Service: Clinical Trial Site Management is the largest segment, accounting for over 50% of the market, as it involves coordinating and managing clinical trial sites to ensure efficient trial execution.Phase: Phase III clinical trials hold a significant market share due to the larger number of patients involved and the extensive data collection required.Sponsor: Pharmaceutical and Biopharmaceutical companies are the primary sponsors of clinical trials, seeking to develop and commercialize new drugs. Recent developments include: April 2019: The WuXi AppTec acquired a clinical research services company, Pharmapace, Inc., to expand its Biometrics offerings in clinical research with data management, statistical programming, clinical data integration, biostatistics, and medical writing.. Notable trends are: Growing focus on patient recruitment, engagement, and retention, driving the need for innovative support services tailored to patient needs, the market growth..

This data description contains code (written in the R programming language), as well as processed data and results presented in a research article (see references). No raw data are provided and the data that are made available cannot be linked to study participants. The sample consists of 180 of 308 eligible participants (adult primary care patients in Sweden, living with chronic illness) who responded to a Swedish web-based questionnaire at two time points. Using a confirmatory factor analysis, we calculated latent factor scores for 9 constructs, based on 34 questionnaire items. In this dataset, we share the latent factor scores and the latent profile analysis results. Although raw data are not shared, we provide the questionnaire item, including response scales. The code that was used to produce the latent factor scores and latent profile analysis results is also provided.

The study was performed as part of a research project exploring how the use of eHealth services in chronic care influence interaction and collaboration between patients and healthcare. The purpose of the study was to identify subgroups of primary care patients who are similar with respect to their experiences of co-care, as measured by the DoCCA scale (von Thiele Schwarz, 2021). Baseline data were collected after patients had been introduced to an eHealth service that aimed to support them in their self-care and digital communication with healthcare; follow-up data were collected 7 months later. All patients were treated at the same primary care center, located in the Stockholm Region in Sweden.

Cited reference: von Thiele Schwarz U, Roczniewska M, Pukk Härenstam K, Karlgren K, Hasson H, Menczel S, Wannheden C. The work of having a chronic condition: Development and psychometric evaluation of the Distribution of Co-Care Activities (DoCCA) Scale. BMC Health Services Research (2021) 21:480. doi: 10.1186/s12913-021-06455-8

The DATASET consists of two files: factorscores_docca.csv and latent-profile-analysis-results_docca.csv.

• factorscores_docca.csv: This file contains 18 variables (columns) and 180 cases (rows). The variables represent latent factors (measured at two time points, T1 and T2) and the values are latent factor scores. The questionnaire data that were used to produce the latent factor scores consist of 20 items that measure experiences of collaboration with healthcare, based on the DoCCA scale. These items were included in the latent profile analysis. Additionally, latent factor scores reflecting perceived self-efficacy in self-care (6 items), satisfaction with healthcare (2 items), self-rated health (2 items), and perceived impact of e-health (4 items) were calculated. These items were used to make comparisons between profiles resulting from the latent profile analysis. Variable definitions are provided in a separate file (see below).

• latent-profile-analysis-results_docca.csv: This file contains 14 variables (columns) and 180 cases (rows). The variables represent profile classifications (numbers and labels) and posterior classification probabilities for each of the identified profiles, 4 profiles at T1 and 5 profiles at T2. Transition probabilities (from T1 to T2 profiles) were not calculated due to lacking configural similarity of profiles at T1 and T2; hence no transition probabilities are provided.

The ASSOCIATED DOCUMENTATION consists of one file with variable definitions in English and Swedish, and four script files (written in the R programming language):

• variable-definitions_swe-eng.xlsx: This file consists of four sheets. Sheet 1 (scale-items_original_swedish) specifies the questionnaire items (in Swedish) that were used to calculate the latent factor scores; response scales are included. Sheet 2 (scale-items_translated_english) provides an English translation of the questionnaire items and response scales provided in Sheet 1. Sheet 3 (factorscores_docca) defines the variables in the factorscores_docca.csv dataset. Sheet 4 (latent-profile-analysis-results) defines the variables in the latent-profile-analysis-results_docca.csv dataset.

• R-script_Step-0_Factor-scores.R: R script file with the code that was used to calculate the latent factor scores. This script can only be run with access to the raw data file which is not publicly shared due to ethical constraints. Hence, the purpose of the script file is code transparency. Also, the script shows the model specification that was used in the confirmatory factor analysis (CFA). Missingness in data was accounted for by using Full Information Maximum Likelihood (FIML).

• R-script_Step-1_Latent-profile-analysis.R: R script file with the code that was used to run the latent profile analyses at T1 and T2 and produce profile plots. This code can be run with the provided dataset factorscores_docca.csv. Note that the script generates the results that are provided in the latent-profile-analysis-results_docca.csv dataset.

• R-script_Step-2_Non-parametric-tests.R: R script file with the code that was used to run non-parametric tests for comparing exogenous variables between profiles at T1 and T2. This script uses the following datasets: factorscores_docca.csv and latent-profile-analysis-results_docca.csv.

• R-script_Step-3_Class-transitions.R: R script file with the code that was used to create a sankey diagram for illustrating class transitions. This script uses the following dataset: latent-profile-analysis-results_docca.csv.

Software requirements: To run the code, the R software environment and R packages specified in the script files need to be installed (open source). The scripts were produced in R version 4.2.1.

The Functional Service Provider (FSP) market is experiencing robust growth, driven by increasing outsourcing trends within the biopharmaceutical and clinical research sectors. The market, valued at $844 million in 2025, is projected to exhibit significant expansion throughout the forecast period (2025-2033). While the specific Compound Annual Growth Rate (CAGR) is not provided, considering the industry's average growth rates and the factors driving FSP adoption, a conservative estimate of 7-9% CAGR is plausible. This growth is fueled by several key factors: the rising complexity of clinical trials, the need for specialized expertise in areas like data management and statistical programming, and the increasing pressure on pharmaceutical companies to reduce operational costs and accelerate drug development timelines. The demand for FSPs is particularly strong in North America and Europe, driven by a high concentration of biopharmaceutical companies and a well-established regulatory landscape. Segments such as clinical monitoring and data management are expected to witness significant growth, reflecting the growing need for efficient data handling and regulatory compliance. The competitive landscape is characterized by a mix of large multinational corporations and specialized niche players. Key players like IQVIA, Parexel, and Covance maintain strong market positions due to their extensive service portfolios and global reach. However, smaller, specialized FSPs are also gaining traction by focusing on specific therapeutic areas or offering advanced analytical capabilities. The market is anticipated to see continued consolidation, with larger companies acquiring smaller firms to expand their service offerings and geographical reach. While regulatory hurdles and data security concerns represent potential restraints, the overall market outlook for FSPs remains exceptionally positive, promising substantial growth and opportunity in the coming years. The continued focus on innovation, particularly within technology-driven solutions for clinical trial management, further reinforces the market's growth trajectory.

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Descriptive statistics for four of the variables used in this study.

BackgroundImmunotherapy dramatically increases patient survival and radically changes the way lung cancer is treated. Through intricate network analysis of disease-related symptoms, symptom networks are able to statistically analyze and depict the links between different symptoms. Through symptom network analysis, this study seeks to pinpoint the primary symptoms that immunotherapy-treated lung cancer survivors encounter. Furthermore, by leveraging the synergistic relationships between symptoms, it aims to investigate the target actions for precise interventions, offering important insights for the creation of a successful symptom management program.MethodsWe assessed the symptoms of 249 lung cancer patients undergoing immunotherapy at Zhejiang Cancer Hospital between February and October of 2024. The evaluation was conducted using the Lung Cancer-Specific Module and the Anderson Symptom Assessment Scale (Chinese version). Following the use of exploratory factor analysis to discover symptom clusters, we calculated the centrality indices and created a network structure using the R programming language that displayed the connections between the symptoms. After controlling for factors, we constructed contemporaneous networks that had all 17 symptoms.ResultsFour symptom clusters—respiratory, emotional, gastrointestinal, and neuro-perceptual—were shown to be generalized. The three most prevalent symptoms, as determined by nodal intensity, were sadness (rs = 7.43), cough (rs = 6.65) and nausea (rs = 6.73). The most common symptoms at bridge intensity were nausea (rs = 4.69), cough (rs = 4.72), and sadness (rs = 5.69). The network’s overall strength and structure did not significantly differ between the male and female groups, or between those who had or did not have a history of smoking.ConclusionThis study demonstrates that the symptom burden is significant among survivors of lung cancer immunotherapy, with sadness, cough, and nausea playing crucial roles in the multidimensional symptom network. Interventions focused on addressing sadness can effectively reduce the severity of the entire symptom network, while early intervention for cough and nausea can alleviate the symptom management burden for patients. Additionally, identifying more predictable symptoms can aid in selecting appropriate targets for symptom management. Healthcare professionals can utilize these symptom patterns to deliver evidence-based and precise symptom management for survivors of lung cancer immunotherapy.

BackgroundImmunotherapy dramatically increases patient survival and radically changes the way lung cancer is treated. Through intricate network analysis of disease-related symptoms, symptom networks are able to statistically analyze and depict the links between different symptoms. Through symptom network analysis, this study seeks to pinpoint the primary symptoms that immunotherapy-treated lung cancer survivors encounter. Furthermore, by leveraging the synergistic relationships between symptoms, it aims to investigate the target actions for precise interventions, offering important insights for the creation of a successful symptom management program.MethodsWe assessed the symptoms of 249 lung cancer patients undergoing immunotherapy at Zhejiang Cancer Hospital between February and October of 2024. The evaluation was conducted using the Lung Cancer-Specific Module and the Anderson Symptom Assessment Scale (Chinese version). Following the use of exploratory factor analysis to discover symptom clusters, we calculated the centrality indices and created a network structure using the R programming language that displayed the connections between the symptoms. After controlling for factors, we constructed contemporaneous networks that had all 17 symptoms.ResultsFour symptom clusters—respiratory, emotional, gastrointestinal, and neuro-perceptual—were shown to be generalized. The three most prevalent symptoms, as determined by nodal intensity, were sadness (rs = 7.43), cough (rs = 6.65) and nausea (rs = 6.73). The most common symptoms at bridge intensity were nausea (rs = 4.69), cough (rs = 4.72), and sadness (rs = 5.69). The network’s overall strength and structure did not significantly differ between the male and female groups, or between those who had or did not have a history of smoking.ConclusionThis study demonstrates that the symptom burden is significant among survivors of lung cancer immunotherapy, with sadness, cough, and nausea playing crucial roles in the multidimensional symptom network. Interventions focused on addressing sadness can effectively reduce the severity of the entire symptom network, while early intervention for cough and nausea can alleviate the symptom management burden for patients. Additionally, identifying more predictable symptoms can aid in selecting appropriate targets for symptom management. Healthcare professionals can utilize these symptom patterns to deliver evidence-based and precise symptom management for survivors of lung cancer immunotherapy.

Pregnancy is a condition of broad interest across many medical and health services research domains, but one not easily identified in healthcare claims data. Our objective was to establish an algorithm to identify pregnant women and their pregnancies in claims data. We identified pregnancy-related diagnosis, procedure, and diagnosis-related group codes, accounting for the transition to International Statistical Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis and procedure codes, in health encounter reporting on 10/1/2015. We selected women in Merative MarketScan commercial databases aged 15–49 years with pregnancy-related claims, and their infants, during 2008–2019. Pregnancies, pregnancy outcomes, and gestational ages were assigned using the constellation of service dates, code types, pregnancy outcomes, and linkage to infant records. We describe pregnancy outcomes and gestational ages, as well as maternal age, census region, and health plan type. In a sensitivity analysis, we compared our algorithm-assigned date of last menstrual period (LMP) to fertility procedure-based LMP (date of procedure + 14 days) among women with embryo transfer or insemination procedures. Among 5,812,699 identified pregnancies, most (77.9%) were livebirths, followed by spontaneous abortions (16.2%); 3,274,353 (72.2%) livebirths could be linked to infants. Most pregnancies were among women 25–34 years (59.1%), living in the South (39.1%) and Midwest (22.4%), with large employer-sponsored insurance (52.0%). Outcome distributions were similar across ICD-9 and ICD-10 eras, with some variation in gestational age distribution observed. Sensitivity analyses supported our algorithm’s framework; algorithm- and fertility procedure-derived LMP estimates were within a week of each other (mean difference: -4 days [IQR: -13 to 6 days]; n = 107,870). We have developed an algorithm to identify pregnancies, their gestational age, and outcomes, across ICD-9 and ICD-10 eras using administrative data. This algorithm may be useful to reproductive health researchers investigating a broad range of pregnancy and infant outcomes.

Characteristics of study participants among patients visiting oncology centers in Addis Ababa, Ethiopia.

Convergent validity of latent factors in perceived access to health-care services in patients with cervical cancer visiting oncology centers in Addis Ababa, Ethiopia.

Measures of fit indices of latent constructs in perceived access to health-care services among patients with cervical cancer visiting oncology centers in Addis Ababa, Ethiopia.

Factor-level reliability test assessment of indicators on perceived access to healthcare among patients visiting oncology centers in Addis Ababa, Ethiopia.

Study participants interviewed by country and their respective year of survey.