Open access Protocol Decision Analysis in SHared decision making for Thromboprophylaxis during Pregnancy (DASH-­TOP): a sequential explanatory mixed methods pilot study protocol Brittany Humphries,1 Montserrat León-­García,2,3 Shannon Bates,4 Gordon Guyatt,1,4 Mark Eckman,5 Rohan D'Souza,6,7,8 Nadine Shehata,9 Susan Jack,1,10 Pablo Alonso-­Coello,2,11 Feng Xie ‍ ‍1,12 To cite: Humphries B, ABSTRACT León-­García M, Bates S, Strengths and limitations of this study Introduction Decision analysis is a quantitative et al. Decision Analysis in approach to decision making that could bridge the gap ►► The mixed methods approach will evaluate the SHared decision making for Thromboprophylaxis during between decisions based solely on evidence and the application of decision analysis to shared decision Pregnancy (DASH-­TOP): unique values and preferences of individual patients, making surrounding prophylactic low molecular a sequential explanatory a feature especially important when existing evidence weight heparin (LMWH) for women who are preg- mixed methods pilot cannot support clear recommendations and there is nant or considering pregnancy and have experi- study protocol. BMJ Open a close balance between harms and benefits for the enced a venous thromboembolism (VTE) for whom 2021;11:e046021. doi:10.1136/ treatments options under consideration. Low molecular guidelines recommend a shared decision-­making bmjopen-2020-046021 weight heparin (LMWH) for the prevention of venous process regarding thromboprophylaxis. ►► Prepublication history and thromboembolism (VTE) during pregnancy represents one ►► Women who are pregnant or considering pregnancy additional maerial for this paper such situation. The objective of this paper is to describe and have experienced a VTE will be specifically tar- is available online. To view these geted since guidelines recommend that treatment the rationale and methodology of a pilot study that will files, please visit the journal decisions regarding prophylactic LMWH be made online (http://​dx.​doi.​org/​10.​ explore the application of decision analysis to a shared decision-­making process involving prophylactic LMWH using a shared decision-­making process. 1136/​bmjopen-​2020-​046021). ►► The integration of quantitative and qualitative re- for pregnant women or those considering pregnancy who Received 18 October 2020 search methods to evaluate a complex intervention have experienced a VTE. Revised 21 December 2020 will offer greater insights than either method alone. Methods and analysis We will conduct an international, Accepted 11 March 2021 ►► A small sample size and the recruitment of partic- mixed methods, explanatory, sequential study, including ipants from two high-­income countries limits the quantitative data collection and analysis followed by generalisability of study findings. qualitative data collection and analysis. In step I, we will ►► The results of this pilot study will be used to inform ask women who are pregnant or considering pregnancy a randomised controlled trial. and have experienced VTE to participate in a shared decision-­making intervention for prophylactic LMWH. The intervention consists of three components: a direct choice exercise, a values elicitation exercise and a personalised INTRODUCTION decision analysis. After administration of the intervention, Thromboprophylaxis during pregnancy we will ask women to make a treatment decision and Venous thromboembolism (VTE) is a condi- © Author(s) (or their measure decisional conflict, self-­efficacy and satisfaction. tion in which a blood clot forms in the deep employer(s)) 2021. Re-­use In step II, which follows the analysis of quantitative veins of the leg, groin or arm (deep vein permitted under CC BY-­NC. No data, we will use the results to inform the qualitative thrombosis (DVT)) and travels to the lungs commercial re-­use. See rights interview. Step III will be a qualitative descriptive study (pulmonary embolism (PE)). Globally, VTE and permissions. Published by that explores participants’ experiences and perceptions of BMJ. is a leading cause of maternal morbidity and the intervention. In step IV, we will integrate findings from mortality.1 2 In high-­ income countries, the For numbered affiliations see the qualitative and quantitative analyses to obtain meta-­ incidence of VTE is 1.2 in 1000 pregnancies end of article. inferences. and deaths in 1.1 per 100 000 deliveries.3 Correspondence to Ethics and dissemination Site-­specific ethics boards Women with prior VTE are at an increased Dr Feng Xie; have approved the study. All participants will provide ​fengxie@​mcmaster.​ca and risk of thrombosis during subsequent preg- informed consent. The research team will take an Dr Pablo Alonso-­Coello; nancies,4 5 although the magnitude of integrated approach to knowledge translation. ​palonso@​santpau.​cat this risk remains uncertain given that the Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 1 Open access existing evidence base is informed by studies with major evidence.22 A variety of methods can help patients appraise limitations.6 their values regarding treatment options under consider- Because it does not traverse the placenta and is asso- ation. For example, patients can complete a ranking exer- ciated with a low risk of heparin-­induced thrombocyto- cise to express the relative importance of each outcome. paenia and osteopaenia, low molecular weight heparin Although eliciting patients’ values is important, it does (LMWH) represents the preferred treatment option for not ensure that they are incorporated into the decision-­ the prevention of VTE during pregnancy over other anti- making process. Furthermore, patients and providers thrombotic therapies, such as unfractionated heparin.7–9 then face the cognitive challenge of weighing the harms It is, however, expensive and requires daily subcutaneous and benefits of each treatment option alongside their injections.10 Prophylactic LMWH may be associated with probabilities of occurring and the patient’s values and an increased risk of major bleeding, especially around preferences.23–25 the time of delivery and may limit access to regional anal- gesia.7 11 There continue to be challenges in determining Decision analysis the appropriate pregnancy-­specific dose and role of labo- Decision analysis is a decision support technology that ratory monitoring. could bridge the gap between decisions based solely on Due to uncertainty regarding available evidence as well evidence and the values and preferences of individual as the costs associated with administration and moni- patients. It involves structuring a decision problem using toring, the American Society of Hematology recommends an analytical framework (eg, decision tree) that includes that the decision between prophylactic LMWH during key clinical outcomes associated with each treatment pregnancy versus expectant management involving no option as well as the natural course of untreated illness LMWH be made using a shared decision-­making process.9 along with their associated probabilities and utilities or The use of decision aids is suggested for this type of condi- other weighting factors based on patients’ values and tional recommendation (ie, recommendation based on preferences. Probabilities, a measure of likelihood that weak evidence) as they may help individuals make deci- an event will occur,26 are obtained from published studies sions consistent with their risks, values and preferences. and may themselves be personalised based on individual Despite this call for shared decision-­making, a paucity of patient’s clinical risk profile. Values and preferences for decision support tools exist to help patients engage in health outcomes, which are expressed as health utilities, treatment decisions for VTE.12 can be obtained directly from patients.27 Utility assessment involves comparisons to anchor health states (ie, perfect Shared decision making and the clinical encounter health and death) and may involve gambles that entail Shared decision-­making is a continuum process through a risk of undesirable outcomes, or trade-­ offs between which patients and clinicians work together to make a quality and quantity of life. Utility scores can be elicited treatment decision.13 Although there is no consensus directly from patients using exercises such as the standard regarding the definition of shared decision-­ making,14 gamble, time trade-­off and visual analogue scale.28 Utility three elements are considered necessary: (1) recognising scores typically are anchored 1 representing ‘perfect a decision is required; (2) knowing and understanding health’ and 0 representing the ‘dead’ state. Utility scores the best available evidence; and (3) incorporating patient can be used in a personalised decision analysis to calculate values and preferences into the decision.15 Although a the quality-­adjusted life years (QALYs) for each treatment key element is the incorporation of patient values and option under consideration. For example, if a treatment preferences into the decision,15 studies indicate they are results in a life expectancy of 10 years, but the quality often ignored or poorly understood by providers.16 17 of life for the resulting health state has a utility of 0.9, A number of decision support technologies to facilitate the treatment would yield 9.0 QALYs. If multiple treat- shared decision-­making exist. Decision aids are the most ment options are being considered, the treatment with commonly used tool. Available in a variety of formats the highest quality-­ adjusted life expectancy represents (eg, online, print and video), decision aids are meant to the best option.27 Thus, patient-­specific probabilities and inform patients regarding treatment options and their utility scores can be used in a decision analysis to calculate associated potential benefits, harms and costs.18 19 These personalised results and obtain an explicit guidance state- tools often entail an implicit method of values clarifica- ment to facilitate decision making.29 30 tion, in which patients are encouraged to think about what’s important to them.20 A systematic review of 105 Decision analysis for shared decision making randomised controlled trials involving 31 043 patients Several studies found a disagreement between the results found that, while decision aids helped patients make of the decision analysis and treatment guidelines.31–33 more informed decisions,21 there is uncertainty as to how This suggests that guidelines, despite including a range these tools support the process of integrating patient of inputs (clinical evidence and patient preferences) and values and preferences into the decision-­making process. potential outcomes, might not adequately respond to The International Patient Decision Aid Standards individual patient treatment decisions. Collaboration recommends that decision aids include a Current evidence, although limited, shows the poten- value elicitation exercise alongside the presentation of tial for personalised decision analysis to improve shared 2 Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 Open access decision making in clinical contexts.31–41 While decision methods question.44 The following research questions analysis is increasingly being used to inform the manage- will guide the conduct of this study: ment of pregnancy-­related conditions,42 its use is fraught ►► Quantitative research question with challenges. These include competing interests of –– Using a shared decision-­making process that incor- mother and fetus, the use of appropriate time horizons porates decision analysis, what is the level of deci- and challenges with using QALYs as an outcome measure sion quality among women that are pregnant or for combined maternal–fetal health states.43 As a result, considering pregnancy who have experienced VTE it is unclear how or if decision analysis should be incor- and must decide whether to take LMWH? porated within shared decision making surrounding VTE ►► Qualitative research question and pregnancy, which necessitates a robust approach to –– What are the experiences and perceptions related shared decision-­making given the close balance between to a shared decision-­ making process that incor- benefits and harms of LMWH and limitations of available porates decision analysis among women that are evidence. pregnant or considering pregnancy who have ex- perienced VTE and must decide whether to take Objective LMWH? The objective of this study is to explore the application of ►► Mixed methods research question decision analysis to a shared decision-­making process for –– How do the qualitative findings provide an en- the decision of using prophylactic LMWH for pregnant hanced understanding of quantitative results on women or those considering pregnancy who have expe- decision quality to evaluate the application of de- rienced a VTE. The study will also inform the design and cision analysis to a shared decision-­making process execution of a future randomised controlled trial that will among women who are pregnant or considering evaluate the additional value of decision analysis in the pregnancy and have experienced a VTE and must clinical encounter for the same decision. decide whether to take LMWH? Figure 1 presents the study flow of this multicentre, METHODS AND ANALYSIS single-­ arm, intervention pilot study. In step I, we will In this study, we will use an explanatory sequential administer the intervention and measure decision quality mixed methods design, which consists of quantitative among women who are pregnant or considering preg- data collection and analysis followed by qualitative data nancy and have experienced a VTE and must decide collection and analysis. This type of study design will whether to take prophylactic LMWH. Step II follows allow for a comprehensive evaluation of decision analysis the analysis of quantitative data, whereby we will use the within a shared decision-­making encounter for VTE and quantitative results to inform qualitative data collection. pregnancy. In step III, we will conduct a qualitative descriptive study In a mixed methods study, separate research ques- with all participants to explore their experiences and tions are specified for the quantitative and qualitative perceptions related to the intervention. In step IV, we will components of the study, as well as an overarching mixed integrate findings from the quantitative and quantitative Figure 1 Study flow diagram for the DASH-­TOP study. DASH-­TOP, Decision Analysis in SHared decision making for Thromboprophylaxis during Pregnancy. Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 3 Open access analyses to obtain inferences that add insight beyond Since greater priority will be given to the qualitative what could be understood from either dataset on their component of this mixed methods study, the sample size own (meta-­inferences).44 will be determined in consideration of the amount of information required to address the qualitative research Step I: administration of intervention and quantitative data question.47 There are no formal rules for calculating a collection priori a sample size for qualitative studies, instead an esti- Study design mate of the number of participants required is provided. In step I, we will ask women who are pregnant or consid- We have estimated that we will need an initial purposeful ering pregnancy and have experienced VTE to participate sample of 30 women (n=5 per site) given that this is a in a shared decision-­ making intervention for prophy- fairly homogenous population of women with respect to lactic LMWH. The intervention will use a personalised their medical diagnoses and who are making these deci- decision analysis as a complementary approach to a deci- sions within pregnancy. However, as data collection and sion aid. Since this study aims to pilot test the use of the analysis is concurrent in qualitative studies, if we deter- decision analysis tool and explore preliminary findings, a mine towards the end of our recruitment that certain control group was not included because the study was not concepts are not fully saturated, then the decision will designed to compare effects between intervention and be made to continue recruitment until we reach satura- control groups. tion or ‘the point at which the data collection process no longer offers any new or relevant data’.48 Study setting The study will recruit women from hospitals in Canada Intervention (n=2 sites) and Spain (n=4 sites). The decision to conduct The intervention will explore the decision-­making process an international study was made in consideration of the comparing strategies of administering prophylactic small size of the target population. Since it is estimated LMWH once daily when pregnancy is confirmed and that complications due to VTE occur in approximately 1 continuing until delivery7 versus expectant management in 1000 pregnancies,45 an international study will increase without LMWH. The intervention has been designed feasibility of recruitment and generalisability of results. with three components: a direct choice exercise, values In a previous study conducted by the research team, elicitation exercise and personalised decision analysis. All we compared the use of direct choice and decision anal- participants in the study will receive the intervention. A ysis among 123 women with a history of VTE who were member of the research team will deliver the intervention pregnant or planning pregnancy. During this study, we and collect data in person or online using a web-­based observed some differences between countries, including platform. The process is expected to take between 1 and Canada and Spain.46 Given that the research teams 1.5 hours. Through this interview, some women may find in these two countries have conducted similar studies the additional counselling and information received together and staff at the selected study sites are already helpful in making their decision about the use of LMWH. trained, these two countries were selected for the pilot All women will participate in the study prior to meeting sites. with their healthcare provider so that they can apply this new information to their decision and follow-­up with a Participants healthcare professional if they have any questions. We do The target population consists of women with a prior not anticipate there is any risk associated with this inter- VTE who are pregnant or planning pregnancy and who view in general nor with the length of time required to have been referred for counselling regarding prophy- complete the interview; however, we do inform partici- lactic LMWH. Women will be eligible for inclusion if pants that they can stop participation at any point during they have a history of lower extremity DVT or PE, are the intervention and are free to not respond to questions currently pregnant or planning pregnancy and consid- they are uncomfortable with. ering LMWH. Women will be excluded from the study To start, women will be asked to consider four health if they are currently receiving thromboprophylaxis or states relevant to this decision: (1) use of LMWH; (2) therapeutic anticoagulation, are less than 18 years of age, major obstetrical bleed; (3) DVT; and (4) PE. Health have contraindications or intolerances to LMWH, or are state descriptions are available in online supplemental unwilling and/or unable to provide informed consent. appendix 1. Women will then complete a direct choice At each site, the clinician will review medical charts to exercise that includes the review of an interactive elec- identify eligible women based on their pregnancy status tronic decision aid developed using the MagicApp plat- and presence of a previous blood clot. The clinician will form.49 The decision aid describes the harms and benefits contact potential participants and ask if they are willing of LMWH for prevention of pregnancy-­related VTE. In to be approached by a member of the research. With the line with International Patient Decision Aid Standards woman’s permission, the research team will contact the (IPDAS) recommendations, information is presented in woman by telephone to explain the study, confirm eligi- numeric and graphic format.22 Figure 2 presents a screen- bility and schedule the interview. shot of the decision aid. 4 Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 Open access Figure 2 Screenshot of decision aid. This screenshot presents women with their estimated risk of experiencing a deep vein thrombosis (DVT). Risks are presented in both numerical and graphical format. Numerically, the risk of DVT during pregnancy is 5.5%. This means that, out of 1000 women, approximately 55 will experience a DVT if they do not take low molecular weight heparin (LMWH) and 9 will experience a DVT if they do take LMWH. Overall, 46 fewer women will experience a blood clot when taking LMWH compared with not taking LMWH. The graphic represents a room of 1000 women. The 945 figures who are coloured in grey represent those women who were not destined to experience a DVT and would take daily injections of medication for the rest of their pregnancy with no benefit. The nine black figures represent women who will take the medication regularly and still experience a DVT during pregnancy because LMWH is not 100% effective. The orange figures represent the 46 women who would have experienced a DVT in their pregnancy and will avoid the blood clot because they took LMWH. The overall certainty of the evidence informing these estimates is low due to the types of studies that were conducted and the small sample sizes. The direct choice exercise will be followed by three The visual analogue scale and ranking tasks will serve value elicitation exercises (rank ordering, visual analogue as warm-­up exercises, with the standard gamble deter- scale and standard gamble)50 that will be completed using mining the value rating inputted into the decision Gambler II software.51 Women will consider the four health analytic model. The standard gamble is considered to be states listed above. For the ranking exercise, women will a gold standard in preference elicitation methods as it has rank the health states from most to least preferred. For demonstrated acceptability and reliability,52 53 as well as the visual analogue scale, women will place each health established theoretical underpinnings of expected utility state along a ‘feeling thermometer’ that represents their theory.54 55 Unlike ranking and the visual analogue scale, preference on a scale of 0 (dead) to 100 (perfect health). it evaluates preferences under conditions of uncertainty. Figure 3 presents a screenshot of the visual analogue scale Once the utilities for a given patient have been exercise. The standard gamble uses a poison pill analogy obtained, they will be inputted into the decision analytic to describe a gamble in which the patient can accept an model along with patient-­specific probabilities for VTE intermediate health state, such as a PE, or take a medi- risk sourced from clinical guidelines.9 The decision cation that can prevent that from occurring. The patient analytical model is a Markov state transition model that need only take a single pill from the bottle, but unfor- examines two treatment options under consideration: tunately some varying number of pills in the bottle are prophylactic LMWH versus expectant management ‘poison’ and may result in death. If a patient is indifferent without LMWH.10 The model has a lifetime time horizon between, for instance, a 0.1 risk of getting a poison pill to and a 6-­week cycle length to simulate both antepartum avoid a PE and prophylactic LMWH, then the utility of and lifetime events. The model will be personalised that health state would be calculated as (1–0.1) or 0.9.35 according to women’s age and risk of VTE and utilities Death will be used as the anchor for standard gambles for each health state.56 57 Based on this information, the relating to major obstetrical bleed, DVT and PE health decision analytical model will estimate the QALYs for states. In the standard gamble for the use of LMWH, DVT each treatment option. The treatment with the greatest will be the anchor. expected QALYs will represent the recommended Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 5 Open access Figure 3 Screenshot of visual analogue scale. This screenshot demonstrates a visual analogue scale where participants are asked to place each health state along a ‘feeling thermometer’ that represents their preference on a scale of 0 (dead) to 100 (perfect health). In this hypothetical example, pulmonary embolism, deep vein thrombosis and major bleed are rated as 20, 30 and 50 out of 100, respectively. strategy. Figure 4 presents a screenshot of the decision Decision Self-­Efficacy Scale is an 11-­item instrument that analysis recommendation. measures confidence or belief in one’s ability in decision making. Items are given a value between 0 and 4, with 0 Data collection indicating ‘not at all confident’ and 4 ‘very confident’. At the start of the study, the research team will document These values are combined into a summary score that women’s age, level of education, pregnancy status, preg- ranges from 0 for ‘not confident’ to 100 for ‘extremely nancy number, details regarding prior VTE (eg, type of confident’. The Satisfaction with Decision Scale measures event and presence of precipitating clinical risk factors), satisfaction with healthcare decisions. It is a six-­ item type and duration of treatment for prior VTE, and expe- instrument that generates a summary score, ranging from rience with LMWH (eg, bruising and heparin-­induced 6 for ‘low level of decision satisfaction’ to 30 for ‘high thrombocytopaenia). level of decision satisfaction’. While it is acknowledged After completing the direct choice, values elicitation that these measures of decision quality have limitations,63 exercises and receiving the results of their personalised they are widely used and have been validated in different decision analysis, women will be asked to make a prelim- patient populations.58 64–68 Selecting three measures will inary treatment decision. Women will make a final deci- provide a starting point for understanding different sion during the consultation with their provider. Women facets of the shared decision-­making process. The self-­ will complete a self-­administered questionnaire to eval- administered questionnaire is available in online supple- uate decision quality using the Modified Decisional mental appendix 2. Conflict Scale,58 Decision Self-­Efficacy Scale59–61 and Satis- faction with Decision Scale.62 The Decisional Conflict Data analysis Scale is a 16-­item instrument that includes five subscales: We will use descriptive statistics to summarise women’s informed, values, support, uncertainty and effective deci- age, level of education, pregnancy status, number and sion. Response options, which range from 0 for ‘strongly characteristics of previous VTE and experience with agree’ to 4 for ‘strongly disagree’, are combined into a LMWH. We will assess decision quality using the scores summary score that ranges from 0 to 100, with 100 indi- obtained from the Decisional Conflict Scale, Self-­Efficacy cating an extremely high level of decisional conflict. The Scale and Satisfaction with Decision Scale. We will 6 Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 Open access Figure 4 Screenshot of decision analysis recommendation. This screenshot shows how the personalised decision analysis results are presented to participants. In this example, the decision analytic framework calculated that the average quality-­ adjusted life year (QALY) expected for treatment with low molecular weight heparin (LMWH) was −1 compared with expectant management without LMWH. In this case, no LMWH would be the recommended strategy because it has the greatest expected QALYs and represents the treatment option that maximises the woman’s quality of life based on available clinical evidence and the patient’s preferences. examine concordance between the woman’s decision description is a method of inquiry that explores individ- regarding LMWH and recommendation from the person- uals’ perceptions and experiences of a phenomenon.70 alised decision analysis. The aim is to generate rich and straightforward descrip- tions of an experience that is rooted in the language used Step II: integration of quantitative results and planning of by participants.71 qualitative data collection Based on the results of step I, women will be stratified Participants into four categories according to their treatment decision After completion of the multicentre, single-­arm, interven- (LMWH and no LMWH), decision analysis recommen- tion study, all women will be invited to participate in the dation (LMWH and no LMWH) and the concordance/ qualitative descriptive study.72 Women will be stratified discordance between the two. Figure 5 presents the cate- into four categories according to their treatment deci- gorisation matrix. This is a form of integration in mixed sion, decision analysis recommendation and the concor- methods research where one dataset (ie, quantitative) is dance/discordance between the two. analysed and used to inform subsequent data collection (ie, qualitative).69 The purpose is to identify what quanti- Data collection tative results need further explanation. In-­depth, semistructured interviews will be conducted to explore women’s experiences and perceptions as they Step III: qualitative descriptive study relate to: (1) the decision-­making process; (2) the direct Study design choice exercise; (3) the personalised decision analysis; Step III is a qualitative descriptive study that will occur and (4) their knowledge of LMWH. Explicitly linking after the quantitative component is complete. Qualitative to the quantitative results, four versions of the interview Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 7 Open access Figure 5 Categorisation matrix based on quantitative results. LMWH, low molecular weight heparin. guide have been designed to address women’s treatment To ensure reliability, two team members will code the decision, decision analysis recommendation and the transcripts using this codebook. They will document and concordance/discordance between the two. The inter- discuss any inconsistencies. NVivo software76 will facilitate view guide was developed and tested by the research team, this analysis. Qualitative data collection and coding will which comprised clinicians, patients and researchers with be conducted in the language of origin. Canadian and expertise in pregnancy-­ related VTE, shared decision Spanish team members will work collaboratively on devel- making and qualitative methods. It is available in online oping a single codebook for the Spanish and English supplemental appendix 3. A member of the research analyses, discussing emerging themes and discrepancies team will conduct the interviews, which will be audio-­ as part of this process. An investigator with proficiency recorded and transcribed verbatim. in English and Spanish will compare the summaries of findings and write a synthesis of themes that highlights Data analysis important differences if they exist. Data from the interviews will be analysed using a method of thematic analysis. Thematic analysis refers to the Step IV: integration of quantitative and qualitative findings systematic search for and identification of themes that are Once the quantitative and qualitative analyses have been present in data.73 The aim is to obtain an understanding analysed separately, the findings will be integrated using of a phenomenon by identifying themes or patterns a joint display. A joint display is a table in which the quan- through a process of coding.74 Given the exploratory titative and qualitative data are displayed alongside each nature of this qualitative descriptive study, there will be other to enable an explicit comparison between data- no predetermined coding scheme. sets.44 This is a form of integration at the interpretive Two members of the research team with training in level, whereby each data set remains analytically separate qualitative analyses will conduct the thematic analysis from the other.77 The intent is to give a voice to study following the six-­step approach outlined by Braun and participants and ensure that quantitative findings are Clarke75 (ie, familiarising with the data, generating grounded in participants’ experiences. initial codes, searching for themes, reviewing the themes, The joint display will present each theme from the qual- defining and naming themes and producing the final itative analyses according to the mean scores obtained report). This process entails an iterative analysis within from the Decisional Conflict Scale, Self-­Efficacy Scale and and across the transcripts of women from each of the Satisfaction with Decision Scale as well as the women’s four groups outlined in figure 5 to gain insight into why risk of VTE, utility values, treatment decision and deci- there is concordance/discordance between treatment sion analysis recommendation. If there are any outliers or decisions and decision analysis recommendations among differences across groups, the integrated analyses will aim pregnant women with a previous VTE.46 to explain these through qualitative data. For example, Looking for overarching themes and relations between qualitative data can be used to explore any inconsistencies them, the investigators will independently code a sample in utility values that are produced by the standard gamble of the transcripts independently to generate an initial and inputted into the decision analytic framework. codebook and definitions. They will meet with a third There are many reasons an inconsistency could occur. study investigator to discuss emerging themes and defini- Pregnancy can be accompanied by extreme risk aver- tions of codes until consensus on a codebook is reached. sion,78 which could skew the standard gamble utility value 8 Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 Open access Table 1 Checklist of strategies to ensure rigour in the conduct and reporting of the study Research step Criteria Action taken Quantitative Dependability Data collection will be conducted using standardised scripts. component Credibility and Reflexive notes will be taken during data collection to record situational information. confirmability Confirmability Reasons for non-­participation will be noted. Confirmability All statistical analyses will be performed according to a prespecified protocol. Qualitative Dependability Data collection will be conducted used standardised scripts. component Confirmability Reasons for loss to follow-­up will be noted. Credibility and Reflexive notes will be taken during data collection to record situational information. confirmability Dependability A sample of the transcripts (eg, 10%) from each site will be checked against the audio recordings. Credibility More than one person will be involved in the analysis of interview data. Credibility Persons involved in the analysis of interview transcripts will look for disconfirming data while developing themes to ensure that all aspects of the interviews were considered. Dependability An investigator with proficiency in English and Spanish will compare the English and Spanish findings. Mixed Credibility The justification for using a mixed methods approach to answer the research question(s) will methods be described. component Credibility The study design will be described in terms of the purpose, priority and sequence of methods. Credibility The process of integration will be described in terms of where it occurred, how it occurred and who participated in it. Credibility The limitation of one method associated with the presence of the other method will be described. Credibility Insights gained from mixing or integrating methods will be described. Credibility The integration of quantitative and qualitative research methods will occur at multiple points of the mixed methods study (eg, research question, sampling strategy and analysis). Confirmability Each data source will be triangulated to confirm convergence or divergence across datasets and and study sites. dependability Credibility Inconsistencies between quantitative and qualitative findings will be explored. Credibility The inferences derived from the quantitative and qualitative findings will be incorporated into meta-­inferences. Entire study Confirmability An audit trail will be maintained by the research coordinator to document all study decisions (and their rationale) and all sampling, data collection and analysis procedures implemented. Confirmability Any deviations from the published protocol will be noted and justified to promote transparency of the research methods. and subsequent treatment recommendation. A qualita- pregnant women or those considering pregnancy who tive evaluation of a shared decision-­making intervention have experienced a VTE. in atrial fibrillation reported that patients struggled to A joint display is an important tool for establishing grasp the standard gamble concept,79 which could result the relationship between intervention effects and the in utility values that are not representative of patients’ patient experience. If the analysis identifies contradic- preferences. By leveraging qualitative data on women’s tions between quantitative and qualitative findings,80 we experiences and perceptions of the intervention and will verify the methodological rigour of each component decision-­making process, integration will enable the of the study, the comparability of datasets and the delivery research team to obtain meta-­inferences and a deeper of the intervention.81 We will also consider how these understanding of the application of decision analysis contradictions can generate new research questions.82 to shared decision making surrounding LMWH for Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 9 Open access Methodological rigour This study takes an integrated approach to knowledge Although an international study ensures feasibility of translation that applies the principles of knowledge trans- recruitment, it poses a challenge of maintaining consis- lation to the entire research process.85 Eight patients have tency in data collection across sites. We have taken several contributed to the design and prepiloting of data collec- steps in the planning of this study to mitigate this issue. tion materials. We will publish findings in peer-­reviewed At each site, we will have highly qualified personnel who journals and present at key conferences, including meet- have experience working on a shared decision-­making ings of the American Society of Hematology, Interna- project for VTE prophylaxis. Professional translation tional Society on Thrombosis and Haemostasis, Society agencies have translated the scripts and interview guides of Medical Decision Making and Guidelines Interna- developed in English into Spanish. Data collection mate- tional Network. We will also disseminate results within the rials have been tested with three to five patients at each Grading of Recommendations, Assessment, Development site to ensure adequate understanding. To facilitate a stan- and Evaluations (GRADE) community. dardised approach for administering the intervention, we have developed scripts based on feedback from patients, Author affiliations nurses and clinicians. All members of the research team 1 Department of Health Research Methods, Evidence and Impact, McMaster will receive training on how to use required software University, Hamilton, Ontario, Canada 2 programs. Iberoamerican Cochrane Center, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain Once recruitment starts, a range of strategies will be 3 Department of Pediatrics, Obstetrics, Gynaecology and Preventive Medicine, used to verify credibility (internal validity), dependability Universidad Autónoma de Barcelona, Barcelona, Catalunya, Spain (reliability or consistency of findings) and confirmability 4 Department of Medicine, McMaster University, Hamilton, Ontario, Canada (neutrality) of data. A list of these strategies is presented 5 Division of General Internal Medicine and Center for Clinical Effectiveness, in table 1. Given the complexity of mixed methods University of Cincinnati, Cincinnati, Ohio, USA 6 research,83 the rigour of the quantitative, qualitative and Division of Maternal-­Fetal Medicine, Department of Obstetrics & Gynaecology, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada mixed methods components of the study will be assessed 7 Lunenfeld-­Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, separately.84 Canada 8 Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada 9 Departments of Medicine, Laboratory Medicine and Pathobiology, Institute of PATIENT AND PUBLIC INVOLVEMENT Health Policy Management and Evaluation, University of Toronto, Division of Three patients (one from Spain and two from Canada) Hematology, Mount Sinai Hospital, Toronto, Ontario, Canada 10 joined the study team as patient advisors. The Canadian School of Nursing, McMaster University, Hamilton, Ontario, Canada 11 CIBER de Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain Venous Thromboembolism Clinical Trials and Outcomes 12 Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, Research Network allowed our team to use their Partners Ontario, Canada Platform to recruit two Canadian patient-­advisors. As part of this programme, patients receive training on how to Twitter Feng Xie @fengxie_mac participate in the research process. For the Spanish site, a Acknowledgements The authors wish to acknowledge and thank the the patient who is also nurse at one of the participating hospi- patient advisors who contributed to the design of the study and testing of the data tals was asked to participate as an advisor. collection materials. The three patient advisors matched inclusion criteria Contributors The study concept and design were conceived by FX, PA-­C, GG, SB for the study. They reviewed all study materials and met and ME. ME developed the intervention, with support from FX, PA-­C, BH, ML-­G and with the research team to provide feedback on the study SB. BH developed the data collection materials and plan for data analyses, with support from FX, PA-­C, GG, ME, ML-­G, SB, RD, NS and SJ. BH prepared the first design, intervention, scripts and data collection materials. draft of the manuscript. All authors have reviewed the manuscript for critically for The development of the qualitative research questions important intellectual content and offer final approval. and selection of quantitative outcome measures were Funding BH received a doctoral award from the Canadian Institutes for Health directly informed by these patients’ priorities, experience Research (Grant number: GSD – 152327). The study is partially funded by the and preferences. An additional five patients participated Spanish Public Research Institute – Health Institute Carlos III (Grant number: in preliminary testing of the intervention to provide feed- PI17/01874). back on the overall research process and ensure adequate Competing interests ME has received grant funding from the National Institutes of Health (Eunice Kennedy Shriver National Institute of Child Health and Human understanding. Development, and National Center for Advancing Translational Sciences) and Patients will not be directly involved in the recruitment funding for investigator-­initiated research from Bristol-­Myers Squibb. RD has and conduct of the study. The results of the decision anal- received speaking honoraria and grant funding from Ferring Inc and an early career ysis will be communicated with patients participating in grant from the Canadian Institutes for Health Research for projects unrelated to this study. NS has received honoraria and an educational grant from Sanofi. the study. Additional study materials (ie, print outs of ML-­G receives doctoral support from the Spanish Public Research Institute – decision aid and health state descriptions) and published Health Institute Carlos III. SB has acted as a consultant for Leo Pharma Canada results will be made available to participants on request. (manufacturer of low molecular weight heparin) and receives unencumbered salary support through the McMaster University Eli Lilly Canada-­May Cohen Chair in Women’s Health. ETHICS AND DISSEMINATION Patient consent for publication Not required. Site-­specific ethics boards have approved the study. Provenance and peer review Not commissioned; externally peer reviewed. 10 Humphries B, et al. BMJ Open 2021;11:e046021. doi:10.1136/bmjopen-2020-046021 Open access Supplemental material This content has been supplied by the author(s). It has 17 Mühlbacher AC, Juhnke C. Patient preferences versus physicians' not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been judgement: does it make a difference in healthcare decision making? peer-­reviewed. Any opinions or recommendations discussed are solely those Appl Health Econ Health Policy 2013;11:163–80. of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and 18 Akl EA, Oxman AD, Herrin J, et al. Framing of health information messages. Cochrane Database Syst Rev 2011;22:Cd006777. responsibility arising from any reliance placed on the content. Where the content 19 Trevena LJ, Zikmund-­Fisher BJ, Edwards A, et al. Presenting includes any translated material, BMJ does not warrant the accuracy and reliability quantitative information about decision outcomes: a risk of the translations (including but not limited to local regulations, clinical guidelines, communication primer for patient decision aid developers. BMC Med terminology, drug names and drug dosages), and is not responsible for any error Inform Decis Mak 2013;13 Suppl 2:S7. and/or omissions arising from translation and adaptation or otherwise. 20 Fagerlin A, Pignone M, Abhyankar P, et al. Clarifying values: an updated review. BMC Med Inform Decis Mak 2013;13 Suppl 2:S8. Open access This is an open access article distributed in accordance with the 21 Stacey D, Légaré F, Lewis K, et al. Decision AIDS for people facing Creative Commons Attribution Non Commercial (CC BY-­NC 4.0) license, which health treatment or screening decisions. Cochrane Database Syst permits others to distribute, remix, adapt, build upon this work non-­commercially, Rev 2017;19:Cd001431. and license their derivative works on different terms, provided the original work is 22 Elwyn G, O'Connor A, Stacey D, et al. 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