Treatment preferences of patients with recurrent or metastatic cervical cancer: a discrete choice experiment in the US

Graphical abstract

Highlights

• •150 metastatic cervical cancer patients took a quantitative treatment preference survey of two discrete choice experiments.
• •Participants valued 12-month overall survival rate and disease control rate as the most important attributes.
• •Participants were willing to accommodate certain logistical and financial requirements to gain treatment benefits.
• •Participants remained sensitive to out-of-pocket costs and number of visits.
• •These findings can be used to inform shared decision-making and treatment discussions between patients and their care team.

Abstract

Objective

This study assessed treatment preferences of patients with recurrent/metastatic cervical cancer, a disease with poor prognosis.

Methods

A survey with two discrete choice experiments was completed by 150 patients with recurrent/metastatic cervical cancer in the US. Discrete choice experiment 1 included treatment attributes, and discrete choice experiment 2 included risk mitigation plan attributes.

Results

Participants valued 12-month overall survival rate as the most important attribute, followed by disease control rate; both efficacy attributes were rated as more important than the risk of side effects such as peripheral neuropathy and corneal side effects. Participants’ willingness to accept a treatment profile requiring a risk mitigation plan was influenced by the number of clinic visits and out-of-pocket costs.

Conclusions

Patients with recurrent/metastatic cervical cancer prioritize overall survival and disease control rate as the most important attributes. These findings can be used to inform shared decision-making and treatment discussions among patients, clinicians, and the care team.

1. Introduction

Cervical cancer is the fourth most frequent cancer in women, with an estimated 661,000 new diagnoses and 348,200 deaths globally in 2022 (Bray et al., 2024). Cervical cancer is preventable through human papillomavirus vaccination and potentially curable if diagnosed early (Guimarães et al., 2022, Wang et al., 2020, Mix et al., 2021). However, prognosis is poor for patients with recurrent or metastatic cervical cancer (r/mCC) (NCI, 2025). Despite the availability of advanced diagnostic and treatment facilities in the US, patients with r/mCC have a 5-year relative survival rate of only 19 % (NCI, 2025).

In the past decade, several treatments have been developed for patients with r/mCC. Bevacizumab in combination with chemotherapy improved overall survival (OS) compared to chemotherapy alone in patients treated for r/mCC in the first line (1L) (Tewari et al., 2014). In 2018, pembrolizumab received accelerated approval from the US Food and Drug Administration (FDA) for the treatment of patients with r/mCC who experienced progression during or after chemotherapy (second line [2L]) (Chung et al., 2019). Pembrolizumab later received full approval in combination with chemotherapy with or without bevacizumab for patients with persistent or r/mCC whose tumors express programmed cell death ligand 1 (PD-L1; combined positive score [CPS] ≥ 1) in 2021, and in combination with chemoradiotherapy for patients with FIGO 2014 stage III-IVA CC in 2024 (Colombo et al., 2021). Moreover, tisotumab vedotin, received accelerated approval in 2021 for the treatment of patients with r/mCC who progressed on or after chemotherapy, followed by full approval in 2024 (Coleman et al., 2021, Vergote et al., 2024, FDA, 2024). With the approval of tisotumab vedotin, a new class of treatment, known as antibody-drug conjugates, were introduced to gynecologic oncologists who manage CC. These new r/mCC treatments have safety profiles that require additional patient monitoring and entail an increased use of healthcare resources (Kim et al., 2022, Richardson, 2023).

The multiple r/mCC treatments now available to patients differ in their efficacy and potential safety risk profiles. Thus, patients may need to make trade-offs when selecting a treatment. However, limited literature is available on patient preferences for the treatment of cervical cancer. Understanding the trade-offs patients are willing to make between treatment efficacy and risks, as well as the non-clinical burden of treatment (i.e., additional costs, need for travel), may increase our understanding of novel therapies in addressing treatment burden and inform healthcare providers in shared decision-making (Brennan and Strombom, 1998, Grad et al., 2017).

Here we describe the results of a patient preference study evaluating the relative importance of therapeutic and risk mitigation plan attributes of r/mCC treatments to patients, as well as their willingness to receive a treatment with a risk mitigation plan that requires an increased use of healthcare resources.

2. Methods

2.1. Overall study design and participants

This was a quantitative preference study involving patients with r/mCC in the US. The study was conducted as an online survey with two discrete choice experiments (DCEs). The DCEs included therapeutic and risk mitigation plan attributes, selected based on a targeted literature review (Supplementary Fig. S1), a review of clinical evidence, and advice from clinical experts.

Participants were recruited through a third-party healthcare recruitment company via healthcare professional referrals. Adults (≥18 years) with a self-reported diagnosis of r/mCC (stage IVB or ineligible for surgery or chemoradiation) were eligible to participate in the study. Participants had to be living in the US or to have received/be receiving treatment for r/mCC in the US and be able to speak, read, write, and understand English or Spanish. All study materials were available in English and Spanish. Participants were remunerated for their time upon the completion of interviews or online survey.

Cognitive pilot telephone interviews (60 min) were conducted in English or Spanish with 10 patients between March and May 2023 to assess the feasibility, relevance, and robustness of the study design, as well as to confirm the final selection of treatment attributes. Patients reviewed the DCE survey and provided feedback on length, wording, format, structure, and response options. The survey was iteratively refined based on this feedback. Cognitive pilot testing confirmed that participants could make trade-offs between treatment attributes. Out-of-pocket cost levels were revised using Centers for Medicare & Medicaid Services estimates (USD 75 per visit), and a cap of USD 500 per 3-week cycle was introduced in DCE 2.

Findings from the cognitive pilot interviews were used to inform the DCE design and survey language. A quantitative pilot study was conducted with 30 patients with r/mCC between July and August 2023 to collect initial preference data and test the DCE design. The main DCE survey (30 min) was conducted in English and Spanish with 150 patients with r/mCC between July and September 2023 (Supplementary Appendix).

Cognitive pilot interview participants were not eligible for the quantitative pilot and main DCE survey. The study was conducted in accordance with best practice guidelines on preference-based methods from the International Society for Pharmacoeconomics and Outcomes Research (Hauber et al., 2016).

2.2. Attribute and level development

Attributes were included in the DCEs if they were reported as being important to the patients, covered safety aspects relevant to patients, were related to a care plan associated with a new r/mCC treatment, or could differentiate between available r/mCC therapies. The first DCE (DCE 1) involved choosing between two hypothetical treatment options to quantify the relative importance placed on key efficacy and safety attributes during r/mCC treatment selection (Table 2 and Supplementary Fig. S2). The five attributes included in DCE 1 were OS rate at 12 months, disease control rate (DCR; i.e., the proportion of patients with a confirmed complete or partial response and the proportion of patients with stable disease), and risks of peripheral neuropathy, corneal side effects, and conjunctival side effects.

Table 2.

DCE: Final attributes and levels.

Attribute	Description	Levels
DCE 1: Clinical Attributes and Levels
Overall survival rate at 12 months	Percentage of patients alive at 12 months after receiving treatment needed to manage the disease	25 % 40 % 60 %
Chance of treatment shrinking or preventing tumor growth	Percentage of patients who experience control or stabilization of disease (i.e., the chance that the tumor does not grow/shrinks/disappears entirely)	30 % DCR (20 % chance tumor not growing + 10 % tumor shrinking or disappearing) 45 % DCR (30 % chance tumor not growing + 15 % tumor shrinking or disappearing) 70 % DCR (50 % chance tumor not growing + 20 % tumor shrinking or disappearing)
DCE 1: Risk Attributes and Levels
Peripheral neuropathy	Side effects such as tingling, numbness, pain, or swelling in the hands or feet, or muscle weakness in the arms or legs	Tingling, numbness, pain, swelling, or weakness is barely noticeable Constant tingling or numbness, or noticeable pain in hands or feet Weakness or difficulty in using limbs (e.g., needing walking assistance)
Corneal side effects	Side effects such as pain and redness in the eyes, excessive tears, blurred or decreased vision, or sensitivity to light	No discomfort in eyes or change in vision Discomfort in eyes, increase in tearing, or blurred/decreased vision Decreased vision and severe pain in the eye that interferes with normal daily activities
Conjunctival side effects	Side effects such as pink or red eyes, swollen eyelids, itchiness or a burning sensation in the eyes, and/or an urge to rub the eyes	No discomfort in the eyes or change in vision Irritation in the eyes, urge to rub the eyes, increased tearing, or difficulties with vision
DCE 2 Care Plan (non-clinical) Attributes and Levels
Number of doctor visits	The treatment involves a care plan to reduce or manage side effects. The patient will be required to visit a doctor between one and four times every three weeks for eye exams, treatment infusion, and treatment of side effects if needed.	1 visit to the doctor every 3 weeks 2 visits to the doctor every 3 weeks 3 visits to the doctor every 3 weeks 4 visits to the doctor every 3 weeks
Accessibility of eye drops	Patient will be required to use three different kinds of eye drops. Two of the eye drops will be administered at the time of infusion and two of the eye drops at home, while receiving their cancer treatment. There are three ways the patient can obtain the eye drops.	Patient will pick the eye drops at the pharmacy for use at home and will be administered the eye drops at every infusion at the infusion center. Patient will be given the eye drops at their first infusion and will be expected to bring them to every infusion. Patient will pick up the eye drops at the pharmacy and bring them to every infusion.
Out-of-pocket costs	The out-of-pocket costs may vary. For every 3-week period, the patient may be required to pay a certain amount of money for each visit to the doctor.	USD 20 per visit × number of visits USD 75 per visit × number of visits USD 120 per visit × number of visits USD 200 per visit × number of visits

DCE: discrete choice experiment; USD: US dollar; DCR: disease control rate.

The second DCE (DCE 2) assessed participants’ willingness to accept a risk mitigation plan associated with a newly approved treatment with ocular side effects (Table 2 and Supplementary Fig. S3). The three attributes included in DCE 2 were the number of visits to the doctor per 3-week treatment cycle, accessibility of eye drops, and out-of-pocket costs.

2.3. DCE instrument design and survey flow

The levels of DCE attributes were varied according to a D-efficient experimental design generated using Ngene software (ChoiceMetrics, Sydney, Australia) (ChoiceMetrics, 2023). DCE 1 included 21 experimental choice tasks which were split into three blocks of seven. DCE 2 included 18 experimental DCE tasks split into three blocks of six. The order of the DCE choice questions and the order of treatment options (Treatment A and Treatment B) were randomized between participants to mitigate ordering, learning, and fatigue bias (Carlsson et al., 2012, Heidenreich et al., 2021).

Participants began the online survey after providing electronic informed consent. In Section 1 of the survey, participants were presented with background information on r/mCC and its treatments, as well as attributes included in the two DCEs. In Section 2, participants completed randomly allocated block of 13 experimental choice tasks (7 choice tasks from DCE 1 and 6 choice tasks from DCE 2. Examples of experimental choice tasks from DCE 1 and DCE 2 are shown in Supplementary Figs. S2 and S3, respectively. A practice choice task used to familiarize participants with the DCE format before the experimental choice tasks in DCE 1 and DCE 2. One question on choice difficulty and one question on choice certainty was included at the end of the survey to assess the participants’ understanding of the DCE.

In Section 3, participants answered three questions on health literacy and five questions on numeracy (Chew et al., 2004, Lipkus et al., 2001). Participants also answered questions on sociodemographic and clinical characteristics (Supplementary Appendix).

2.4. Validity assessments

Validity analyses were conducted to test participant comprehension, alertness, and stability of choices. In DCE 1, a repeated choice task at the end of the survey tested the stability of participants’ responses. Stability was defined as selecting the same option in both instances. A dominance test assessed rationality by presenting a choice where one treatment was superior on all attributes; participants passed if they selected the superior option. Non-participation was defined as consistently choosing the same option across all DCE tasks, while dominant decision-making was evaluated to assess whether choices were driven by a single attribute.

The time taken by each participant to complete the DCE survey was recorded. To avoid selection bias, participants who failed the internal validity tests were not excluded (Supplementary Appendix).

2.5. Statistical analysis

The sample size in the main DCE was consistent with the sample size for healthcare DCEs reported in the literature (Hauber et al., 2016, Soekhai et al., 2019, Bridges et al., 2011). Descriptive statistics were used to summarize sociodemographics, clinical characteristics, and data quality indicators. R version R 4.0.5 was used to conduct the analyses.

Data from the two DCEs were separately analyzed with correlated mixed logit models specified within the random utility maximization framework (Manski et al., 1959, Thurstone, 1927, McFadden, 1973). The models estimated the effect of marginal changes in attributes on treatment utility. The OS rate, DCR, and out-of-pocket costs were treated as linear variables, such that the models estimated the effect of a 1-unit change (e.g., increasing DCR by 1 %). The remaining attributes were treated as categorical variables and the models estimated the effect of discrete changes (e.g., moving from no to mild/moderate peripheral neuropathy). Model estimates were then used to compute scores of relative attribute importance (RAI) and measures of minimum acceptable benefit (e.g., increase in OS rate) to tolerate an increase in treatment-related risks. A bootstrap method in 1,000 iterations was implemented to calculate 95 % confidence intervals (CIs) for the RAI scores (Efron and Tibshirani, 1986). The Delta method was used to obtain standard errors and 95 % CIs (Supplementary Appendix) (Hole, 2007).

Participants’ willingness to accept an r/mCC treatment was assessed as predicted uptake probabilities, which were calculated using the elicited preferences for the overall sample in DCE 2 (Supplementary Appendix). The effect of participants’ sociodemographic and clinical characteristics on their treatment and risk mitigation plan preferences was investigated with interaction effects between each participant characteristic and DCE attributes (Supplementary Appendix). This analysis examined whether certain subgroups placed greater or lesser importance on an attribute relative to the overall sample.

2.6. Ethics

The study protocol was approved by Salus Institutional Review Board (Salus study number: 22259-01C), and the study was performed in line with the principles of the Declaration of Helsinki. All participants provided electronic informed consent after reading an online informed consent form containing information on the study objectives, participant confidentiality, and whom to contact in case of questions. Participants were informed that they could withdraw at any time without penalty or giving up any benefits to which they were otherwise entitled.

3. Results

3.1. Participants

A total of 150 participants with r/mCC were included in the DCE survey (mean age, 50.1 years; Table 1). Twenty-nine percent were Hispanic or Latino and 37.3 % were non-White, including 21.3 % Black or African American. All participants reported having at least one type of health insurance: 45.3 % of the participants had self-provided health insurance, 30.7 % had employer-provided health insurance, and 16.0 % had health insurance under Medicare/Medicaid. Over half of the participants (52.0 %) reported being first diagnosed with r/mCC at Stage IV. Most participants had received prior chemotherapy (85.3 %) and less than half had received prior immunotherapy (40.7 %). At baseline, some participants reported having experienced treatment-related side effects including peripheral neuropathy (25.3 %), ocular side effects (26.7 %), and increased bleeding (28.0 %).

Table 1.

Sociodemographic and clinical characteristics of participants.

Characteristics	Overall (N = 150)
Age (years)
Mean (SD)	50.1 (7.8)
Ethnicity, n (%)
Hispanic or Latino	43 (28.7)
Not Hispanic or Latino	97 (64.7)
Prefer not to say	10 (6.7)
Race, n (%)
White	80 (53.3)
Black or African American	32 (21.3)
Asian or Asian American	6 (4.0)
Native Hawaiian/other Pacific Islander	12 (8.0)
American Indian/Alaska Native	6 (4.0)
Prefer not to say	14 (9.3)
Health insurance, n (%)
Employer-provided insurance	46 (30.7)
Self-provided insurance	68 (45.3)
Veteran affairs/military healthcare	15 (10.0)
Medicare	6 (4.0)
Medicaid	18 (12.0)
Residence, n (%)
Urban	42 (28.0)
Suburban	78 (52.0)
Rural	30 (20.0)
Time since diagnosis (months)
Mean (SD)	30.9 (35.0)
Median (Q1–Q3)	13.0 (7.0–49.0)
Cervical cancer stage at diagnosis
Pre-cancerous lesion	0 (0.0)
Stage I	15 (10.0)
Stage II	28 (18.7)
Stage III	28 (18.7)
Stage IVA	64 (42.7)
Stage IVB	14 (9.3)
Don’t know	1 (0.7)
Cervical cancer recurred after initial treatment, n (%)
No	91 (60.6)
Yes	59 (39.3)
Current remission status, n (%)
In remission	17 (11.3)
Not in remission	133 (88.6)
Side effects experienced: peripheral neuropathy, n (%)
No	112 (74.7)
Yes, but very mild (barely noticeable)	19 (12.7)
Yes, moderate	14 (9.3)
Yes, severe	5 (3.3)
Side effects experienced: ocular, n (%)
No	110 (73.3)
Yes, but very mild (barely noticeable)	20 (13.3)
Yes, moderate	16 (10.7)
Yes, severe	4 (2.7)
Side effects experienced: bleeding, n (%)
No	108 (72.0)
Yes, but very mild (barely noticeable)	14 (9.3)
Yes, moderate	25 (16.7)
Yes, severe	3 (2.0)
Type of cervical cancer treatments received*, n (%)
Surgery	54 (36.0)
Radiotherapy	16 (10.7)
Chemotherapy and radiotherapy	39 (26.0)
Chemotherapy	128 (85.3)
Immunotherapy	61 (40.7)
Targeted therapy	43 (28.7)
Self-reported overall health, n (%)
Very poor	38 (25.3)
Poor	71 (47.3)
Good	30 (20.0)
Very good	7 (4.7)
Excellent	4 (2.7)

Q: quartile; SD: standard deviation.

*Treatments received for cervical cancer were not mutually exclusive. Note that patients may not always accurately categorize prior treatments.

3.2. Validity assessments

The validity assessments suggested that participants were able to make trade-offs between DCE attributes. Participants demonstrated adequate health literacy and numeracy (Supplementary Table S1). The majority of participants (84.0 %) self-reported being at ease with reading/filling out medical forms/hospital material, and understanding written information. All participants correctly answered at least three out of five questions assessing numeracy, indicating adequate or high numeracy skills (full health literacy and numeracy scores can be found in Supplementary Table S1). Most participants (93.3 %) passed the choice dominance test, choosing the treatment choice with better efficacy and safety (Supplementary Table S2).

3.3. Treatment and risk mitigation care plan preferences

3.3.1. Utility estimates

In DCE 1, clinical efficacy attributes had a larger impact on participants’ preferences than risk attributes. An increase in OS rate at 12 months from 25 % to 60 % was the most important change in an attribute to participants (maximum likelihood estimate [MLE] = 4.8; P < 0.05), followed by an increase in DCR from 30 % to 70 % (MLE = 3.2; P < 0.01) (Fig. 1). Among risk attributes, risks of severe peripheral neuropathy and corneal side effects had a larger impact on participants’ preferences than conjunctival side effects. Participants were most concerned about a change from having no peripheral neuropathy to severe peripheral neuropathy (MLE = −1.0; P < 0.001), followed by a change from no corneal side effects to severe corneal side effects (MLE = −0.9; P < 0.001), and no conjunctival side effects to mild to moderate conjunctival side effects (MLE = −0.5; P < 0.001).

Fig. 1.

DCE 1: Participant preferences for r/mCC treatment attributes and levels (N = 150) All numeric attributes were modelled as linear terms. The figure shows the preference estimates for changes in the attributes, as specified in the DCE. Effects were derived by multiplying each attribute level with its corresponding linear coefficient. The black error bars represent 95 % confidence intervals. Utility estimates (y-axis) capture the impact of an improved attribute level (e.g., an increase in overall survival rate from 25 % to 40 %) on participants’ preferences. Higher estimates indicate a higher desirability, but the absolute value of a utility are not directly interpretable. With each DCE attribute, reference level is indicated by “*” was manually set as a utility estimate of zero. Positive change compared to reference indicates higher preference, and negative change indicates a lower preference, hold all else equal. The disease control rate is defined as the proportion of patients with a confirmed complete response, partial response, or stable disease. The model had a good fit (adjusted McFadden R² = 22.1 %) and was able to explain the choices participants made in the DCE. DCE: discrete choice experiment; r/mCC: recurrent or metastatic cervical cancer; CI: confidence interval.

In DCE 2, participants were first asked about their willingness to accept a treatment with a given benefit-risk profile. With the same benefit-risk profile, patients then answered questions about their preferred choice with varying degree of access convenience. Participants preferred having one visit to the doctor over two visits per 3-week treatment cycle (MLE = 13.3; P < 0.001). Additionally, increasing the number of doctor visits from 2 to 4 visits per cycle (MLE = −7.3; P < 0.001) and out-of-pocket costs from USD 20 to USD 200 per 3-week treatment cycle (MLE = −28.2; P < 0.001) both led to a decrease in acceptability of a treatment, all else being equal (Fig. 2). Participants’ preferences were not significantly impacted by how prescriptions for eye drops were filled.

Fig. 2.

DCE 2: Participant preferences for risk mitigation care plan attributes and levels (N = 150) The figure shows the estimated effect of each attribute over the range used in the DCE. Black bars show the 95 % confidence interval for each utility value. The model had a good fit (adjusted McFadden R² = 49.65 %) and was able to explain the choices that participants made in the DCE. Out-of-pocket cost levels were modeled as linear terms, with effects calculated by multiplying each level by its corresponding coefficient. All other attributes were coded categorically. With each DCE attribute, reference level is indicated by “*” and was manually set as a utility estimate of zero. Positive change compared to reference indicate higher preference, and negative change indicates a lower preference, hold all else equal. Out-of-pocket cost is the cost per doctor visit. *Reference level for each attribute. DCE: discrete choice experiment; CI: confidence interval.

3.3.2. Relative attribute importance

Compared to treatment attributes related to efficacy, those related to risks of side effects were less important to participants during treatment selection (Fig. 3). Improving OS rate at 12 months from 25 % to 60 % (RAI: 45.9 % [95 % CI: 28.3–63.4]) was valued approximately five times more than a reduced risk of peripheral neuropathy (RAI: 9.7 % [95 % CI: 4.0–15.4]) and corneal side effects (RAI: 8.7 % [95 % CI: 3.2–14.3]) and approximately eleven times more than a reduced risk of conjunctival side effects (RAI: 4.2 % [95 % CI: 1.3–7.1]).

Fig. 3.

Relative attribute importance scores in DCE 1 Relative attribute importance scores represent the relative impact of improving each attribute from the worst level to the best level (e.g., improving overall survival rate at 12 months from 25 % to 60 %). A higher relative attribute importance score indicates a greater relative importance of improving the attribute. CI: confidence interval; DCE: discrete choice experiment.

3.3.3. Minimum acceptable benefit of overall survival rate at 12 months

Trade-offs between efficacy and risk attributes were calculated based on participant responses to measure the minimum improvement in treatment efficacy participants would require for them to accept the potential risks associated with treatment. Results showed that participants required a 7.9 % increase in OS rate to tolerate severe peripheral neuropathy (P < 0.05), a 7.2 % increase to tolerate severe corneal side effects (P < 0.05), and a 3.5 % increase to tolerate mild to moderate conjunctival side effects (P < 0.05) (Fig. 4). Similarly, a 12.8 % increase in DCR was required to tolerate severe neuropathy (P < 0.01) and an 11.5 % increase to tolerate severe corneal side effects (P < 0.01) (Supplementary Fig. S4). Participants also required a 4.6 % and 5.6 % improvement in DCR to compensate for mild to moderate corneal and conjunctival side effects, respectively (P < 0.05; Supplementary Fig. S4).

Fig. 4.

Minimum acceptable benefit of overall survival rate at 12 months (DCE 1) Minimum improvements in OS rate at 12 months that participants would require to tolerate an increased risk of side effects. Values were based on the attributes and levels presented in DCE 1. Higher MAB values indicate that participants would require higher treatment efficacy to tolerate an increase in the severity of side effects. For example, to tolerate an increase in risk of severe peripheral neuropathy, participants would require a treatment which offered a 7.9 % increase in OS rate at 12 months. *** P-value < 0.1 %; ** P-value < 1 %; * P-value < 5 %. CI: confidence interval; DCE: discrete choice experiment; OS: overall survival; SD: standard deviation.

3.3.4. Predicted patient uptake of an r/mCC treatment profile with a risk mitigation care plan

Most participants (83.0 %) were willing to accept a treatment with a given benefit-risk profile defined by a 12-month OS rate of 51 %; 24 % of patients experiencing tumor shrinkage/disappearance and an additional 48 % having no tumor growth (DCR of 72 %); and mild or moderate peripheral neuropathy, corneal, and conjunctival side effects. Furthermore, with the above-described benefit-risk profile, most participants (73.0 %) were willing to accept some degree of burden associated with a risk mitigation plan, which included two visits to the doctor per 3-week treatment cycle and an out-of-pocket cost of USD 150 per cycle (an average per-visit cost of USD 75). However, for the same out-of-pocket cost, the predicted uptake decreased to < 50 % when three visits to the doctor per 3-week treatment cycle were required (Supplementary Fig. S5).

3.3.5. Subgroup differences

We conducted subgroup preference heterogeneity analyses based on a range of participant observed characteristics, including age, insurance type, and prior experience with peripheral neuropathy or ocular side effects (Supplementary Figs. S6 and S7); an analysis across race/ethnicity did not show statistical significance across attributes evaluated. Of note, in DCE 1, the preference for corneal side effect appeared to be more heterogeneous, as evidenced by more subgroups having statistically significant differences in RAIs associated with this attribute. Comparing age subgroups (≥45 vs. <45 years), improvement in OS rate at 12 months was significantly more important among the younger subgroup (ΔRAI: 19.6 %; P < 0.05). Participants who had previously experienced peripheral neuropathy were more concerned about the risk of severe peripheral neuropathy than those who had not (ΔRAI: 11.4 %; P < 0.05). In DCE 2, participants who reported self-provided insurance recorded significantly higher concerns associated with an increased number of doctor visits and out-of-pocket costs compared to those with employer-provided/veteran affairs or Medicaid/Medicare insurance (Supplementary Fig. S7).

4. Discussion

This study informs how patients with r/mCC trade-off between treatment efficacy, risks, and burden of treatment (i.e. a risk mitigation plan) when making treatment decisions. Additionally, data from this study was used to model how certain non-clinical factors such as financial or logistical burden, as perceived by patients, may influence treatment preferences. Consistent with published studies on patient preferences for gynecological cancer treatments (Collacott et al., 2021, Havrilesky et al., 2020, Havrilesky et al., 2019, Lee et al., 2016, Havrilesky et al., 2014, Minion et al., 2016), our findings clearly indicate that patients with r/mCC were willing to accept certain risks of side effects if the treatment has the potential to prolong their survival or effectively control the progression of their disease. Amongst cervical cancer patients, who are often younger and socially active, results from this study highlights the importance of having effective treatments that provide longer and active survivorship. This research also showed that patients with r/mCC were willing to accommodate certain logistical and financial requirements to gain r/mCC treatment benefits but were still sensitive to out-of-pocket costs and number of visits.

An important aspect of this study is the inclusion of a diverse patient sample that reflects the epidemiology and sociodemographic characteristics of patients with r/mCC in the US (NCI, 2025). By specifically ensuring participant recruitment from diverse communities and offering Spanish-language versions of study materials, we achieved a fair representation of underinsured and/or uninsured, non-White, and Hispanic participants, thereby improving the generalizability of our findings and insights into preferences of different patient populations. Additionally, we ensured the representativeness of treatment experiences and perspectives among study participants by aiming to enroll 10 % to 20 % of participants with prior exposure to 2L or advanced systemic therapy.

As suggested by the predicted uptake analysis, patients with r/mCC face trade-offs between treatment benefit and the burden of treatment, which is not unexpected. Patients with cervical cancer often require more support with various aspects such as money, housing, food, transportation, and utilities. These needs arise as they navigate the effects of cancer-related treatments, which can often result in work limitations and reduced earnings (unpublished data on file. Family Reach Cervical Advocacy Collaborative). For patients on active treatment, cost of therapy is a significant contributor to the overall financial burden and is often a major factor in a patient’s choice of therapy in their long-term planning for survivorship. Many patient assistance programs exist today to address the cost of therapy challenges. Furthermore, patients also face overwhelming expenses and logistical demands associated with treatment outside of cost of therapy, such as costs for imaging and other follow-up or monitoring visits. As such, these may lead to patients delaying seeking medical attention for milder symptoms, thereby increasing the likelihood of experiencing deterioration of their health status. Previously, Zheng et al have observed a link between financial hardships and increased emergency room visits and lower use of preventative services among cancer survivors (Zheng et al., 2020). In this study, we observed that patients are sensitive to financial burden when a standard treatment requires more than 1 visit every 3 weeks, where additional visits may be associated preventative or symptom management. These findings add to the report from Zheng et al. (Zheng et al., 2020), and further provide insights into the exacerbating health disparities among patients with r/mCC. These findings support a recommendation for the clinical care team to help patients with r/mCC prioritize clinical visits that are essential for their treatment and supportive care, and to offer information and resources to alleviate the financial and logistical barriers patients may encounter.

It is noteworthy that participants with previous experience of side effects associated with treatment or with better overall health were more concerned about corneal side effects. Furthermore, participants with a history of peripheral neuropathy reported heightened concerns regarding the risk of experiencing side effects with a new treatment. This may reflect the trade-offs patients make between avoiding risk of severe side effects and finding ways to manage the impact of severe side effects on their daily activities (e.g., using walkers or wheelchairs to manage challenges in mobility due to peripheral neuropathy). The authors interpret this as indicative of the importance for patients to maintain active and productive lives throughout their treatment journey and survivorship. We propose that physicians and care team members should more consistently consider the patient’s lifestyle and social commitments when discussing treatment options during shared decision-making. Participants reporting self-provided insurance were more sensitive to both increased number of doctor visits and higher out-of-pocket costs compared to participants with employer-provided/veteran affairs insurance or Medicare/Medicaid insurance suggest that a more equitable coverage for care may help mitigate disparities in access to newer treatments that require a risk mitigation plan.

These results provide evidence of differences in preferences among the sample, which indicates that personal preferences should be considered in shared decision-making because prescribing a treatment that is more consistent with a patient’s preferences is more likely to lead to increased treatment satisfaction and potentially increased adherence.

This study has certain limitations. First, attribute confounding is a general risk within DCEs and is usually present if the chosen attributes overlap and/or the influence of preferences for one attribute depends upon levels of other attributes. However, this DCE was designed to minimize attribute confounding by selecting distinct treatment aspects as attributes. Second, preference methods such as DCEs may be subject to hypothetical bias, if the choices made by respondents in the DCE differ from those they would make in real life. Third, the preferences of participants may be systematically different from those who were contacted but declined to participate. Lastly, due to the small sample sizes in some subgroups, some analyses, such as the analysis of differences in participant preferences based on health insurance coverage, may have been limited in their generalizability.

5. Conclusions

Our study findings may be used to guide the selection of r/mCC treatments, emphasize counseling with consideration for the patient’s native language, and improve shared decision-making between patients and clinicians. This research also highlights the importance of engaging patients with r/mCC as essential partners in discussions of their treatment plan and empowering them with information on benefit-risk and burden of treatment to facilitate informed and confident decision-making. These results underscore the importance for clinicians to identify patients’ perspectives and their tolerance of treatment-related risks that may necessitate a care plan. Increased patient education in all facets of treatment options may potentially enhances compliance with life-prolonging treatments. Logistical and financial implications are significant considerations for patients and should be incorporated into discussions with patients.

CRediT authorship contribution statement

Premal H. Thaker: Writing – review & editing, Writing – original draft, Conceptualization. Hui Lu: Conceptualization, Data curation, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing. Yitong J. Zhang: Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. Myrto Trapali: Writing – review & editing, Writing – original draft, Validation, Project administration, Methodology, Conceptualization. Paul Swinburn: Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. Nicolas Krucien: Writing – review & editing, Validation, Software, Methodology, Formal analysis, Data curation. Doris White: Writing – review & editing, Formal analysis. Joslyn Chaiprasert-Paguio: Writing – review & editing, Formal analysis. Bhavana Pothuri: Writing – review & editing, Writing – original draft, Conceptualization. Jie Ting: Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Funding acquisition, Conceptualization.

Ethics approval and consent to participate

This was a quantitative preference study involving patients with r/mCC in the US. The study protocol was approved by Salus Institutional Review Board (Salus study number: 22259-01C). All participants provided electronic informed consent

Funding

This study was funded by Seagen, which was acquired by Pfizer, Inc. in December 2023 and Genmab A/S. The funder was involved in the study conceptualization and design, data collection and analysis, the decision to publish, and preparation of the manuscript. However, the authors remain responsible for the opinions, conclusions, and data interpretation.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: HL, MT, PS, and NK are employees of Evidera. YJZ and JT are employees of Pfizer Inc. PHT has served on advisory boards for Immunogen, AstraZeneca, GSK, Merck, Immuon, Zentalis, Mersana, Seagen, Iovance, and Novocure; done consulting work for Caris; and received grants from Merck and GSK. JC-P is an employee of Elsevier. BP has received grants from AstraZeneca, Celsion, Clovis Oncology, Duality Bio, Eisai, Genentech/Roche, Immunogen, Karyopharm, Merck, Mersana, Onconova, Seagen, Sutro Biopharma, Takeda Pharmaceuticals, Tesaro/GSK, Toray, and VBL Therapeutics; consulting fees from AstraZeneca, BioNTech, Clovis Oncology, Eisai, GOG Foundation, Eli Lilly and Company, Merck, Mersana, Onconova, Seagen, Sutro Biopharma, Tesaro/GSK, and Toray; and travel support from GSK and BioNTech. DW declares that they have no competing interests.

Appendix A. Supplementary data

The following are the Supplementary data to this article: