are planned.  
6.4 
Missing data 
For missing data on outcomes and covariates, we use multiple imputation by chained equations 
(MICE), under the assumption of missing at random (MAR). Ten imputation datasets will be created 

 
 
SAP for PHYTOBRONCH 
Version of SAP: 1 
Based on template: CTU_STA_TEM-11; v5.0.0 
Valid from: 20.03.2025 
Page 15 of 15 
 
and results pooled using Rubin’s rule. The following auxiliary variables will be used to predict missing-
ness: type of recruitment (PCP/walk-in clinic), age, sex, level of education, smoking status, beginning 
of symptoms (number of days), and baseline ABSS. Variables with missing values will be removed 
from the auxiliary list of variables. Terminal events such as death will be used as auxiliary variables. 
Technical issues (e.g., wrong email, reminder not sent) that cause missingness will also be used as 
auxiliary variables. For the PP analyses, we will also use inverse probability of attrition weights, which 
will be combined to inverse probability weights accounting for ICEs by a multiplication. We will use 
stabilized weights, truncated at the 99th percentile. 
6.5 
Safety evaluation 
See section 6.2.2. 
6.6 
Subproject (if applicable) 
Subprojects will be described in their own SAPs, where appropriate.  
6.7 
Statistical software 
Statistical analysis will be performed with Stata 18. If required, some analyses will be performed in R 
(version 4.4.2 or more recent). 
6.8 
Quality control 
An independent statistician will check the code of analyses described in sections 6.2.1 to 6.2.4. 
7. 
Changes from the Protocol 
Not applicable. 
8. 
References 
1. 
Thomas S, Patel D, Bittel B, Wolski K, Wang Q, Kumar A, et al. Effect of high-dose zinc and 
ascorbic acid supplementation vs usual care on symptom length and reduction among ambulatory 
patients with SARS-CoV-2 infection: The COVID A to Z randomized clinical trial. JAMA Netw Open. 
2021;4(2):e210369. 
2. 
Lhopitallier L, Kronenberg A, Meuwly J-Y, Locatelli I, Mueller Y, Senn N, et al. Procalcitonin 
and lung ultrasonography point-of-care testing to determine antibiotic prescription in patients with 
lower respiratory tract infection in primary care: pragmatic cluster randomised trial. BMJ. 
2021;374:n2132. 
3. 
Nduba VN, Mwachari CW, Magaret AS, Park DR, Kigo A, Hooton TM, et al. Placebo found 
equivalent to amoxicillin for treatment of acute bronchitis in Nairobi, Kenya: a triple blind, randomised, 
equivalence trial. Thorax. 2008;63(11):999-1005. 
4. 
Holmberg MJ, Andersen LW. Adjustment for Baseline Characteristics in Randomized Clinical 
Trials. JAMA. 2022;328(21):2155-6. 
5. 
Kahan BC, Jairath V, Doré CJ, Morris TP. The risks and rewards of covariate adjustment in 
randomized trials: an assessment of 12 outcomes from 8 studies. Trials. 2014;15(1):139. 
6. 
Orawo LAo. Confidence intervals for the binomial proportion: A comparison of four methods. 
Open Journal of Statistics. 2021;11(5):806-16. 
7. 
Stuart EA. Matching methods for causal inference: A review and a look forward. Stat Sci. 
2010;25(1):1-21. 
8. 
Zhao Q-Y, Luo J-C, Su Y, Zhang Y-J, Tu G-W, Luo Z. Propensity score matching with R: 
conventional methods and new features. Ann Transl Med. 2021;9(9):812. 
9. 
Nagin DS, Jones BL, Elmer J. Recent advances in group-based trajectory modeling for 
clinical research. Annu Rev Clin Psychol. 2024;20(1):285-305.