Table Of Content
This means that the longitudinal aspects of the study, time and change, are often poorly captured. In particular the reporting of cross-sectional data alone can lead to descriptions of each time point rather than focusing on the changes between time points. Studies may have the explicit aim to focus on one or other aspect of analysis and this will achieve different analysis and reporting. The addition of a theoretical framework can help to guide researchers during analysis to move beyond description. It should be noted that these conclusions apply to choosing amongst balanced ALDs and rely on the assumptions of a linear trend and no cohort effects.
Allows researchers to look at changes over time
Specific to this type of study are the number of cohorts and the extent of overlap between cohorts, whereas common to any longitudinal study, the frequency and timing of measurements also needs to be set. Varying these parameters may produce a large collection of candidate designs, so the question of how to choose the best design arises. In addition, the study may be constrained to a maximum duration, number of participants or number of measurements, and the relative costs of implementing different ALDs will play an important role in choosing between them. Longitudinal qualitative research (LQR) has been an emerging methodology over the last decade with methodological discussion and debate taking place within social research [1]. Longitudinal qualitative research is distinguished from other qualitative approaches by the way in which time is designed into the research process, making change a key focus for analysis [1].
Longitudinal Designs
Issues that seem very important at one time point may change with the perspective of time and processes may change the way experiences are viewed. One off qualitative interviews rely on recall, for example, asking about symptom experience at diagnosis when a patient is several months away from that point. There will always be some element of retrospective discussion in an LQR interview but with a focus on change over time, this can be aided by summarizing or reflecting on the previous interview. As data is collected prospectively, causation, the temporality of cause and effect, and the processes or conditions by which this happens can also be explored in the data [4].
Analyzing data
There have been successes in the strategies we have used and areas in retrospect that we could have worked differently. These factors, together with researcher continuity, were important in helping to maintain good recruitment rates for participants with better health and survival rates throughout the study. Patients were identified by the clinical team at the research site and then approached by a member of the research team to give information about the study. In qualitative studies sufficient participants are required at the last time point to ensure data saturation particularly if any new themes become evident at this point. We also wished to interview carers and this created a significant number of interviews at follow-up.
Recruitment and retention of participants
In contrast to longitudinal quantitative methodologies LQR focuses on individual narratives and trajectories and can capture critical moments and processes involved in change. Saldana [3] identifies the principles that underpin LQR as duration, time and change and emphasizes that time and change are contextual and may transform during the course of a study. Key considerations in undertaking longitudinal qualitative projects in health research, include the use of theory, utilizing multiple methods of analysis and giving consideration to the practical and ethical issues at an early stage. Longitudinal studies thus make observing changes more accurate and are applied in various other fields.
What is a Longitudinal Study?
Multilevel models are useful for hierarchically structured longitudinal data, with lower-level observations (e.g., repeated measures) nested within higher-level units (e.g., individuals). Cohort effects can bias results if not accounted for, especially in accelerated longitudinal designs which assume cohort equivalence. There is little published work about rigour in LQR, and it would be worth investigating whether additional elements should be added to accepted conceptualizations of the quality of qualitative research so judgments can be made about the rigour of research. Research to explore participants’ perspectives of being in a longitudinal study would be valuable as there may be additional burden to the participant, emotional and practical, of being involved in LQR. Eliciting participants’ insights into their experiences of participation may give us greater insight into the method itself.
Longitudinal study designs
Techniques like maximum likelihood estimation and multiple imputation are better alternatives to older methods like listwise deletion. Assumptions about missing data mechanisms (e.g., missing at random) shape the analytic approaches taken. Once you determine the type of longitudinal study you will conduct, you then must determine how, when, where, and on whom the data will be collected. In a retrospective study, you are collecting data on events that have already occurred.
Developing longitudinal qualitative designs: lessons learned and recommendations for health services research
It is also wise to involve patients or service users in the design of the research and ongoing management to get the participants’ perspective of burden and balance research interest with participants’ well being. There was a significant risk in our research that patients would become too unwell to participate or die between interviews. We sought consent from participants to access medical records and were able to check the health status of participants prior to contacting the participants to make arrangements for the next interview to ensure this was done sensitively.
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We eventually made the decision not to interview some carers at follow-up as data was saturated. This created some difficulty with carer participants who valued this ongoing opportunity to ventilate feelings. The oversampling at the beginning (in order to have an adequate number of subjects at the last interview) was not a successful technique and overstretched the researchers and the data collection process unnecessarily. Longitudinal study designs are implemented when one or more responses are measured repeatedly on the same individual or experimental unit. These designs often seek to characterize time trajectories for cohorts and individuals within cohorts. Longitudinal designs may be either randomized where individuals are randomly assigned into different groups or observational where individuals from different well-defined groups are observed over time.
As longitudinal qualitative methods are becoming increasingly used in health services research, the methodological and practical challenges particular to health care settings need more robust approaches and conceptual improvement. Total numbers of subjects (left) and measurements (right) to achieve 90% power, for the designs in Table 2 and the corresponding 6-month interval designs, plotted against duration. The x-axis is number of cohorts for the left plots and overlap for the right plots, and the y-axis is total number of subjects for the top plots and total number of measurements for the bottom plots.
Because of this, these studies often have only a small group of subjects, which makes it difficult to apply the results to a larger population. In the 18th century, Count Philibert Gueneau de Montbeillard conducted the first recorded longitudinal study when he measured his son every six months and published the information in "Histoire Naturelle." Longitudinal studies, a type of correlational research, are usually observational, in contrast with cross-sectional research. Longitudinal research involves collecting data over an extended time, whereas cross-sectional research involves collecting data at a single point. The Framingham Heart study has given us invaluable data pertaining to the incidence of cardiovascular disease and further confirming a number of risk factors. The success of this study was further potentiated by the absence of treatments or modifiers, such as statin therapy and anti-hypertensives.
It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability. Moerbeek2 considered the effect of number of cohorts, extent of overlap and frequency of measurement on power to detect a linear trend, for some specific ALDs. Tekle et al.3 considered D-optimal designs for polynomial trends in age, for the case of non-overlapping cohorts. Fitzmaurice et al.4 discussed cross-sectional and longitudinal effects and proposed a model for detecting differences between these effects, an approach that treats cohort effects as fixed.
In this chapter, I briefly discuss the nature of each of the three designs above and more deeply explore visualization and some analysis techniques for repeated measures design studies via examples of the analyses of two datasets. I conclude with discussion of recent topics of interest in the modeling of longitudinal data including models for intensive longitudinal data, latent class models, and joint modeling of survival and repeated measures data. We have also presented some results for comparing designs when cohort effects are present. These results suggest that when the aim is either to detect cohort effects or to achieve a desired level of precision for estimating the entire vector of fixed effects estimates, there may be an advantage in increasing the number of measurements per subject.
