Abstract
Objective
Despite regulatory initiatives to improve female representation in clinical trials, sex-based disparities persist, particularly in oncology. This is of concern given the rising burden of lung cancer in women. We aimed to quantify female representation in lung cancer clinical trials over the past three decades and to examine whether trial-level differences in female enrolment were associated with differences in reported adverse events (AEs).
Methods
We conducted a systematic review and meta-analysis of randomised controlled trials (RCTs) evaluating drug therapies for lung cancer, identified through PubMed, Embase, the Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from Jan 1, 1993, to April 1, 2025. Two reviewers independently assessed study eligibility, extracted data, and evaluated risk of bias. Enrolment incidence disparity (EID) was calculated to assess female representation. Reported incidence of AEs was analysed in relation to female representation across treatment settings. This study is registered with PROSPERO (CRD42024537925).
Results
Among 44,915 records identified, 636 studies comprising 265,989 participants were included. Most studies were judged to be at low risk of bias (83%), whereas 3% were at high risk. The pooled EID was -3.17% (95% CI: 4.10, -2.24), indicating underrepresentation of women overall, particularly in trials conducted in very high Human Development Index (HDI) settings, including Europe, Oceania, and North America. Underrepresentation was also observed across several trial subgroups, including younger populations, predominantly Caucasian populations, and selected disease-stage strata. Trial-level differences in female representation were associated with differences in reported AE incidence across treatment settings.
Conclusions
Women remain underrepresented in lung cancer RCTs, which may limit the generalisability of trial findings to female patients. Trial-level differences in female representation were associated with differences in reported AE incidence, supporting the need for more inclusive recruitment and more transparent sex-disaggregated safety reporting.
Keywords: Lung cancer, Adverse events, Female, RCTs, Equality, Sex
1. Introduction
Over the past three decades, the Food and Drug Administration (FDA) has prioritised female representation in drug clinical trials due to the risk of underestimating adverse events (AEs) resulting from insufficient female enrolment.1 This underrepresentation may compromise post-marketing drug safety surveillance and adversely affect women’s health.2 Despite the 1993 'Guideline for the Study and Evaluation of Gender Differences in the Clinical Evaluation of Drugs', notable shifts favouring female participation only emerged in 2016, primarily in trials targeting conditions such as osteoporosis.3 However, oncology clinical trials remain male-dominated, with drug trials accounting for 38.4% of the market.4,5 The World Health Organization (WHO) projects a 77% increase in cancer incidence by 2050, driving demand for novel therapies.6 In response, the FDA released 'Study of Sex Differences in the Clinical Evaluation of Medical Products Guidance for Industry' in January 2025 to improve female representation in oncology trials and promote sex-based analysis, superseding the 1993 guidelines.
Lung cancer remains the leading cause of cancer-related deaths globally, ranking first in men and second in women.7 While incidence rates have declined among men in developed countries, a concomitant rise has been observed in women.8 In 2020, female incidence in the USA surpassed males, with a narrowing gap between sexes also observed in Europe.9 This trend highlights the need for accurate efficacy and safety data. Over the past decade, lung cancer therapy has evolved into an increasingly individualised, biomarker-driven paradigm defined by immunotherapy, targeted agents, and combination strategies.10, 11, 12, 13, 14 Although programmed death-ligand 1 (PD-L1) remains the only validated predictive biomarker, host-related factors also influence treatment response and survival, underscoring the growing complexity of modern lung cancer care.10, 11, 12, 13, 14 In this evolving landscape, adequate female representation in clinical trials is essential to ensure accurate assessment of sex-specific differences and the generalisability of clinical evidence.
However, women remain underrepresented in randomised controlled trials (RCTs), especially in lung cancer, limiting the generalisability of findings.4 Previous analyses reveal a consistently low female participation rate in cancer RCTs.15 A 2017 global analysis reported only 39% female enrolment.16 USA lung cancer trials from 2014–2019 included just 40.9% women.17 Given that women experience higher AEs, this underrepresentation may lead to underestimated post-approval risks.18 Addressing this imbalance is crucial for ensuring equitable therapeutic approaches for women. This study aimed to assess female representation in global lung cancer RCTs across regions, populations, and time, and to examine its impact on AEs reporting across treatments. This study aimed to assess female representation in global lung cancer RCTs across regions, populations, and time, and to examine whether trial-level differences in female representation were associated with differences in reported AEs across treatment settings.
2. Materials and methods
2.1. Search strategy and selection criteria
This systematic review and meta-analysis, registered with the International Prospective Register of Systematic Reviews Database (PROSPERO, CRD42024537925), complied with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.19 A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane Central Register of Controlled Trials utilising a combination of terms capturing 'Lung cancer' AND ('Clinical Trial' AND 'Randomised'). The search was limited to full-text articles, English-language abstracts, and studies published between January 1, 1993, and April 1, 2025. Additionally, ClinicalTrials.gov was searched to identify completed or terminated interventional trials with available results. The complete search strategy is detailed in the Supplementary Materials (pp 2–3). The systematic review included RCTs evaluating pharmacological interventions (such as chemotherapy, immunotherapy, or targeted therapy) in patients with primary lung cancer. Eligible comparators included placebo, monotherapy, combination therapy, or best supportive care (BSC). Only RCTs meeting phase II-III sample size criteria and initiating recruitment after 1993 were included. Detailed inclusion and exclusion criteria are specified in the Supplementary Materials (pp 3–4).
2.2. Data analysis
Literature search results were deduplicated using EndNote 20, followed by manual verification. Publications and registry records from the same clinical trial were identified using trial registration numbers, study identifiers, enrolment periods, and population characteristics to ensure data independence. When multiple reports originated from the same trial or overlapping cohort, data were extracted only once from the most comprehensive or most recent source, and studies with smaller sample sizes, shorter follow-up, or lower quality were excluded. Two reviewers screened all abstracts and full-text articles for inclusion in the study, and disagreements were resolved by a third investigator. Data from the included studies were initially extracted in Excel by one author and subsequently reviewed by a second reviewer.
A standardised data extraction form (Supplementary Materials pp 4–5) was employed to collect relevant information. This included trial characteristics (first author, publication year, trial recruitment year, country/region, continent, Human Development Index [HDI], trial phase, treatments), cancer characteristics (cancer stage, histological type), and population characteristics (female participation, median/mean age, race/ethnicity, smoking status, Eastern Cooperative Oncology Group performance status [ECOG PS], proportions of participants with specific metastatic sites [liver, pleura, bone, adrenal, lymph node, brain, skin, multiple sites], and proportions with prior exposure to chemotherapy, surgery, or radiotherapy). For categorical variables, the predominant category was utilised for definition, while missing data were designated as 'Not Reported (NR)'.
The primary outcome of this study was the enrolment incidence disparity (EID), defined as the difference between the female participation rate in a trial and the estimated female proportion among lung cancer patients derived from GLOBOCAN 2020 continent data, stratified by continent and lung cancer subtype (Supplementary Materials pp 6–7). Female representation is classified as sufficient (EID ≥ −0.2 times the female proportion) or insufficient (EID < −0.2 times) (Supplementary Materials pp 8).
Methodological quality was independently assessed by two reviewers using the Cochrane Risk of Bias Assessment Tool (RoB 2). This evaluation encompassed five domains: randomisation process, deviations from intended interventions, missing outcome data, outcome measurement, and selection of the reported result. Any disagreements in risk of bias judgments were resolved through discussion with a third reviewer.
Descriptive statistics were used to summarise trial, cancer, and population characteristics. Because EID reflects a structural measure of female representation rather than a treatment effect, pooling across diverse intervention types was methodologically appropriate. A random-effects model utilising the inverse variance method (DerSimonian-Laird estimator for 95% confidence intervals [CIs]) was employed to quantitatively synthesise EID values, with stratified analyses conducted based on trial, cancer, and population characteristics. Heterogeneity was therefore summarised descriptively using I² statistics and Cochran’s Q test for the overall pooled analysis and key subgroup analyses, primarily to enhance transparency rather than to serve as the sole basis for interpretation. Sensitivity analyses assessed the influence of studies with high or ambiguous risk of bias and compared results using GLOBOCAN 2020 HDI data or Global Burden of Disease (GBD) continent data (Supplementary Materials pp 6–7). The incidence of grade ≥ 3 AEs for each treatment regimen was analysed in relation to female representation using formal statistical testing, with two-sided P-values reported as part of an exploratory analysis. Given the multiple parallel comparisons across treatment classes and AE categories, these P values should be interpreted cautiously and not as confirmatory evidence. Statistical significance was set at P < 0.05. All analyses were performed using Python (version 4.2.0) and R (version 4.2.0).
3. Results
The search identified 24,182 studies after removing duplicates. Following abstract screening, 1365 full-text articles were assessed for eligibility. Ultimately, 489 publications met the inclusion criteria, along with 488 records retrieved from ClinicalTrials.gov. After excluding 341 duplicates, a total of 269 phase II and 367 phase III clinical trials were included (Fig. 1).
Fig. 1.
Study selection. RCT, random clinical trial.
The majority of studies investigated non-small cell lung cancer (NSCLC; n = 570) compared with small cell lung cancer (SCLC; n = 66) (Supplementary Materials pp 11–35). Studies exhibited a global distribution, predominantly originating from North America (41.7%), Europe (26.1%), and Asia (23.3%), and were conducted across 38 countries. The USA (38.8%), China (11.3%), Japan (7.5%), and Germany (4.4%) accounted for the largest proportion of studies. The included populations were primarily Caucasian (76.6%) or Asian (23.3%), comprising non-smokers (9.4%) and smokers (36.8%). Patients with Stage IV lung cancer were frequently represented (48.0%), with the majority being under 65 years of age (82.2%) and exhibiting an ECOG PS of 0 or 1 (71.1%). Explicit documentation of multiple metastases was limited (5.5%). Consistent reporting of individual metastatic sites was lacking, with data provided only for liver (12.4%), pleural (2.7%), bone (7.8%), adrenal (3.9%), lymph node (2.2%), skin (0.5%), and brain (11.3%) metastases reported. Information concerning prior treatment was often omitted, particularly adjuvant chemotherapy (17.0%), surgery (14.9%), and radiotherapy (15.7%). A complete summary of study characteristics is provided in Table 1.
Table 1.
Characteristics of included studies.
| Characteristics | Number of studies, No. (%) |
Number of participants, No. (%) |
Number of females, No. (%) |
|---|---|---|---|
| Total | 636 | 265,989 | 92,650 |
| Trial characteristics | |||
| Continent | |||
| North America | 265 (41.7) | 118,484 (44.5) | 42,876 (46.3) |
| Europe | 166 (26.1) | 63,855 (24) | 19,884 (21.5) |
| Asia | 148 (23.3) | 54,209 (20.4) | 19,063 (20.6) |
| South America | 29 (4.6) | 20,919 (7.9) | 7719 (8.3) |
| Oceania | 27 (4.2) | 8467 (3.2) | 3090 (3.3) |
| Africa | 1 (0.2) | 55 (0.0) | 18 (0.0) |
| HDI | |||
| Very high | 544 (85.5) | 230,995 (86.8) | 80,520 (86.9) |
| High | 67 (10.5) | 26,465 (9.9) | 8782 (9.5) |
| Medium | 23 (3.6) | 7858 (3.0) | 3154 (3.4) |
| Low | 2 (0.3) | 671 (0.3) | 194 (0.2) |
| Clinical trial phase | |||
| III | 367 (57.7) | 223,301 (84.0) | 76,438 (82.5) |
| II | 269 (42.3) | 42,688 (16.0) | 16,212 (17.5) |
| Cancer characteristics | |||
| Cancer type | |||
| NSCLC | 570 (89.6) | 240,966 (90.6) | 85,440 (92.2) |
| SCLC | 66 (10.4) | 25,023 (9.4) | 7210 (7.8) |
| TNM | |||
| I | 13 (2.0) | 8175 (3.1) | 2425 (2.6) |
| II | 11 (1.7) | 8091 (3.0) | 3304 (3.6) |
| III | 20 (3.1) | 9302 (3.5) | 3081 (3.3) |
| IV | 305 (48.0) | 131,533 (49.5) | 43,940 (47.4) |
| NR | 287 (45.1) | 108,888 (40.9) | 39,900 (43.1) |
| Population characteristics | |||
| Age, years | |||
| < 65 | 523 (82.2) | 225,546 (84.8) | 77,622 (83.8) |
| 65 | 64 (10.1) | 19,281 (7.2) | 7237 (7.8) |
| NR | 49 (7.7) | 21,162 (8.0) | 7791 (8.4) |
| Race | |||
| Asian | 148 (23.3) | 54,515 (20.5) | 20,432 (22.1) |
| Caucasian | 487 (76.6) | 211,419 (79.5) | 72,200 (77.9) |
| Black | 1 (0.2) | 55 (0.0) | 18 (0.0) |
| Smoking status | |||
| Smoker | 234 (36.8) | 113,187 (42.6) | 36,342 (39.2) |
| Non-smoker | 60 (9.4) | 20,728 (7.8) | 11,231 (12.1) |
| NR | 342 (53.8) | 132,074 (49.7) | 45,077 (48.7) |
| ECOG PS | |||
| 0/1 | 452 (71.1) | 196,880 (74.0) | 66,738 (72.0) |
| At least 2 | 14 (2.2) | 4048 (1.5) | 1012 (1.1) |
| NR | 170 (26.7) | 6,5061 (24.5) | 24,900 (26.9) |
| Multiple metastases | |||
| Yes | 35 (5.5) | 11,376 (4.3) | 3336 (3.6) |
| No | 12 (1.9) | 5829 (2.2) | 1724 (1.9) |
| NR | 589 (92.6) | 248,784 (93.5) | 87,590 (94.5) |
| Liver metastases | |||
| Yes | 4 (0.6) | 701 (0.3) | 310 (0.3) |
| No | 75 (11.8) | 35,327 (13.3) | 10,973 (11.8) |
| NR | 557 (87.6) | 229,961 (86.5) | 81,367 (87.8) |
| Pleura metastases | |||
| No | 17 (2.7) | 8378 (3.1) | 2773 (3.0) |
| NR | 619 (97.3) | 257,611 (96.9) | 89,877 (97.0) |
| Bone metastases | |||
| Yes | 2 (0.3) | 614 (0.2) | 181 (0.2) |
| No | 48 (7.5) | 21,057 (7.9) | 6428 (6.9) |
| NR | 586 (92.1) | 244,318 (91.9) | 86,041 (92.9) |
| Adrenal metastases | |||
| Yes | 2 (0.3) | 207 (0.1) | 81 (0.1) |
| No | 23 (3.6) | 9765 (3.7) | 3055 (3.3) |
| NR | 611 (96.1) | 256,017 (96.3) | 89,514 (96.6) |
| Lymph nodes metastases | |||
| Yes | 1 (0.2) | 182 (0.1) | 32 (0.0) |
| No | 13 (2.0) | 4218 (1.6) | 1528 (1.6) |
| NR | 622 (97.8) | 261,589 (98.3) | 91,090 (98.3) |
| Skin metastases | |||
| No | 3 (0.5) | 2249 (0.8) | 876 (0.9) |
| NR | 633 (99.5) | 263,740 (99.2) | 91,774 (99.1) |
| Brain metastases | |||
| Yes | 5 (0.8) | 2264 (0.9) | 705 (0.8) |
| No | 67 (10.5) | 32,732 (12.3) | 10,489 (11.3) |
| NR | 564 (88.7) | 230,993 (86.8) | 81,456 (87.9) |
| Prior chemotherapy | |||
| Yes | 59 (9.3) | 32,483 (12.2) | 11,774 (12.7) |
| No | 49 (7.7) | 26,598 (10.0) | 7969 (8.6) |
| NR | 528 (83.0) | 206,908 (77.8) | 72,907 (78.7) |
| Prior surgery | |||
| Yes | 26 (4.1) | 16,029 (6.0) | 5577 (6.0) |
| No | 69 (10.8) | 31,835 (12.0) | 9208 (9.9) |
| NR | 541 (85.1) | 218,125 (82.0) | 77,865 (84.0) |
| Prior radiotherapy | |||
| Yes | 11 (1.7) | 2827 (1.1) | 1284 (1.4) |
| No | 89 (14.0) | 42,982 (16.2) | 12,524 (13.5) |
| NR | 536 (84.3) | 220,180 (82.8) | 78,842 (85.1) |
| Risk of bias | |||
| High | 21 (3.3) | 9921 (3.7) | 3711 (4.0) |
| Some concerns | 84 (13.2) | 21,847 (8.2) | 7309 (7.9) |
| Low | 531 (83.5) | 234,221 (88.1) | 81,630 (88.1) |
Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; HDI, Human Development Index; NR, not reported; NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer.
Chemotherapy was the predominant treatment regimen studied (67.1%), followed by targeted therapy (27.5%), chemotherapy with targeted therapy (17.5%), immunotherapy (11.8%), chemotherapy with immunotherapy (8.0%), immunotherapy with targeted therapy (1.7%), and combinations of chemotherapy, immunotherapy, and targeted therapy (1.1%). Among the 636 studies, 3.3% were assessed as having a high risk of bias, 13.2% had some concerns, and 83.5% were judged to have a low risk of bias (Supplementary Materials pp 35–36).
Female representation in lung cancer RCTs was inadequate (EID, −3.17%; 95% CI: −4.10, −2.24) (Table 2), displaying an increasing trend before 2015, followed by a decreasing trend thereafter (Fig. 2). Substantial between-study heterogeneity was observed across pooled analyses (I², 100.0%; Cochran’s Q, P < 0·001), as expected for EID as a structural enrolment metric rather than a treatment-effect estimate. Sensitivity analyses of studies with low risk of bias confirmed the robustness of this finding (Supplementary Materials pp 36–39). Trials enrolling TNM stage III patients were unrepresented (EID, −1.46; 95% CI: −8.93, 6.02), whereas those including patients with prior surgery were represented (EID, 0.83; 95% CI: −4.01, 5.67).
Table 2.
Enrollment incidence disparity and female representation in lung cancer trials.
| Characteristics | GLOBOCAN continent data |
GLOBOCAN HDI data |
GBD continent data |
|||
|---|---|---|---|---|---|---|
| Enrollment incidence disparity, % (95% CI) | Representation | Enrollment incidence disparity, % (95% CI) | Representation | Enrollment incidence disparity, % (95% CI) | Representation | |
| Trial characteristics | ||||||
| Continent | ||||||
| North America | −8.25 (−9.35, −7.15) | Unrepresented | −7.09 (−8.19, −5.99) | Unrepresented | 2.60 (1.53, 3.66) | Represented |
| Europe | −3.06 (−4.46, −1.67) | Unrepresented | 5.30 (4.00, 6.59) | Represented | −5.32 (−6.71, −3.94) | Unrepresented |
| Asia | 6.82 (4.78, 8.86) | Represented | 5.60 (3.55, 7.66) | Represented | 2.97 (1.04, 4.90) | Represented |
| South America | −0.63 (−2.86, 1.59) | Unrepresented | 2.46 (0.22, 4.69) | Represented | 4.52 (2.27, 6.78) | Represented |
| Oceania | −8.16 (−11.21, −5.10) | Unrepresented | 7.73 (4.69, 10.76) | Represented | −0.50 (−3.54, 2.55) | Unrepresented |
| Africa | 6.16 (−2.14, 14.45) | Represented | 10.28 (1.99, 18.58) | Represented | 4.03 (−4.27, 12.33) | Represented |
| HDI | ||||||
| Very high | −4.80 (−5.74, −3.86) | Unrepresented | −0.94 (−1.86, −0.02) | Unrepresented | −0.56 (−1.35, 0.23) | Unrepresented |
| High | 7.63 (4.52, 10.75) | Represented | 6.72 (3.86, 9.57) | Represented | 8.94 (5.94, 11.94) | Represented |
| Medium | 5.24 (−1.53, 12.00) | Represented | 5.09 (−1.73, 11.90) | Represented | 5.89 (−0.87, 12.66) | Represented |
| Low | 1.12 (−3.63, 5.88) | Represented | 1.80 (−2.90, 6.51) | Represented | −9.43 (−14.18, −4.68) | Unrepresented |
| Clinical trial phase | ||||||
| III | −4.21 (−5.46, −2.97) | Unrepresented | −1.24 (−2.44, −0.03) | Unrepresented | −0.81 (−1.75, 0.14) | Unrepresented |
| II | −1.80 (−3.15, −0.45) | Unrepresented | 1.83 (0.52, 3.14) | Represented | 2.37 (1.02, 3.73) | Represented |
| Cancer characteristics | ||||||
| Cancer type | ||||||
| NSCLC | −2.46 (−3.43, −1.49) | Unrepresented | 0.72 (−0.22, 1.66) | Represented | 1.23 (0.47, 2.00) | Represented |
| SCLC | −9.34 (−12.43, −6.25) | Unrepresented | −5.48 (−7.90, −3.06) | Unrepresented | −5.28 (−7.83, −2.73) | Unrepresented |
| TNM | ||||||
| I | −5.13 (−9.87, −0.39) | Unrepresented | −2.43 (−6.95, 2.08) | Unrepresented | −5.15 (−8.67, −1.63) | Unrepresented |
| II | −1.15 (−6.38, 4.09) | Unrepresented | 1.08 (−4.48, 6.64) | Represented | 2.77 (−1.35, 6.89) | Represented |
| III | 1.04 (−5.79, 7.87) | Represented | 3.34 (−3.23, 9.91) | Represented | 1.52 (−4.67, 7.72) | Represented |
| IV | −3.51 (−4.88, −2.14) | Unrepresented | −0.25 (−1.56, 1.06) | Unrepresented | −1.09 (−2.12, −0.05) | Unrepresented |
| NR | −3.06 (−4.35, −1.77) | Unrepresented | 0.31 (−0.98, 1.60) | Represented | 2.41 (1.25, 3.56) | Represented |
| Population characteristics | ||||||
| Age, years | ||||||
| < 65 | −3.51 (−4.59, −2.44) | Unrepresented | −0.12 (−1.16, 0.92) | Unrepresented | 0.20 (−0.65, 1.04) | Represented |
| At least 65 | −1.09 (−3.27, 1.10) | Unrepresented | 1.19 (−1.04, 3.42) | Represented | 2.16 (0.19, 4.13) | Represented |
| NR | −3.17 (−5.69, −0.65) | Unrepresented | 0.30 (−1.97, 2.57) | Represented | 1.57 (−0.85, 3.99) | Represented |
| Race | ||||||
| Asian | 6.17 (4.03, 8.31) | Represented | 5.57 (3.42, 7.72) | Represented | 4.16 (2.11, 6.20) | Represented |
| Caucasian | −5.89 (−6.73, −5.05) | Unrepresented | −1.52 (−2.41, −0.64) | Unrepresented | −0.49 (−1.25, 0.27) | Unrepresented |
| Black | 6.16 (−2.14, 14.45) | Represented | 10.28 (1.99, 18.58) | Represented | 4.03 (−4.27, 12.33) | Represented |
| Smoking status | ||||||
| Smoker | −6.07 (−7.19, −4.95) | Unrepresented | −4.61 (−5.78, −3.43) | Unrepresented | −2.05 (−2.92, −1.17) | Unrepresented |
| Non-smoker | 21.36 (18.05, 24.68) | Represented | 21.95 (18.60, 25.29) | Represented | 22.62 (18.89, 26.35) | Represented |
| NR | −5.09 (−6.18, −4.01) | Unrepresented | −0.17 (−1.22, 0.87) | Unrepresented | −1.16 (−2.18, −0.14) | Unrepresented |
| ECOG PS | ||||||
| 0/1 | −3.17 (−4.30, −2.05) | Unrepresented | −0.24 (−1.33, 0.84) | Unrepresented | −0.19 (−1.08, 0.70) | Unrepresented |
| At least 2 | −12.29 (−17.01, −7.57) | Unrepresented | −5.98 (−11.50, −0.45) | Unrepresented | −11.19 (−16.40, −5.97) | Unrepresented |
| NR | −2.43 (−3.76, −1.09) | Unrepresented | 1.44 (0.08, 2.80) | Represented | 3.51 (2.27, 4.75) | Represented |
| Multiple metastases | ||||||
| Yes | −7.84 (−11.22, −4.46) | Unrepresented | −2.77 (−5.30, −0.25) | Unrepresented | −4.31 (−7.54, −1.08) | Unrepresented |
| No | −9.90 (−17.75, −2.04) | Unrepresented | −4.30 (−12.88, 4.28) | Unrepresented | −7.85 (−15.55, −0.15) | Unrepresented |
| NR | −2.76 (−3.72, −1.79) | Unrepresented | 0.34 (−0.60, 1.29) | Represented | 1.02 (0.26, 1.79) | Represented |
| Liver metastases | ||||||
| Yes | −5.32 (−13.81, 3.17) | Unrepresented | 3.74 (−5.66, 13.14) | Represented | 2.70 (−4.80, 10.20) | Represented |
| No | −5.90 (−8.51, −3.29) | Unrepresented | −3.70 (−6.23, −1.16) | Unrepresented | −2.84 (−5.07, −0.61) | Unrepresented |
| NR | −2.78 (−3.77, −1.79) | Unrepresented | 0.59 (−0.37, 1.54) | Represented | 1.02 (0.24, 1.80) | Represented |
| Pleura metastases | ||||||
| No | −9.19 (−13.72, −4.66) | Unrepresented | −2.90 (−7.94, 2.13) | Unrepresented | −4.69 (−8.35, −1.03) | Unrepresented |
| NR | −3.05 (−4.00, −2.11) | Unrepresented | 0.13 (−0.79, 1.05) | Represented | 0.67 (−0.09, 1.43) | Represented |
| Bone metastases | ||||||
| Yes | −9.09 (−15.16, −3.01) | Unrepresented | 3.02 (2.09, 3.94) | Represented | −6.07 (−8.84, −3.29) | Unrepresented |
| No | −7.27 (−10.32, −4.21) | Unrepresented | −3.13 (−6.12, −0.13) | Unrepresented | −3.86 (−6.79, −0.94) | Unrepresented |
| NR | −2.81 (−3.78, −1.85) | Represented | 0.34 (−0.59, 1.28) | Represented | 0.95 (0.19, 1.71) | Represented |
| Adrenal metastases | ||||||
| Yes | −1.76 (−7.25, 3.74) | Unrepresented | 3.50 (0.82, 6.18) | Represented | 4.17 (−5.34, 13.67) | Represented |
| No | −9.92 (−15.60, −4.24) | Unrepresented | −2.17 (−7.90, 3.55) | Unrepresented | −6.95 (−11.54, −2.35) | Unrepresented |
| NR | −2.92 (−3.86, −1.98) | Unrepresented | 0.16 (−0.75, 1.07) | Represented | 0.84 (0.10, 1.58) | Represented |
| Lymph nodes metastases | ||||||
| Yes | −15.91 (−16.66, −15.15) | Unrepresented | −3.94 (−4.70, −3.19) | Unrepresented | −18.50 (−19.26, −17.74) | Unrepresented |
| No | 1.67 (−4.27, 7.60) | Represented | 4.85 (−0.76, 10.47) | Represented | 3.14 (−2.62, 8.89) | Represented |
| NR | −3.25 (−4.19, −2.31) | Unrepresented | −0.01 (−0.92, 0.91) | Unrepresented | 0.54 (−0.22, 1.29) | Represented |
| Skin metastases | ||||||
| No | 1.69 (−14.08, 17.46) | Represented | 6.34 (−7.41, 20.08) | Represented | 4.77 (−5.85, 15.40) | Represented |
| NR | −3.20 (−4.13, −2.27) | Unrepresented | 0.05 (−0.86, 0.95) | Represented | 0.54 (−0.21, 1.29) | Represented |
| Brain metastases | ||||||
| Yes | −6.34 (−10.87, −1.80) | Unrepresented | −5.54 (−10.41, −0.68) | Unrepresented | 0.28 (−8.67, 9.24) | Represented |
| No | −3.85 (−6.63, −1.06) | Unrepresented | −3.55 (−6.15, −0.94) | Unrepresented | −1.01 (−3.17, 1.15) | Unrepresented |
| NR | −3.06 (−4.05, −2.06) | Unrepresented | 0.60 (−0.36, 1.56) | Represented | 0.76 (−0.04, 1.56) | Represented |
| Prior chemotherapy | ||||||
| Yes | −3.62 (−6.56, −0.69) | Unrepresented | −2.43 (−5.42, 0.57) | Unrepresented | 1.80 (−0.28, 3.89) | Represented |
| No | −6.91 (−9.46, −4.36) | Unrepresented | −5.30 (−7.83, −2.77) | Unrepresented | −3.34 (−5.44, −1.24) | Unrepresented |
| NR | −2.78 (−3.84, −1.72) | Unrepresented | 0.85 (−0.17, 1.87) | Represented | 0.79 (−0.07, 1.64) | Represented |
| Prior surgery | ||||||
| Yes | −0.34 (−4.85, 4.18) | Unrepresented | 2.48 (−1.61, 6.57) | Represented | 0.80 (−2.85, 4.44) | Represented |
| No | −7.82 (−9.68, −5.97) | Unrepresented | −3.40 (−5.12, −1.68) | Unrepresented | −5.68 (−7.42, −3.95) | Unrepresented |
| NR | −2.69 (−3.74, −1.63) | Unrepresented | 0.44 (−0.60, 1.47) | Represented | 1.38 (0.55, 2.20) | Represented |
| Prior radiation | ||||||
| Yes | 6.20 (−1.66, 14.07) | Represented | 5.96 (−1.68, 13.59) | Represented | 7.60 (0.70, 14.51) | Represented |
| No | −7.20 (−9.01, −5.39) | Unrepresented | −2.73 (−4.45, −1.00) | Unrepresented | −4.83 (−6.40, −3.25) | Unrepresented |
| NR | −2.63 (−3.69, −1.57) | Unrepresented | 0.47 (−0.56, 1.51) | Represented | 1.38 (0.55, 2.21) | Represented |
| Total | −3.17 (−4.10, −2.24) | Unrepresented | 0.08 (−0.82, 0.99) | Represented | 0.56 (−0.19, 1.31) | Represented |
Abbreviations: CI, confidence interval; ECOG PS, Eastern Cooperative Oncology Group performance status; HDI, Human Development Index; NR, not reported; NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer.
Fig. 2.
Relationship between enrolment incidence disparity and trial recruitment year.
Subgroup analyses consistently revealed female underrepresentation across various subpopulations (Table 2). Global data indicated regional disparities in female representation. Asia demonstrated adequate representation (6.82; 4.78 to 8.86), while Europe, Oceania, and North America exhibited significant underrepresentation, with EIDs of −3.06 (−4.46 to −1.67), −8.16 (−11.21 to −5.10), and −8.25 (−9.35 to −7.15), respectively (Fig. 3). With respect to the HDI, nations classified as having high HDI showed adequate female representation (7.63; 4.52 to 10.75), whereas those with very high HDI demonstrated inadequate representation (−4.80; −5.74 to −3.86). Further analyses revealed sex disparities across clinical trial phases, with phase II (−1.80; −3.15 to −0.45) and phase III trials (−4.21; −5.46 to −2.97) demonstrating significant underrepresentation of females.
Fig. 3.
Enrolment incidence disparity and female representation across various trial characteristics. HDI, Human Development Index.
Analyses of studies involving NSCLC and SCLC consistently revealed significant female underrepresentation, with EIDs of −2.46 (−3.43 to −1.49) for NSCLC and −9.34 (−12.43 to −6.25) for SCLC. Studies on patients with TNM stages I and IV, and those not reporting TNM staging, also exhibited female underrepresentation, with EIDs of −5.13 (−9.87 to −0.39), −3.51 (−4.88 to −2.14), and −3.06 (−4.35 to −1.77), respectively. Research involving patients aged ≤ 65 years and those with missing age data indicated underrepresentation, with EIDs of −3.51 (−4.59 to −2.44) and −3.17 (−5.69 to −0.65). Studies focusing on Asian populations showed adequate female representation (6.17; 4.03 to 8.31), whereas those focusing on Caucasian populations revealed underrepresentation (−5.89; −6.73 to −5.05). Research on smokers and individuals not disclosing smoking status indicated female underrepresentation, with EIDs of −6.07 (−7.19 to −4.95) and −5.09 (−6.18 to −4.01), respectively, while non-smoker studies showed substantial female representation (21.36; 18.05 to 24.68). Studies involving patients with an ECOG PS of 0 or 1 demonstrated female underrepresentation (−3.17; −4.30 to −2.05), with more pronounced underrepresentation observed in studies with ECOG PS ≥ 2 (−12.29; −17.01 to −7.57). Additionally, research that excluded ECOG PS also exhibited underrepresentation (−2.43; −3.76 to −1.09).
Studies on patients with multiple metastases showed notable female underrepresentation (−7.84; −11.22 to −4.46). A similar trend was observed in patients without multiple metastases (−9.90; −17.75 to −2.04) and in studies that did not report multiple metastatic status (−2.76; −3.72 to −1.79). Female underrepresentation was also noted in studies involving patients without liver metastases (−5.90; −8.51 to −3.29) and in those not reporting liver metastasis status (−2.78; −3.77 to −1.79). Studies on patients without pleural metastases and those not reporting pleural metastasis status showed underrepresentation, with EIDs of −9.19 (−13.72 to −4.66) and −3.05 (−4.00 to −2.11), respectively. Similar patterns of female underrepresentation were observed in studies involving patients with bone metastases (−9.09; −15.16 to −3.01), without bone metastases (−7.27; −10.32 to −4.21) and in those not reporting bone metastases status (−2.81; −3.78 to −1.85). Female underrepresentation was also identified in studies on patients without adrenal metastases (−9.92; −15.60 to −4.24), as did studies lacking reports on adrenal metastatic status (−2.92; −3.86 to −1.98). Studies involving patients with lymph node metastases showed pronounced underrepresentation (−15.91; −16.66 to −15.15), as did those not reporting lymph node status (−3.25; −4.19 to −2.31). Studies not reporting skin or brain metastasis status demonstrated female underrepresentation, with EIDs of −3.20 (−4.13 to −2.27) and −3.06 (−4.05 to −2.06), respectively. Likewise, studies on patients with and without brain metastases also showed female underrepresentation, with EIDs of −6.34 (−10.87 to −1.80) and −3.85 (−6.63 to −1.06), respectively.
Studies on patients treated with adjuvant chemotherapy showed female underrepresentation (−3.62; −6.56 to −0.69), with greater underrepresentation in chemotherapy-naïve patients (−6.91; −9.46 to −4.36). A similar trend persists when chemotherapy history is unreported (−2.78; −3.84 to −1.72). Female representation was markedly diminished in studies involving patients without prior surgery (−7.82; −9.68 to −5.97), with a similar trend observed in those not reporting prior surgical history (−2.69; −3.74 to −1.63). Likewise, studies on patients without prior radiotherapy showed female underrepresentation (−7.20; −9.01 to −5.39), as did those not reporting prior radiotherapy history (−2.63; −3.69 to −1.57).
GLOBOCAN HDI-based analyses indicated higher female representation in studies conducted in Europe (5.30; 4.00 to 6.59), South America (2.46; 0.22 to 4.69), and Oceania (7.73; 4.69 to 10.76); as well as phase II clinical trials (1.83; 0.52 to 3.14); studies not reporting ECOG PS (1.44; 0.08 to 2.80); and studies involving patients with bone metastases (3.02; 2.09 to 3.94) or adrenal metastases (3.50; 0.82 to 6.18) (Table 2). Similarly, the GBD continent-based results showed higher representation in studies from South America (4.52; 2.27 to 6.78); phase II trials (2.37; 1.02 to 3.73); studies involving NSCLC (1.23; 0.47 to 2.00); and studies not reporting TNM staging (2.41; 1.25 to 3.56), ECOG PS (3.51; 2.27 to 4.75), multiple metastatic status (1.02; 0.26 to 1.79), liver metastases (1.02; 0.24 to 1.80), bone metastases (0.95; 0.19 to 1.71), adrenal metastases (0.84; 0.10 to 1.58), prior surgery history (1.38; 0.55 to 2.20), or prior radiotherapy history (1.38; 0.55 to 2.21). Studies on patients aged > 65 years also showed higher female representation (2.16; 0.19 to 4.13). In contrast, lower female representation was observed in studies from countries or regions with high HDI (−9.43; −14.18 to −4.68).
Further examination indicated that female representation across different characteristics fluctuated over time, with no significant upward or downward trend observed between 1993 and 2025 (Supplementary Materials pp 40).
Trial-level underrepresentation of women was associated with differences in reported incidences of grade 3 or higher AEs across treatment settings (Table 3). Because these analyses involved multiple parallel comparisons across treatment classes and AE categories, the reported P values should be interpreted cautiously and viewed as exploratory rather than confirmatory. Several AEs showed lower reported incidence in trials with inadequate female representation than in trials with more adequate representation within specific treatment settings. These included neutropenia in studies of chemotherapy combined with programmed cell death ligand-1 (PD-L1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4) inhibitors, as well as chemotherapy combined with PD-L1 inhibitors; thrombocytopenia in trials of chemotherapy alone or chemotherapy combined with proteasome inhibitors; leukopenia in studies of chemotherapy plus vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKIs) and VEGFR TKI monotherapy; nausea in mesenchymal-epithelial transition factor (MET) TKI, VEGFR TKI, and placebo-controlled trials; vomiting in studies combining chemotherapy with epidermal growth factor receptor (EGFR) and VEGFR TKIs; diarrhoea and constipation in VEGFR TKI studies; asthenia and fatigue with best supportive care, programmed cell death-1 (PD-1) inhibitors, and VEGFR TKIs; and pneumonia in studies of chemotherapy plus PD-1 inhibitors, PD-1 plus VEGFR TKIs, and VEGFR TKI monotherapy. These patterns indicate variation in reported AE incidence according to trial-level female representation, but should not be interpreted as evidence of causality.
Table 3.
The difference in adverse events rates between trials with adequate versus inadequate female representation across various treatments.
| Adverse events | Treatments | Female underrepresentation |
Female representation |
P-value | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Number of studies | Number of participants | Number of adverse events | Incidence of adverse events, % (95% CI) | Number of studies | Number of participants | Number of adverse events | Incidence of adverse events, % (95% CI) | |||
| Neutropenia | Chemotherapy + Proteasome inhibitors | 1 | 52 | 10 | 19.23 (8.52, 29.94) | 1 | 80 | 4 | 5.00 (0.22, 9.78) | 0.0210 |
| VEGFR TKIs | 5 | 1497 | 257 | 29.14 (18.07, 40.22) | 28 | 8539 | 298 | 4.34 (3.12, 5.56) | < 0.0001 | |
| PARP inhibitors | 1 | 118 | 66 | 55.93 (46.98, 64.89) | 1 | 146 | 7 | 4.18 (0.34, 8.02) | 0.0090 | |
| Chemotherapy + PD-L1 inhibitors + CTLA-4 inhibitors | 2 | 661 | 27 | 6.66 (4.20, 9.11) | 1 | 151 | 53 | 35.10 (27.49, 42.71) | 0.0200 | |
| Chemotherapy + PD-L1 inhibitors | 5 | 1703 | 127 | 7.32 (2.07, 12.56) | 9 | 2963 | 451 | 20.65 (13.84, 27.45) | 0.0080 | |
| Thrombocytopenia | Chemotherapy | 212 | 64,147 | 3082 | 6.80 (6.18, 7.42) | 246 | 62,291 | 4240 | 10.71 (9.83, 11.58) | < 0.0001 |
| Chemotherapy + Proteasome inhibitors | 1 | 52 | 8 | 15.38 (5.58, 25.19) | 1 | 80 | 52 | 65.00 (54.55, 75.45) | < 0.0001 | |
| VEGFR TKIs | 5 | 1497 | 113 | 14.71 (9.98, 19.44) | 28 | 8539 | 171 | 2.56 (1.61, 3.52) | < 0.0001 | |
| EGFR TKIs | 25 | 7643 | 26 | 9.96 (6.33, 13.60) | 95 | 22,135 | 133 | 2.52 (1.55, 3.50) | < 0.0001 | |
| Anemia | VEGFR TKIs | 5 | 1497 | 227 | 27.29 (19.91, 34.67) | 28 | 8539 | 361 | 4.41 (3.10, 5.72) | < 0.0001 |
| Placebo | 35 | 9262 | 152 | 6.58 (4.20, 8.96) | 45 | 10,256 | 181 | 3.16 (2.02, 4.29) | 0.0130 | |
| Leukopenia | Chemotherapy | 246 | 62,291 | 4889 | 18.59 (17.22, 19.96) | 212 | 64,147 | 4304 | 12.80 (11.66, 13.93) | 0.0580 |
| Chemotherapy + EGFR TKIs | 19 | 3488 | 256 | 15.50 (9.65, 21.35) | 10 | 2592 | 93 | 4.39 (1.53, 7.25) | 0.1100 | |
| Chemotherapy + PD-L1 inhibitors + CTLA-4 inhibitors | 1 | 151 | 38 | 25.17 (18.24, 32.09) | 2 | 661 | 10 | 2.42 (0.91, 3.94) | 0.2300 | |
| Chemotherapy + VEGFR TKIs | 41 | 12,006 | 664 | 12.38 (10.22, 14.54) | 10 | 2453 | 266 | 41.98 (21.22, 62.74) | 0.3000 | |
| PD-1 inhibitors | 17 | 3950 | 122 | 14.51 (5.05, 23.98) | 22 | 6286 | 4 | 0.39 (−0.00, 0.78) | 0.1400 | |
| VEGFR TKIs | 28 | 8539 | 79 | 1.63 (0.70, 2.56) | 5 | 1497 | 93 | 12.72 (10.30, 15.14) | 0.1100 | |
| Nausea | Chemotherapy + PD-L1 inhibitors | 9 | 2963 | 123 | 4.01 (2.14, 5.88) | 5 | 1703 | 11 | 0.88 (−0.02, 1.79) | 0.0310 |
| EGFR TKIs | 95 | 22,135 | 314 | 1.45 (1.10, 1.80) | 25 | 7643 | 48 | 0.87 (0.47, 1.28) | 0.0060 | |
| MET TKIs | 3 | 328 | 1 | 0.47 (−0.45, 1.39) | 1 | 175 | 23 | 13.14 (8.14, 18.15) | 0.1300 | |
| PD-L1 inhibitors | 7 | 1168 | 10 | 7.46 (3.01, 11.91) | 16 | 5039 | 11 | 0.36 (0.13, 0.60) | 0.0710 | |
| Placebo | 45 | 10,256 | 98 | 0.82 (0.50, 1.15) | 35 | 9262 | 40 | 2.24 (0.94, 3.54) | 0.0140 | |
| VEGFR TKIs | 28 | 8539 | 266 | 3.19 (2.06, 4.33) | 5 | 1497 | 97 | 13.26 (10.79, 15.72) | 0.1000 | |
| Vomiting | Chemotherapy + EGFR TKIs | 19 | 3488 | 68 | 1.86 (1.27, 2.45) | 10 | 2592 | 89 | 4.32 (2.71, 5.93) | 0.0250 |
| Chemotherapy + PD-L1 inhibitors | 9 | 2963 | 50 | 1.73 (0.88, 2.58) | 5 | 1703 | 6 | 0.38 (0.04, 0.73) | 0.0130 | |
| EGFR TKIs | 95 | 22,135 | 221 | 1.28 (1.00, 1.55) | 25 | 7643 | 45 | 0.68 (0.32, 1.03) | 0.0060 | |
| PD-1 inhibitors + VEGFR TKIs | 3 | 458 | 20 | 25.00 (15.51, 34.49) | 3 | 679 | 5 | 1.30 (0.15, 2.45) | 0.2400 | |
| VEGFR TKIs | 28 | 8539 | 93 | 0.90 (0.48, 1.33) | 5 | 1497 | 42 | 5.65 (3.97, 7.33) | 0.0470 | |
| Diarrhea | Chemotherapy + PD-L1 inhibitors | 9 | 2963 | 71 | 4.37 (1.98, 6.76) | 5 | 1703 | 21 | 1.38 (0.35, 2.42) | 0.0300 |
| EGFR TKIs | 95 | 22,135 | 916 | 4.12 (3.46, 4.77) | 25 | 7643 | 176 | 2.28 (1.57, 2.99) | 0.0180 | |
| Placebo | 45 | 10,256 | 117 | 2.09 (1.18, 2.99) | 35 | 9262 | 15 | 0.54 (0.18, 0.90) | 0.0160 | |
| VEGFR TKIs | 28 | 8539 | 177 | 2.25 (1.47, 3.03) | 5 | 1497 | 70 | 9.62 (7.47, 11.76) | 0.0740 | |
| Constipation | PD-1 inhibitors + VEGFR TKIs | 3 | 458 | 23 | 28.75 (18.83, 38.67) | 3 | 679 | 1 | 0.54 (−0.52, 1.60) | 0.0050 |
| VEGFR TKIs | 28 | 8539 | 55 | 0.89 (0.33, 1.44) | 5 | 1497 | 40 | 5.48 (3.83, 7.14) | < 0.0001 | |
| Asthenia | BSC | 13 | 2961 | 11 | 0.65 (0.27, 1.04) | 8 | 1396 | 9 | 11.54 (4.45, 18.63) | 0.0070 |
| Chemotherapy + MEK1/2 kinase inhibitors | 2 | 335 | 22 | 8.76 (5.27, 12.26) | 2 | 253 | 2 | 1.19 (−0.45, 2.83) | 0.0310 | |
| VEGFR TKIs | 28 | 8539 | 111 | 3.83 (2.04, 5.62) | 5 | 1497 | 96 | 13.07 (7.29, 18.85) | 0.0050 | |
| Fatigue | Chemotherapy | 246 | 62,291 | 1861 | 4.76 (4.28, 5.24) | 212 | 64,147 | 1512 | 4.05 (3.60, 4.50) | 0.0070 |
| EGFR TKIs | 95 | 22,135 | 468 | 3.61 (2.82, 4.40) | 25 | 7643 | 97 | 2.02 (1.34, 2.70) | 0.0160 | |
| PD-1 inhibitors | 17 | 3950 | 29 | 0.77 (0.45, 1.08) | 22 | 6286 | 101 | 1.91 (1.02, 2.80) | 0.0110 | |
| Placebo | 45 | 10,256 | 206 | 2.07 (1.32, 2.82) | 35 | 9262 | 56 | 1.03 (0.48, 1.59) | 0.0100 | |
| VEGFR TKIs | 28 | 8539 | 279 | 4.05 (2.66, 5.44) | 5 | 1497 | 86 | 11.80 (9.45, 14.14) | 0.0770 | |
| Infection | EGFR TKIs | 95 | 22,135 | 43 | 0.85 (0.42, 1.28) | 25 | 7643 | 5 | 0.27 (−0.01, 0.55) | 0.0280 |
| Pneumonia | Chemotherapy + PD-1 inhibitors | 10 | 1875 | 22 | 1.93 (0.69, 3.18) | 17 | 3997 | 172 | 6.91 (4.63, 9.18) | 0.0500 |
| Chemotherapy + VEGFR TKIs | 41 | 12,006 | 180 | 3.84 (2.48, 5.20) | 10 | 2453 | 3 | 0.66 (−0.08, 1.41) | 0.0320 | |
| PD-1 inhibitors + VEGFR TKIs | 3 | 458 | 1 | 1.45 (−1.37, 4.27) | 3 | 679 | 53 | 7.31 (4.52, 10.09) | 0.0590 | |
| VEGFR TKIs | 28 | 8539 | 61 | 1.53 (0.83, 2.23) | 5 | 1497 | 39 | 5.36 (3.72, 6.99) | 0.0380 | |
| Dyspnea | Chemotherapy | 246 | 62,291 | 630 | 4.07 (3.38, 4.76) | 212 | 64,147 | 215 | 2.50 (1.90, 3.10) | 0.0010 |
| EGFR TKIs | 95 | 22,135 | 108 | 3.33 (2.18, 4.48) | 25 | 7643 | 15 | 1.01 (0.05, 1.96) | 0.0030 | |
| VEGFR TKIs | 28 | 8539 | 57 | 2.65 (1.00, 4.30) | 5 | 1497 | 47 | 6.33 (3.45, 9.21) | 0.0360 | |
| Epistaxis | VEGFR TKIs | 28 | 8539 | 3 | 0.51 (−0.08, 1.09) | 5 | 1497 | 39 | 5.23 (3.42, 7.05) | < 0.0001 |
| Anorexia | Chemotherapy | 246 | 62,291 | 935 | 3.21 (2.76, 3.66) | 212 | 64,147 | 623 | 2.25 (1.87, 2.64) | 0.0100 |
| Chemotherapy + EGFR TKIs | 19 | 3488 | 81 | 4.13 (2.33, 5.93) | 10 | 2592 | 25 | 0.90 (0.08, 1.72) | 0.0320 | |
| PD-L1 inhibitors | 7 | 1168 | 9 | 6.72 (2.48, 10.95) | 16 | 5039 | 3 | 0.37 (−0.09, 0.84) | 0.0630 | |
| Placebo | 45 | 10,256 | 67 | 1.59 (0.80, 2.38) | 35 | 9262 | 18 | 0.53 (0.10, 0.96) | 0.0110 | |
| VEGFR TKIs | 28 | 8539 | 110 | 2.69 (1.52, 3.87) | 5 | 1497 | 85 | 11.67 (9.34, 14.01) | 0.0900 | |
| Alopecia | Chemotherapy | 246 | 62,291 | 297 | 6.55 (4.92, 8.18) | 212 | 64,147 | 727 | 12.04 (9.74, 14.33) | < 0.0001 |
| VEGFR TKIs | 28 | 8539 | 378 | 20.27 (6.29, 34.24) | 5 | 1497 | 313 | 42.99 (39.40, 46.59) | 0.0040 | |
| Peripheral sensory neuropathy | VEGFR TKIs | 28 | 8539 | 96 | 6.64 (0.31, 12.98) | 5 | 1497 | 321 | 44.09 (40.49, 47.70) | < 0.0001 |
| Pain | VEGFR TKIs | 28 | 8539 | 17 | 0.44 (0.17, 0.71) | 5 | 1497 | 196 | 26.90 (23.68, 30.12) | < 0.0001 |
| Rash | Chemotherapy | 246 | 62,291 | 152 | 1.14 (0.78, 1.49) | 212 | 64,147 | 62 | 0.63 (0.36, 0.90) | 0.0270 |
| Hypokalemia | Chemotherapy + VEGFR TKIs | 41 | 12,006 | 4 | 0.34 (−0.45, 1.12) | 10 | 2453 | 9 | 3.69 (1.32, 6.05) | 0.0100 |
| Stomatitis | Chemotherapy | 246 | 62,291 | 130 | 2.16 (1.36, 2.95) | 212 | 64,147 | 34 | 0.73 (0.29, 1.17) | 0.0020 |
Abbreviations: BSC, best supportive care; CTLA-4, cytotoxic T-lymphocyte associated 4; EGFR, epidermal growth factor receptor; MET, mesenchymal-epithelial transition factor; MEK 1/2, mitogen-activated protein kinase 1/2; PARP, poly (ADP-ribose) polymerase; PD-1, programmed cell death-1; PD-L1, programmed cell death-ligand 1; TKIs, tyrosine kinase inhibitors; VEGFR, vascular endothelial growth factor receptor.
Conversely, several AEs showed higher reported incidence in trials with inadequate female representation within specific treatment settings. These included neutropenia in studies evaluating chemotherapy combined with proteasome inhibitors, VEGFR TKIs, and poly (ADP-ribose) polymerase (PARP) inhibitors; thrombocytopenia in VEGFR and EGFR TKI trials; anaemia in VEGFR TKI and placebo-controlled trials; leukopenia in chemotherapy studies and in trials incorporating EGFR TKIs or PD-L1/CTLA-4/PD-1 inhibitors; nausea and vomiting in selected chemoimmunotherapy and EGFR TKI settings; diarrhoea in trials of chemotherapy plus PD-L1 inhibitors, chemotherapy plus EGFR TKIs, and placebo; and constipation in PD-1 and VEGFR TKI studies. Additional differences were observed for asthenia, fatigue, infection, pneumonia, anorexia, rash, and stomatitis in selected treatment settings.
4. Discussion
The ongoing underrepresentation of women in lung cancer clinical trials remains a critical barrier to equitable clinical practice and the effective translation of trial findings into routine care.20, 21, 22 Addressing this disparity is urgent, given the growing evidence of sex-specific differences in lung cancer pathogenesis, pharmacological responses, and toxicity profiles.22, 23, 24 Failure to account for these differences risks the development of therapies primarily optimized for men, potentially resulting in suboptimal efficacy and increased AEs in women, while hindering personalised treatment and perpetuating health inequities.25 This long-standing nature of this issue has prompted policy changes worldwide. Notably, the European Union introduced the 2016 Sex and Gender Equity in Research guidelines and Horizon 2020, which mandates sex equality in research.26 In 2023, the WHO issued guidance on best practices for clinical trials, emphasising the inclusion of women to ensure research validity across diverse populations.27 Despite regulatory initiatives aimed at improving representativeness in clinical research, sex disparities persist in oncology trials, particularly in early-phase studies and investigations of novel therapeutics. Restrictive eligibility criteria and sex-specific exclusions continue to limit equitable participation. Policy mandates alone appear insufficient without robust monitoring mechanisms, transparent sex-disaggregated reporting, and clear accountability frameworks. Our findings demonstrate a sustained sex imbalance in lung cancer trials, with only modest improvements over time and incomplete translation of policy commitments into practice.
Geographical and ethnic variation appears to substantially shape patterns of female participation in lung cancer clinical trials. Studies conducted in Asia, or those enrolling predominantly Asian populations, frequently demonstrate sex distributions that approximate, and in some cases exceed, the expected proportion of women based on regional lung cancer epidemiology. In contrast, trials undertaken in Europe, Oceania, and North America, particularly those involving largely White populations, more commonly show female underrepresentation when evaluated against contemporaneous regional incidence data. Such disparities have important implications for the generalisability and external validity of trial findings, and point to a potentially complex interaction between sex and ethnicity in enrolment patterns. Multiple factors are likely to contribute to these regional differences. Variations in histological subtype distribution, smoking prevalence, and the frequency of actionable genomic alterations across populations may influence both the expected proportion of women with lung cancer and their eligibility for specific trials. Restrictive eligibility criteria, disparities in trial infrastructure, and inconsistent reporting of race and ethnicity are also likely to shape enrolment patterns.28, 29, 30 For instance, lung cancer incidence among never-smoking Asian women has been reported to be higher than that observed in Western populations, which may contribute to differences in sex distribution in biomarker-driven trials.31, 32, 33 The interrelationship between smoking status, tumour genomic profiles, and sex-specific eligibility criteria remains incompletely characterised and warrants further investigation. Notably, female underrepresentation appears more pronounced in phase II and III studies, particularly those evaluating novel therapeutics, possibly reflecting concerns regarding reproductive toxicity as well as misalignment between enrolment patterns and underlying disease burden.34,35 Ensuring that trials are both inclusive and generalisable will require more deliberate design strategies, systematic and transparent reporting of sex- and ethnicity-stratified data, equitable allocation of research infrastructure, and coordinated multinational efforts within an increasingly globalised precision oncology landscape.
Female underrepresentation in NSCLC and SCLC clinical trials, particularly pronounced in SCLC, reflects broader enrolment disparities. Such histology-specific disparities may affect therapeutic generalisability and suggest variation in sex-specific enrolment across tumour biology and trial design. In NSCLC, racial and ethnic factors, along with the exclusion of elderly patients and those with comorbidities like renal impairment, reduce participation among Black and Hispanic/Latino individuals, limiting the generalisability of findings.36,37 Stricter eligibility criteria in SCLC trials further exacerbate sex-based imbalances.38 However, the sex-specific impact of eligibility criteria across histological subtypes remains underexamined. Although the incidence of lung cancer with age, disparities in trial participation persist across younger groups,39,40 with additional barriers posed by comorbid conditions such as chronic obstructive pulmonary disease and advanced disease at diagnosis.41,42 Most trials lack adaptive or comorbidity-adjusted enrolment, limiting real-world representativeness. Furthermore, regions such as northern China—where young women with a family history of lung cancer and EGFR/ALK mutations are underrepresented—face recruitment challenges, hindering access to targeted therapies.43 These selective enrolment practices restrict the applicability of trial results to a more diverse lung cancer population. In an era of biomarker-driven lung cancer care, broader, real-world-aligned eligibility criteria are essential to ensure representative and generalisable trials.
The underrepresentation of women with an ECOG PS of 2 or higher and those with extensive metastatic disease may constrain the delivery of personalised medicine, as individuals with greater disease burden are more likely to be excluded under conventional eligibility criteria. The exclusion of patients with oligometastatic or high-burden disease also limits the evaluation of immunotherapies, for which potential sex-specific differences in response remain insufficiently defined.44, 45, 46, 47, 48, 49, 50 In prioritising internal validity, stringent eligibility frameworks tend to favour patients with more favourable prognoses, thereby widening the gap between trial populations and routine clinical practice.51, 52, 53, 54, 55 Moreover, biases related to adjuvant chemotherapy efficacy, genotype-driven trials, and long-term follow-up challenges exacerbate sex-specific biases, necessitating a reassessment of trial design and recruitment strategies to ensure equitable representation and improve generalisability across sexes, regardless of prior therapy exposure.56, 57, 58, 59 A critical reassessment of trial design and recruitment strategies is therefore warranted to promote equitable representation and strengthen the generalisability of findings across sexes, irrespective of prior treatment exposure. Progress towards truly equitable precision oncology will depend on better integration of real-world populations, more adaptive eligibility approaches, and systematic sex-stratified evaluation of outcomes.
The observed female underrepresentation in lung cancer clinical trials evaluating various therapeutic modalities, including BSC, VEGFR TKIs, PD-1/PD-L1 inhibitors, CTLA-4 inhibitors, EGFR TKIs, MET TKIs, and chemotherapy, necessitates greater attention to sex-specific AEs. Differences in pharmacokinetics, hormonal milieu, immune function, reporting behaviours, and eligibility criteria may influence both enrolment patterns and the safety signals observed in trials. Consequently, imbalanced participation may compromise the generalisability of adverse event estimates, particularly as most studies remain insufficiently powered for sex-stratified safety analyses.60,61 Biological differences between sexes, including variation in hormonal regulation, drug metabolism, and immune response, may contribute to distinct toxicity profiles, such as haematological and gastrointestinal adverse events, fatigue, and pneumonitis. However, adverse event reporting is also shaped by treatment-related factors, dosing strategies, comorbidity burden, and the intensity and duration of follow-up. The exclusion of older women and those with comorbid conditions may further distort observed safety distributions. Strengthening adverse event assessment will require more balanced sex representation, systematic and transparent sex-disaggregated reporting, and closer integration of pharmacovigilance with biomarker-informed approaches to enable more precise toxicity modelling and reduce sex-based disparities in treatment risk assessment. These findings should be interpreted as trial-level associations rather than causal effects, because the analyses were ecological in structure and based on aggregate study-level reporting rather than individual patient-level data.
Persistent sex disparities in lung cancer trials call for substantive reform to ensure that advances in targeted therapies and immunotherapies do not inadvertently perpetuate existing inequities. Addressing these gaps will require enforceable and coordinated action rather than reliance on voluntary guidance. Regulatory bodies should mandate sex-disaggregated analyses in all randomised controlled trials, and funding agencies should consider implementing accountable inclusion targets supported by feasibility-adjusted enrolment frameworks. Trial protocols should incorporate explicit recruitment strategies, pre-specified sex-based enrolment targets, stratified randomisation where appropriate, and routine sex-disaggregated reporting of safety outcomes to enable adequately powered subgroup analyses. In contexts where substantial imbalance is anticipated, adaptive or enrichment designs may enhance representativeness and external validity. Embedding sex-specific metrics across research training, regulatory oversight, and publication standards will be essential for sustained progress. Investigators should align enrolment targets with regional disease epidemiology and ensure consistent reporting of sex-stratified efficacy and safety outcomes to improve interpretability. Clinicians interpreting trial findings should remain mindful of potential imbalances in representation, particularly when extrapolating toxicity data to underrepresented groups. Finally, strengthened post-marketing pharmacovigilance and the integration of real-world evidence with sex-stratified analyses will be critical to advancing equitable and precise safety assessment in routine practice.
Multiple factors influence female participation in clinical trials, including concerns about randomisation, placebo effects, unknown AEs, transportation barriers, and treatment-related costs. This study assessed the association between female underrepresentation and reported adverse event incidence and emphasised the need for more inclusive trial designs. However, this study has some limitations. First, inclusion was restricted to randomised controlled trials meeting predefined sample size thresholds and to English-language publications or trials registered on ClinicalTrials.gov, which may have introduced selection bias and influenced estimates of female participation. Second, most included studies were conducted in high-income settings, thereby limiting the generalisability of our findings to low- and middle-income countries, where differences in health-care access, socioeconomic context, and disease epidemiology may shape both enrolment patterns and treatment outcomes. Third, heterogeneity in therapeutic modalities and the emphasis on first-line treatment may constrain the applicability of our findings to later-line or combination regimens, in which toxicity profiles and eligibility criteria may differ. Fourth, the use of incidence rather than prevalence data as the epidemiological benchmark may underestimate sex-based differences in adverse events, particularly in advanced disease characterised by cumulative toxicities. In addition, inconsistent reporting of grade 3 or higher adverse events across trials may have introduced reporting bias. Fifth, although sensitivity analyses accounted for temporal trends and variation in Human Development Index, residual confounding cannot be excluded. Trial-level characteristics, including age distribution, performance status, line of therapy, and comorbidity burden, may interact with both sex-based enrolment patterns and adverse event incidence. Incomplete reporting of baseline variables, such as prior treatment exposure, ECOG performance status, and metastatic burden, further limited our ability to evaluate these interactions comprehensively. Because individual patient-level data were unavailable and reporting practices varied across studies, adjustment for these potential confounders was not feasible. Accordingly, our findings should be interpreted with caution and warrant validation in prospective investigations incorporating standardised sex-disaggregated and baseline reporting frameworks. In addition, the adverse event analyses involved multiple exploratory comparisons across treatment classes and event categories, increasing the possibility of type I error. These findings should therefore be interpreted as hypothesis-generating and require confirmation in studies with individual patient-level data and prespecified sex-stratified analyses.
5. Conclusions
In conclusion, women remain underrepresented in lung cancer clinical trials, which may limit the generalisability of trial findings to female patients. Trial-level differences in female representation were associated with differences in reported adverse event incidence across treatment settings. These findings support the need for more inclusive enrolment strategies, routine sex-disaggregated reporting, and further validation in studies with individual patient-level data to improve the reliability and equity of clinical evidence in lung cancer.
CRediT authorship contribution statement
Yena Gan: Conceptualization, Methodology, Writing – review & editing, Formal analysis, Data curation, Writing – original draft. Rui Tian: Conceptualization, Methodology, Writing – review & editing, Conceptualization, Methodology. Sheng Han: Conceptualization, Methodology, Writing – review & editing, Supervision. Hongmei Zeng: Conceptualization, Methodology, Writing – review & editing, Supervision. Feng Sun: Conceptualization, Methodology, Writing – review & editing, Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
Data are available upon reasonable request and with the permission of the collaborating authors.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jncc.2026.03.006.
Appendix. Supplementary materials
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