CALY-SWE: Discrete choice experiment and time trade-off data for a representative Swedish value set

The data consist of two parts: Time trade-off (TTO) data with one row per TTO question (5 questions), and discrete choice experiment (DCE) data with one row per question (6 questions). The purpose of the data is the calculation of a Swedish value set for the capability-adjusted life years (CALY-SWE) instrument. To protect the privacy of the study participants and to comply with GDPR, access to the data is given upon request. The data is provided in 4 .csv files with the names: - tto.csv (252 kB) - dce.csv (282 kB) - weights_final_model.csv (30 kB) - coefs_final_model.csv (1 kB) The first two files (tto.csv, dce.csv) contain the time trade-off (TTO) answers and discrete choice experiment (DCE) answers of participants. The latter two files (weight_final_model.csv, coefs_final_model.csv) contain the generated value set of CALY-SWE weights, and the pertaining coefficients of the main effects additive model. Background: CALY-SWE is a capability-based instrument for studying Quality of Life (QoL). It consists of 6 attributes (health, social relations, financial situation & housing, occupation, security, political & civil rights) and provides the option to gives for attribute answers on 3 levels (Agree, Agree partially, Do not agree). A configuration or state is one of the 3^6 = 729 possible situations that the instrument describes. Here, a config is denoted in the form of xxxxxx, one x for each attribute in order above. X is a digit corresponding to the level of the respective attribute, with 3 being the highest (Agree), and 1 being the lowest (Do not agree). For example, 222222 encodes a configuration with all attributes on level 2 (Partially agree). The purpose of this dataset is to support the publication of the CALY-SWE value set and to enable reproduction of the calculations (due to privacy concerns we abstain from publishing individual level characteristics). A value set consists of values on the 0 to 1 scale for all 729, each of represents a quality weighting where 1 is the highest capability-related QoL, and 0 the lowest capability-related QoL. The data contains answers to two types of questions: TTO and DCE. In TTO questions, participants iteratively chose a number of years between 1 to 10. A choice of 10 years is equivalent to living 10 years with full capability (state configuration 333333) in the capability state that the TTO question describes. The answer on the 0 to 1 scale is then calculated as x/10. In the DCE questions, participants were given two states and they chose a state that they found to be better. We used a hybrid model with a linear regression and a logit model component, where the coefficients were linked through a multiplicative factor, to obtain the weights (weights_final_model.csv). Each weight is calculated as constant + the coefficients for the respective configuration. Coefficients for level 3 encode the difference to level 2, and coefficients for level 2 the difference to the constant. For example, for the weight for 123112 is calculated as constant + socrel2 + finhou2 + finhou3 + polciv2 (No coefficients for health, occupation, and security involved as they are on level 1 that is captured in the constant/intercept). To assess the quality of TTO answers, we calculated a score per participant that takes into account inconsistencies in answering the TTO question. We then excluded 20% of participants with the worst score to improve the TTO data quality and signal strength for the model (this is indicated by the 'included' variable in the TTO dataset). Details of the entire survey are described in the preprint “CALY-SWE value set: An integrated approach for a valuation study based on an online-administered TTO and DCE survey” by Meili et al. (2023). Please check this document for updated versions. Ids have been randomized with preserved linkage between the DCE and TTO dataset. Data files and variables: Below is a description of the variables in each CSV file. - tto.csv: config: 6 numbers representing the attribute levels. position: The number of the asked TTO question. tto_block: The design block of the TTO question. answer: The equivalence value indicated by the participant, ranging from 0.1 to 1 in steps of 0.1. included: If the answer was included in the data for the model to generate the value set. id: Randomized id of the participant. - dce.csv: config1: Configuration of the first state in the question. config2: Configuration of the second state in the question. position: The number of the asked TTO question. answer: Whether state 1 or 2 was preferred. id: Randomized id of the participant. - weights_final_model.csv config: 6 numbers representing the attribute levels. weight: The weight calculated with the final model. ciu: The upper 95% credible interval. cil: The lower 95% credible interval. - coefs_final_model.csv: name: Name of the coefficient, composed of an abbreviation for the attribute and a level number (abbreviations in the same order as above: health, socrel, finhou, occu, secu, polciv). value: Continuous, weight on the 0 to 1 scale. ciu: The upper 95% credible interval. cil: The lower 95% credible interval.本数据集包含两部分：一是每行为1个时间权衡（Time trade-off, TTO）问题的数据集，共包含5个问题；二是每行为1个离散选择实验（Discrete Choice Experiment, DCE）问题的数据集，共包含6个问题。本数据集用于计算能力调整寿命年（Capability-Adjusted Life Years, CALY-SWE）量表的瑞典价值集（CALY-SWE）。为保护研究参与者的隐私并符合通用数据保护条例（General Data Protection Regulation, GDPR）的要求，本数据集需通过申请方可获取。 本数据集以4个.csv文件形式提供，文件名为： - tto.csv（252 kB） - dce.csv（282 kB） - weights_final_model.csv（30 kB） - coefs_final_model.csv（1 kB） 前两个文件（tto.csv、dce.csv）包含参与者的时间权衡（TTO）应答数据与离散选择实验（DCE）应答数据。后两个文件（weights_final_model.csv、coefs_final_model.csv）包含CALY-SWE量表的最终价值集权重，以及主效应加法模型的对应系数。 ### 背景 CALY-SWE量表是一种用于研究生活质量（Quality of Life, QoL）的基于能力的工具。它包含6个维度：健康、社会关系、财务状况与住房、职业、安全感、政治与公民权利，且每个维度的应答可选择3个等级：同意、部分同意、不同意。 一个配置（或状态）是该量表描述的729种可能情境之一，即3^6=729种。本研究中，配置以6位字符串的形式表示，每一位字符对应上述顺序的一个维度。每位字符为数字，代表对应维度的等级：3为最高等级（同意），1为最低等级（不同意）。例如，222222编码表示所有维度均处于等级2（部分同意）的配置。 本数据集的目的是支持CALY-SWE价值集的发布，并便于研究人员复现计算过程（由于隐私顾虑，我们未发布参与者的个体层面特征）。价值集包含0至1区间内所有729种配置的权重值，每个权重代表一种能力相关生活质量的加权得分：1代表最高的能力相关生活质量，0代表最低的能力相关生活质量。 本数据集包含两类问题的应答数据：时间权衡（TTO）与离散选择实验（DCE）。 在时间权衡（TTO）问题中，参与者需迭代选择1至10年的时长。选择10年等价于在该TTO问题对应的能力状态中，以完全能力状态（配置333333）生活10年。最终转换为0至1区间的得分计算公式为x/10，其中x为参与者选择的年数。 在离散选择实验（DCE）问题中，参与者会被呈现两个状态，并选择其认为更优的状态。 我们采用结合线性回归与logit模型组件的混合模型，通过乘性因子关联两类模型的系数，以计算得到权重（存储于weights_final_model.csv文件中）。每个权重的计算公式为：常数项 + 对应配置的各维度系数。等级3的系数代表与等级2的差值，等级2的系数代表与常数项的差值。例如，配置123112的权重计算方式为：常数项 + socrel2 + finhou2 + finhou3 + polciv2（健康、职业与安全感维度的等级为1，其系数已包含在常数项/截距中，故无需额外添加系数）。 为评估TTO应答的质量，我们为每位参与者计算了得分，以考量其TTO应答中的不一致性。随后我们剔除了得分最差的20%的参与者，以提升TTO数据的质量与模型的信号强度（该筛选过程可通过TTO数据集中的included变量体现）。本次完整调查的细节已在Meili等人2023年的预印本《CALY-SWE价值集：基于线上时间权衡与离散选择实验调查的估值研究整合方法》中进行了描述，请查阅该文档以获取更新版本。 所有参与者的ID已进行随机化处理，且保留了DCE数据集与TTO数据集之间的关联关系。 ### 数据文件与变量 以下为每个CSV文件中变量的说明： - tto.csv： config：6位数字，代表各维度的等级。 position：所询问的TTO问题的编号。 tto_block：TTO问题的设计区组。 answer：参与者给出的等效值，取值范围为0.1至1，步长为0.1。 included：标识该应答是否被纳入用于生成价值集的模型训练数据中。 id：参与者的随机化ID。 - dce.csv： config1：问题中第一个状态的配置。 config2：问题中第二个状态的配置。 position：所询问的TTO问题的编号。 answer：标识参与者更偏好状态1还是状态2。 id：参与者的随机化ID。 - weights_final_model.csv： config：6位数字，代表各维度的等级。 weight：通过最终模型计算得到的权重。 ciu：95%可信区间上限。 cil：95%可信区间下限。 - coefs_final_model.csv： name：系数名称，由维度缩写与等级数字组成（维度缩写顺序与前述一致：health健康、socrel社会关系、finhou财务状况与住房、occu职业、secu安全感、polciv政治与公民权利）。 value：0至1区间内的连续权重值。 ciu：95%可信区间上限。 cil：95%可信区间下限。