Category: 3. Business

By Gordon Gottsegen

Retail investors’ dip-buying remains as strong as ever despite tariffs, Middle East tensions and economic uncertainty

Tariffs, market volatility and war in the Middle East couldn’t slow down the buying spree by individual investors, as they traded a record amount of stocks in the first half of the year.

Retail investors cumulatively bought around $3.4 trillion worth of equities over the first half of 2025, according to data from Nasdaq. At the same time, they sold about $3.2 trillion worth – bringing the total traded to over $6.6 trillion.

This represents a strong bias toward buying into the market versus taking money out, despite high levels of market volatility in the first half of the year. Tariff announcements from President Donald Trump spooked markets, while investors weighed the possibility of global trade wars leading to an economic slowdown and higher inflation. The Dow Jones Industrial Average DJIA and S&P 500 SPX entered a correction, while the Nasdaq Composite COMP fell into bear-market territory. Some investors said it was “the toughest investment climate” they ever experienced.

Yet retail investors’ behavior showed that they were bullish in the face of all of this. Cumulative net retail inflows hit $137.6 billion into U.S. single stocks and exchange-traded funds the first half of the 2025, according to Nasdaq.

Data from capital-markets research firm Vanda Research differed slightly from the Nasdaq data, but it showed the same general trend. Vanda found that investors net purchased $155.3 billion worth of single stocks and ETFs in the first half of 2025. This was the largest-ever net inflow of retail investor cash since Vanda started keeping track in 2014. Inflows surpassed the previous high of $152.8 billion reached in the first half of 2021, when the meme-stock mania and pandemic stimulus checks drove hordes of everyday investors into the stock market.

In 2025, buying was driven by two things, Vanda said: the “American exceptionalism” trade and a record amount of dip-buying in response to Trump’s “liberation day” tariffs. Buzzy U.S. stocks – like Nvidia (NVDA), Tesla (TSLA) and Palantir (PLTR) – topped the charts of the most actively traded tickers throughout the first half, but retail investors also poured significant amounts of capital into index-tracking ETFs like SPDR S&P 500 ETF Trust SPY and Invesco QQQ Trust Series I QQQ.

Read more: Fourth of July holiday highlights 4 reasons ‘American exceptionalism’ isn’t going anywhere

The average daily inflow was roughly $1.3 billion, according to Vanda, which represents a 21.6% jump from the average in 2024.

This level of stock-buying hasn’t exactly hurt investors’ performance either. Vanda estimated that the average retail portfolio was up 6.2% so far in 2025, which was closely in line with the 6.1% that the S&P 500 gained in the first half of this year.

“Retail investors remain a major force in the market. Participation is at record highs, the dip-buying bias is fully intact, and engagement with single names – particularly high-beta and leveraged plays – continues to rise. Performance is holding up, and risk appetite is anything but shy. Nothing seems to stop this retail train,” Marco Iachini, Vanda Research’s senior vice president of research, wrote in a note.

-Gordon Gottsegen

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Chinese chain Luckin Coffee opened its first two U.S. locations this week, betting that mobile-only ordering and creative flavors can lure customers away from Starbucks.

Both new Luckin stores are based in Manhattan, and at the midtown location on Wednesday, Sam Liu took a sip of her jasmine cold brew.

“I’ve never tried anything like it,” she said.

Liu said she’d hoped for more seating — the small shop has only three tables — and was initially confused by Luckin’s in-app ordering system, which means customers can’t order directly from a barista.

“I thought I just order at the counter, but I realized everyone was standing around looking at their phone,” Liu said.

Luckin is China’s largest coffee chain, with more than twice as many locations as Starbucks there. Its two New York City stores are its first foray outside Asia, where it has over 24,000 locations across the region. By comparison, there are over 17,000 Starbucks in the United States.

Its CEO, Guo Jinyi, called the U.S. “a strategically important market” for the company’s expansion in a press release heralding the two new locations Wednesday. “We are excited to introduce a diverse and unique coffee experience to American consumers.”

The company, which didn’t respond to a request for comment, has touted its ambitions to expand globally but hasn’t publicly detailed its next moves in the U.S. or other markets.

The chain has gained success overseas through creative drinks like alcohol-infused coffees and fruit lattes, along with its smartphone-centric ordering model. The app-based approach makes it easier to track inventory, send personalized appeals to consumers and serve drinks quickly, said John Zolidis, an analyst who tracks Luckin and Starbucks at the brokerage firm he founded, Quo Vadis Capital.

“Luckin was able to develop an incredible muscle with regard to product innovation, and they have been very creative in China,” he said.

Zolidis said how Luckin fares on Starbucks’ home turf will depend on its ability to differentiate its menu from other major U.S. coffee chains and smaller, independent cafes. Its American lineup already includes distinctive drinks like blood orange cold brew and coconut lattes.

“These orange drinks, or one of their most successful, a coconut cloud latte — that’s how you get trial [customers] from the U.S.,” Zolidis said.

Luckin faced financial troubles during the pandemic. It was delisted from Nasdaq in 2020 after its stock plunged following an internal investigation that found an executive had falsified revenue reports. The company filed for bankruptcy in the U.S. the following year but emerged from proceedings in 2022 and its sales have soared since, reaching $4.7 billion worldwide in fiscal year 2024, a 38.4% increase from 2023.

Starbucks, by contrast, is struggling in both the U.S. and China. Its same-store sales in the U.S. declined 2% and its sales in China 8% in fiscal year 2024, and it reported in April that its quarterly profit was half of what it pulled in for the same period last year. The Seattle-based chain is reportedly looking to partially sell its business in China while revamping its U.S. strategy to focus on customer experience and human connection, in contrast with Luckin’s model.

“We veered away from, I think, owning the idea of the ‘third place,’ the coffeehouse experience, making sure that the customer was front and center,” Starbucks CEO Brian Niccol told NBC News in June.

A Starbucks spokesperson declined to comment.

Zolidis said that whereas Starbucks aims in both the U.S. and China to appeal to customers looking for higher-end coffee served in an inviting setting, Luckin has successfully positioned itself as the “everyman’s coffee” in China, with low prices and small, grab-and-go storefronts.

After taking the train in from Hoboken, New Jersey, to check out the new one in midtown, Samantha Coy said the trip was worth it. She had enjoyed Luckin in China previously and was eager to order one of its fruit drinks.

“I’m surprised Starbucks hasn’t tried to bring that over to the U.S.,” Coy said. “I hope they stay open.”

Zolidis said he thinks Luckin is well-positioned to gain a foothold in America.

“They’ve been able to operate and grow incredibly quickly in the Chinese market, much faster than I would have thought possible, and they’ve been able to sustain it and develop a strong financial model so they can fund their expansion in the U.S.,” Zolidis said. “They wouldn’t be coming here to try it if they didn’t think they had a shot of owning part of the market.”

The six-month, full-time paid program offers aspiring AI researchers from adjacent fields like physics or neuroscience a pathway into the AI industry, rather than recruiting individuals already deeply invested in AI research and work. According to Jackie Hehir, OpenAI’s research residency program manager, residents aren’t those seeking Ph.Ds in machine learning or AI, nor are they employees of other AI labs. Instead, she said in a program info session, “they’re really passionate about the space.”

So what’s in it for OpenAI? Hot talent at cut-rate prices. While the six-figure salary puts OpenAI residents in the top 5% of American workers, it’s a bargain in the rarefied world of AI, where the bidding war for talent has some companies tossing around nine-figure bonuses.

By offering a foothold into the AI world, OpenAI appears to be cultivating talent deeply embedded in the company’s mission. This strategy, spearheaded by CEO Sam Altman, has long been part of the company’s approach to retaining employees and driving innovation. One former OpenAI staffer described the employee culture to Business Insider as “obsessed with the actual mission of creating AGI,” or artificial general intelligence.

Mission driven or not, OpenAI’s residents are also compensated handsomely, earning an annualized salary of $210,000, which translates to around $105,000 for the six-month program. The company also pays residents to relocate to San Francisco. Unlike internships, the program treats participants as full-fledged employees, complete with a full suite of benefits. Nearly every resident who performs well receives a full-time offer, and, according to Hehir, every resident offered a full-time contract so far has accepted. Each year, the company welcomes around 30 residents. 

The qualifications for residents at OpenAI are somewhat unconventional. In fact, the company claims there are no formal education or work requirements. Instead, they hold an “extremely high technical bar” at parity to what they look for in full-time employees as it pertains to math and programming.

“While you don’t need to have a degree in advanced mathematics, you do need to be really comfortable with advanced math concepts,” Hehir said.

As OpenAI attempts to build talent from the ground up, its rivals, namely Meta, are pulling out all the stops to poach top AI talent with reports alleging that Meta CEO Mark Zuckerberg personally identified top OpenAI staff on what insiders dubbed “The List” and attempted to recruit them with offers exceeding $100 million in signing bonuses.

Meta’s compensation packages for AI talent can reportedly reach over $300 million across four years for elite researchers. The flood of cash has ignited what some insiders call a “summer of comp FOMO,” as AI specialists weigh whether to stay loyal to their current employers or leave for record-breaking paydays.

Zuckerberg’s methods have had some success, poaching a number of OpenAI employees for Meta’s new superintelligence team. In response to news of the employees’ departure, OpenAI’s chief research officer, Mark Chen, told staff that it felt like “someone has broken into our home and stolen something.”

Meanwhile, OpenAI CEO Sam Altman called Meta’s recruitment tactics “crazy,” warning that money alone won’t secure the best people. “What Meta is doing will, in my opinion, lead to very deep cultural problems,” Altman told employees in a leaked internal memo this week. 

Ultimately, cultivating new talent, rather than trying to outbid the likes of Meta, may prove a more sustainable path for OpenAI in its quest to stay highly mission-oriented while supporting an industry grappling with a scarcity of top-tier talent. Estimates suggest there are only about 2,000 people worldwide capable of pushing the boundaries of large language models and advanced AI research. Whether the talent cultivated by Altman and OpenAI will remain loyal to the firm remains unknown. But Altman says that AI “missionaries will beat mercenaries.”

Participants

Twenty-four healthy, community-dwelling older adults, consisting of 15 men and 9 women with a mean age of 72.1 ± 4.1 years, participated (Table 1). The eligibility criteria of this study were as follows: age ≥ 65 years; ability to walk independently without assistive devices; and absence of conditions that could significantly influence gait, such as neurological disorders (e.g., Alzheimer’s disease, Parkinson’s disease, or stroke), severe cardiovascular or respiratory impairments with symptoms during daily activities (e.g., heart failure, chronic obstructive pulmonary disease), or musculoskeletal problem that disable independent gait (e.g., joint replacement, spinal surgery, or advanced arthritis).

Sample size calculation

The required sample size was determined based on the population within-subject standard deviation (PWSD). The number of subjects was determined to estimate PWSD within 10% of the population value ((frac{1.96}{sqrt{2nleft(m-1right)}}leq0.1,;m=the;number;of;observations;per;subject)) using the variance of PSWD ((frac{{sigma }_{w}}{sqrt{2nleft(m-1right)}}, {sigma }_{w}=PWSD)) [21]. A sample size of 24 was required for all of the distances with nine or more observations per subject (m ≥ 9) except for the two longest distances (4.9 and 5-m).

Muscle mass and strength assessments

Participants underwent bioimpedance analysis using an InBody S10 device (InBody Co., Ltd., Seoul, South Korea) to determine height-adjusted appendicular skeletal muscle mass. Muscle strength was assessed by measuring handgrip and isometric knee extension strength. Handgrip strength was measured using a Takei 5401 Digital Dynamometer (Takei Scientific Instruments Co., Ltd., Niigata, Japan) in a standing position with the elbow fully extended. Isometric knee extension strength was evaluated using a TKK-5710e tension meter (Takei Scientific Instruments Co., Ltd., Niigata, Japan); during measurement, participants were seated on a chair with a dynamometer anchored to it, maintaining knee flexion at 90°. Both measurements were conducted bilaterally, with each side assessed twice and a 1-min rest period between attempts. Participants were instructed to exert maximum effort for each measurement, and the highest reading was used in the analysis. All procedures were conducted by a single trained assessor following the recommendations of Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People [9, 10].

Physical performance assessments

Physical performance was evaluated using the Short Physical Performance Battery (SPPB) [22], the 30-s chair stand test [23], the five-times sit-to-stand test [24], and the timed up-and-go test [25]. All assessments were conducted by a single trained assessor in a spacious setting under consistent environmental conditions, following the protocols of the Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People [9, 10].

10-m gait speed test and data acquisition

Participants walked along a 10-m walkway, which included a 2-m acceleration zone for a dynamic start and a 2-m deceleration zone at the end. They were instructed to walk at their usual pace on a hard surface while wearing comfortable footwear. The 10-m walk was repeated three times, with a minimum rest period of 2 min between trials. Recordings were captured using an Apple iPad Pro 11 2nd Generation (Apple, Inc., Cupertino, CA, USA) equipped with RGB cameras arranged perpendicularly to the walking path at a distance of 3.8 m and a height of 0.8 m. Videos were recorded in the sagittal plane (resolution: 800 × 600 pixels; 30 fps; Fig. 1).

Gait analysis using 2D pose estimation

A customized pose estimation model (ViFive, Inc., Boulder, CO, USA) was used, which tracked 14 key body points using an architecture adapted from a standard stacked hourglass model [26]. We introduced multiple objectives to enhance the context, accuracy, speed, and stability of the model, which are vital for musculoskeletal assessment. The classification model included a random forest classifier with optimized features to increase accuracy and speed while reducing the model size. Pixel-per-meter estimation used markers at 2 and 8 m (Fig. 1). The CoM of each subject was determined using the weighted sums of the body segment centers of mass (Fig. 2a).

Gait speed estimation

Gait speed was measured using two independent methods for validation: manually with a stopwatch and using pose estimation algorithms. Manually assessed speed was determined by an evaluator using a stopwatch to record the time taken for the subjects to pass by the markers set at 2 and 8 m. Pose estimation gait speed was calculated by dividing the distance covered between frames by the elapsed time using either the CoM or the leading foot as reference points. CoM-referenced measurements simulate those obtained via conventional motion capture system, whereas leading foot-referenced measurements simulate those made using walkway or pressure sensors such as GAITRite® (CIR Systems Inc., Franklin, NJ, USA).

Gait speed measurement validation

Gait speeds measured using a stopwatch and pose estimation were compared using a linear mixed-effects model, with speed over 6 (manual) or 5 m (pose estimation) per trial as the dependent variable and with subject random effect to account for multiple tests from each subject. The intraclass correlation coefficient (ICC) was used to evaluate absolute agreement between gait speed measurements obtained via the two methods for the same walking trials.

Change of uncertainty with measured distance

A 5-m walk video of a skeleton with 14 key points was extracted from each recording using our pose estimation algorithm. This was further edited by cropping at 0.1-m intervals to generate 4.9- to 1.0-m segments. One 5.0-m walk video generated two 4.9-m segments, three 4.8-m segments, and so forth, up to 41 segments for a 1.0-m walk, culminating in 861 segments of varying distance (Fig. 2b,c).

The variability of gait speed across the measured distances was defined as the within-subject standard deviation (WSD) for each measured distance, calculated as the square root of the mean-square error in a one-way analysis of variance, where groups combined subjects with distance intervals. Three gait speed data from three measurements were collected for each group to avoid underestimating within-subject variation due to overlapping distances when distance intervals are not considered. For example, for a 4.7-m walk, four gait speed measurements were obtained at 4.7-m distances (0–4.7, 0.1–4.8, 0.2–4.9, and 0.3–5.0 m), and within-subject variation at a 4.7-m distance was estimated by considering different distance intervals.

Determination of minimum required distance

To determine the minimum required distance, we utilized WSD at each measured distance. Given that confidence intervals (CI) quantify variability, we computed the half-width of the CI using WSD and the critical value corresponding to the chosen confidence level. Specifically, the 95% CI was calculated as 1.96 × WSD, and the 90% CI as 1.64 × WSD. For the measurement to be clinically meaningful, the half-width of the CI, reflecting gait speed variability, had to remain below the MCID of 0.1 m/s [27, 28]. Thus, the minimum required distance was defined as the shortest distance at which this criterion was met, ensuring that gait speed measurements remained within an acceptable range of variability.

Factors affecting gait speed variability

CoM trajectory was plotted as distance against time for each test. Linear regression analysis provided a trend line and the mean squared error (MSE) for each subject. As MSE quantifies deviations from the trend line, lower MSE values indicate less variability in gait speed, leading to a shorter minimum required distance. We investigated whether epidemiological, anthropometric, or clinical variables were associated with MSE using linear regression following Pearson’s correlation for continuous variables and point-biserial correlation for dichotomous variables to identify subject characteristics influencing the minimum required distance. All processing and statistical analyses were conducted using MATLAB R2023b (MathWorks, Natick, MA, USA) and SAS 9.4 (SAS Institute, Cary, NC, USA), with statistical significance set at p < 0.05.

• Shares of AMD and Micron Technology have soared impressively in the past three months.

• Both are set to benefit from identical end markets, but one of them is growing at a much faster pace.

• The valuation will make it clear which of these semiconductor stocks is worth buying right now.

• 10 stocks we like better than Micron Technology ›

The demand for artificial intelligence (AI) chips has been increasing at a nice pace in the past few years. Major cloud service providers (CSPs), hyperscalers, and governments have been spending a lot of money on shoring up their cloud infrastructure so that they can run AI workloads.

This explains why the businesses of Advanced Micro Devices (NASDAQ: AMD) and Micron Technology (NASDAQ: MU) have gained terrific traction in recent quarters. As a result, shares of both these chip designers have clocked impressive gains in the past three months. AMD has jumped 32% during this period, and Micron stock is up 36%.

But if you had to put your money into just one of these AI semiconductor stocks right now, which one should it be? Let’s find out.

AMD designs chips that go into personal computers (PCs), servers, and gaming consoles, and for other applications such as robotics, automotive, and industrial automation. AI has created impressive demand for the company’s chips in these areas, leading to healthy growth in its top and bottom lines.

The company’s revenue in the first quarter of 2025 was up by 36% from the year-ago period to $7.4 billion, while non-GAAP earnings per share shot up by 55% to $0.96. This solid growth was primarily driven by the data center and PC markets, which accounted for 81% of its top line. AMD’s data center revenue was up by 57% from the year-ago period, while the PC business reported a 68% increase.

In the data center business, AMD sells both central processing units (CPUs) and graphics processing units (GPUs) that are deployed in AI servers. The demand for both these products is strong, which is evident from the terrific growth the company recorded in Q1. Importantly, AMD estimates that the market for AI accelerator chips in data centers could create a $500 billion annual revenue opportunity in 2028.

So, the outstanding growth that AMD clocked in the data center business in Q1 seems sustainable, especially considering that it generated $12.6 billion in revenue from data center chip sales last year — nearly double the 2023 revenue. AMD is pushing the envelope on the product development front with new chips that are expected to pack in a serious performance upgrade and may even help it take market share away from Nvidia.

Meanwhile, AMD’s consistent market share gains in PC CPUs make it a solid bet on the secular growth of the AI PC market, which is expected to clock an annual growth rate of 42% in shipments through 2028. All this indicates that AMD is on track to take advantage of the growing adoption of AI chips in multiple applications, and that’s expected to lead to an acceleration in its bottom-line growth.

Consensus estimates are projecting a 17% jump in AMD’s earnings this year, followed by a bigger jump of 45% in 2026. As such, this semiconductor company is likely to remain a top AI stock in the future as well.

Micron Technology manufactures and sells memory chips that are used for both computing and storage purposes, and the likes of AMD and Nvidia are its customers. In fact, just like AMD, Micron’s memory chips are used in AI accelerators such as GPUs and custom processors, PCs, and the smartphone and automotive end markets.

Micron has been witnessing outstanding demand for a type of chip known as high-bandwidth memory (HBM), which is known for its ability to transmit huge amounts of data at high speeds. This is the reason why HBM is being deployed in AI accelerators, and the demand for this memory type is so strong that the likes of Micron have already sold out their capacity for this year.

Not surprisingly, Micron is ramping up its HBM production capacity, and it’s going to increase its capital expenditure to $14 billion in the current fiscal year from $8.1 billion in the previous one. The company’s focus on improving its HBM production capacity is a smart thing to do from a long-term perspective, as this market is expected to grow to $100 billion in annual revenue by 2030, compared to $35 billion this year.

Micron’s memory chips are used in PCs and smartphones as well. Apart from the growth in unit volumes that AI-enabled PCs and smartphones are expected to create going forward, the amount of memory going into these devices is also expected to increase. CEO Sanjay Mehrotra remarked on the company’s latest earnings conference call:

AI adoption remains a key driver of DRAM content growth for smartphones, and we expect more smartphone launches featuring 12 gigabytes or more compared to eight gigabytes of capacity in the average smartphone today.

Similarly, AI-enabled PCs are expected to sport at least 16GB of DRAM to run AI workloads, up by a third when compared to the average DRAM content in PCs last year. So, just like AMD, Micron is on its way to capitalizing on multiple AI-focused end markets. However, it is growing at a much faster pace than AMD because of the tight memory supply created by AI, which is leading to a nice increase in memory prices.

The favorable pricing environment is the reason why Micron’s adjusted earnings more than tripled in the previous quarter to $1.91 per share on the back of a 37% increase in its top line. Analysts are forecasting a 6x jump in Micron’s earnings in the current fiscal year, and they have raised their earnings expectations for the next couple of years as well.

So, Micron stock seems poised to sustain its impressive growth momentum, thanks to the AI-fueled demand for HBM.

Both AMD and Micron are growing at solid rates, with the latter clocking a much faster pace thanks to the favorable demand-supply dynamics in the memory industry. What’s more, Micron is trading at a significantly cheaper valuation compared to AMD, despite its substantially stronger growth.

Investors looking for a mix of value and growth can pick Micron over AMD, considering the former’s attractive valuation and the phenomenal earnings growth that it can deliver. However, one can’t go wrong with AMD either. The company should be able to justify its valuation in the long run, considering its ability to clock stronger earnings growth.

Before you buy stock in Micron Technology, consider this:

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Better Artificial Intelligence (AI) Stock: Advanced Micro Devices vs. Micron Technology was originally published by The Motley Fool

Introduction

Maintenance therapy is recommended for patients diagnosed with chronic obstructive pulmonary disease (COPD). These treatments include long-acting muscarinic antagonists (LAMAs) and long-acting beta₂-agonists (LABAs), with an inhaled corticosteroid (ICS) added according to the frequency of exacerbations.¹ Currently, several single-inhaler triple combinations of these treatment classes are available.

Besides the general recommendations for which inhaler combination to use, the question of when to initiate maintenance therapy with these inhalers has been put forward. Recent observational studies have investigated the comparative effectiveness of prompt versus delayed timing of initiating single-inhaler triple inhaler therapy after a COPD exacerbation.^2–6 These studies found that prompt initiation of single-inhaler triple therapy was associated with significant reductions in the rates of moderate and severe exacerbations, compared with delayed initiation. Such observational studies present major methodological challenges related to the definition of timing of initiation in relation to the timing of the outcome events, that can result in time-related biases.

We review these studies and discuss methodological aspects of their study design that can introduce bias in the results. We illustrate the biases using a general practice clinical database and present results of the analysis using an approach that avoids these biases.

The Published Studies

As the published observational studies used a similar design, we describe the first one in detail to explain the approach.² The Mannino study evaluated the impact of prompt versus delayed initiation of single-inhaler triple therapy (SITT) with fluticasone furoate, umeclidinium, and vilanterol, following a COPD exacerbation, using a US claims database. Patients with a COPD exacerbation between September 2017 and September 2019 were identified, with the first exacerbation occurring in that period taken as the index exacerbation. The index date was taken as the date of discharge for exacerbations requiring hospitalisation and the date of the physician visit for moderate exacerbations. The study cohort was formed exclusively from those who initiated a SITT within 6 months after the index date, with SITT timing classified as prompt (initiation within 30 days after the index date; N = 529) or delayed (initiation 31–180 days after the index date; N = 1,375). Patients were aged 40 years or more at the index date, had at least 12 months of continuous health insurance coverage before index (baseline), no exacerbation and no SITT prescription during this baseline period. The subjects needed at least 6 months of coverage after the index. Subjects were followed from the index date until the end of the observation period for the occurrence of COPD exacerbations and other outcomes. The technique of inverse probability of treatment weighting was used to adjust the rate ratio of these outcomes for differences in baseline characteristics between the prompt and delayed groups. Patients in the prompt initiation group had a 21% lower rate of COPD exacerbation (rate ratio 0.79; 95% CI: 0.65–0.94) and a 28% lower incidence of a first exacerbation (hazard ratio 0.72; 95% CI: 0.62–0.83) compared with delayed initiators.

Methodological Issues

A randomized trial of this question would enroll patients at the index exacerbation and randomly allocate them to either the prompt or delayed treatment strategy. The allocation of the two groups is thus known at the time of randomization (index date) with outcome events counted as of this time, and which can thus occur prior to treatment initiation. The published observational studies, on the other hand, had to peek into “future” to define the two treatment groups, such as treatment initiation within 30 days after the index (prompt) or at 31–180 days after the index (delayed). This use of “future” time creates several methodological challenges and can lead to potential biases in observational studies. We use the Mannino study described in detail above as an example to explain the methodological issues.²

The first methodological issue with the observational studies involves the outcome (exacerbations) allowed to occur prior to the initiation of triple therapy, which can introduce protopathic bias.⁷ Indeed, some physicians may wait for a second exacerbation, namely the first during follow-up, as an indication to initiate triple therapy, as per guidelines.¹ Thus, it may not be the treatment that led to the exacerbation, but the reverse. In particular, patients with early exacerbations will likely receive their triple inhaler after day 30, when the exacerbation ended, and thus more likely to be classified in the “delayed” treatment group, a bias compounded by the longer duration of this delayed period. This could explain the observational study’s reported median times to the first COPD exacerbation of 367 versus 200 days for the prompt and delayed initiation groups, respectively.² Moreover, the corresponding Kaplan–Meier curve shows that around 10% of the delayed group had their first exacerbation in the first 30 days, the period defining “prompt” treatment, and that 48% of that group had their first exacerbation before 6 months, the end of the “delayed” treatment period.

The second methodological issue relates to the cohort selection that imposes a period of continuous health insurance coverage after the index date, resulting in potential selection bias from immortal time.^8,9 Indeed, the cohort included only patients with at least 6 months of coverage after the index date, thus excluding patients who die in this 6-month period, even if they had initiated triple therapy and had exacerbations before death. By this criterion, patients must survive to initiate treatment, whether prompt or delayed, even if they had exacerbations before death. This imposition of 6 months of coverage after an index that inherently excludes deaths can result in selection bias that could favor one group over the other. The Mannino study reports that 25% of the original 668,011 subjects were excluded because they had less than 6 months of eligibility, with no information on mortality.²

Study Design to Avoid Bias

The study design that avoids these biases must attempt to emulate the randomized trial when using these observational data, while not looking into the future. The key with the randomized trial is that the timing of inhaler initiation, namely allocation to a prompt or delayed treatment regimen, is known at the time of randomisation, so that the two groups can be properly compared on the rate of exacerbation during a prespecified follow-up period after the index exacerbation. In this case, exacerbation events can precede the inhaler initiation but will follow the random treatment group assignment time. The difference in the rates between the two groups will provide the effect of prompt versus delayed treatment.

The observational study must thus also seek to allocate “exposure” (prompt or delayed inhaler initiation) at the index date to avoid the methodological biases raised above when this exposure allocation is based on looking in the future. To accomplish this, it is important to recognize that a subject who has not yet received the inhaler at the index date could, in fact, belong to both the prompt and delayed group at that time point and up until inhaler therapy is initiated. To resolve this dilemma, the concept of “cloning” is used in observational research to assign each patient to the two possible treatment strategies (prompt and delayed inhaler initiation) that the patient could belong to at the index date.^10–12 Thus, the patient is “cloned” so that their data are included twice in the analysis, though the follow-up will be censored according to the timing of the inhaler initiation and the outcome events, which could be counted in one, two, or none of the groups depending on where the clones are censored.

As an illustration for the outcome of time to first exacerbation, consider, for example, a patient who initiates triple therapy on day 15, thus within 30 days (Figure 1, subject 1). This patient will generate two clones, a “prompt” clone that will be considered as exposed to the prompt treatment strategy for the entire follow-up, whose follow-up will end at the time of the event, and a “delayed” clone that will be considered as exposed to the delayed treatment strategy and censored at 14 days, the day at which we still did not know the membership of its parent. The second example (subject 2) is a patient who initiates triple therapy on day 60 (during the 31–180-day period). This patient will generate two clones, the first is a “prompt” clone that will be censored at 30 days, the time they can no longer be prompt, and a “delayed” clone that will be classified as “delayed” exposure for the entire follow-up, whose follow-up will end at the time of the event (Figure 1, subject 2). The third example (subject 3) does not initiate triple therapy at all during follow-up. The “prompt” clone will be censored at 30 days and the delayed clone follow-up will end at the time of the event within the 31–180 period (Figure 1, subject 3). The fourth example (subject 4) is a patient who initiates triple therapy on day 35 (during the 31–180-day period), who has their first exacerbation on day 20. The two generated clones, one prompt and one delayed, will both have an outcome event at day 20, at which point their follow-up stops (Figure 1, subject 4). Figure 2 displays the corresponding cloning patterns illustrating the situation where the outcome involves the frequency of exacerbations over time.

Figure 1 Illustration of cloning approach for the analysis of time to first exacerbation.

Figure 2 Illustration of cloning approach for the analysis of the frequency of exacerbations.

This cloning approach addresses the bias resulting from peeking into the future to define exposure. Nonetheless, simply computing the corresponding cumulative incidences or rates of exacerbation for the two cloned treatment strategies will still produce bias from giving equal weights to the clones. This is addressed by accounting for the artificial censoring of the clones at specific times, which can be done using inverse probability of censoring weights (ICPW).¹¹

Illustration

We formed a cohort of patients with COPD from the Clinical Practice Research Datalink (CPRD), a primary care database from the United Kingdom (UK) that contains primary care medical records for over 50 million people enrolled in more than 1800 general practices. These data have shown to be of high quality, including for studies of COPD.^13–17

The study cohort included all patients with a diagnosis of COPD, treated with maintenance therapy, at or after age 40 who had a moderate or severe exacerbation of COPD after 15 September 2017, the year single-inhaler triple therapy became available in the UK. A moderate exacerbation was defined by a new prescription for prednisolone, while a severe exacerbation was defined as a hospitalization for COPD (ICD-10: J41, J42, J43, J44). The first such exacerbation defined the index date. All subjects had to have at least one year of medical history prior to the index date (baseline period). Patients receiving triple therapy, either in a single inhaler or multiple inhalers, in the year before the exacerbation defining cohort entry, were excluded. All subjects were followed for up to one year after the index date, with follow-up ending at death, 31 March 2021, or the end of the patient’s registration in the practice, whichever occurred first.

The covariates measured at baseline included age, sex, body mass index (BMI), smoking status and alcohol abuse. The severity of COPD was measured using the type of index exacerbation (moderate, severe), the number of COPD hospitalisations and the use of other respiratory drugs (SABA, SAMA, theophylline), during the one-year baseline period, as well as by the percent predicted FEV₁. A prescription for prednisolone, LABA, LAMA, ICS, and respiratory antibiotics in the month prior to the index date were also considered. Baseline co-morbidity in the one-year baseline period was measured using clinical diagnoses, hospitalizations, and prescriptions (Table 1).

Table 1 Baseline Characteristics of the Overall Study Cohort of 91,958 Subjects with an Index Exacerbation, Generating 91,958 Clones of Prompt and 91,958 Clones of Delayed Initiation of Single-Inhaler Triple Therapy, After Weighing by Inverse Probability of Censoring

Each cohort subject was cloned and assigned to both the prompt initiators arm (initiation within 30 days of the index date) and to the delayed initiators arm (initiation 31–180 days after the index date). Within each treatment group, clones were artificially censored at the time that the treatment they received was no longer compatible with their group membership. To account for the bias introduced by this artificial censoring mechanism, IPCW was estimated by pooled logistic regression, separately for prompt initiators at day 30, for delayed initiators at index date, between day 1 and day 30 and at day 180, as a function of the baseline covariates. IPCW estimation for prompt initiators, delayed initiators at index date and at day 180, was based on time updated values of the baseline covariates and the type of initial exacerbation (moderate or severe). For the delayed initiators, estimation of IPCW between day 1 and day 30 also included the time since cohort entry (linear, quadratic and cubic terms). Only prompt initiators starting treatment between day 16 and 30 were upweighted to replace clones censored on day 30. Similarly, only delayed initiators between day 152 and 180 were upweighted to replace clones censored on day 180. In addition to the artificial censoring associated with the cloning process, patients were also censored when they initiated triple therapy in multiple inhalers (same day prescription for LABA, LAMA and ICS). To account for this type of censoring another IPCW was estimated in the full cohort and before cloning, using pooled logistic regression and with the same set of variables. Final time-varying weights for each section of person-time were obtained from the cumulative product of all IPCWs.

For data analysis, we used the Cox proportional hazards model to compare the incidence of an exacerbation during the one-year follow-up, weighted for the inverse of the probability of censoring. The corresponding 95% confidence intervals (CI) for the hazard ratios were obtained using the non-parametric bootstrap method based on 1000 random samples. Similarly, weighted cumulative incidence curves were estimated over the one-year follow-up, as well as differences and ratios of cumulative incidence at 3-month time points during follow-up.

We also replicated the approach used in the Mannino study, as described above.² Briefly, the study cohort included exclusively the subjects who had a prompt (within 30 days after the index date) or delayed (31–180 days after the index date classification) treatment initiation, restricted to those who had no exacerbation prior to the index date and at least 6 months of coverage after the index date. Inverse probability of treatment weighting, with stabilised weights, was used to adjust the hazard ratio of exacerbation, using the bootstrap method based on 1000 random samples outcomes to estimate the corresponding confidence interval. Since our study cohort on which the cloning analysis was based included subjects with exacerbations prior to the index date, we repeated the analysis, but not restricted to those who had no exacerbation prior to the index date and at least 6 months of coverage after the index date. The study protocol was approved by CPRD’s Research Data Governance Committee (protocol # 23_002846) and the Research Ethics Board of the Jewish General Hospital (protocol # JGH-2024-3847), Montreal Canada.

Results

The overall study cohort included 91,958 eligible subjects who had an exacerbation after September 2017. There were 4,876 new-users of single-inhaler triple therapy within 180 days after the index COPD exacerbation. Of these, 1,394 were prompt initiators and 3,482 delayed initiators of single-inhaler triple therapy, with 87,082 who either received it after 180 days or not at all. The prompt initiators appear more severe, with lower FEV₁ percent predicted and more likely to have a hospitalised exacerbation as the index event (Table S1).

The cloning of these subjects generated 91,958 prompt initiator clones and the same number of delayed initiator clones, including 426 clones who initiated triple therapy in multiple inhalers on the index date and 419 clones who initiated single-inhaler triple therapy on the index date (Table 1).

Of the 91,532 clones assigned to the prompt initiation strategy, 18,748 had a moderate or severe exacerbation after the index date, during 86,431 person-months of follow-up, resulting in an unadjusted incidence rate of a first exacerbation of 0.217 per patient per month (Table 2). The corresponding incidence rate for clones assigned to the delayed initiation strategy was 0.130 per patient per month. After weighing for censoring, the incidence rates are 0.101 and 0.103 per person per month, respectively, for the clones assigned to the prompt and delayed initiation regimens (HR 0.98; 95% CI: 0.80–1.19). The adjusted cumulative incidence curves of a moderate or severe exacerbation, weighted for censoring, are displayed in Figure 3. For severe exacerbations, the weighted incidence rates are 0.011 and 0.008 per person per month for the clones assigned to the prompt and delayed initiation regimens, respectively (HR 1.26; 95% CI: 0.81–1.96), with the corresponding adjusted cumulative incidence curves displayed in Figure 4. For the cumulative incidence curves given in Figures 3 and 4 the estimates of the differences and ratios of these cumulative incidences between the prompt and delayed initiators at different time points in follow-up are displayed in Table 3.

Table 2 Hazard Ratio of a Moderate or Severe Exacerbation for Prompt versus Delayed Initiation of Single-Inhaler Triple Therapy, Estimated Using Cloning to Define the Treatment Strategy Over Time, Weighed by Inverse Probability of Censoring

Table 3 Difference and Ratio of the Cumulative Incidence of Exacerbation Over Follow-up Time Comparing Prompt versus Delayed Initiation of Single-Inhaler Triple Therapy, Estimated Using Cloning to Define the Treatment Strategy Over Time, Weighted by Inverse Probability of Censoring

Figure 3 Cumulative incidence of the first moderate or severe exacerbation, for the prompt and delayed initiators, using the cloning approach, weighted by inverse probability of censoring.

Figure 4 Cumulative incidence of the first severe exacerbation, for the prompt and delayed initiators, using the cloning approach, weighted by inverse probability of censoring.

For the replication of the Mannino study, the analysis was restricted to the 2,650 new-users of single-inhaler triple therapy within 6 months after the index COPD exacerbation, with no prior exacerbations and at least 6 months of follow-up. Of these, 809 were prompt initiators of single-inhaler triple therapy and 1,841 were delayed initiators, who were similar in terms of baseline clinical characteristics after weighing (Table 4). The hazard ratio of a first moderate or severe exacerbation was 0.73 (95% CI: 0.65–0.81), comparing prompt with delayed initiation, while it was 0.58 (95% CI: 0.46–0.74) for a severe exacerbation (Table 5). These results were similar after removing the restrictions of 6 months of coverage and no prior exacerbations (Table 5).

Table 4 Baseline Characteristics of the 809 Prompt and 1,841 Delayed Initiators of Single-Inhaler Triple Therapy, Identified from the Cohort of 91,958 Subjects with an Index Exacerbation, Crude and Weighted by Inverse Probability of Treatment, Used to Replicate the Approach of Mannino

Table 5 Hazard Ratio of Moderate or Severe Exacerbation for Prompt versus Delayed Initiation of Single-Inhaler Triple Therapy, Used to Replicate the Approach of Mannino, Estimated Using the Cox Proportional Hazards Model, Adjusted by Inverse Probability of Treatment Weighing

Discussion

In this large-scale real-world study, we found that prompt treatment with single-inhaler triple therapy after a COPD exacerbation was not more effective than delayed treatment on reducing the incidence of a subsequent exacerbation. We showed that the methods used by previous studies that suggested significant effectiveness with prompt therapy, were affected by major time-related biases that favored the prompt treatment group.^2,3,6 For example, our illustration showed that, using the corrected approach, the hazard ratio of a COPD exacerbation was 0.98 (95% CI: 0.80–1.19) with prompt versus delayed treatment, while the corresponding HR with the time-related biased method employed by the previous studies was a significant 0.73 (95% CI: 0.65–0.81).

The time-related biases affecting the previous studies first involved “peeking into the future” to define prompt and delayed treatment, with a return to time zero to start follow-up for outcome exacerbation events. This approach thus allowed treatment initiation occurring after outcome events, which introduces protopathic bias.⁷ Indeed, multiple exacerbations, the study outcome, are an indication to initiate triple therapy, as recommended by the GOLD guidelines.¹ The other source of bias, namely selection bias from immortal time, resulting from imposing 6 months of coverage after the index date, was present in two of the studies to date.^2,6 This criterion excludes subjects who died during this period, when they could have initiated triple therapy and had exacerbations, which could be differential in the two treatment groups. While other studies did not impose this 6-month condition, they did introduce protopathic bias.^3–5 Our bias analysis showed that this 6-month imposition did not affect the findings, implying that the major time-related bias in these studies is protopathic bias, a bias present in all studies.

This approach has also been used in several other observational studies have investigated the comparative effectiveness of prompt versus delayed initiation of triple therapy, though including triple therapy in multiple inhalers after a COPD exacerbation.^18–22 These studies also found that prompt initiation of triple therapy was associated with significant reductions in the rates of exacerbations, and related costs, compared with delayed initiation. These findings are thus also affected by the same protopathic bias.

The “cloning” approach that we used is specifically designed to avoid the protopathic bias resulting from peeking into the future to define the treatment strategy. This cloning approach emulates a randomized trial by allocating the treatment strategy, prompt or delayed initiation of triple therapy, as of the index date, thus not looking in the future. Cloning involves creating data replicates of each patient, one for each of the study treatment strategies (in this case, prompt and delayed initiation) that the patient could belong to at the index date.^10–12 The patient’s data are thus included twice in the analysis, censored by the timing of exposure and outcome events. While the same outcome events can be counted in multiple regimens in this approach, a censor-weighted data analysis and the bootstrap can account for the replicated data.

Our study has some limitations typical to observational studies. The inhaler information is based on written prescriptions and can thus introduce some exposure misclassification, including on the timing of the actual treatment initiation, which can lag behind the prescription date. Thus, the 30- and 180-day thresholds used to define prompt and delayed treatment will necessarily be affected by this misclassification, which should differentially affect the shorter prompt treatment period. Indeed, we can assume that some subjects with a prescription date just prior to 30 days will be misclassified as prompt initiators if they in fact initiate their inhaler after 30 days. The use of censoring weights, however, because they are calculated using the observed exposure timing, does not account for this misclassification. To account for such exposure measurement error would require validating exposure in a subset of the study population and incorporating sensitivity analyses alongside censoring weights. Also, the outcome of a moderate exacerbation is defined only based on a prescription for prednisolone which, while a common practice in the UK, could introduce some misclassification. Our study also has strengths, besides the cloning approach that avoids the time-related biases of previous studies. Indeed, this approach is not affected by confounding as the cloning results in the same patients being replicated, making the two comparison groups identical on all subject characteristics.

In conclusion, this large-scale real-world study found that prompt treatment with single-inhaler triple therapy after a COPD exacerbation was not more effective than delayed treatment on reducing the incidence of a subsequent exacerbation. We showed that the methods used by previous studies that suggested significant effectiveness with prompt therapy, were affected by time-related biases that favored the prompt treatment group. For example, using the corrected approach, we found no reduction in the risk of a COPD exacerbation with prompt versus delayed treatment, while the time-related biased method used in previous studies suggested a significant 27% reduction in the outcome event.

Data Sharing Statement

This study is based in part on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency. The data are provided by patients and collected by the UK National Health Service as part of their care and support. Because electronic health records are classified as “sensitive data” by the UK Data Protection Act, information governance restrictions (to protect patient confidentiality) prevent data sharing via public deposition. Data are available with approval through the individual constituent entities controlling access to the data. Specifically, the primary care data can be requested via application to the Clinical Practice Research Datalink (https://www.cprd.com).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was funded in part by grants from the Canadian Institutes of Health Research (CIHR) and the Canadian Foundation for Innovation (CFI). Pr. Suissa is the recipient of the Distinguished James McGill Professorship award.

Disclosure

SS attended, in the last three years, scientific advisory committee meetings or received speaking fees from AstraZeneca, Boehringer-Ingelheim, Novartis, and Panalgo. The authors report no other conflicts of interest in this work.

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Welcome to this special edition of Neurology News Network. I’m Marco Meglio.

Newly announced interim data from a phase 1 study showed that treatment with salanersen (Biogen), an investigational antisense oligonucleotide administered once a year, was safe and led to slowing of neurodegeneration in patients with spinal muscular atrophy (SMA) previously on gene therapy. Based on these findings, Biogen plans to test the therapy in phase 3 studies, the design of which is being discussed with the FDA. Building on the mechanism of action of nusinersen (Spinraza; Biogen), salanersen is designed to enhance potency and allow for once-yearly dosing, offering potential improvements in convenience and efficacy. Mechanistically, salanersen targets the SMN2 gene, modulating its splicing to increase production of functional survival motor neuron (SMN) protein, which is deficient in SMA.

Edgewise Therapeutics has announced positive topline data from the MESA open-label extension study, with results showing that treatment with investigational sevasemten led to notable improvements in North Star Ambulatory Assessment (NSAA) scores over an 18-month period in patients with Becker muscular dystrophy (BMD). In addition, the company shared encouraging data from its phase 2 LYNX and FOX trials of Duchenne muscular dystrophy (DMD), as well as completion of a Type C meeting with the FDA, paving a path for sevasemten to become the first approved therapy for BMD. MESA, an open-label extension, featured 99% of eligible participants from the previously completed ARCH, CANYON, GRAND CANYON, or DUNE trials. In the latest data update, results showed a 0.8-point increase in NSAA scores among participants from CANYON, the major phase 2 trial of sevasemten, after 18 months of treatment. More notably, those who switched from placebo to sevasemten demonstrated a 0.2-point improvement since their crossover.

In a new announcement from UCB, fenfluramine (Fintepla), an FDA-approved antiseizure medication, met its primary and secondary end points in the phase 3 GEMZ trial of patients with CDKL5 deficiency disorder (CDD). Based on these data, the company plans to submit an application for fenfluramine to become a potential treatment option for patients living with CDD. Between baseline and the titration plus maintenance phase, fenfluramine demonstrated statistically significant changes relative to placebo on the primary end point of percent change in countable motor seizure frequency. In this phase 3, double-blind, placebo-controlled, fixed-dose study, fenfluramine continued to show a safety profile that was consistent with its previous indications in Dravet syndrome (DS) and Lennox-Gastaut syndrome (LGS).

For more direct access to expert insight, head to NeurologyLive.com. This has been Neurology News Network. Thanks for watching.