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\begin{document}

\title{Early model-based precision-dosing at home to guide adalimumab therapy}



\author[1]{Paul de Klaver}%
\author[2]{Ron Keizer}%
\author[3]{Rob ter Heine}%
\author[4]{Frank Hoentjen}%
\author[5]{Paul Boekema}%
\author[6]{Inge Kuntzel}%
\author[7]{Tiny Schaap}%
\author[8]{Annick de Vries}%
\author[8]{Karien Bloem}%
\author[8]{Theo Rispens}%
\author[9]{Lisa Smits}%
\author[10]{Luc Derijks}%
\affil[1]{Máxima Medical Center}%
\affil[2]{Insight Rx}%
\affil[3]{Radboud University Medical Center, Radboud Institute for Health sciences}%
\affil[4]{University of Alberta}%
\affil[5]{Maxima Medical Centre Location Veldhoven}%
\affil[6]{Maxima Medical Centre}%
\affil[7]{Sanquin Bloedvoorziening}%
\affil[8]{Sanquin Research}%
\affil[9]{Universitair Medisch Centrum Sint Radboud}%
\affil[10]{Maxima Medical Center}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
1. Aims Underdosing of adalimumab can result in non-response and poor
disease control. In this study we investigated the prediction of
adalimumab levels with population pharmacokinetic model-based Bayesian
forecasting early in therapy. This way underexposed non-responders can
possibly be identified early to optimise disease control. 2. Methods A
literature study was performed to identify adalimumab pharmacokinetic
models. With data from a previous pharmacokinetic adalimumab study a
model was evaluated retrospectively. In the prospective phase, a
fit-for-purpose evaluation of the model was performed for rheumatologic
and inflammatory bowel disease patients with peak, trough and control
adalimumab samples obtained by a volumetric absorptive microsampling
technique and administration data from an electronic needle container.
Steady state adalimumab levels were predicted from peak and trough
levels collected after the first adalimumab administration. Predictive
performance was calculated with mean prediction error (MPE) and
normalized root mean square error (RMSE). 3. Results An existing
pharmacokinetic model was selected with external validation for the
prospective phase. Thirty-six patients (22 rheumatologic and 14 IBD)
were included in our study. After stratification for absence of
anti-adalimumab antibodies, the calculated MPE was -2.6\% and normalised
RMSE 24.0\%. Concordance between predicted and measured adalimumab serum
levels falling within or outside the therapeutic window was 75\%. Three
patients (8.3\%) developed detectable levels of anti-adalimumab
antibodies. 4. Conclusion This prospective study demonstrates that
adalimumab levels at steady state can be predicted from early samples.
This concept enables early precision dosing at home to guide therapy.%
\end{abstract}%



\sloppy


\textbf{Early model-based precision-dosing at home to guide adalimumab
therapy}

Running title: early prediction of adalimumab levels

Paul A.G. de Klaver\textsuperscript{1}, Ron J.
Keizer\textsuperscript{2}, Rob ter Heine\textsuperscript{3}, Frank
Hoentjen\textsuperscript{4}, Paul J. Boekema\textsuperscript{5}, Inge
Kuntzel\textsuperscript{6}, Tiny Schaap\textsuperscript{7}, Annick de
Vries\textsuperscript{8}, Karien Bloem\textsuperscript{9}, Theo
Rispens\textsuperscript{10}, Lisa Smits\textsuperscript{11}, Luc J.J.
Derijks\textsuperscript{12}

Author's institutional affiliations and email address:

1 M\selectlanguage{ngerman}áxima Medical Center, Department of Pharmacy and Clinical
Pharmacology, Veldhoven, the Netherlands, p.deklaver@mmc.nl

2 InsightRx Inc, San Francisco, CA, US, ron@insight-rx.com

3 Radboud University Medical Center, Radboud Institute for Health
Sciences, Department of Pharmacy, Nijmegen, the Netherlands,
R.terHeine@radboudumc.nl

4 Radboud University Medical Center, Department of Gastroenterology,
Nijmegen, the Netherlands, Division of Gastroenterology, Department of
Medicine, University of Alberta, Edmonton, Canada, hoentjen@ualberta.ca

5 Máxima Medical Center, Department of Gastroenterology, Veldhoven, the
Netherlands, P.Boekema@mmc.nl

6 Máxima Medical Center, Department of Rheumatology, Eindhoven, the
Netherlands, I.Kuntzel@mmc.nl

7 Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The
Netherlands, j.schaap@sanquin.nl.

8 Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The
Netherlands, annick.devries@sanquin.nl

9 Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The
Netherlands, K.Bloem@sanquin.nl

10 Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The
Netherlands,

Department of Immunopathology, Sanquin Research, Amsterdam, The
Netherlands, and Landsteiner Laboratory, Academic Medical Centre,
University of Amsterdam, Amsterdam, The Netherlands,
T.Rispens@sanquin.nl

11 Radboud University Medical Center, Department of Gastroenterology,
Nijmegen, the Netherlands, Division of Gastroenterology,
Lisa.Smits@radboudumc.nl

12 Máxima Medical Center, Department of Pharmacy and Clinical
Pharmacology, Veldhoven, the Netherlands, l.derijks@mmc.nl

Ethical statements:

Data availability statement: raw data were generated at Máxima Medical
Center and Radboud University Medical Center. Derived data supporting
the findings of this study are available from the corresponding author
{[}PK{]} on request.

Funding statement: n.a.

Conflict of interest disclosure: the authors declare that there is no
conflict of interest

Ethics approval statement: the study was approved by the local ethics
committee.

Patient consent statement: all patients provided written informed
consent.

Clinical trial registration: the trial was registered in the Netherlands
Trial Register with trial registry number NTR 7692
(www.trialregister.nl).

Keywords: adalimumab, model based precision dosing, Inflammatory bowel
disease, rheumatology

Total word count excluding summary\emph{:} 2144

\begin{itemize}
\tightlist
\item
  What is already known about this subject
\item
  Capillary adalimumab sampling can be done at home
\item
  Adalimumab treatment can be optimised with therapeutic drug monitoring
\item
  Underdosing of adalimumab can lead to poor disease control and
  non-response
\item
  What this study adds
\item
  Steady state adalimumab concentrations can be predicted after a single
  dose
\item
  Home capillary sampling and electronic needle container can be
  combined to monitor treatment at home
\item
  MAP Bayesian forecasting can be used for precision dosing of
  adalimumab
\item
  Abstract
\item
  Aims
\end{itemize}

Underdosing of adalimumab can result in non-response and poor disease
control. In this study we investigated the prediction of adalimumab
levels with population pharmacokinetic model-based Bayesian forecasting
early in therapy. This way underexposed non-responders can possibly be
identified early to optimise disease control.

Methods

A literature study was performed to identify adalimumab pharmacokinetic
models. With data from a previous pharmacokinetic adalimumab study a
model was evaluated retrospectively. In the prospective phase, a
fit-for-purpose evaluation of the model was performed for rheumatologic
and inflammatory bowel disease patients with peak, trough and control
adalimumab samples obtained by a volumetric absorptive microsampling
technique and administration data from an electronic needle container.
Steady state adalimumab levels were predicted from peak and trough
levels collected after the first adalimumab administration. Predictive
performance was calculated with mean prediction error (MPE) and
normalized root mean square error (RMSE).

Results

An existing pharmacokinetic model was selected with external validation
for the prospective phase. Thirty-six patients (22 rheumatologic and 14
IBD) were included in our study. After stratification for absence of
anti-adalimumab antibodies, the calculated MPE was -2.6\% and normalised
RMSE 24.0\%. Concordance between predicted and measured adalimumab serum
levels falling within or outside the therapeutic window was 75\%. Three
patients (8.3\%) developed detectable levels of anti-adalimumab
antibodies.

Conclusion

This prospective study demonstrates that adalimumab levels at steady
state can be predicted from early samples. This concept enables early
precision dosing at home to guide therapy.

``Clinical trial registry number: Netherlands Trial Register, NTR 7692''

Keywords: model-based precision-dosing adalimumab

Introduction

Adalimumab is a fully human recombinant IgG1k monoclonal antibody
against Tumor Necrosis Factor (TNF) alpha. It is approved for moderate
to severe inflammatory bowel disease (IBD) and the rheumatic diseases
rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing
spondylitis (SpA) with poor response to conventional immunosuppressants.
Adalimumab is administered subcutaneously. For RA, PsA and SpA the
licensed dose is 40 mg every other week, without induction therapy. For
IBD the licensed induction dose is either 80 mg followed by 40 mg after
two weeks or 160 mg followed by 80 mg after two weeks, the latter
induction scheme being used more frequently in clinical practice. The
licensed maintenance dose is 40 mg every other week.

Up to 30\% of patients with IBD do not respond to initial treatment with
TNF\selectlanguage{greek}α \selectlanguage{english}antagonists. It is important to differentiate between true primary
non-responders (pharmacodynamic failure) and underexposed non-responders
(pharmacokinetic failure), to intervene early in latter cases and adjust
dosage to the individual patient. This serves patient satisfaction,
disease control and drug expenses.

Target adalimumab trough-levels can range from 5-12 mg/L and therapeutic
drug monitoring (TDM) can be performed in routine clinical practice,
most often reactively during the maintenance phase of therapy.
Population pharmacokinetic models have been developed and could
theoretically be used for serum level prediction at steady state and
therefore early dosage prediction, but these models have not yet reached
clinical practice.

In the current study, we investigated the feasibility of predicting
adalimumab levels with population pharmacokinetic model-based Bayesian
forecasting early in therapy. This can be used to identify underdosed
non-responders as soon as possible to optimise disease control in
clinical practice.

Materials and Methods

2.1 Population pharmacokinetic model selection

A 3-step-approach as described by \emph{ter Heine} \emph{et al} was
used. For step 1, identification of models, a PubMed search for a
population pharmacokinetic adalimumab model was performed and FDA
registration data were evaluated. In step 2, an expert panel of
pharmacometricians and clinical pharmacologists retrospectively
evaluated the predictive performance of the pharmacokinetic models with
data from a published study with IBD patients in M\selectlanguage{ngerman}áxima Medical Center
using Nonlinear Mixed Effects Modelling (NONMEM) version 7.4, executed
through the Pirana workbench. Final model selection was based on
Goodness of fit evaluation in line with best practice. Step 3 in this
strategy is described below as the prospective observational cohort
study.

2.2 Study design and population

This multicentre prospective observational cohort study aimed to collect
data from 40 patients \selectlanguage{english}[?] 18 years with IBD or RA, SpA and PsA starting
with adalimumab from March 2019 up to August 2020.

Patients were recruited from Rheumatology and Gastroenterology
departments of Maxima Medical Center, Veldhoven/Eindhoven, the
Netherlands and Gastroenterology department of Radboud University
Medical Center (UMC), Nijmegen, the Netherlands. Adalimumab was dosed
according to label and local clinical care pathways.

Pregnancy, known allergy for adalimumab or excipients and previous
adalimumab use were exclusion criteria. For each drop-out a new patient
was recruited. Patients weight, gender, date of birth and indication for
treatment with adalimumab were collected.

The workflow is shown in figure 1.

2.3 Sampling

Sampling was done with a volumetric absorptive microsampling (VAMS)
method. All patients were provided with 3 sampling sets for capillary
blood microsampling at home. A sampling set consists of two 20
microliter Mitra\textsuperscript{TM} microsamplers (Neotyrex, Torrance,
USA) and a BD microtainer 2 mm contact-activated lancet (BD, Dublin,
Ireland). Patients were asked to perform capillary sampling at home 5
days, 13 days and 12 weeks after first adalimumab administration (Figure
1). Patients could receive sampling reminders for each sampling moment
per email or text message on request. Samples were returned and stored
under refrigerated conditions until analysis at Sanquin Diagnostic
Services (Amsterdam, the Netherlands). Patients completed the trial upon
returning the third sample.

2.4 Drug administration monitoring

All patients were required to use a an electronic needle container
(HealthBeacon Injection Care Management System\textsuperscript{TM},
Health Beacon Ltd, Dublin, Ireland). Electronic needle containers were
provided as part of standard care to all patients in this study. The
electronic needle container is a device intended to monitor and improve
compliance for patients on therapy with injectables. It reports the date
and time a syringe is dropped in the device after use. The electronic
needle container reports were automatically sent to Maxima Medical
Center with secure mail.

2.5 Measurement of adalimumab and anti-adalimumab antibody
concentrations

\emph{Mitra\textsuperscript{TM} tips processing.}

Blood from Mitra\textsuperscript{TM} tips was eluted by overnight
incubation in 0.5--1 ml PBS containing 0.05\% Tween and 0.05\%
NaN\textsubscript{3} gently shaking at room temperature. Eluates were
kept at 4\selectlanguage{english}*C until further measurements were performed as previously
described.

\emph{Concentration measurements}

Concentration measurements of adalimumab in patient samples were
performed by a validated ELISAs at Sanquin Diagnostic Services
(Amsterdam, the Netherlands). In short, TNF\selectlanguage{greek}α \selectlanguage{english}is captured to the ELISA
plate by a coating with mouse-anti-TNF\selectlanguage{greek}α-\selectlanguage{english}antibody. Hereafter adalimumab
derived from the patient samples is captured and detected by a biotin
labelled anti-idiotype polyclonal antibody in combination with HPR
coupled to streptavidin and TMB. The concentration of anti-adalimumab
antibodies (ADA) was measured by radioimmunoassay. In short, antibodies
from patient samples are captured by protein A sepharose and detected
with radio-labelled F(ab')\textsubscript{2} fragment of adalimumab.
Anti-adalimumab antibodies were only measured if adalimumab
concentration (back-calculated to serum) was below 5 mg/L (cascade
principle). The concentrations were back-calculated to serum
concentrations by taking into account the exact volume of the absorbent
Mitra\textsuperscript{TM} tip in combination with the volume of elution
buffer and a fixed haematocrit value of 0.42. For all underfilled
samples, for which the correction factor was unknown, the potassium
concentration was measured in eluates of completely filled tips and in
eluates of the underfilled tips of the same patient. The blood volume
present in the eluate was calculated with these potassium concentrations
and used for calculation of adalimumab concentration.

2.6 Fit-for-purpose evaluation and statistical analysis

The predictive performance of the selected models to predict the steady
state adalimumab serum concentration at 12 weeks from the measurements
at day 5 and day 13 was investigated. The adalimumab concentration
measured in the eluate of the Mitra\textsuperscript{TM} tip performed at
week 12 was considered the true serum level value and compared with the
individual model-predicted value. The primary outcome of this analysis
was a precise and accurate prediction defined as mean prediction error
(MPE) and normalised root mean square error (RMSE) \textless{} 25\%. We
defined normalized RMSE as RMSE divided by range (maximal dependent
variable minus minimal dependent variable). Additionally, we calculated
normalised RMSE defined as RMSE divided by average true values for all
patients without detectable ADA. The 95\% Confidence intervals (CI) are
defined as 1.96\selectlanguage{ngerman} x standard error (SE) for MPE. Standard
error for RMSE is defined as \(\sqrt{1/2n}\text{\ x}\) normalised RMSE, where n
represents the degrees of freedom.

The clinical applicability of early prediction of steady state
adalimumab levels was evaluated by dividing all predictions into four
classes: true positive (prediction and measured value within therapeutic
range), true negative (prediction and measured value outside therapeutic
range), false positive (prediction in therapeutic range, measured value
outside therapeutic range), false negative (prediction outside
therapeutic range, measured value in therapeutic range).

Secondary outcome of this analysis was fitting a new model to the
collected pharmacokinetic data. The pharmacokinetic parameters were
estimated with NONMEM version 7.4 (ICON plc, Dublin, Ireland) and PsN
version 5.2.6. (https://github.com/UUPharmacometrics/PsN) Diagnostic
plots were prepared in R (R Foundation for Statistical Computing,
Vienna, Austria). Model predictive ability was assessed using the
proseval tool in PsN.

2.7 Ethical considerations

The study was approved by the local ethics committee and all patients
provided written informed consent. The trial was registered in the
Netherlands Trial Register with trial registry number NTR 7692
(www.trialregister.nl).

Results

3.1 Population pharmacokinetic model selection

Based on the literature search and the external evaluation of existing
models with our retrospective dataset, the model by Ternant \emph{et
al}was selected for use in this prospective analysis. Prediction
corrected visual predictive check (VPC) used for the goodness of fit
evaluation for this model is shown in figure 2. Other VPCs of the model
as well as the model code are shown in the appendix.

3.2 Patients

A total of 56 patients were included in the trial. Drop-out rate in this
trial was 20 patients (36\%). Data of 36 patients were included in the
prospective analysis. Inclusion was stopped at 36 patients because of
the COVID pandemic. Twenty-two patients carried a diagnosis of rheumatic
disease and 14 IBD. Baseline characteristics of patients included in the
analysis are shown in table 1.

3.3 Fit-for-purpose evaluation

The predictive performance analysis resulted in an MPE of 294\% (95\% CI
261\% to 326\%) and a normalised RMSE of 80\% (95\% CI 61\% to 99\%).
When stratified for absence of ADA, the MPE was-2.6\% (95\% CI -3.9\% to
-1.4\%) and normalised RMSE 24.0\% (95\% CI 18.4\% to 29.6\%).

When calculating normalised RMSE defined as RMSE divided by average true
values for patients without measured ADA, we found an RMSE of 42.5\%
(95\% CI 37.5\% to 47.6\%)

Clinical applicability evaluation resulted in 75\% true predictions.
Full results from the clinical applicability evaluation are shown in
table 2.

The results of parameter estimation based on the newly collected
adalimumab levels and ADA titers collected in this study are shown in
table 3.

3.3 Immunogenicity

Three patients in our cohort developed ADA at steady state 12 weeks
after start of adalimumab therapy. None of these patients had received
biologicals before and none of these patients were on combination
therapy with other immunosuppressive drugs.

3.4 Feasibility at home

The combination of an electronic needle container and capillary blood
microsampling enabled us to remotely monitor patient's medication
treatment. Exclusion from the analysis was mostly caused by home
sampling errors by a minority of patients resulting in samples
unsuitable for concentration measurement. Other reasons were needle drop
registration issues with health beacon occurred and some patients failed
to provide a complete set of three samples. These issues should be
addressed to increase feasibility at home.

Discussion

In this study we demonstrated the possibility of predicting steady state
adalimumab concentrations, based on early single peak and trough levels
only, resulting in a correct prediction (therapeutic -- subtherapeutic)
in the vast majority of cases without ADA. After stratification for ADA
our primary outcome measures for bias and precision were met for
patients without ADA. It should be noted that ADA development is not
predictable in clinical practice. The application of MAP Bayesian
forecasting early in therapy in combination with an electronic needle
container and home capillary sampling is unique and enables us to fully
remotely monitor the patient's medication treatment at home from
pharmacokinetic point of view. Self-management can be of value for
patients with chronic conditions on adalimumab treatment to reduce the
number of visits to the clinic.

The foremost clinical implication of our study is the possibility of an
early adalimumab dose optimisation for patients with predicted
subtherapeutic levels. Since we did not measure clinical response, our
prediction does not account for non-response due to other reasons.

This study shows that the population pharmacokinetic model selected
(which is based on adalimumab concentrations measured in serum) could be
used in combination with a VAMS method with capillary blood for
adalimumab sampling. This makes sampling more accessible for patients.
This method has been compared to venepuncture for adalimumab and has
been studied in IBD patients at home before with reliable results.

A drawback of the current VAMS technique is underfilling of the tips. In
case of underfilling, it is a challenge to calculate the concentration
that equals the serum concentration. For patients with at least one
correctly filled sample, other underfilled samples were corrected for
volume by potassium levels in both the correctly filled sample and the
underfilled sample(s). Patients with potassium-corrected samples did not
perform worse in our model then patients uncorrected samples, although
this could not be statistically proven due to the small number of
patients. For future research with home monitoring of anti-TNF serum
concentrations, a more robust sampling method (e.g. wet blood collection
with microsampling tubes) is recommended to avoid these sampling and
correction issues.

We used an electronic needle container to collect data on timing of
adalimumab administration. Unfortunately, the electronic needle
container was not able to generate a report in all cases. Therefore, on
a few occasions interpolations for the timing of adalimumab
administration were necessary. We do not expect this will influence the
outcome of our study since adalimumab has a long terminal elimination
half-life and it concerned only a single administration in a series of
administrations. For implementation of our adalimumab monitoring
concept, other systems such as mobile health apps may be good
alternatives.

Conclusion

In this study we have demonstrated prospectively that our model is
fit-for-purpose for early prediction of adalimumab levels at steady
state. This concept enables early precision dosing at home to guide
therapy.

Acknowledgements: We thank Wil Adriaans, Louise Merry-Meier and
Antoinette Piepenbrock-van Schooten for their efforts for the inclusion
of patients in this trial.

Conflict of interest disclosure: the authors declare that there is no
conflict of interest

Funding information: This study was funded by Máxima Medical Center

Data availability statement: raw data were generated at Máxima Medical
Center and Radboud University Medical Center. Derived data supporting
the findings of this study are available from the corresponding author
{[}PK{]} on request.

References

Tables

Table 1:

Patient demographics at baseline\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
& N = 36\tabularnewline
\midrule
\endhead
Crohn's disease (N, \%) & 8 (22\%)\tabularnewline
Ulcerative colitis (N, \%) & 6 (17\%)\tabularnewline
Rheumatoid arthritis (N, \%) & 11 (31\%)\tabularnewline
Ankylosing spondylitis (N, \%) & 4 (11\%)\tabularnewline
Psoriatic Arthritis (N, \%) & 7 (19\%)\tabularnewline
Male (N, \%) & 13 (36\%)\tabularnewline
Age (yr) & 51.5 (42-58)\tabularnewline
Weight (kg) & 78 (64-89)\tabularnewline
\bottomrule
\end{longtable}

Categorical values are reported as count (percentage of total)
Continuous values are reported as median values (interquartile range)

Table 2:

Concordance between predicted and measured adalimumab serum levels
falling within or outside the therapeutic window (defined as 5 --12
mg/L)\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
& N = 36\tabularnewline
\midrule
\endhead
True positive & 21 (58\%)\tabularnewline
True negative & 6 (17\%)\tabularnewline
False positive & 4 (11\%)\tabularnewline
False negative & 5 (14\%)\tabularnewline
\bottomrule
\end{longtable}

true positive (prediction and measured value within therapeutic range),
true negative (prediction and measured value outside therapeutic range),
false positive (prediction in therapeutic range, measured value outside
therapeutic range3), false negative (prediction outside therapeutic
range, measured value in therapeutic range).

Table 3:

Model parameter estimates\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Parameter & Value & Unit\tabularnewline
\midrule
\endhead
CL/F & 0.319 & L/day (/70kg)\tabularnewline
V/F & 9.830 & L(/70kg)\tabularnewline
KA & 0.376 & /hr\tabularnewline
AAA-CL & 4.350 &\tabularnewline
IIV CL & 46.400 & \%\tabularnewline
IIV V & 54.900 & \%\tabularnewline
Proportional & 0.145 & \%\tabularnewline
Additive & 0.617 & mg/L\tabularnewline
\bottomrule
\end{longtable}

Figures

Figure 1.

Workflow\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 2.

Prediction-corrected visual predictive check (pc-VPC) for the model by
Ternant et al. for the retrospective data. The shaded area's in this
figure show the 95\% of the 5th, 50th and 95th of data simulated from
the model, calculated for each of the bins in the plot. The dashed lines
indicate those same percentiles for the observed data. Dots indicate the
observed adalimumab concentrations.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/430672/articles/713596-early-model-based-precision-dosing-at-home-to-guide-adalimumab-therapy}

Appendices

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/430672/articles/713596-early-model-based-precision-dosing-at-home-to-guide-adalimumab-therapy}

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/430672/articles/713596-early-model-based-precision-dosing-at-home-to-guide-adalimumab-therapy}

\textbf{Hosted file}

\verb`image5.emf`  available at \url{https://authorea.com/users/430672/articles/713596-early-model-based-precision-dosing-at-home-to-guide-adalimumab-therapy}

\textbf{Hosted file}

\verb`image6.emf`  available at \url{https://authorea.com/users/430672/articles/713596-early-model-based-precision-dosing-at-home-to-guide-adalimumab-therapy}

Model code:

\$PROBLEM final model Adalimumab in adults (De Klaver et al.)

\$INPUT ID TIME EVID MDV DV AMT RATE SEX AGE WT ATA

\$DATA dataset.csv IGNORE=@

; TIME in hours

; WT: TBW in kg

; SEX: 0 = FEMALE, 1 = MALE

\$SUBROUTINE ADVAN2 TRANS2 ; linear 1-cmt oral

\$PK

; typical values

TVCL = (THETA(1)/24) * (WT/70)**0.75 * THETA(4)**ATA

TVV = THETA(2) * (WT/70)

TVKA = THETA(3)

KA = TVKA/24

; IIV

CL = TVCL * EXP(ETA(1))

V = TVV * EXP(ETA(2))

; scaling

S2=V

\$ERROR

IPRED = F

Y = IPRED*(1+ERR(1)) + ERR(2)

\$THETA

0.318775 ; 1 CL/F (L/day)

9.83155 ; 2 V/F (L)

0.375643 ; 3 KA (/day)

4.34751 ; 4 AAA\textasciitilde{}CL

\$OMEGA

0.215418 ; 1 IIV CL

0.301728 ; 2 IIV V

\$SIGMA

0.0210676 ; 1 Proportional error

0.380199 ; 2 Additive error

\$ESTIMATION METHOD=1 INTERACTION NOABORT MAXEVAL=9999 SIGDIG=3 PRINT=5

\$COVARIANCE UNCOND

STROBE Statement---checklist of items that should be included in reports
of observational studies\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Item No.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Recommendation\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Page No.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Relevant text from manuscript\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Title and abstract}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(\emph{a}) Indicate the study's design with a commonly used term in the
title or the abstract\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(\emph{b}) Provide in the abstract an informative and balanced summary
of what was done and what was found\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2,3\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Introduction\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Introduction\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Introduction\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Introduction\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Background/rationale\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Explain the scientific background and rationale for the investigation
being reported\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4,5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Objectives\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
State specific objectives, including any prespecified hypotheses\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4,5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Methods\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Methods\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Methods\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Methods\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Study design\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Present key elements of study design early in the paper\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6-10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Setting\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Describe the setting, locations, and relevant dates, including periods
of recruitment, exposure, follow-up, and data collection\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6,7\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Participants\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(\emph{a}) \emph{Cohort study}---Give the eligibility criteria, and the
sources and methods of selection of participants. Describe methods of
follow-up \emph{Case-control study}---Give the eligibility criteria, and
the sources and methods of case ascertainment and control selection.
Give the rationale for the choice of cases and controls
\emph{Cross-sectional study}---Give the eligibility criteria, and the
sources and methods of selection of participants\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
6,7\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\par\null\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(\emph{b}) \emph{Cohort study}---For matched studies, give matching
criteria and number of exposed and unexposed \emph{Case-control
study}---For matched studies, give matching criteria and the number of
controls per case\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
N.A.\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Variables\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Clearly define all outcomes, exposures, predictors, potential
confounders, and effect modifiers. Give diagnostic criteria, if
applicable\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9,10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Data sources/ measurement\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
8*\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
For each variable of interest, give sources of data and details of
methods of assessment (measurement). Describe comparability of
assessment methods if there is more than one group\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{9,10}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Bias\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Describe any efforts to address potential sources of bias\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6-10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Study size\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Explain how the study size was arrived at\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6,7\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Continued on next page\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Quantitative variables\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Explain how quantitative variables were handled in the analyses. If
applicable, describe which groupings were chosen and why\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6-10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Statistical methods\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(\emph{a}) Describe all statistical methods, including those used to
control for confounding\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9,10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(\emph{b}) Describe any methods used to examine subgroups and
interactions\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
N.A.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(\emph{c}) Explain how missing data were addressed\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
N.A.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\par\null\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(\emph{d}) \emph{Cohort study}---If applicable, explain how loss to
follow-up was addressed \emph{Case-control study}---If applicable,
explain how matching of cases and controls was addressed
\emph{Cross-sectional study}---If applicable, describe analytical
methods taking account of sampling strategy\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
N.A.\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(\emph{e}) Describe any sensitivity analyses\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
N.A.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Results\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Results\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Results\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Results\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Results\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Participants\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
13*\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(a) Report numbers of individuals at each stage of study---eg numbers
potentially eligible, examined for eligibility, confirmed eligible,
included in the study, completing follow-up, and analysed\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11, table 1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(b) Give reasons for non-participation at each stage\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(c) Consider use of a flow diagram\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Table 3\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Descriptive data\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
14*\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(a) Give characteristics of study participants (eg demographic,
clinical, social) and information on exposures and potential
confounders\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Table 1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(b) Indicate number of participants with missing data for each variable
of interest\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
N.A.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(c) \emph{Cohort study}---Summarise follow-up time (eg, average and
total amount)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
N.A.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Outcome data\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
15*\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Cohort study}---Report numbers of outcome events or summary
measures over time\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Case-control study---}Report numbers in each exposure category, or
summary measures of exposure\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{10-13}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Cross-sectional study---}Report numbers of outcome events or
summary measures\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Main results\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(\emph{a}) Give unadjusted estimates and, if applicable,
confounder-adjusted estimates and their precision (eg, 95\% confidence
interval). Make clear which confounders were adjusted for and why they
were included\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(\emph{b}) Report category boundaries when continuous variables were
categorized\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
N.A.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(\emph{c}) If relevant, consider translating estimates of relative risk
into absolute risk for a meaningful time period\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
N.A.\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Continued on next page\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Other analyses & 17 & Report other analyses done---eg analyses of
subgroups and interactions, and sensitivity analyses & N.A.
&\tabularnewline
Discussion & Discussion & Discussion & Discussion &
Discussion\tabularnewline
Key results & 18 & Summarise key results with reference to study
objectives & 12,13 &\tabularnewline
Limitations & 19 & Discuss limitations of the study, taking into account
sources of potential bias or imprecision. Discuss both direction and
magnitude of any potential bias & 13 &\tabularnewline
Interpretation & 20 & Give a cautious overall interpretation of results
considering objectives, limitations, multiplicity of analyses, results
from similar studies, and other relevant evidence & 13,14
&\tabularnewline
Generalisability & 21 & Discuss the generalisability (external validity)
of the study results & &\tabularnewline
Other information & Other information & & &\tabularnewline
Funding & 22 & Give the source of funding and the role of the funders
for the present study and, if applicable, for the original study on
which the present article is based & N.A. &\tabularnewline
\bottomrule
\end{longtable}

*Give information separately for cases and controls in case-control
studies and, if applicable, for exposed and unexposed groups in cohort
and cross-sectional studies.

\textbf{Note:} An Explanation and Elaboration article discusses each
checklist item and gives methodological background and published
examples of transparent reporting. The STROBE checklist is best used in
conjunction with this article (freely available on the Web sites of PLoS
Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at
http://www.annals.org/, and Epidemiology at http://www.epidem.com/).
Information on the STROBE Initiative is available at www.-statement.org.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
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\includegraphics[width=0.70\columnwidth]{figures/table1/table1}

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\includegraphics[width=0.70\columnwidth]{figures/Table3/Table3}

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