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1 Quality ofLife Theory
5
tors are factors such as mobility and ability to continue normal life despite illness. Functional indexes in medicine started in the 1930s to assess parameters such as the ability to perform ADL. The NewYork Heart Association score was one of the rst functional indices developed in order to evaluate the functional capacity of patients with heart disease [39]. Many specialties have adapted this functional index and formulated similar tools to determine the impact of chronic disease, cancer and surgical procedures on func­tion [40–44]. Psychological indicators are fac­tors such as mood, happiness, anxiety and loneliness [45]. Psychologists use scoring sys­tems to assess the quality of life in patients sur­viving with long term illnesses [37, 46–48]. This shows the multifaceted input required to mea­sure HRQOL which has presented challenges in being able to quantify and measure across patient populations.
HRQOL questionnaires were developed in the 1970s and were adapted for chronic illnesses from a quality of life score rst created by John Flanagan, an American psychologist [49–51]. A number of validated HRQOL questionnaires have since been developed and are commonly used by healthcare professionals to calculate HRQOL [3]. These questionnaires are self­administered and provide an overall numerical score that can be compared in patients with simi­lar health conditions and treatment across a pop­ulation, combining objective and subjective indicators.
HRQOL is generally only measured during points of direct contact between patients and their healthcare providers and therefore refers to a set point in time. This means that healthcare providers do not have a dynamic insight into HRQOL following new diagnosis, treatment or through a period of chronic illness and therefore lack data that represents the spectrum of disease or illness. In addition, patient questionnaires measuring QOL are distributed at varying times along the patient journey which makes it dif-
cult to compare QOL among patient groups even with the same illness. Moreover, individuals have different expectations of illness and well­ness which can be dependent on baseline func­tion and health. It is also important to remember that as QOL is a dynamic concept patient per­ceptions and expectations may change over time [52]. Furthermore, it can be argued that QOL measurement tools are not patient centred as they describe QOL through markers developed by healthcare professionals to provide a qualitative or quantitative marker of QOL.Questionnaires are usually limited in their scope as they also restrict the patient’s ability to answer outside of the pre-determined options [53]. In addition, due to translation and language differences it can be conceived that the essence and nuances of QOL questionnaires may be lost in particular parts of a population. This lack of comparability and transferability of data is often compounded due to cultural variations in health- related behaviour, making certain questions incompatible or irrel­evant due to different models of health beliefs in some populations [54].
Conclusion (Fig.1.3)
Quality of life is a dynamic concept that has evolved through time. Advent of modern technol­ogy and a changing population demographic will lead to changes in the denition of QOL to encompass factors pertinent to the society of the time. HRQOL measurement tools consequently play an important role as indicators of patient preference and opinion regarding their own health and wellbeing. In addition, they are there­fore also useful potential markers of cost effec­tiveness of healthcare intervention. Thus, modern day medicine must consider HRQOL and look at the patient holistically when making healthcare decisions about management of patients to ensure that high quality patient care and outcomes are delivered.
6
There is no universal definition
of QOL, it is subjective and
encompasses many parameters
such as social, physical, mental
well-being, cultural norms and
personal perspectives
HRQOL focuses specifically on
the impact of disease on a wide
range of factors in a patient’s life
E. Khanderia and V. Patel
Traditionally, outcomes such as
morbidity and mortality were
considered to be indicators of
population health. However, the
importance of HRQOL in
patients due to the changing
demographics of society and
availability of treatment is
imperative in making decisions
that are in the best interest of
patients
Measurement of HRQOL
enables healthcare
professionals to determine the
impact of illness and healthcare
interventions on patients through
examination of differing aspects
of their life and not at mortality
alone
Validated HRQOL questionnaires are used by healthcare professionals to
collect information about
HRQOL during the patient
journey and are indicators of
patient opinion and preference
Fig. 1.3 Conclusions
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1966. p.93–108.
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Statistical Methods forPROMS andQoL
BhaminiVadhwana andMunirTarazi
2
Introduction
Statistical analyses for patient reported outcome measures (PROMs), predominantly health related quality of life (QoL) assessments, are varied and can impact the overall conclusions. Psychometric evaluations from PROMs tools can be open to statistical interpretation. A fundamental under­standing of the PROMs instrument scales, scor­ing methods, and handling of multiple longitudinal data are crucial in generating valu­able results to guide patient-centred care. PROMs are used to assess symptoms, functional domains, general health perceptions, and QoL [1]. Statistical analysis from PROMs are used in research to provide insights into the impacts of disease and its treatment, and clinically are used to enhance patient-centred care and incorporate the patient’s perspective in health system perfor­mance evaluation [1, 2].
B. Vadhwana (*) · M. Tarazi Department of Surgery and Cancer, Imperial College London, London, UK e-mail: b.vadhwana@imperial.ac.uk;
m.tarazi@imperial.ac.uk
Models forLongitudinal Data Analysis
Repeated Measures Model
Repeated measures describe multiple assess­ments following a clinical treatment over discrete time points, where each time point is dened as a categorical variable. Longitudinal studies of this nature typically follow a model of 2–4 assess­ments, for example, pre-treatment, 6 weeks and 12 weeks post-treatment, with a xed follow-up duration. This is normally dictated by timing of patient visits and hospital protocols. Risk of biases must be considered and eliminated. For example, health measures immediately following surgery can offer a false representation due to the pain and anxiety associated with surgery itself. On the other hand, if the assessment windows are too wide, there is a risk of introducing irrelevant variables which can inuence the statistical power of the study. Analysis of the repeated mea­sures model considers missing data and varying time periods between assessments. A variance­covariance model around the repeated measures as a time category is employed as a linear mixed model. Multiple imputation analysis using the Markov Chain Monte Carlo (MCMC) technique can be used to account for missing data.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Athanasiou et al. (eds.), Patient Reported Outcomes and Quality of Life in Surgery,
https://doi.org/10.1007/978-3-031-27597-5_2
9
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B. Vadhwana and M. Tarazi
Growth Curve Models
A growth curve model uses quality of life mea­sures as a function of time, where there is varia­tion in timings of assessments between patients, or a large quantity of assessments. There is modelling of health related changes over time as a continuous variable. A mixed-effects model analytical approach is used.
Statistical Analytical Methods
Basic Scoring Systems
Many PROMs instruments are based on basic scoring systems where an ordered Likert scale can be used to compute a score for a particular outcome. For example, a positively phrased state­ment such as “I enjoy my hobbies” will be scored more highly. It is important to consider nega­tively phrased items such as “I am in pain.” A higher score in this case reects a more negative outcome. In these scenarios, reverse scoring should be performed where the reported value is subtracted from the maximum score in that state­ment (for example a 4-point Likert scale means the maximum score is 4).
Basic Statistical Analyses
PROMs comprise predominantly Likert or visual analogue scales. Both methods produce scores where higher scores correlate with a better health related quality of life and lower scores with a negative perception. Statistical analysis can be performed for individual dimensions and general quality of life ratings. It is important to deter­mine whether the data is parametric or non-para­metric to inform which statistical test to use. Parametric data refers to a “normal distribution” of data, where the spread of data is similar on either side of the mid-point, resembling symme­try. In these cases, parametric tests include t-tests, Chi squared tests and analysis of variance (ANOVA) for repeat measures from the same population. Where data does not follow a “nor-
mal distribution” and is skewed towards one end of the bell curve, non-parametric tests must be selected, such as Mann Whitney U test, Wilcoxon signed rank test, Kruskal Wallis test, and Friedmans test for repeated measures. Data nor­mality can be assessed using the Kolmogorov­Smirnov or Shapiro Wilk tests. For comparable paired datasets which follow a normal distribu­tion, for example pre- and post-treatment, paired t-tests can be utilised. When data is non-para­metric, for example when comparing different treatment groups, Mann Whitney U test can be used. Repeated measures in a longitudinal model can use regression analysis (linear or multiple), and studies comparing different groups can use ANOVA tests [3].
Mixed-Eects Models
A general linear model (GLM) approach is adapt­able for both models where time can be categori­cal or continuous. A mixed model for repeated measures presents a measured health outcome and known covariates over a xed time period. Growth model curves also use this analytical strategy as varying outcomes are allowed over time.
A mixed-effects model combines xed and random effects. The xed component represents the mean trajectory and the expected response of the outcome measure over time (Fig.2.1). The random component reects the variance of patients’ responses, around the average response.
Fig. 2.1 Mixed effects model. Black line = average response of all participants. Coloured lines=individual participant responses, with circles representing actual scores
Polynomial Piecewise linear
2 Statistical Methods forPROMS andQoL
11
The variability is twofold between patients; the initial value (for example, pre-treatment) and the degree of change (for example, perceived improvement in mobility). Finally, the residual effects encompass variability within individuals, and address potential outliers.
For growth curve models, two modelling plat­forms can be used; (1) polynomial models and (2) piecewise linear models. Polynomial models present an approximate curve tting the observed multiple assessed time points. The more time points there are, the higher likelihood that the trajectory will deviate from a linear path. However, the coefcients will vary at different points of the curve. Piecewise linear regression models use linear patterns over short time peri­ods. Deviation from a truly linear path represents changes in quality of life measures at a dened time point following clinical intervention (Fig.2.2).
Generalised Estimating Equations
Generalised estimating equations (GEE) were developed by Liang and Zeger to analyse longitu­dinal data (repeated measures) from large cohorts [4]. Missing data must be at random to avoid sta­tistical bias. It is considered an extension of GLM, and models the average response of the population rather than the within-subject varia­tion. The model demonstrates how much the average population response would change with
one-unit increase of the co-variance. For exam­ple, with an additional 1 month post treatment, GEE could estimate the average change in health outcome measures [5].
Minimally Important Dierence
The minimally important difference (MID) describes the lowest threshold at which a differ­ence in an outcome measure is perceived to be important to the patient, even without reaching statistical signicance [6]. This can be a measure of improvement or harm. Although there is no accepted consensus, an effect size of 0.2–0.5 is considered sufcient [7]. This may vary between instruments.
Ceiling-Floor Eects
Multi-attribute based PROMs are successfully used in clinical research trials for assessment of quality of life following clinical interventions. However, the data can be skewed by the ceiling and oor effects. The ceiling effect describes a situation where the majority of patients score on the highest of the scale, thereby losing discrimi­nation of the quality measured [8]. Similarly, the oor effect describes the majority of values on the lower end of the scale. These domains maybe not accurately reect the real-life situation in specic diseases.
Fig. 2.2 Growth curve models; Polynomial and Piecewise linear models. Blue line=control group. Green line=inter­ventional group
12
B. Vadhwana and M. Tarazi
Missing Data andImputation Methods
Missing data presents difculties in statistical analyses and raises questions about the value of the outcomes. The two main types of missing data include: (1) patients omitting certain ques­tions, and (2) a large quantity of missing data from multiple variables overall. Missing data can be classied into one of three categories. Firstly, data missing completely at random (MCAR) does not correlate to the observed data [9–11]. For example, it may be an administrative error. Secondly, data missing at random (MAR) have a systematic relationship between observed data and the nature of the missing outcomes. Finally, data missing not at random (MNAR) has a strong association between the missing values and the cohort. The cause of the missing values is depen­dent on the patient and their environment. It is crucial to identify the latter to inform analytical strategies [11]. Missing data greater than 10% should stimulate thoughts about the nature of the missing data. Missing data can lead to a selection bias with onward analysis, and therefore imputa­tion techniques must be used with caution. For example, missed questionnaires due to treatment toxicity or post-operative complications can bias the health related quality of life towards more positive representation.
Single and multiple imputation methods exist for different levels of missing data. Several single imputation methods can be performed for a sin­gle missing value. Most commonly, the mean of the observed data, the last value carried forward, or the minimum value carried forward are calcu­lated to replace a missing value. With a small number of missing items, the half rule can be uti­lised which allows the mean of the overall data as a substitute, with the caveat that the patient has completed at least 50% of the questionnaire. A misconception with this method is that the miss­ing value is considered as if it were a true value. The variance of the data is reduced which increases the likelihood of type 1 errors.
Multiple imputation methods integrates a level of uncertainty into the statistical calculation and therefore addresses the underestimation of
single imputation analyses. It is of maximum benet when there is strong additional clinical data to correlate with PROMs outcomes. At least 3–20 sets of data values are analysed and the results combined using Rubins rules to achieve precision estimates [9].
Quality Adjusted Life Years (QALYs)
Quality Adjusted Life Years
Health related quality of life (HRQoL) outcomes provides a platform for: (1) assessment of patient- centred quality of life, and (2) as a metric of time to inform quality adjusted survival (QAS) and quality adjusted life years (QALYs). Both components can be combined to assess the quality adjusted time without symptoms and tox­icity (Q-TWiST). The advantage of the Q-TWiST is the integrated evaluation of risk (measured as QoL; quality) and benet (measured as survival; quantity). These can be translated into an eco­nomic scale to analyse cost utility of clinical interventions. Scoring systems such as the time­trade- off (TTO) or standard gamble (SG) convert patient multi-attribute measures to utility values [12, 13]. Common examples of well-established questionnaires with multi-attribute measures includes the EuroQoL EQ-5D-5L and SF-36 forms [14, 15]. Here, patients put a direct value on their own health state. TTO demonstrates how much time a patient would sacrice to be in perfect health for a given time period [16]. SG utility is based on the patient valuing two treat­ment options at an equal level [17]. This is based on formulas derived from health related quality of life scores from the general population, based on geographical location. An area under the curve is generated (utility value versus time) to estimate the average expected course of each patient treatment group. Alternatively, imputing QALYs from individual patient-specic values of health status can be calculated for use in uni­variate analysis. In general, a QALYs gained are an adjustment in the utility value (quality of life compared to the general population) as a direct result of clinical treatment, multiplied by the
QA
ey=
()
×
()
Qm
th
=
2 Statistical Methods forPROMS andQoL
Fig. 2.3 Graph demonstrating quality­adjusted life years with a targeted clinical intervention
13
length of the treatment effect. This provides a tangible value to understand the improvement or regression a clinical intervention has to a patient’s life (Fig.2.3).
LY utility value Qyears of lif
aximum of perfect heal
:1
Two methods for QALY assessment can be employed: the recall period and the trapezoidal approximation. Ideally, completed quality of life measures in a timely fashion until death would yield the most accurate data. On the other hand, data can be collected on recall of mea­sures in the past week/month and calculates an average utility. Both methods work towards the nal time point of death. The major challenges of these methodologies are missing data and limited follow- up. Clinical trials assessing qual­ity of life have a limited follow-up period post­operatively, with the majority of patients not receiving follow- up until death. Therefore, QALYs must be adjusted to reect a relatively short follow-up time. The minimum follow-up time encompasses all possible post-surgery sce­narios, but for fair approximation, the median follow-up period is recommended. Repeated measures over time, for example consecutive quality of life measures to assess the longitudi­nal benets in the post- operative period, can be valuable to identify time points that benets from surgery can be seen.
Cost-Utility Analysis
In recent decades, cost effectiveness and cost utility analyses have steered allocation of health resources. With emerging novel treatment strate­gies, economic evaluations have become more important to determine the relationship between the cost and patient benets. More specically, a cost-utility analysis (CUA) compares the costs to outcome measures in the form of QALY which produces a value between 0 and 1 (1=perfect health, 0=death) [18]. From a clinical perspec­tive, CUA provides a broad platform to under­stand where funding is directed and how valuable a particular intervention is for the said popula­tion. For example, we know breast cancer screen­ing is cost-effective for early detection and treatment, however, endoscopic screening for gastric cancer is expensive, invasive, and not without risks. A fundamental understanding of this can stimulate novel research in diagnostics. It is important to note that CUA should not deter from clinical needs. For example, it is more cost effective to treat appendicitis with antibiotics than perform an appendicectomy, however clini­cal judgement and practices surpass this [19]. In a similar fashion, CUA recommends that EVAR should not be selected over open AAA surgery, however, this has not translated to clinical prac­tice [20].
CUA is selected when the outcome measure
is QALYs. It is useful when comparing differ-
14
In
−
()
()
Fig. 2.4 Cost effectiveness acceptability curve demonstrating the willingness to pay for a more cost-effective treatment strategy; Treatment A>Treatment B
B. Vadhwana and M. Tarazi
ent treatment strategies, for example surgery versus curative chemotherapy in cancer. A Markov model decision tree incorporates all possible health outcomes for both treatment groups including costings. An incremental cost effectiveness ratio (ICER) value is calculated to inform the treatment leading to the most desir­able health outcome. There are differences
cremental cost effectiveness ratio
=
QALY of surgery QALY of chemotherapy
Non-traditional Quality ofLife Assessment Methods
More recent innovative methods of obtaining real-time health related quality of life data via social media monitoring is being trialled [22]. A wealth of information relating to symptomatol­ogy, treatments, effects on daily activities and lifestyle are shared within online communities. Renner etal. generated a social media listening algorithm to assimilate relevant data based on specic domains already established in quality of life instruments: physical, psychological, activi­ties, social and nancial. General impact on qual­ity of life was identied at a sensitivity of 0.83 and specicity of 0.74. It is important to consider the wider resources available in the development
globally in how healthcare systems justify the costs in relation to QALY.The UK threshold is approximately £20,000–30,000 per QALY, the USA US$50,000–100,000 per QALY and AU$35,000–50,000 per QALY [21]. Considering these thresholds, an ICER less than or equal to the relevant threshold is cost­effective (Fig.2.4).
cost of surgery cost
oof chemotherapy
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of PROMs and consider the ease of access for patients to contribute data. However, this may be limiting to the technology averse population.
Limitations
Quality of life research is wholly dependent on the target patient population and there are many limitations which must be considered. Many PROMs instruments are questionnaire based which are largely subjective. Differing opinions can be due to geographical location, confounding patient factors and other lifestyle inuences. Patient populations across the world with the same disease process may prioritise quality of life indicators differently, largely inuenced by socioeconomic status and education. Patient
2 Statistical Methods forPROMS andQoL
15
related confounding include acute life events, concurrent illnesses and life stressors which may not be identied by the questionnaires. PROMs may also be biased towards patients who under­stand the need for research and are willing to par­ticipate. Other biases include cultural and language barriers. Many PROMs instruments consist of at least 20–30 items, and in some cases, multiple questionnaires. Respondent fatigue is a known phenomenon where participants become tired completing the questionnaires, and the qual­ity of the responses deteriorate. Therefore, design of new PROMs tools, and study design should address this. Studies using multiple question­naires can randomise the order of questionnaires given to patients to reduce bias from respondent fatigue. In addition, recent decades have seen evolutions in the relationship between cost analy­ses and QALYs in health economic modelling to guide health resources and understand patient impact. However, there is a deciency in tools to marry up surgical outcomes with PROMs. There is a need to develop robust methods to quantify surgery specic health outcomes with patient perceived health outcomes.
• Longitudinal data analyses, repeated mea­sures model or growth curve model, assesses health related quality of life changes over time in response to a treatment.
• Health related quality of life outcomes can be used as a metric of time to inform quality adjusted survival (QAS) and quality adjusted life years (QALYs).
• Quality of life years gained are an adjustment in utility value, demonstrating the quality of life compared to the general population, as a result of clinical treatment multiplied by the length of its effect.
• Cost effectiveness and cost utility analyses steer allocation of health resources and con­siders how valuable a particular intervention is for a target population.
• An incremental cost-effective ratio (ICER) is calculated to inform the treatment leading to the most desirable health outcome.
• Quality of life research is wholly dependent on patients voluntary participation, and can be biased by geographical location, confounding patient factors and lifestyle inuences.
Conclusion
PROMs represent a multi-dimensional evaluation of health related quality of life including symp­tomatology, treatments, functional status and socioeconomics. All domains assessed will have diverse trajectories over time, and therefore, the primary outcome measures including timepoints must be clearly dened. Conclusions generated from PROMs instruments can facilitate clinical decisions and ultimately improving patient care. This multi-faceted approach to patient care includes treatment regimens, adverse effects, and patient experiences.
Summary Points
• Statistical analyses from PROMs used in research provides valuable insights into the impacts of disease and treatment and incorpo­rate the patient’s perspective in health system performance evaluation.
References
1. McKenna SP, Heaney A, Wilburn J, Stenner AJ. Measurement of patient-reported outcomes. 1: the search for the Holy Grail. J Med Econ. 2019;22:516–22.
2. Al Sayah F, Jin X, Johnson JA. Selection of patient- reported outcome measures (PROMs) for use in health systems. J Patient Rep Outcomes. 2021;5:99.
3. Hamel JF, Saulnier P, Pe M, Zikos E, Musoro J, Coens C, Bottomley A.A systematic review of the quality of statistical methods employed for analysing quality of life data in cancer randomised controlled trials. Eur J Cancer. 2017;83:166–76.
4. Liang KY, Zeger SL. Longitudinal data analy­sis using generalized linear models. Biometrika. 1986;73:13–22.
5. Ballinger GA.Using generalized estimating equations for longitudinal data analysis. Organ Res Methods. 2004;7:127–50.
6. Johnston BC, Ebrahim S, Carrasco-Labra A, Furukawa TA, Patrick DL, Crawford MW, Hemmelgarn BR, Schunemann HJ, Guyatt GH, Nesrallah G.Minimally important difference estimates and methods: a proto­col. BMJ Open. 2015;5:e007953.
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