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\begin{document}

\title{Trends in race and sex reporting in lung cancer phase III clinical
trials}



\author[1]{Faaiq Aslam}%
\author[2]{Rami Manochakian}%
\author[2]{Yanyan Lou}%
\author[3]{Gerardo Colon-Otero}%
\author[4]{Taimur Sher}%
\affil[1]{Mayo Clinic Alix School of Medicine}%
\affil[2]{Mayo clinic}%
\affil[3]{Mayo Clinic Florida}%
\affil[4]{Mayo Clinic}%


\vspace{-1em}



  
  \date{May 23, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Background: To understand the changing trends in reporting of race and
sex as a demographic variable in phase III lung cancer clinical trials
published over the last 35 years. Methods: A total of 426 articles
reporting results of phase 3 lung cancer clinical trials published from
1984 to 2019 were identified in PubMed. Statistical analysis on trends
over time on percentage of minority and female participation were
performed. Results: Only 137 (32.2\%) of the 426 studies analyzed
reported race of participants. Among those studies, we found that the
mean participation rate of white participants was significantly higher
(82.65\%) (p \textless{} 0.001). We found a decrease in African American
participants and an increase in Asian participants over time. When
looking at sex, we found that although the rate of male participation
(69.02\%) was significantly higher than that of female participation
(30.98\%), the female participation has improved with time at a rate of
0.65\% per year. Conclusions: We found that the reporting and
participation of minority races continues to lag that of other
demographic factors like sex in phase III clinical trials in lung
cancer. Especially in African Americans, where the participation in lung
cancer phase III clinical trials has declined despite the rising
incidence in lung cancer.%
\end{abstract}%



\sloppy


\emph{Title:} Trends in race and sex reporting in lung cancer phase III
clinical trials

\emph{Authors:} Faaiq N Aslam \textsuperscript{a}, Rami
Manochakian\textsuperscript{b}, Yanyan Lou \textsuperscript{b}, Gerardo
Colon-Otero\textsuperscript{b}, Taimur Sher \textsuperscript{b}

\textsuperscript{a} Mayo Clinic Alix School of Medicine, 4500 San Pablo
Rd S, Jacksonville, FL USA 32224. Email: aslam.faaiq@mayo.edu. ORCID:
0000-0001-9748-7545

\textsuperscript{b} Mayo Clinic Florida, 4500 San Pablo Rd S,
Jacksonville, FL USA 32224

Emails:

Faaiq N Aslam: aslam.faaiq@mayo.edu

Rami Manochakian: manochakian.rami@mayo.edu

Yanyan Lou: lou.yanyan@mayo.edu

Gerardo Colon-Otero: gcolonotero@mayo.edu

Taimur Sher: sher.taimur@mayo.edu

\emph{This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.}

\emph{Correspondence:} Address to Taimur Sher MD, Mayo Clinic Florida,
4500 San Pablo Rd S. Jacksonville, FL 32224 (sher.taimur@mayo.edu)

\textbf{Statements and Declarations}

\emph{Ethics Approval and Consent to Participate}

Not Applicable

\emph{Consent for Publication}

Not Applicable

\emph{Availability of Data and Materials}

The datasets used and/or analyzed during the current study are available
from the corresponding author on reasonable request.

\emph{Competing Interests}

The authors have no relevant financial or non-financial interests to
disclose.

\emph{Funding}

The authors declare that no funds, grants, or other support were
received during the preparation of this manuscript.

\emph{Author's Contributions}

Faaiq N Aslam and Taimur Sher were instrumental in conceptualization,
methodology, investigation, data curation, formal analysis,
visualization, and writing---original draft.

Rami Manochakian, Yanyan Lou, and Gerardo Colon-Otero were instrumental
in conceptualization, methodology, data curation, formal analysis, and
writing---review \& editing.

All authors read and approved the final manuscript.

\emph{Acknowledgements}

Not Applicable

\textbf{Abstract}

\emph{Background:} To understand the changing trends in reporting of
race and sex as a demographic variable in phase III lung cancer clinical
trials published over the last 35 years.

\emph{Methods:} A total of 426 articles reporting results of phase 3
lung cancer clinical trials published from 1984 to 2019 were identified
in PubMed. Statistical analysis on trends over time on percentage of
minority and female participation were performed.

\emph{Results:} Only 137 (32.2\%) of the 426 studies analyzed reported
race of participants. Among those studies, we found that the mean
participation rate of white participants was significantly higher
(82.65\%) (\emph{p \textless{}} 0.001). We found a decrease in African
American participants and an increase in Asian participants over time.
When looking at sex, we found that although the rate of male
participation (69.02\%) was significantly higher than that of female
participation (30.98\%), the female participation has improved with time
at a rate of 0.65\% per year.

\emph{Conclusions:} We found that the reporting and participation of
minority races continues to lag that of other demographic factors like
sex in phase III clinical trials in lung cancer. Especially in African
Americans, where the participation in lung cancer phase III clinical
trials has declined despite the rising incidence in lung cancer.

\textbf{Keywords:} Lung cancer, clinical trials, disparities, NIH
Revitalization Act, race, sex

\textbf{1. Background}

Clinical trials are an essential means of improving outcomes of cancer
patients. Racial minorities and females have been underrepresented in
clinical trials through time. Historically, such disparities have been a
problem of multiracial societies such as US, but with globalization and
human migration, health disparities are becoming a global concern. In
February 2020 the European Parliament in its report on health
inequalities in the European Union identified addressing growing health
inequalities a priority {[}1{]}. The National Institute of Health (NIH)
of the United States was tasked to address these growing challenges
through the NIH Revitalization Act of 1993. An important mandate of the
NIH Revitalization Act was adequate reporting on minority and vulnerable
population in clinical trials. Despite these important efforts, various
studies over the ensuing two decades continue to report persistent
disparities in clinical trials {[}2-4{]}.

As we enter the third decade of the 21\textsuperscript{st} century, with
increasing awareness and advancements in communications and global
connectivity, an expectation would be that these disparities would be
resolving. We set out to evaluate the landscape of race and sex
reporting in phase III clinical trials in patients with lung cancer. We
selected lung cancer for our study because as the leading cause of
cancer death in the US, it has represented a significant proportion of
phase III cancer studies (14\%) and is seen across the entire spectrum
of human races and sex {[}5-10{]}.

The purpose of our study was to determine the historical trends and
current landscape of reporting and representation of females and
minorities in lung cancer phase III clinical trials. We chose to study
lung cancer phase III clinical trials because lung cancer is the most
common cause of cancer-related death and manifests differently depending
on one's background, thus adequate reporting and representation are
essential {[}5-8{]}. Specifically, we looked at participation rates of
different races and sex over a 35-year time-period (1984-2019).

\textbf{2. Methods}

\emph{2.1 Data Collection}

To obtain participation data for lung cancer phase III clinical trials,
the publically available database, PubMed, was queried using the search
terms ''((lung{[}Title{]} OR pulmonary{[}Title{]}) AND (phase
3{[}Title{]} OR phase III{[}Title{]})) NOT (review) AND
(clinicaltrial{[}Filter{]} OR randomizedcontrolledtrial{[}Filter{]}) AND
(fft{[}Filter{]})'' on April 22nd, 2020. This search yielded 724
publications, from which 426 publications were analyzed as they met the
following inclusion criteria:

The study was a Phase III Lung Cancer Clinical Trial

The study did not present data from a previous clinical trial already
recorded in our database (to avoid duplicate recording of data)

The 426 publications were subsequently used to collect year of
publication and demographic data relating to participants' race and sex
from the study's demographic table to construct the database for the
analysis (Figure 1). Most of the studies the search yielded were
multinational studies, which was determined by reviewing the author
affiliations of these publications. The review of publically available
data could not classify them into discrete categories of U.S. vs other
regions. This study was considered exempt for IRB review per
institutional policies.

\textbf{Figure 1.} CONSORT diagram illustrating the study selection
process.

For race, the number of White, Asian, African American, and Hispanic
participants were recorded. The category ``Other'' was also used to
include any participants that were listed as Other in the original study
or did not belong to the White, Asian, African American, or Hispanic
groups. The final category of ``Unknown'' was used to include any
participants that were listed as Unknown in the publication. The number
of participants in each category were then converted to the percent of
the total participant pool each category represented and were then used
to compare participation rates across studies. The studies that did not
report demographic data on racial background were annotated as such. For
sex, the total number of males and females was recorded. These values
were subsequently used to calculate the percent of the participants'
pool that was male or female, and the studies that did not report
demographic data regarding sex were annotated as such. The data
collected was then used to determine the percent of studies that
reported participant race or sex, differences in the rate of
participation among racial groups and between sex, and how participation
rates of have changed over time.

\emph{2.2 Statistical Analysis}

Statistical analysis was done using the SciPy stats package and figure
generation was done using the MatPlotLib package for python {[}11,
12{]}. To determine the rate of reporting of race, the percentage of
studies that reported race as a demographic and those that did not
report race were calculated. Out of the studies that did report race,
the percent of those studies that reported on each racial group was also
determined. The same method was also used to determine the rate of
reporting of sex.

To determine whether disparities in clinical trials' participation
exist, the mean participation rate of each race and sex across all the
studies that reported race or sex was calculated and the 95\% confidence
interval for the mean of each demographic was determined. Then, to
compare participation rates among different races, the percent of white
participation from each study was compared with the percent
participation of the remaining races (Asian, African American, Hispanic,
Other, and Unknown) using a 2-sample t-test and a p-value \textless{}
0.01 was considered statistically significant. A one-way ANOVA test was
also used to determine whether a statistically significant difference in
participation among the Asian, African American, Hispanic, Other, and
Unknown groups exists and a p-value \textless{} 0.01 was considered
statistically significant. To compare participation rates between sex, a
2-sample t-test was used and a p-value \textless{} 0.01 was considered
statistically significant.

Lastly, to determine how participation rate has changed over time, the
participation rates of different races from each individual study was
plotted on a scatter plot using the MatPlotLib package {[}12{]}. Using
those datapoints, the SciPy stats package was used to calculate the
Pearson correlation coefficient, the p-value (with \emph{p
\textless{}}0.05 considered statistically significant), and to draw a
line of best fit {[}11{]}. The data points for White participation were
plotted with each of the remaining races to serve as a comparison as to
how participation rates have changed with time.

\textbf{3. Results}

\emph{3.1 Rates of race reporting and participation trends}

We analyzed the demographic data from a total of 426 lung cancer phase
III clinical trials. It was not possible for us to identify US only
studies as most studies had authorships from multiple institutions
across various regions in the US, Europe, and Asia in various
combinations. Only 137 studies (32.2\%) reported race as a demographic
(Figure 2A) The remaining 289 studies (67.8\%) did not report race.
Interestingly, we found that from all the studies we analyzed, no study
published before 1992 reported demographic data on patient race (Figure
2C). After 1992, more studies started reporting race (Figure 2C).
However, race reporting did not appear to improve over time since 1993
(Figure 2C). Out of the 137 studies that reported demographic data on
participants' race, 128 studies (93.4\%) reported the rate of White
participation, 97 studies (70.8\%) reported the rate of Asian
participation, 92 studies (67.2\%) reported the rate of African American
participation, 23 studies (16.8\%) reported rate of Hispanic
participation, 106 studies (77.4\%) reported a race that fit into the
other category, and 22 studies (16.1\%) reported the rate of
participants whose race was unknown (Figure 2B).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2.} (A) Pie chart illustrating percentage of studies
analyzed that reported race as a patient characteristic. (B) Histogram
reporting the percentage of studies (of the studies that reported
multiple races) that reported participation rates of each race category.
(C) Histogram showing percent of studies published each year that
reported demographic data on participant race.

The white participants represented 82.55\% (95\% CI 80.39\%-84.71\%) of
study participants (Table 1) (Figure 3A). The mean rates of
participation of Asian, African American, Hispanic, Others, and Unknown
were 14.03\% (95\% CI 11.02\%-17.04\%), 5.53\% (95\% CI 4.50\%-6.56\%),
3.97\% (95\% CI 2.13\%-5.81\%), 3.99\% (95\% CI 3.21\%-4.77\%), and
2.46\% (95\% CI 0.85\%-4.08\%) respectively (\emph{p \textless{}} 0.001)
(Table 1) (Figure 3A).

\textbf{Table 1.} Mean Rates of Participation of each Race across
analyzed Lung Cancer Phase III Clinical Trials.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Race\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Mean Participation Rate of Each Race\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
95\% Confidence Interval\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Pearson Correlation Coefficient (Change in Participation rate with
Time)\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\emph{p}-value\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
White\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
82.55\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
80.39\% - 84.71\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
m = -0.51\% per year r = -0.23, \emph{p \textless{}} 0.01\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2 sample t-test (white vs. remaining races) \emph{p \textless{}}
0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Asian\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
14.03\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
11.02\%-17.04\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
m = 1.07\% per year r = 0.279, \emph{p \textless{}} 0.01\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
One-way ANOVA test (among remaining races) \emph{p \textless{}}
0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
African American\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
5.53\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
4.50\%-6.56\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
m = -0.30\% per year r = -0.34, \emph{p \textless{}} 0.001\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Hispanic\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
3.97\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2.13\%-5.81\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
m = No Change with Time r = -0.01, \emph{p =}0.97\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Other\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
3.99\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
3.21\%-4.77\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
m = No Change with Time r = -0.04, \emph{p =} 0.65\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Unknown\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2.46\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.85\%-4.08\%\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
m = No Change with Time r = 0.14, \emph{p =} 0.55\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

When looking at how participation rates changed with time, we found that
the rate of white participants decreased at a rate of 0.51\% per year (r
= -0.23, \emph{p \textless{}} 0.01) (Figure 3B). This decrease in
participation was accompanied by an increase in the rate of Asian
participants by a rate of 1.07\% per year (r = 0.279, \emph{p
\textless{}} 0.01) (Figure 3C). However, the rate of African American
participants decreased at a rate of -0.30\% per year (r = -0.34, \emph{p
\textless{}} 0.001) (Figure 3D). The rates of participation among
Hispanics (r = -0.01, \emph{p =} 0.97), the other category (r =
-0.04,\emph{p =} 0.65), and the unknown category (r = 0.14, \emph{p =}
0.55) did not change with time (Figure 3E, 3F, 3G), although we did not
have enough studies report Hispanic demographics to draw meaningful
conclusions about their representation.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3.} (A) Box Plots illustrating the percentage of
participants of each race in all the studies analyzed that reported
race. (B) A scatter plot with a Pearson correlation coefficient of r =
-0.23 illustrating how the percent of white participants has decreased
with time. (C) A scatter plot with a Pearson correlation coefficient
showing that the percent of Asian participants has increased with time
(r = 0.279) and percent of white participants has decreased with time (r
= -0.23). (D) A scatter plot with a Pearson correlation coefficient
showing that the percent of African American participants has decreased
with time (r = -0.34) and percent of white participants has decreased
with time (r = -0.23). (E) A scatter plot with a Pearson correlation
coefficient showing that the percent of Hispanic participants has not
changed with time (r = -0.01) and percent of white participants has
decreased with time (r = -0.23). (F) A scatter plot with a Pearson
correlation coefficient showing that the percent of other participants
has not changed with time (r = -0.04) and percent of white participants
has decreased with time (r = -0.23). (G) A scatter plot with a Pearson
correlation coefficient showing that the percent of unknown participants
has not changed with time (r = 0.14) and percent of white participants
has decreased with time (r = -0.23).

\emph{3.2 Rates of sex reporting and participation trends}

From the 426 studies that were analyzed, 420 studies (98.8\%) reported
sex as a patient demographic (Figure 4A). Among those 420 studies, the
mean rate of participation for males was 69.02\% (95\% CI 67.66\% -
70.38\%) and the mean for females was 30.98\% (95\% CI 29.62\% -
32.34\%), yielding a statistically significant difference in
participation rates between males and females (\emph{p
\textless{}}0.001) (Table 2) (Figure 4B).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 4.} (A) Pie chart illustrating percentage of studies
analyzed that reported sex as a patient characteristic. (B) Box Plots
illustrating the percentage of participants of each sex in all the
studies analyzed that reported sex (p-value \textless{} 0.001). (C) A
scatter plot with a Pearson correlation coefficient showing that the
percent of male participants has decreased with time (r = -0.31) and
percent of female participants has increased with time (r = 0.31).

When looking at the change of participation rates over time, we found
that the disparity between male and female participation has
significantly improved. The rate of male participation has decreased by
a rate of 0.65\% a year since 1985 (r = -0.31, \emph{p
\textless{}}0.001) and the rate of female participation has increased by
a rate of 0.65\% a year since 1985 (r = 0.31, \emph{p \textless{}}
0.001) (Figure 4C). Moreover, we found that the mean rate of
participation from 2015-2019 for males was 65.21\% and the mean rate of
participation for females was 34.79\%, which is markedly improved from
the mean rate of participation from 1985-1995 when the mean
participation rate was 73.60\% for males and 26.40\% for females (Table
2).

\textbf{Table 2.} Mean Rates of Participation of each Sex across all
analyzed Lung Cancer Phase III Clinical Trials.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Sex\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Mean Participation rate across all studies\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
95\% Confidence Interval\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\emph{p}-value\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Pearson Correlation Coefficient (Change in Participation rate with
Time)\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Mean from 1985-1995\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Mean from 2015-2019\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Male\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
69.02\%\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
67.66\% - 70.38\%\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2 sample t-test (Males vs. Females) \emph{p \textless{}} 0.001\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
m = -0.65\% per year r = -0.31, \emph{p \textless{}} 0.001\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
73.60\%\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
65.21\%\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Female\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
30.98\%\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
29.62\% - 32.34\%\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
m = 0.65\% per year r = 0.31, \emph{p \textless{}} 0.001\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
26.40\%\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
34.79\%\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{4. Discussion}

Our study is the first one to look at the reporting trends in race and
sex in phase III lung cancer trials over last 35 years. The results of
our analysis further substantiate the evidence that continues to
highlight the disparities surrounding race in clinical trials.

Examining the trends in reporting of sex, we find more positive change
over time. In the studies we reviewed, 98.8\% reported participant sex.
Analyzing these trends, we noted progressive increase in women
representation since 1985. While the trends over time are promising,
females are still underrepresented with a mean participation rate of
34.97\% from 2015-2019. This is despite females accounting for 48.9\% of
lung cancer in the United States {[}13{]}. Similar patterns have also
been seen in other, non-cancer clinical trials as well. For example,
cardiovascular and lipid lowering agent clinical trials also showed
underrepresentation of females at a participation rate of 33\%-38\%
{[}14, 15{]}.

We were surprised to find that no phase III lung cancer study, in our
database, between 1984-1992 reported on race. In reviewing the
literature for evidence, we encountered significant variability in race
reporting across different studies. As an example, Brahan et al. found
significant increases in race reporting from 29.6\% to 63.5\% in
pediatric studies conducted in 1991-1993 and 2000-2002 {[}16{]}. In a
2011 study, Geller et al. looked at 86 clinical trials across nine
high-impact journals and found that one-fifth of the studies failed to
report the distribution of participant race {[}17{]}. Unlike the
findings from these studies, our findings showed that a significantly
larger proportion of lung cancer phase III clinical trials did not
report the distribution of participant race. There can be several
reasons for such degree of variance. Pediatric clinical studies,
especially pediatric oncology studies, have more centralized review and
sponsoring processes, and are more likely to be funded by the NIH.
Logically, such studies are more likely to comply with the NIH policies
including the provisions of the NIH Revitalization Act and hence the
significant change in the race and sex reporting as noted by Brahan et
al. This increase in race reporting, while encouraging, falls short of
the ideal as a third of pediatric oncology studies still did not report
race as a variable. Another reason for lack of reporting on race may be
that majority of the studies are multinational in nature and may include
geographical areas with less cultural heterogeneity. This is likely an
important limitation of our study. However, the rapid globalization and
human migration over the last two decades calls for a more acute
evaluation. The results of our study in the context of recent
demographic changes highlight the significance of addressing this trend
as health inequality and disparities are becoming more of a global
issue.

Our findings of minority underrepresentation in lung cancer phase III
clinical trials have also been observed in other studies as well. A 2004
study looking at race-based disparities in cancer clinical trials also
found White participants to represent 85.6\% of all participants and
another study found White participants to represent 76.3\% of all
participants in cancer drug clinical trials {[}4, 18{]}. One might
question the generalizability of this finding in the context of the
demographic make-up of the population and the epidemiology of the
disease under study. For lung cancer, this is especially important as
the incidence in African Americans, the largest minority population in
the United States, is 59.5 per 100,000 compared to 61.6 per 100,000 in
Whites {[}13{]}. Loree et al in 2019 evaluated the reporting of race and
other demographic variables in the landmark clinical trials leading to
the approval of antineoplastic therapies and found a similar finding of
decline in African American participation between 2008 and 2018
{[}18{]}.

The principles of justice, beneficence and the medicine's professional
code of conduct require diversity and inclusion as mission critical to
its social contract with the society. These higher moral values call out
for all efforts to ensure adequate access of all segments of society to
the benefits of biomedical research. While it may be debatable that
non-reporting is not synonymous with not-being included, it is strong
enough evidence to support the conclusion of non-inclusion. Although
positive changes have been made in the reporting of sex, there is
clearly room for much improvement when it comes to the reporting of
race. The responsibility to move the needle forward falls on shoulders
of all stakeholders. The clinicians and patient care teams should review
and discuss the importance of race and sex reporting with the patients
in the broader context of its value to the society and in addressing the
disparities. The clinical trials' sponsors should work with the study
teams to ensure recording sex and race amongst other demographic
information in case report forms. The medical publishers should mandate
the reporting of race and sex as a criterion for acceptance of
publication.

Our study does have \emph{limitations} . Given that this study was a
retrospective analysis, it carries the same weaknesses associated with
such studies. As noted in the methods section, we could not reliably
decipher which studies were solely U.S. based versus from other regions
as majority of the manuscripts had author affiliations from various
institutions across the globe.

\textbf{5. Conclusions}

In conclusion, we found that disparities in reporting and participation
of minority racial groups continue to exist. We believe that requiring
reporting of patient demographic data in clinical trials, in compliance
with the NIH Revitalization Act, will improve transparency and the rate
of reporting. Furthermore, it will critically inform health policy
makers and other stakeholders to address the root causes of
underrepresentation of minorities by addressing social barriers and
hesitancy to participate in clinical trials {[}19{]}.

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