Category: 3. Business

In this first-ever multi-site user-experience study for POC diagnosis of vaginitis, the MVP test demonstrated consistent performance across user types (P > 0.082) and users’ educational status (P > 0.050) based on PPA and NPA estimates for all 4 targets in the MVP test. User feedback showed that 15/19 (79%) of users found the Xpress System easy to set up, 18/19 (96%) found the MVP test instructions easy to follow, and all 19/19 (100%) of users responded that they “agree/strongly agree” that it was easy to perform the MVP test.

Highly sensitive molecular diagnostics historically have had a long turnaround time (hours which results in a practical time for the patient of days), required expensive equipment, and required skilled operators. POC NAAT-based diagnostic tests, particularly those with CLIA-waived designation, have the potential to lessen the time from collection to patient management, to improve accuracy of same-day treatment, and to minimize long term follow up healthcare costs. The Xpert^® Xpress MVP test is the first CLIA-waived, FDA-cleared POC NAAT test designed to diagnose vaginal infections in symptomatic women within an hour with minimal hands-on time. Understanding the ease-of-use of these tests by operators with varying skill sets is important for effective implementation of testing strategies.

Our study was limited by the artificial nature of the testing since the performance of the assay was under evaluation and multiple comparators tests were being performed. In most cases, the tests were not performed in a manner that reflects the routine clinical workflow, which might involve a sample-first collection and testing process [5]. Fuller and colleagues found that by having patients collect samples for chlamydia/gonorrhea testing prior to engagement with a healthcare provider and beginning the test based on intake triage questions, the time to results was more offset in part by routine clinical wait times. In the clinic, they described the additional time for patients that resulted from a 90-minute test to be, on average, 46 min. Similarly, Gettinger, et al. found that a 30-minute chlamydia/gonorrhea test would add, on average, only 11 min to a visit in a very efficient clinic setting [6]. However, to utilize sample-first testing, means that the staff performing the testing may be different from the type of staff in this analysis and they may not be as comfortable with the process as the staff engaged in this research project. Additional research is needed into the implementation strategies that will best support utilization of POC tests such as the one used in this study.

Evaluation of a CLIA-waived test also impacted how new users implemented the MVP test. The absence of written competency checklist or verification of adherence to the IFU occurred prior to testing in the study reflects the practice for generalizable use and as such, our study findings may apply more broadly to the use of other POC tests as well. In contrast, users were only allowed to use the IFU and QRI for operation instructions whereas, in a real-world setting, they would have been able to contact the manufacturer for assistance.

Additionally, due to secondary nature of the analyses, the number of specimens in some of the user type categories and the educational level categories were small (large 95% confidence intervals) and hence the analyses were exploratory in nature. Thus, real-world implementation studies will be needed to assess ease of use for a variety of potential testers. This type of evaluation has been performed for chlamydia/gonorrhea testing, demonstrating the variability of performance based on the testing sites which highlights the importance of oversight and quality management to indicate when retraining or training updates may be necessary [3, 7]. Finally, it is important to note the we did not assess the cost implications of adoption because of the nature of this secondary analysis. This important factor in adoption and sustainability must be evaluated in future research efforts.

Specialty

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Study design

This multicenter cluster RCT with a special case of a stepped-wedge design with two arms and one step used a PAR approach in Dutch nursing homes and is part of the RID study. The full study protocol has been published elsewhere [45]. This report follows the CONSORT guidelines [46].

The stepped-wedge design [47] had an overall duration of 16 months and comprised two 8-month phases, with measurements taken at baseline, 8 months, and 16 months. Phase one started with 16 nursing homes randomized to either the RID intervention group or the control group (usual care). Phase two started after 8 months with the nursing homes in the control group crossing over to the RID intervention group and the other eight nursing homes continuing with the RID intervention (Fig. 1). An independent statistician performed computer-generated blinded randomization in fixed blocks: round 1 (6 homes; blocks, 2-2-2) and round 2 (10 homes; blocks, 4-2-4) [45].

Setting and participants

In the Netherlands, nursing homes provide dementia care in special care units (DSCUs). An elderly care physician typically has responsibility for any medical treatment, working in close collaboration with a psychologist, nurse practitioner, and nursing staff with varying levels of education and responsibilities. Homes may also employ physical, occupational, and activity therapists to improve wellbeing, functioning, and quality of life [48, 49]. In 2015, The Dutch government implemented a major reform aiming for elderly persons to stay as long as possible in their own homes. Residential care homes, taking care of elderly persons with moderate levels of impairment, were shutting down. Consequently, the threshold for admission to a nursing home increased. Only persons with complex health care problems in need of 24-hour surveillance and multidisciplinary care are eligible for admission. As a result, residents often have a quite short length of stay and relatively high mortality rates. In this respect, Dutch nursing homes may be different as compared to nursing homes in other countries [48].

We recruited nursing homes online after attending a national kick-off conference with presentations and an information market. An intake telephone call was then scheduled to assess the suitability of each home for inclusion, with 16 homes included by their order of application. DSCUs delivering care for residents with Korsakoff syndrome, acquired brain injury and Down’s syndrome were excluded. Units delivering care for young-onset dementia were also excluded. No age restrictions were imposed within the DSCUs providing care for residents with dementia at an older age. Each nursing home participated with a few large-scale units or multiple small-scale units. Nursing home residents were eligible for participation if they had a diagnosis of dementia and a life expectancy of at least 3 months, as judged by a physician. All eligible residents were approached for participation, including newly admitted residents, after the study began. More information can be found in the study protocol [45].

RID intervention

A detailed description of the RID intervention can be found elsewhere [50]. The RID intervention involved forming a multidisciplinary project team with an internal project leader, a physician, a psychologist, and a nursing staff representative, together with a certified external coach to guide the cyclical process across four phases. Each intervention started with researchers executing a problem analysis on the management of neuropsychiatric symptoms and the appropriateness and percentage of current psychotropic drug use in their home (observation phase). The team then evaluated this tailored information and formulated specific goals under the guidance of the external coach (reflection phase), before operationalizing the goals into an action and implementation plan (planning phase). Finally, each nursing home implemented a set of interventions (action phase).

In some cases, there were differences between participating DSCUs within a nursing home, regarding the problem analysis or the potential solutions. Implementation was allowed to be tailored to a given DSCU, although in practice, most nursing homes developed and executed one action and implementation plan for all the participating DSCUs within their nursing home. The actions implemented by each nursing home varied based on their tailored problem analysis, but they generally targeted multidisciplinary and methodical working (including person-centered interventions), education and training, and adaptations to the living environment [50]. For the nursing homes that started in the RID intervention group in phase one, the measurement at 8 months was treated as an interim analysis that triggered the repetition of all four phases of the PAR cycle during the second phase of the trial (Fig. 1). Nursing homes that started in the control group in phase one provided care as usual for the first 8 months and entered an intervention cycle in phase two.

Sample size

The sample size was based on the primary outcome (inappropriateness of psychotropic drug use). To detect a reduction of 5 points (standard deviation 15) on the Appropriateness of Psychotropic Drug Use in Dementia (APID) index with a power of 0.80, a two-sided α value of 0.05, and an average of 25 residents per nursing home, we estimated the need for 16 clusters (nursing homes). Not taking clustering into account, we needed to include 284 residents who used psychotropic drugs. However, allowing for the multilevel design with two measurements after baseline, an intraclass correlation coefficient of 0.1, a calculated design factor of 1.28, and a 10% cluster dropout, this increased to 364 residents. Given that an estimated 60% of residents with dementia are prescribed psychotropic drugs [17], we needed to include 607 residents (i.e., psychotropic drug users and non-users). We attempted to mitigate the expected 40% loss to follow-up by enrolling newly admitted residents throughout the study [45].

Outcomes and data collection

Data on age, sex, dementia diagnosis, length of stay in the current DSCU, and number of psychotropic drugs were collected from each participant’s medical record. Both outcomes (inappropriateness– and percentage of psychotropic drug use) were also extracted from the medical records of residents. A team of (junior) researchers with educational backgrounds in medicine, psychology and health sciences collected data. The research team together pilot tested scoring of inappropriate psychotropic drug use by means of the APID index. Psychotropic drug usage included prescriptions of antipsychotics, anxiolytics, hypnotics, antidepressants, anticonvulsants and anti-dementia drugs. Anticonvulsants and antidementia drugs are listed as psychotropics drugs because they could have been prescribed to treat agitation in dementia and psychosis in Lewy Body dementia, respectively. Psychotropic drugs were grouped according to the Anatomical Therapeutic Chemical classification [51]. We excluded psychotropic drugs used pro re nata. If residents died or relocated more than 2 months after the measurements at baseline or 8 months, we collected any recorded data on psychotropic drug use at the next measurement.

The primary outcome was the inappropriateness of psychotropic drug use, as measured with the APID index. The APID index was developed by an expert panel based on the items of the Medication Appropriateness Index. The index has been evaluated among DSCU residents in the Netherlands [52, 53]. The APID rates the appropriateness of psychotropic drug use for residents with neuropsychiatric symptoms and dementia. Therefore, psychotropic drugs given for dementia, sleeping disorders, or delirium are included in the scoring, but those given for other psychiatric disorders are excluded. The APID instrument contains seven domains: indication, evaluation, dosage, drug-drug interaction, drug-disease interaction, duplication, and therapy duration. Using data from medical records, each domain is scored 0, 1, or 2 to reflect “appropriate,” “marginally appropriate,” and “inappropriate” usage, respectively. During the development, an expert panel weighted the relative importance of each single domain on a scale from one to ten, resulting in different ranges per domain: indication (range 0-18.8), evaluation (range 0-19.2), dosage (range 0-13.4), drug-drug interactions (range 0-11.6), drug-disease interactions (range 0-13.2), duplication (range 0-14.4), and therapy duration (range 0-12.2). These single domains can be incorporated into a weighted sum score using mean weights. The APID sum score ranges from 0 (fully appropriate) to 102.8 (fully inappropriate) per rated psychotropic drug. Hence, lower scores indicate more appropriate psychotropic drug use [52]. The APID index applies different rules regarding the indication and evaluation domains for prescriptions that are started prior to nursing home admission and for prescriptions started at the DSCU of the nursing home. For example, for psychotropic drugs that are started at the current DSCU the normal rules apply: a (correct) indication needs to be found within two months after starting the psychotropic drug. To assess the indication of a psychotropic drug that is started before admission to the DSCU, a 6-month period is allowed. Moreover, the indication is still considered appropriate even if an indication is lacking or incorrect if the 6-month period has not yet expired. The rationale behind this, according to the expert panel that developed the APID index, was that the physician should be given enough time to set an indication and to evaluate the usage of psychotropic drugs that were prescribed prior to nursing home admission.

The secondary outcome was the percentage of psychotropic drug use, evaluated as a binary variable (i.e., yes/no).

Data about neuropsychiatric symptoms were collected using the Neuropsychiatric Inventory-Nursing Home version (NPI-NH) [54]. A member of the nursing staff filled in paper versions of the questionnaire in the presence of a researcher. The NPI-NH assesses the frequency (score, 1–4), severity (score, 1–3), and caregiver distress (score, 0–5) for 12 psychiatric and behavioral symptoms. Item scores are generated by multiplying the frequency and severity [1,2,3,4,5,6,7,8,9,10,11,12], with possible scores ranging from 0 to 144, where a higher score indicates more frequent and severe neuropsychiatric symptoms [55].

Statistical analysis

IBM SPSS, version 25 (IBM Corp., Armonk, NY, USA), was used to prepare the datasets and perform the descriptive statistics. Stata software, version 17.0, was used for all other analyses. Descriptive statistics were used to summarize the characteristics of residents at baseline by treatment arm, with data included for newly recruited residents at 8- and 16-months’ follow-up.

For the primary outcome, data was used from the residents using psychotropic drugs, with single psychotropic drug prescriptions as the level of observation. We compared the inappropriateness of psychotropic drug use between the intervention and control groups using multilevel models to accommodate the hierarchical data structure. These models were used to adjust for the clustering of residents within nursing homes (random intercept at the nursing home level) and for the correlation of the repeated measures and multiple prescriptions within residents (random intercept at the resident level). The dependent variable was set as the change in APID index score between two consecutive measurements. The analysis was adjusted for the number of psychotropic drugs per resident, sex, baseline NPI-NH total score, length of stay in the DSCU at baseline (in months), and time in the study arm. Residents were evaluated in four groups: full duration, later enrollment, early drop out, and later enrollment with early drop out. Time and the interaction of time with treatment were included as fixed effects. The model compared changes in the APID index sum score between baseline and either 8- or 16 months. Multilevel models were fitted with the restricted maximum likelihood method, and effect estimates are presented with 95% confidence intervals (CIs) and p values. Newly admitted residents were included at 8- and 16-month’s follow-up, but, considering that change scores were used for the primary outcome, data was only taken into account when residents were included in at least two measurements.

A different dataset and structure were used to evaluate the secondary outcome, percentage of psychotropic drug use. This dataset included all residents (psychotropic drug users and non-users) with observations at the resident level. Data of residents included at 8- and 16-month’s follow-up was taken into account. Psychotropic drug use between the control and intervention groups was compared by logistic generalized estimating equations (GEE), accounting for the clustering of repeated measurements within residents. GEE was used because it generates population average estimates that are preferable for intervention studies [56]. The model contained psychotropic drug use (yes/no) at 8 and 16 months as the dependent variables and assessed the main effect by group (intervention vs. control). We intended to correct for baseline NPI-NH sum score and baseline psychotropic drug use. Given the possibility of collinearity between these variables, they were added to the model one by one. Many residents were not included at the baseline measurement, which led to missing data; however, imputation was not feasible because the data concerned the period before admission. Two GEE models were ultimately executed: (1) analysis of all cases without correction for the NPI-NH sum score and psychotropic drug use at baseline, and (2) analysis of complete cases only, with subsequent correction for the NPI-NH sum score and psychotropic drug use at baseline. Adjustments were made for sex, length of DSCU stay (in months), and time in the study arm (full duration, later enrolment, early drop out, and later enrolment with early drop out; for all cases only). In addition to overall psychotropic drug usage, we performed post hoc analyses for psychotropic drug subgroups: antipsychotics, anxiolytics, antidepressants and hypnotics. We did not perform analyses for anticonvulsants and anti-dementia drugs separately, because of the small sample sizes within these groups. Several models were executed for each subgroup, in line with the analysis of overall usage. The models adjusted for confounders and containing all cases are considered the main models for both the pre-specified and post hoc analyses.

Finally, we conducted sensitivity analyses for the primary and secondary outcomes that considered the results of the process evaluation by excluding nursing homes with tardy or low implementation (n = 4) [50].

There were some deviations from the study protocol [45], see Additional file 1.

Gold prices surged on Saturday, both internationally and in the domestic market, as investors flocked to the precious metal amid mounting global economic uncertainties.

According to market data, the price of gold in the international bullion market rose by a staggering $61 per ounce, pushing the rate to an unprecedented $3,363 per ounce. The sharp uptrend reflects growing concerns over a weakening US dollar and fears of a potential global economic slowdown.

In response to the international spike, domestic gold prices also recorded a significant increase. In Pakistan, the price of gold jumped by Rs6,100 per tola, reaching Rs359,000. Similarly, the rate for 10 grams climbed by Rs5,229 to settle at Rs307,784.

On Friday, the per tola price had briefly dipped by Rs100, settling at Rs352,900 before rebounding sharply by the end of the day.

Meanwhile, silver prices also posted gains, with the per tola rate rising by Rs53 to close at Rs3,953.

 

The Omani Rial (OMR) is trading at a rate of Rs 748.2. in the open market against the Pakistani Rupee (PKR), on Saturday, August 2, 2025.

On August 02, 2025, the exchange rates for the Omani Rial are set at a buying rate of 738.2 and a selling rate of 748.2.

1,000 Omani Rial in Pakistani rupees

At today’s selling rate, 1000 Omani Rial equals approximately ₨. 748,200. This conversion is vital for individuals sending money from Oman to Pakistan, ensuring their families receive the maximum value.

The Omani Rial holds significant importance for Pakistan, especially in the realm of remittances. With over 360,000 Pakistani expatriates residing in Oman, the currency exchange between OMR to PKR plays a crucial role in supporting families back home.

These remittances are often used for essential needs such as education, healthcare, and housing, and they also contribute to Pakistan’s foreign exchange reserves and GDP.

Read More: Bitcoin (BTC) to Pakistani Rupee (PKR) Rates for August 02, 2025
Impact of Remittances on Pakistan

The financial remittances sent home by Pakistani workers in Oman play a pivotal role in the socioeconomic well-being of their families. These funds are often used for essential needs such as education, healthcare, and housing, thereby improving the quality of life for recipients back in Pakistan. On a macroeconomic level, remittances contribute significantly to Pakistan’s foreign exchange reserves and national GDP, making them a crucial element of the country’s financial health.

Note: This data is provided for informational and estimated purposes only and is not meant for trading or financial guidance. Always verify prices with your broker before engaging in any transactions or investments. The exchange rate should not be taken as investment advice, and no recommendation is made to buy, sell, or hold any securities or financial products.

 

Dogecoin (Doge) is priced at PKR 56.11, as of 2:35 PM (Pakistan Standard Time) on August 02, 2025. This shows it has dropped from its previous closing price of PKR 58.37 on August 01, meaning its value has decreased.

On August 02, the price of Dogecoin (DOGE) in US dollars (USD) is $0.20 in the open market, which is lower than its closing price of $0.22 on the previous day.

What is Cryptocurrency?

Cryptocurrency is a form of digital currency that employs encryption techniques, known as cryptography, to secure transactions. Unlike traditional currencies that are regulated by governmental authorities, cryptocurrencies are decentralised and typically operate on blockchain technology, enabling individuals to send, receive, or store value online without the need for intermediaries such as banks.

Notable examples include Dogecoin (DOGE), Bitcoin, and Ripple, among others, each characterised by distinct regulations and use cases.

Read More: XRP to PKR: Conversion Rate; August 02, 2025

What is Dogecoin (DOGE)?

Dogecoin (DOGE) is a kind of cryptocurrency, which is basically digital money. It was created in December 2013 by Jackson Palmer, an Australian software developer, and Billy Markus, a programmer from Portland, Oregon. They came up with the idea during a casual conversation as a fun project.

It features a playful Shiba Inu dog logo and was intended to be a lighthearted version of Bitcoin. Though it began as a joke, Dogecoin quickly attracted many fans, and a lot of people now take it seriously.

Even with its silly beginnings, Dogecoin has a strong community and has been used for charity and fundraising. If you’re interested in how it compares to Bitcoin or want to learn how to start using it, I can help you with that!

Note: Dogecoin (Doge) prices are subject to significant volatility and may fluctuate rapidly. For accurate and up-to-date market information or financial guidance, please consult a qualified professional or a trusted exchange platform. We do not accept liability for any investment decisions made based on this information.

Study selection

The search returned 543 records from the Embase database and 344 from the MEDLINE database (Fig. 1); 611 records remained for title and abstract screening after removing duplicates. Following this, 92 articles were excluded for reasons detailed in Fig. 1, leaving 519 records for full text screening. The full text could not be retrieved for one article and so this study was excluded. The main reasons articles were excluded at the full text screening stage were because QoL was not collected longitudinally (n = 153), no formal statistical analysis was performed (n = 36), or the QoL was not treated as the outcome measure (n = 14). A total of 271 articles met the eligibility criteria and were included in the review. Due to limited available resources, a pragmatic decision was made to initially select 15 articles to be second screened by three reviewers (including two reviewers screening the same five articles); discrepancies were found in two instances and were resolved, with the second reviewer agreeing with the original decision. Similarly, data were extracted from a further 15 articles by two reviewers (including both reviewers extracting the same five articles); discrepancies were found in < 6% of data points collected (one question = one data point). Discrepancies were the result of the clarity of reporting or misreading of articles and not due to a difference in interpretation of methods used. Again, after discussion, the second reviewer agreed with the original extraction. Based on the level of agreement observed and given the reviewers all agreed with the original screening/extraction, the team felt there was no need for further double screening or extraction.

Fifty-six records appeared in the initial search but were not identified in the final search due to the database record being updated in the interim period: thirty-five of these records did not have either of the QLQ-C30 or QLQ-LC13 questionnaires indexed or mentioned in the title or abstract; nine did not have the required wording/indexing to identify them as an RCT or observational study; seven were classified as conference abstracts; four had the date of publication updated to 2023; and one record was removed from both MEDLINE and Embase databases.

Study characteristics

The characteristics of the eligible studies included in the review are presented in Table 1. The majority of included studies were parallel group RCTs (161/271, 59%), followed by cohort studies (84/271, 31%). The remaining studies were other RCT designs, single arm trials, or used individual patient data from multiple RCTs, multiple cohort studies, or combined data from cohorts and RCTs. The median number of patients included in each study was 201 (IQR 90 to 508). Quality of life was assessed at baseline in 96% (261/271) of included studies, and the median number of post-baseline/randomisation assessments was 4 (IQR 3 to 6). Most studies used just the QLQ-C30 questionnaire, none used just the QLQ-LC13 questionnaire, and 16 studies used both. In all cases the analysis method for the two questionnaires was the same. Therefore, without loss of generality, the results that follow refer to the QLQ-C30 questionnaire only. Very few studies specified a primary outcome that was a QoL score derived from the QLQ-C30 questionnaire (34/271, 13%). Of the 271 studies included in the review, 131 (48%) defined an MCID; the majority of these stated an MCID that related to within-patient change in QoL score (84/131, 64%), 28% (37/131) defined an MCID representing a between-group difference, and 8% (10/131) specified MCIDs for both within-patient changes and between-group differences.

Synthesis of results

Just over half of studies (138/271, 51%) analysed all scores derived from the QLQ-C30 questionnaire, 28% (76/271) selected a subset of scores and 4% (11/271) only analysed summary scores (Table 2). Around three-quarters of studies (207/271, 76%) did not use any methods to account for missing questionnaires or state the assumed missing data mechanism. Only 23 studies (8%) explicitly used a method to account for missing data due to death, such as joint modelling of the QoL score and survival or defining death as an event in a time to event (TTE) analysis. The remaining studies treated data truncated by death in the same way as other missing data.

Overall, the most utilized statistical model was a linear mixed effects model, with 45% (121/271) of included studies applying this approach in at least one analysis (Fig. 2). Following this, the models/methods used most often were TTE analyses (54/271, 20%), t-tests (44/271, 16%) and Mann–Whitney U/Wilcoxon rank-sum tests (38/271, 14%). Thirteen studies used unweighted GEEs (5%), nine used constrained longitudinal data analysis (3%), three used growth mixture models (1%), three used PMM (1%) and three used joint longitudinal survival models (1%). Just over two-thirds (189/271, 70%) of studies applied at least one longitudinal analysis method. Thirty-eight studies performed both longitudinal and cross-sectional analyses. Multiple cross-sectional analyses were undertaken in 98 studies (36%), and of these, adjustment for multiplicity was made in 10% (10/98). The most common method of multiplicity adjustment was the Bonferroni correction. Of the 175 studies that applied at least one longitudinal analysis model (excluding TTE analyses), 40 (23%) estimated treatment effects at multiple time points regardless of the statistical significance of the time x treatment interaction; 3/175 estimated treatment effects at multiple time points only if the time x treatment interaction was statistically significant (Table 2).

Table 2 summarises analysis methods by study type (RCT vs other). One-hundred and seventy-two studies were classified as RCTs and 99 were classified as other. Linear mixed effect models were used slightly more frequently in RCTs (81/172, 47%) compared to other studies (40/99, 40%). Similarly, TTE analyses, such as time to deterioration in QoL score by the MCID, were also more likely to be performed in RCTs compared to other studies, with 28% (49/172) of RCTs included in the review using this method, compared to 5% (5/99) of other studies. The use of the other commonly applied statistical methods (e.g., t-tests and Mann–Whitney U/Wilcoxon rank-sum tests) was comparable between RCTs and other studies.

Covariates or confounding variables, such as the baseline QoL score, were accounted for in at least one QoL analysis in 47% (75/161) of RCTs and 53% (50/94) of other studies (Table 2). Effect sizes and 95% CIs or standard errors (SEs) from at least one statistical model were presented in 56% of studies included in the review (99/172, 58% of RCTs vs 53/99, 54% of other studies). Overall, 40% of RCTs and other studies presented effect sizes and a measure of precision from all statistical models; 44% of studies did not present this information for any statistical analysis presented, either because the output from the chosen analysis did not include this information, or the authors chose not to present it.

Further details about the analyses performed are presented in Table 3. The majority of studies (248/270, 92%) analysed or used data from all time points collected, either in one or separate analyses. Most studies used the QoL score as the outcome measure (216/271, 80%), followed by change from baseline (56/271, 21%). Of those applying a longitudinal method of analysis (excluding TTE analyses), the majority fitted time as a categorical variable as recommended by the SISAQOL consortium (150/168, 89%). Of the 54 using TTE analyses, 16 (30%) used Cox proportional hazards models to estimate the difference between groups, compared to 32 (59%) using the log-rank test.

In relation to the specific challenges faced when analysing QLQ-C30, 165/267 (62%) of studies analysed a single item symptom score (a score that takes only four possible values, Table 3). Of these, 21% (33/158) used a method of analysis that accounted for the ordinal nature of the single item score. The remaining treated the score in the same way as other functioning or symptom scales analysed. Methods used included: non-parametric methods (26/33, 79%), logistic regression (5/33, 15%), ordinal logistic regression (2/33, 6%) and two-part models (1/33, 3%). Similar approaches were used to account for a possible peak at one end of the score distribution; methods used included non-parametric methods, ordinal logistic and logistic regression, two-part models, and GEE Tweedie models (Table 3). Few studies mentioned checking of the assumptions of their chosen analysis method and/or examining model fit (36/233, 15%).

Ten of the included studies cited the SISAQOL guidelines; 255/271 analysed at least one outcome that the SISAQOL consortium had published a recommended analysis method for. Just under one third of studies (73/255, 29%) followed the recommended approach for all outcomes, 17% (44/255) followed recommendations for some outcomes but not all, and 54% (138/255) did not follow the recommended analysis methods.

Of the 34 studies (17 RCTs, 17 other designs) that used QoL as a primary outcome, only 14/34 (41%) studies (7 RCTS, 7 other designs) clearly defined the QoL outcome of interest e.g. QLQ-C30 fatigue or global health status, and the relevant time frame (Table 4). The statistical analysis was appropriate for the specified estimand in 27/34 (79%) studies (13 RCTS, 14 other designs) and 15 of these 27 studies reported an effect estimate and measure of precision (5 RCTS, 10 other designs). Twenty-three studies reported deaths; nine included patients who died in their analyses, nine performed cross-sectional analyses and so excluded deaths if they occurred prior to the time point(s) analysed, and five excluded deaths from all primary outcome analyses.