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Background Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. Interpretation Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. Funding British Heart Foundation Data Science Centre, led by Health Data Research UK.

Background Joint modelling of longitudinal and time‐to‐event data is often preferred over separate longitudinal or time‐to‐event analyses as it can account for study dropout, error in longitudinally measured covariates, and correlation between longitudinal and time‐to‐event outcomes. The joint modelling literature focuses mainly on the analysis of single studies with no methods currently available for the meta‐analysis of joint model estimates from multiple studies. Methods We propose a 2‐stage method for meta‐analysis of joint model estimates. These methods are applied to the INDANA dataset to combine joint model estimates of systolic blood pressure with time to death, time to myocardial infarction, and time to stroke. Results are compared to meta‐analyses of separate longitudinal or time‐to‐event models. A simulation study is conducted to contrast separate versus joint analyses over a range of scenarios. Results Using the real dataset, similar results were obtained by using the separate and joint analyses. However, the simulation study indicated a benefit of use of joint rather than separate methods in a meta‐analytic setting where association exists between the longitudinal and time‐to‐event outcomes. Conclusions Where evidence of association between longitudinal and time‐to‐event outcomes exists, results from joint models over standalone analyses should be pooled in 2‐stage meta‐analyses.

IntroductionAggregate data meta-analyses have shown heterogeneous treatment effects for cognitive–behavioural therapy (CBT) for patients with schizophrenia spectrum diagnoses. This heterogeneity could stem from specific intervention or patient characteristics that could influence the clinical effectiveness of CBT, termed treatment effect modifiers. This individual participant data meta-analysis will investigate a range of potential treatment effect modifiers of the efficacy of CBT.Methods and analysisWe will perform a systematic review and meta-analysis of studies investigating CBT versus treatment as usual, or CBT versus other psychosocial interventions, for patients with schizophrenia spectrum diagnoses. The Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE and the online clinical trials registers of the US government, European Union, WHO and Current Controlled Trials will be searched. Two researchers will screen titles and abstracts identified by the search. Individual participant data will be requested for any eligible study, for the primary outcome (overall psychotic symptoms), secondary outcomes and treatment effect modifiers. Data will be checked and recoded according to an established statistical analysis plan. One-stage and two-stage random effects meta-analyses investigating potential treatment effect modifiers will be conducted. A list of potential treatment effect modifiers for CBT will be produced, motivating future research into particular modifiers.Ethics and disseminationThis study does not require ethical approval as it is based on data from existing studies, although best ethical practice for secondary analysis of clinical data will be followed. The findings will be submitted for publication in peer-reviewed journals, and promoted to relevant stakeholders.PROSPERO registration numberCRD42017060068.

BACKGROUND: This is an updated version of the original Cochrane Review published in Issue 12, 2016. This review is one in a series of Cochrane Reviews investigating pair‐wise monotherapy comparisons. Epilepsy is a common neurological condition in which abnormal electrical discharges from the brain cause recurrent unprovoked seizures. It is believed that with effective drug treatment, up to 70% of individuals with active epilepsy have the potential to become seizure‐free and go into long‐term remission shortly after starting drug therapy, the majority of which may be able to achieve remission with a single antiepileptic drug (AED). The correct choice of first‐line AED for individuals with newly diagnosed seizures is of great importance and should be based on the highest‐quality evidence available regarding the potential benefits and harms of various treatments for an individual. Topiramate and carbamazepine are commonly used AEDs. Performing a synthesis of the evidence from existing trials will increase the precision of results of outcomes relating to efficacy and tolerability, and may help inform a choice between the two drugs. OBJECTIVES: To review the time to treatment failure, remission and first seizure with topiramate compared with carbamazepine when used as monotherapy in people with focal onset seizures (simple or complex focal and secondarily generalised), or generalised onset tonic‐clonic seizures (with or without other generalised seizure types). SEARCH METHODS: For the latest update we searched the Cochrane Register of Studies (CRS Web), which includes the Cochrane Epilepsy Group Specialized Register and the Cochrane Central Register of Controlled Trials (CENTRAL); MEDLINE (Ovid); ClinicalTrials.gov; and the WHO International Clinical Trials Registry Platform (ICTRP) to 22 May 2018. We imposed no language restrictions. We also contacted pharmaceutical companies and trial investigators. SELECTION CRITERIA: Randomised controlled trials (RCTs) comparing monotherapy with either topiramate or carbamazepine in children or adults with focal onset seizures or generalised onset tonic‐clonic seizures (with or without other generalised seizure types). DATA COLLECTION AND ANALYSIS: This was an individual participant data (IPD), review. Our primary outcome was time to treatment failure. Our secondary outcomes were time to first seizure post‐randomisation, time to six‐month remission, time to 12‐month remission, and incidence of adverse events. We used Cox proportional hazards regression models to obtain trial‐specific estimates of hazard ratios (HRs), with 95% confidence intervals (CIs), using the generic inverse variance method to obtain the overall pooled HR and 95% CI. MAIN RESULTS: IPD were available for 1151 of 1239 eligible individuals from two of three eligible studies (93% of the potential data). A small proportion of individuals recruited into these trials had 'unclassified seizures;' for analysis purposes, these individuals are grouped with those with generalised onset seizures. For remission outcomes, a HR < 1 indicated an advantage for carbamazepine, and for first seizure and treatment failure outcomes, a HR < 1 indicated an advantage for topiramate. The main overall results for the primary outcome, time to treatment failure, given as pooled HR adjusted for seizure type were: time to failure for any reason related to treatment 1.16 (95% CI 0.97 to 1.38); time to failure due to adverse events 1.02 (95% CI 0.82 to 1.27); and time to failure due to lack of efficacy 1.46 (95% CI 1.08 to 1.98). Overall results for secondary outcomes were time to first seizure 1.11 (95% CI 0.96 to 1.29); and time to six‐month remission 0.88 (0.76 to 1.01). There were no statistically significant differences between the drugs. A statistically significant advantage for carbamazepine was shown for time to 12‐month remission: 0.84 (95% CI 0.71 to 0.99). The results of this review are applicable mainly to individuals with focal onset seizures; 81% of individuals included within the analysis experienced seizures of this type at baseline. For individuals with focal onset seizures, a statistically significant advantage for carbamazepine was shown for time to failure for any reason related to treatment (HR 1.21, 95% CI 1.01 to 1.46), time to treatment failure due to lack of efficacy (HR 1.47, 95% CI 1.07 to 2.02), and time to 12‐month remission (HR 0.82, 95% CI 0.69 to 0.99). There was no statistically significant difference between topiramate and carbamazepine for 'time to first seizure' and 'time to six‐month remission'. Evidence for individuals with generalised tonic‐clonic seizures (9% of participants contributing to the analysis), and unclassified seizure types (10% of participants contributing to the analysis) was very limited; no statistically significant differences were found but CIs were wide; therefore we cannot exclude an advantage to either drug, or a difference between drugs. The most commonly reported adverse events with both drugs were drowsiness or fatigue, "pins and needles" (tingling sensation), headache, gastrointestinal disturbance and anxiety or depression. The rate of adverse events was similar across the two drugs. We judged the methodological quality of the included trials generally to be good; however, there was some evidence that the open‐label design of the larger of the two trials may have influenced the treatment failure rate within the trial. Hence, we judged the certainty of the evidence for treatment failure to be moderate for individuals with focal onset seizures and low for individuals with generalised onset seizures. For efficacy outcomes (first seizure, remission), we judged the certainty of evidence from this review to be high for individuals with focal onset seizures and moderate for individuals with generalised onset or unclassified seizures. AUTHORS' CONCLUSIONS: For individuals with focal onset seizures, there is moderate‐certainty evidence that carbamazepine is less likely to be withdrawn and high‐certainty evidence that 12‐month remission will be achieved earlier than with topiramate. We did not find any differences between the drugs in terms of the other outcomes measured in the review and for individuals with generalised tonic‐clonic seizures or unclassified epilepsy; however, we encourage caution in the interpretation of results including small numbers of participants with these seizure types. Future trials should be designed to the highest quality possible and take into consideration masking, choice of population, classification of seizure type, duration of follow‐up, choice of outcomes and analysis, and presentation of results.

Background: Joint modeling of longitudinal and time‐to‐event data is often advantageous over separate longitudinal or time‐to‐event analyses as it can account for study dropout, error in longitudinally measured covariates, and correlation between longitudinal and time‐to‐event outcomes. The current literature on joint modeling focuses mainly on the analysis of single studies with a lack of methods available for the meta‐analysis of joint data from multiple studies. Methods: We investigate a variety of one‐stage methods for the meta‐analysis of joint longitudinal and time‐to‐event outcome data. These methods are applied to the INDANA dataset to investigate longitudinally measured systolic blood pressure, with each of time to death, time to myocardial infarction, and time to stroke. Results are compared to separate longitudinal or time‐to‐event meta‐analyses. A simulation study is conducted to contrast separate versus joint analyses over a range of scenarios. Results: The performance of the examined one‐stage joint meta‐analytic models varied. Models that accounted for between study heterogeneity performed better than models that ignored it. Of the examined methods to account for between study heterogeneity, under the examined association structure, fixed effect approaches appeared preferable, whereas methods involving a baseline hazard stratified by study were least time intensive. Conclusions: One‐stage joint meta‐analytic models that accounted for between study heterogeneity using a mix of fixed effects or a stratified baseline hazard were reliable; however, models examined that included study level random effects in the association structure were less reliable.

SummaryObjective To compare quality-of-life (QoL) outcomes over 2 years following initiation of treatment with a standard or newer antiepileptic drug (AED) in adults with new-onset epilepsy. To examine the impact of seizure remission and failure of initial treatment on QoL outcomes measured over 2 years. Methods We conducted a pragmatic, randomized, unblinded, multicenter, parallel-group clinical trial (the Standard and New Antiepileptic Drugs [SANAD] trial) comparing clinical and cost effectiveness of initiating treatment with carbamazepine versus lamotrigine, gabapentin, oxcarbazepine and topiramate, and valproate versus lamotrigine and topiramate. QoL data were collected by mail at baseline, 3 months, and at 1 and 2 years using validated measures. These data were analyzed using longitudinal data models. Continuous QoL measures, time to 12-month remission and time to treatment withdrawal were explored using joint models. Results Baseline questionnaires were returned by 1,575 adults; 1,439 returned the 3-month questionnaire, 1,274 returned the 1-year questionnaire, and 1,121 returned the 2-year questionnaire. There were few statistically significant differences between drugs over 2 years in QoL outcomes. Significant association was identified between QoL scores over the 2-year time frame and the risk of experiencing a 12-month remission or treatment withdrawal over that period. Significance The choice of initial treatment had no significant effect on QoL by 2-year follow-up. However, overall QoL was reduced with continued seizures, adverse events, and failure of the initial treatment.

BACKGROUND: This is an updated version of the original Cochrane Review published in 2017. Epilepsy is a common neurological condition with a worldwide prevalence of around 1%. Approximately 60% to 70% of people with epilepsy will achieve a longer‐term remission from seizures, and most achieve that remission shortly after starting antiepileptic drug treatment. Most people with epilepsy are treated with a single antiepileptic drug (monotherapy) and current guidelines from the National Institute for Health and Care Excellence (NICE) in the United Kingdom for adults and children recommend carbamazepine or lamotrigine as first‐line treatment for focal onset seizures and sodium valproate for generalised onset seizures; however, a range of other antiepileptic drug (AED) treatments are available, and evidence is needed regarding their comparative effectiveness in order to inform treatment choices. OBJECTIVES: To compare the time to treatment failure, remission and first seizure of 12 AEDs (carbamazepine, phenytoin, sodium valproate, phenobarbitone, oxcarbazepine, lamotrigine, gabapentin, topiramate, levetiracetam, zonisamide, eslicarbazepine acetate, lacosamide) currently used as monotherapy in children and adults with focal onset seizures (simple focal, complex focal or secondary generalised) or generalised tonic‐clonic seizures with or without other generalised seizure types (absence, myoclonus). SEARCH METHODS: For the latest update, we searched the following databases on 12 April 2021: the Cochrane Register of Studies (CRS Web), which includes PubMed, Embase, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform (ICTRP), the Cochrane Central Register of Controlled Trials (CENTRAL), the Cochrane Epilepsy Group Specialised Register and MEDLINE (Ovid, 1946 to April 09, 2021). We handsearched relevant journals and contacted pharmaceutical companies, original trial investigators and experts in the field. SELECTION CRITERIA: We included randomised controlled trials of a monotherapy design in adults or children with focal onset seizures or generalised onset tonic‐clonic seizures (with or without other generalised seizure types). DATA COLLECTION AND ANALYSIS: This was an individual participant data (IPD) and network meta‐analysis (NMA) review. Our primary outcome was 'time to treatment failure', and our secondary outcomes were 'time to achieve 12‐month remission', 'time to achieve six‐month remission', and 'time to first seizure post‐randomisation'. We performed frequentist NMA to combine direct evidence with indirect evidence across the treatment network of 12 drugs. We investigated inconsistency between direct 'pairwise' estimates and NMA results via node splitting. Results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs) and we assessed the certainty of the evidence using the CiNeMA approach, based on the GRADE framework. We have also provided a narrative summary of the most commonly reported adverse events. MAIN RESULTS: IPD were provided for at least one outcome of this review for 14,789 out of a total of 22,049 eligible participants (67% of total data) from 39 out of the 89 eligible trials (43% of total trials). We could not include IPD from the remaining 50 trials in analysis for a variety of reasons, such as being unable to contact an author or sponsor to request data, data being lost or no longer available, cost and resources required to prepare data being prohibitive, or local authority or country‐specific restrictions. No IPD were available from a single trial of eslicarbazepine acetate, so this AED could not be included in the NMA. Network meta‐analysis showed high‐certainty evidence that for our primary outcome, ‘time to treatment failure’, for individuals with focal seizures; lamotrigine performs better than most other treatments in terms of treatment failure for any reason and due to adverse events, including the other first‐line treatment carbamazepine; HRs (95% CIs) for treatment failure for any reason for lamotrigine versus: levetiracetam 1.01 (0.88 to 1.20), zonisamide 1.18 (0.96 to 1.44), lacosamide 1.19 (0.90 to 1.58), carbamazepine 1.26 (1.10 to 1.44), oxcarbazepine 1.30 (1.02 to 1.66), sodium valproate 1.35 (1.09 to 1.69), phenytoin 1.44 (1.11 to 1.85), topiramate 1.50 (1.23 to 1.81), gabapentin 1.53 (1.26 to 1.85), phenobarbitone 1.97 (1.45 to 2.67). No significant difference between lamotrigine and levetiracetam was shown for any treatment failure outcome, and both AEDs seemed to perform better than all other AEDs. For people with generalised onset seizures, evidence was more limited and of moderate certainty; no other treatment performed better than first‐line treatment sodium valproate, but there were no differences between sodium valproate, lamotrigine or levetiracetam in terms of treatment failure; HRs (95% CIs) for treatment failure for any reason for sodium valproate versus: lamotrigine 1.06 (0.81 to 1.37), levetiracetam 1.13 (0.89 to 1.42), gabapentin 1.13 (0.61 to 2.11), phenytoin 1.17 (0.80 to 1.73), oxcarbazepine 1.24 (0.72 to 2.14), topiramate 1.37 (1.06 to 1.77), carbamazepine 1.52 (1.18 to 1.96), phenobarbitone 2.13 (1.20 to 3.79), lacosamide 2.64 (1.14 to 6.09). Network meta‐analysis also showed high‐certainty evidence that for secondary remission outcomes, few notable differences were shown for either seizure type; for individuals with focal seizures, carbamazepine performed better than gabapentin (12‐month remission) and sodium valproate (six‐month remission). No differences between lamotrigine and any AED were shown for individuals with focal seizures, or between sodium valproate and other AEDs for individuals with generalised onset seizures. Network meta‐analysis also showed high‐ to moderate‐certainty evidence that, for ‘time to first seizure,’ in general, the earliest licensed treatments (phenytoin and phenobarbitone) performed better than the other treatments for individuals with focal seizures; phenobarbitone performed better than both first‐line treatments carbamazepine and lamotrigine. There were no notable differences between the newer drugs (oxcarbazepine, topiramate, gabapentin, levetiracetam, zonisamide and lacosamide) for either seizure type. Generally, direct evidence (where available) and network meta‐analysis estimates were numerically similar and consistent with confidence intervals of effect sizes overlapping. There was no important indication of inconsistency between direct and network meta‐analysis results. The most commonly reported adverse events across all drugs were drowsiness/fatigue, headache or migraine, gastrointestinal disturbances, dizziness/faintness and rash or skin disorders; however, reporting of adverse events was highly variable across AEDs and across studies. AUTHORS' CONCLUSIONS: High‐certainty evidence demonstrates that for people with focal onset seizures, current first‐line treatment options carbamazepine and lamotrigine, as well as newer drug levetiracetam, show the best profile in terms of treatment failure and seizure control as first‐line treatments. For people with generalised tonic‐clonic seizures (with or without other seizure types), current first‐line treatment sodium valproate has the best profile compared to all other treatments, but lamotrigine and levetiracetam would be the most suitable alternative first‐line treatments, particularly for those for whom sodium valproate may not be an appropriate treatment option. Further evidence from randomised controlled trials recruiting individuals with generalised tonic‐clonic seizures (with or without other seizure types) is needed.

Background Meta-analyses based on individual participant data (IPD-MAs) allow more powerful and uniformly consistent analyses as well as better characterisation of subgroups and outcomes, compared to those which are based on aggregate data (AD-MAs) extracted from published trial reports. However, IPD-MAs are a larger undertaking requiring greater resources than AD-MAs. Researchers have compared results from IPD-MA against results obtained from AD-MA and reported conflicting findings. We present a methodology review to summarise this empirical evidence . Objectives To review systematically empirical comparisons of meta-analyses of randomised trials based on IPD with those based on AD extracted from published reports, to evaluate the level of agreement between IPD-MA and AD-MA and whether agreement is affected by differences in type of effect measure, trials and participants included within the IPD-MA and AD-MA, and whether analyses were undertaken to explore the main effect of treatment or a treatment effect modifier. Search methods An electronic search of the Cochrane Library (includes Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effectiveness, CENTRAL, Cochrane Methodology Register, HTA database, NHS Economic Evaluations Database), MEDLINE, and Embase was undertaken up to 7 January 2016. Potentially relevant articles that were known to any of the review authors and reference lists of retrieved articles were also checked. Selection criteria Studies reporting an empirical comparison of the results of meta-analyses of randomised trials using IPD with those using AD. Studies were included if sufficient numerical data, comparing IPD-MA and AD-MA, were available in their reports. Data collection and analysis Two review authors screened the title and abstract of identified studies with full-text publications retrieved for those identified as eligible or potentially eligible. A ‘quality’ assessment was done and data were extracted independently by two review authors with disagreements resolved by involving a third author. Data were summarised descriptively for comparisons where an estimate of effect measure and corresponding precision have been provided both for IPD-MA and for AD-MA in the study report. Comparisons have been classified according to whether identical effect measures, identical trials and patients had been used in the IPD-MA and the AD-MA, and whether the analyses were undertaken to explore the main effect of treatment, or to explore a potential treatment effect modifier. Effect measures were transformed to a standardised scale (z scores) and scatter plots generated to allow visual comparisons. For each comparison, we compared the statistical significance (at the 5% two-sided level) of an IPD-MA compared to the corresponding AD-MA and calculated the number of discrepancies. We examined discrepancies by type of analysis (main effect or modifier) and according to whether identical trials, patients and effect measures had been used by the IPD-MA and AD-MA. We calculated the average of differences between IPD-MA and AD-MA (z scores, ratio effect estimates and standard errors (of ratio effects)) and 95% limits of agreement. Main results From the 9330 reports found by our searches, 39 studies were eligible for this review with effect estimate and measure of precision extracted for 190 comparisons of IPD-MA and AD-MA. We classified the quality of studies as ‘no important flaws’ (29 (74%) studies) or ‘possibly important flaws’ (10 (26%) studies). A median of 4 (interquartile range (IQR): 2 to 6) comparisons were made per study, with 6 (IQR 4 to 11) trials and 1225 (542 to 2641) participants in IPD-MAs and 7 (4 to 11) and 1225 (705 to 2541) for the AD-MAs. One hundred and forty-four (76%) comparisons were made on the main treatment effect meta-analysis and 46 (24%) made using results from analyses to explore treatment effect modifiers. There is agreement in statistical significance between the IPD-MA and AD-MA for 152 (80%) comparisons, 23 of which disagreed in direction of effect. There is disagreement in statistical significance for 38 (20%) comparisons with an excess proportion of IPD-MA detecting a statistically significant result that was not confirmed with AD-MA (28 (15%)), compared with 10 (5%) comparisons with a statistically significant AD-MA that was not confirmed by IPD-MA. This pattern of disagreement is consistent for the 144 main effect analyses but not for the 46 comparisons of treatment effect modifier analyses. Conclusions from some IPD-MA and AD-MA differed even when based on identical trials, participants (but not necessarily identical follow-up) and treatment effect measures. The average difference between IPD-MA and AD-MA in z scores, ratio effect estimates and standard errors is small but limits of agreement are wide and include important differences in both directions. Discrepancies between IPD-MA and AD-MA do not appear to increase as the differences between trials and participants increase. Authors' conclusions IPD offers the potential to explore additional, more thorough, and potentially more appropriate analyses compared to those possible with AD. But in many cases, similar results and conclusions can be drawn from IPD-MA and AD-MA. Therefore, before embarking on a resource-intensive IPD-MA, an AD-MA should initially be explored and researchers should carefully consider the potential added benefits of IPD.

Background Current NICE guidelines for adults and children recommend carbamazepine or lamotrigine as first-line treatment for partial onset seizures and valproate for generalised onset seizures; however a range of other anti-epileptic drug treatments are available, and evidence is needed regarding their comparative effectiveness in order to inform treatment choices. Objectives To compare the clinical effectiveness of 10 antiepileptic drugs (carbamazepine, phenytoin, valproate, phenobarbitone, oxcarbazepine, lamotrigine, gabapentin, topiramate, levetiracetam, zonisamide) currently used as monotherapy in children and adults with partial-onset seizures or generalised tonic-clonic seizures with or without other generalised seizure types. Methods We performed an individual participant data network meta-analysis of randomised monotherapy trials published up to July 2016. Outcomes were time to 12 month remission and time to treatment failure. We included data from 77 trials, 18 045 participants. Results Results support current NICE guidance that carbamazepine and lamotrigine are suitable first-line treatments for individuals with partial onset seizures and demonstrates that levetiracetam may be suitable alternative. Results also support the use of valproate as the first-line treatment for individuals with generalised tonic-clonic seizures (with or without other generalised seizure types) and also demonstrates that lamotrigine and levetiracetam would be suitable alternatives, particularly for those of child bearing potential.

Background Joint models for longitudinal and time-to-event data are commonly used to simultaneously analyse correlated data in single study cases. Synthesis of evidence from multiple studies using meta-analysis is a natural next step but its feasibility depends heavily on the standard of reporting of joint models in the medical literature. During this review we aim to assess the current standard of reporting of joint models applied in the literature, and to determine whether current reporting standards would allow or hinder future aggregate data meta-analyses of model results. Methods We undertook a literature review of non-methodological studies that involved joint modelling of longitudinal and time-to-event medical data. Study characteristics were extracted and an assessment of whether separate meta-analyses for longitudinal, time-to-event and association parameters were possible was made. Results The 65 studies identified used a wide range of joint modelling methods in a selection of software. Identified studies concerned a variety of disease areas. The majority of studies reported adequate information to conduct a meta-analysis (67.7% for longitudinal parameter aggregate data meta-analysis, 69.2% for time-to-event parameter aggregate data meta-analysis, 76.9% for association parameter aggregate data meta-analysis). In some cases model structure was difficult to ascertain from the published reports. Conclusions Whilst extraction of sufficient information to permit meta-analyses was possible in a majority of cases, the standard of reporting of joint models should be maintained and improved. Recommendations for future practice include clear statement of model structure, of values of estimated parameters, of software used and of statistical methods applied.